Source code for holoviews.core.element

from itertools import groupby
import numpy as np

import param

from .dimension import Dimension, Dimensioned, ViewableElement
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 baseclass for all ViewableElement types, with an x- and y-dimension. Subclasses should define the data storage in the constructor, as well as methods and properties, which define how the data maps onto the x- and y- and value dimensions. """ group = param.String(default='Element', constant=True)
[docs] def hist(self, dimension=None, num_bins=20, bin_range=None, adjoin=True, individually=True, **kwargs): """ The hist method generates a histogram to be adjoined to the Element in an AdjointLayout. By default the histogram is computed along the first value dimension of the Element, however any dimension may be selected. The number of bins and the bin_ranges and any kwargs to be passed to the histogram operation may also be supplied. """ 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, individually=individually, 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): """ Subclasses may override this to signal that the Element contains no data and can safely be dropped during indexing. """ return True __bool__ = __nonzero__
[docs] @classmethod def collapse_data(cls, data, function=None, kdims=None, **kwargs): """ Class method to collapse a list of data matching the data format of the Element type. By implementing this method HoloMap can collapse multiple Elements of the same type. The kwargs are passed to the collapse function. The collapse function must support the numpy style axis selection. Valid function include: np.mean, np.sum, np.product, np.std, scipy.stats.kurtosis etc. Some data backends also require the key dimensions to aggregate over. """ raise NotImplementedError("Collapsing not implemented for %s." % cls.__name__)
[docs] def closest(self, coords): """ Class method that returns the exact keys for a given list of coordinates. The supplied bounds defines the extent within which the samples are drawn and the optional shape argument is the shape of the numpy array (typically the shape of the .data attribute) when applicable. """ return coords
[docs] def sample(self, samples=[], **sample_values): """ Base class signature to demonstrate API for sampling Elements. To sample an Element supply either a list of samples or keyword arguments, where the key should match an existing key dimension on the Element. """ raise NotImplementedError
[docs] def reduce(self, dimensions=[], function=None, **reduce_map): """ Base class signature to demonstrate API for reducing Elements, using some reduce function, e.g. np.mean, which is applied along a particular Dimension. The dimensions and reduce functions should be passed as keyword arguments or as a list of dimensions and a single function. """ 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 table(self, datatype=None): """ Converts the data Element to a Table, optionally may specify a supported data type. The default data types are 'numpy' (for homogeneous data), 'dataframe', and 'dictionary'. """ if datatype and not isinstance(datatype, list): datatype = [datatype] from ..element import Table return Table(self, **(dict(datatype=datatype) if datatype else {}))
def dframe(self, dimensions=None): import pandas as pd column_names = dimensions if dimensions else self.dimensions(label=True) dim_vals = OrderedDict([(dim, self[dim]) for dim in column_names]) return pd.DataFrame(dim_vals) def mapping(self, kdims=None, vdims=None, **kwargs): length = len(self) if not kdims: kdims = self.kdims if kdims: keys = zip(*[self.dimension_values( for dim in self.kdims]) else: keys = [()]*length if not vdims: vdims = self.vdims if vdims: values = zip(*[self.dimension_values( for dim in vdims]) else: values = [()]*length return OrderedDict(zip(keys, values)) def array(self, dimensions=[]): if dimensions: dims = [self.get_dimension(d, strict=True) for d in dimensions] else: dims = [d for d in self.kdims + self.vdims if d != 'Index'] 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)
[docs]class Tabular(Element): """ Baseclass to give an NdMapping objects an API to generate a table representation. """ __abstract = True @property def rows(self): return len(self) + 1 @property def cols(self): return len(self.dimensions())
[docs] def pprint_cell(self, row, col): """ Get the formatted cell value for the given row and column indices. """ 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): """ Returns the cell type given a row and column index. The common basic cell types are 'data' and 'heading'. """ return 'heading' if row == 0 else 'data'
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.""") 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).""")
[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( 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( == 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 = dim if isinstance(dim, Dimension) else Dimension(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)]))