Source code for holoviews.element.util

import itertools

import param
import numpy as np
import pandas as pd

from ..core import Dataset, OrderedDict
from ..core.boundingregion import BoundingBox
from import default_datatype, PandasInterface
from ..core.operation import Operation
from ..core.sheetcoords import Slice
from ..core.util import (
    cartesian_product, datetime_types, is_cyclic, is_nan,
    one_to_one, sort_topologically

[docs]def split_path(path): """ Split a Path type containing a single NaN separated path into multiple subpaths. """ path = path.split(0, 1)[0] values = path.dimension_values(0) splits = np.concatenate([[0], np.where(np.isnan(values))[0]+1, [None]]) subpaths = [] data = PandasInterface.as_dframe(path) for i in range(len(splits)-1): end = splits[i+1] slc = slice(splits[i], None if end is None else end-1) subpath = data.iloc[slc] if len(subpath): subpaths.append(subpath) return subpaths
[docs]def compute_slice_bounds(slices, scs, shape): """ Given a 2D selection consisting of slices/coordinates, a SheetCoordinateSystem and the shape of the array returns a new BoundingBox representing the sliced region. """ xidx, yidx = slices ys, xs = shape l, b, r, t = scs.bounds.lbrt() xdensity, ydensity = scs.xdensity, scs.ydensity xunit = (1./xdensity) yunit = (1./ydensity) if isinstance(l, datetime_types): xunit = np.timedelta64(int(round(xunit)), scs._time_unit) if isinstance(b, datetime_types): yunit = np.timedelta64(int(round(yunit)), scs._time_unit) if isinstance(xidx, slice): l = l if xidx.start is None else max(l, xidx.start) r = r if xidx.stop is None else min(r, xidx.stop) if isinstance(yidx, slice): b = b if yidx.start is None else max(b, yidx.start) t = t if yidx.stop is None else min(t, yidx.stop) bounds = BoundingBox(points=((l, b), (r, t))) # Apply new bounds slc = Slice(bounds, scs) # Apply scalar and list indices l, b, r, t = slc.compute_bounds(scs).lbrt() if not isinstance(xidx, slice): if not isinstance(xidx, (list, set)): xidx = [xidx] if len(xidx) > 1: xdensity = xdensity*(float(len(xidx))/xs) ls, rs = [], [] for idx in xidx: xc, _ = scs.closest_cell_center(idx, b) ls.append(xc-xunit/2) rs.append(xc+xunit/2) l, r = np.min(ls), np.max(rs) elif not isinstance(yidx, slice): if not isinstance(yidx, (set, list)): yidx = [yidx] if len(yidx) > 1: ydensity = ydensity*(float(len(yidx))/ys) bs, ts = [], [] for idx in yidx: _, yc = scs.closest_cell_center(l, idx) bs.append(yc-yunit/2) ts.append(yc+yunit/2) b, t = np.min(bs), np.max(ts) return BoundingBox(points=((l, b), (r, t)))
[docs]def reduce_fn(x): """ Aggregation function to get the first non-zero value. """ values = x.values if isinstance(x, pd.Series) else x for v in values: if not is_nan(v): return v return np.NaN
[docs]class categorical_aggregate2d(Operation): """ Generates a gridded Dataset of 2D aggregate arrays indexed by the first two dimensions of the passed Element, turning all remaining dimensions into value dimensions. The key dimensions of the gridded array are treated as categorical indices. Useful for data indexed by two independent categorical variables such as a table of population values indexed by country and year. Data that is indexed by continuous dimensions should be binned before aggregation. The aggregation will retain the global sorting order of both dimensions. >> table = Table([('USA', 2000, 282.2), ('UK', 2005, 58.89)], kdims=['Country', 'Year'], vdims=['Population']) >> categorical_aggregate2d(table) Dataset({'Country': ['USA', 'UK'], 'Year': [2000, 2005], 'Population': [[ 282.2 , np.NaN], [np.NaN, 58.89]]}, kdims=['Country', 'Year'], vdims=['Population']) """ datatype = param.List(default=['xarray', 'grid'], doc=""" The grid interface types to use when constructing the gridded Dataset.""") @classmethod def _get_coords(cls, obj): """ Get the coordinates of the 2D aggregate, maintaining the correct sorting order. """ xdim, ydim = obj.dimensions(label=True)[:2] xcoords = obj.dimension_values(xdim, False) ycoords = obj.dimension_values(ydim, False) if xcoords.dtype.kind not in 'SUO': xcoords = np.sort(xcoords) if ycoords.dtype.kind not in 'SUO': return xcoords, np.sort(ycoords) # Determine global orderings of y-values using topological sort grouped = obj.groupby(xdim, container_type=OrderedDict, group_type=Dataset).values() orderings = OrderedDict() sort = True for group in grouped: vals = group.dimension_values(ydim, False) if len(vals) == 1: orderings[vals[0]] = [vals[0]] else: for i in range(len(vals)-1): p1, p2 = vals[i:i+2] orderings[p1] = [p2] if sort: if vals.dtype.kind in ('i', 'f'): sort = (np.diff(vals)>=0).all() else: sort = np.array_equal(np.sort(vals), vals) if sort or one_to_one(orderings, ycoords): ycoords = np.sort(ycoords) elif not is_cyclic(orderings): coords = list(itertools.chain(*sort_topologically(orderings))) ycoords = coords if len(coords) == len(ycoords) else np.sort(ycoords) return np.asarray(xcoords), np.asarray(ycoords) def _aggregate_dataset(self, obj): """ Generates a gridded Dataset from a column-based dataset and lists of xcoords and ycoords """ xcoords, ycoords = self._get_coords(obj) dim_labels = obj.dimensions(label=True) vdims = obj.dimensions()[2:] xdim, ydim = dim_labels[:2] shape = (len(ycoords), len(xcoords)) nsamples = grid_data = {xdim: xcoords, ydim: ycoords} ys, xs = cartesian_product([ycoords, xcoords], copy=True) data = {xdim: xs, ydim: ys} for vdim in vdims: values = np.empty(nsamples) values[:] = np.NaN data[] = values dtype = default_datatype dense_data = Dataset(data, kdims=obj.kdims, vdims=obj.vdims, datatype=[dtype]) concat_data = obj.interface.concatenate([dense_data, obj], datatype=dtype) reindexed = concat_data.reindex([xdim, ydim], vdims) if not reindexed: agg = reindexed df = PandasInterface.as_dframe(reindexed) df = df.groupby([xdim, ydim], sort=False).first().reset_index() agg = reindexed.clone(df) # Convert data to a gridded dataset for vdim in vdims: grid_data[] = agg.dimension_values(vdim).reshape(shape) return agg.clone(grid_data, kdims=[xdim, ydim], vdims=vdims, datatype=self.p.datatype) def _aggregate_dataset_pandas(self, obj): index_cols = [ for d in obj.kdims] df =, sort=False).first() label = 'unique' if len(df) == len(obj) else 'non-unique' levels = self._get_coords(obj) index = pd.MultiIndex.from_product(levels, names=df.index.names) reindexed = df.reindex(index) data = tuple(levels) shape = tuple(d.shape[0] for d in data) for vdim in obj.vdims: data += (reindexed[].values.reshape(shape).T,) return obj.clone(data, datatype=self.p.datatype, label=label) def _process(self, obj, key=None): """ Generates a categorical 2D aggregate by inserting NaNs at all cross-product locations that do not already have a value assigned. Returns a 2D gridded Dataset object. """ if isinstance(obj, Dataset) and obj.interface.gridded: return obj elif obj.ndims > 2: raise ValueError("Cannot aggregate more than two dimensions") elif len(obj.dimensions()) < 3: raise ValueError("Must have at two dimensions to aggregate over" "and one value dimension to aggregate on.") obj = Dataset(obj, datatype=['dataframe']) return self._aggregate_dataset_pandas(obj)
[docs]def circular_layout(nodes): """ Lay out nodes on a circle and add node index. """ N = len(nodes) if not N: return ([], [], []) circ = np.pi/N*np.arange(N)*2 x = np.cos(circ) y = np.sin(circ) return (x, y, nodes)
[docs]def quadratic_bezier(start, end, c0=(0, 0), c1=(0, 0), steps=50): """ Compute quadratic bezier spline given start and end coordinate and two control points. """ steps = np.linspace(0, 1, steps) sx, sy = start ex, ey = end cx0, cy0 = c0 cx1, cy1 = c1 xs = ((1-steps)**3*sx + 3*((1-steps)**2)*steps*cx0 + 3*(1-steps)*steps**2*cx1 + steps**3*ex) ys = ((1-steps)**3*sy + 3*((1-steps)**2)*steps*cy0 + 3*(1-steps)*steps**2*cy1 + steps**3*ey) return np.column_stack([xs, ys])
[docs]def connect_edges_pd(graph): """ Given a Graph element containing abstract edges compute edge segments directly connecting the source and target nodes. This operation depends on pandas and is a lot faster than the pure NumPy equivalent. """ edges = graph.dframe() = 'graph_edge_index' edges = edges.reset_index() nodes = graph.nodes.dframe() src, tgt = graph.kdims x, y, idx = graph.nodes.kdims[:3] df = pd.merge(edges, nodes, left_on=[], right_on=[]) df = df.rename(columns={ 'src_x', 'src_y'}) df = pd.merge(df, nodes, left_on=[], right_on=[]) df = df.rename(columns={ 'dst_x', 'dst_y'}) df = df.sort_values('graph_edge_index').drop(['graph_edge_index'], axis=1) cols = ["src_x", "src_y", "dst_x", "dst_y"] edge_segments = list(df[cols].values.reshape(df.index.size, 2, 2)) return edge_segments
[docs]def connect_tri_edges_pd(trimesh): """ Given a TriMesh element containing abstract edges compute edge segments directly connecting the source and target nodes. This operation depends on pandas and is a lot faster than the pure NumPy equivalent. """ edges = trimesh.dframe().copy() = 'trimesh_edge_index' edges = edges.drop("color", errors="ignore", axis=1).reset_index() nodes = trimesh.nodes.dframe().copy() = 'node_index' nodes = nodes.drop(["color", "z"], errors="ignore", axis=1) v1, v2, v3 = trimesh.kdims x, y, idx = trimesh.nodes.kdims[:3] df = pd.merge(edges, nodes, left_on=[], right_on=[]) df = df.rename(columns={ 'x0', 'y0'}) df = pd.merge(df, nodes, left_on=[], right_on=[]) df = df.rename(columns={ 'x1', 'y1'}) df = pd.merge(df, nodes, left_on=[], right_on=[]) df = df.rename(columns={ 'x2', 'y2'}) df = df.sort_values('trimesh_edge_index').drop(['trimesh_edge_index'], axis=1) return df[['x0', 'y0', 'x1', 'y1', 'x2', 'y2']]
[docs]def connect_edges(graph): """ Given a Graph element containing abstract edges compute edge segments directly connecting the source and target nodes. This operation just uses internal HoloViews operations and will be a lot slower than the pandas equivalent. """ paths = [] for start, end in graph.array(graph.kdims): start_ds = graph.nodes[:, :, start] end_ds = graph.nodes[:, :, end] if not len(start_ds) or not len(end_ds): raise ValueError('Could not find node positions for all edges') start = start_ds.array(start_ds.kdims[:2]) end = end_ds.array(end_ds.kdims[:2]) paths.append(np.array([start[0], end[0]])) return paths