Source code for holoviews.element.util

import itertools

import param
import numpy as np

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

try:
    import pandas as pd
    from ..core.data import PandasInterface
except:
    pd = None

try:
    import dask
except:
    dask = None

try:
    import xarray as xr
except:
    xr = None



[docs]def compute_edges(edges): """ Computes edges from a number of bin centers, throwing an exception if the edges are not evenly spaced. """ edges = np.asarray(edges) if edges.dtype.kind == 'i': edges = edges.astype('f') widths = np.diff(edges) if np.allclose(widths, widths[0]): width = widths[0] else: raise ValueError('Centered bins have to be of equal width.') edges -= width/2. return np.concatenate([edges, [edges[-1]+width]])
[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(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 pd and 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(['xarray', 'grid'] if xr else ['grid'], doc=""" The grid interface types to use when constructing the gridded Dataset.""") def _get_coords(self, 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) # 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 xcoords, ycoords def _aggregate_dataset(self, obj, xcoords, ycoords): """ Generates a gridded Dataset from a column-based dataset and lists of xcoords and ycoords """ dim_labels = obj.dimensions(label=True) vdims = obj.dimensions()[2:] xdim, ydim = dim_labels[:2] shape = (len(ycoords), len(xcoords)) nsamples = np.product(shape) 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[vdim.name] = values dtype = 'dataframe' if pd else 'dictionary' 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 pd: df = PandasInterface.as_dframe(reindexed) df = df.groupby([xdim, ydim], sort=False).first().reset_index() agg = reindexed.clone(df) else: agg = reindexed.aggregate([xdim, ydim], reduce_fn) # Convert data to a gridded dataset grid_data = {xdim: xcoords, ydim: ycoords} for vdim in vdims: grid_data[vdim.name] = agg.dimension_values(vdim).reshape(shape) return agg.clone(grid_data, kdims=[xdim, ydim], vdims=vdims, datatype=self.p.datatype) 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.") dtype = 'dataframe' if pd else 'dictionary' obj = Dataset(obj, datatype=[dtype]) xcoords, ycoords = self._get_coords(obj) return self._aggregate_dataset(obj, xcoords, ycoords)