from itertools import groupby import numpy as np import pandas as pd import param from .dimension import Dimensioned, ViewableElement, asdim from .layout import Composable, Layout, NdLayout from .ndmapping import NdMapping from .overlay import CompositeOverlay, NdOverlay, Overlayable from .spaces import GridSpace, HoloMap from .tree import AttrTree from .util import get_param_values 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 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 == (): return self else: raise NotImplementedError("%s currently does not support getitem" % type(self).__name__) def __bool__(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.') 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 def sample(self, samples=None, 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 """ if samples is None: samples = [] raise NotImplementedError def reduce(self, dimensions=None, 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. """ if dimensions is None: dimensions = [] 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] 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 """ 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 = dict([(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 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) 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()) 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]) 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' 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 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 = () 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().__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({_k for _k, _v in locals().items() if isinstance(_v, type) and issubclass(_v, Dimensioned)})