Source code for

from collections import defaultdict

import numpy as np

from .. import util
from ..dimension import dimension_name
from ..element import Element
from ..ndmapping import NdMapping, item_check, sorted_context
from .dictionary import DictInterface
from .interface import DataError, Interface
from .util import dask_array_module, finite_range, get_array_types, is_dask

[docs]class GridInterface(DictInterface): """ Interface for simple dictionary-based dataset format using a compressed representation that uses the cartesian product between key dimensions. As with DictInterface, the dictionary keys correspond to the column (i.e. dimension) names and the values are NumPy arrays representing the values in that column. To use this compressed format, the key dimensions must be orthogonal to one another with each key dimension specifying an axis of the multidimensional space occupied by the value dimension data. For instance, given an temperature recordings sampled regularly across the earth surface, a list of N unique latitudes and M unique longitudes can specify the position of NxM temperature samples. """ types = (dict,) datatype = 'grid' gridded = True @classmethod def init(cls, eltype, data, kdims, vdims): if kdims is None: kdims = eltype.kdims if vdims is None: vdims = eltype.vdims if not vdims: raise ValueError('GridInterface interface requires at least ' 'one value dimension.') ndims = len(kdims) dimensions = [dimension_name(d) for d in kdims+vdims] vdim_tuple = tuple(dimension_name(vd) for vd in vdims) if isinstance(data, tuple): if (len(data) != len(dimensions) and len(data) == (ndims+1) and len(data[-1].shape) == (ndims+1)): value_array = data[-1] data = {d: v for d, v in zip(dimensions, data[:-1])} data[vdim_tuple] = value_array else: data = {d: v for d, v in zip(dimensions, data)} elif (isinstance(data, list) and data == []): if len(kdims) == 1: data = dict([(d, []) for d in dimensions]) else: data = dict([(, np.array([])) for d in kdims]) if len(vdims) == 1: data[vdims[0].name] = np.zeros((0, 0)) else: data[vdim_tuple] = np.zeros((0, 0, len(vdims))) elif not any(isinstance(data, tuple(t for t in interface.types if t is not None)) for interface in cls.interfaces.values()): data = {k: v for k, v in zip(dimensions, zip(*data))} elif isinstance(data, np.ndarray): if data.shape == (0, 0) and len(vdims) == 1: array = data data = dict([(, np.array([])) for d in kdims]) data[vdims[0].name] = array elif data.shape == (0, 0, len(vdims)): array = data data = dict([(, np.array([])) for d in kdims]) data[vdim_tuple] = array else: if data.ndim == 1: if eltype._auto_indexable_1d and len(kdims)+len(vdims)>1: data = np.column_stack([np.arange(len(data)), data]) else: data = np.atleast_2d(data).T data = {k: data[:, i] for i, k in enumerate(dimensions)} elif isinstance(data, list) and data == []: data = {d: np.array([]) for d in dimensions[:ndims]} data.update({d: np.empty((0,) * ndims) for d in dimensions[ndims:]}) elif not isinstance(data, dict): raise TypeError('GridInterface must be instantiated as a ' 'dictionary or tuple') validate_dims = list(kdims) if vdim_tuple in data: if not isinstance(data[vdim_tuple], get_array_types()): data[vdim_tuple] = np.array(data[vdim_tuple]) else: validate_dims += vdims for dim in validate_dims: name = dimension_name(dim) if name not in data: raise ValueError(f"Values for dimension {dim} not found") if not isinstance(data[name], get_array_types()): data[name] = np.array(data[name]) kdim_names = [dimension_name(d) for d in kdims] if vdim_tuple in data: vdim_names = [vdim_tuple] else: vdim_names = [dimension_name(d) for d in vdims] expected = tuple([len(data[kd]) for kd in kdim_names]) irregular_shape = data[kdim_names[0]].shape if kdim_names else () valid_shape = irregular_shape if len(irregular_shape) > 1 else expected[::-1] shapes = tuple([data[kd].shape for kd in kdim_names]) for vdim in vdim_names: shape = data[vdim].shape error = DataError if len(shape) > 1 else ValueError if vdim_tuple in data: if shape[-1] != len(vdims): raise error('The shape of the value array does not match the number of value dimensions.') shape = shape[:-1] if (not expected and shape == (1,)) or (len(set((shape,)+shapes)) == 1 and len(shape) > 1): # If empty or an irregular mesh pass elif len(shape) != len(expected): raise error('The shape of the %s value array does not ' 'match the expected dimensionality indicated ' 'by the key dimensions. Expected %d-D array, ' 'found %d-D array.' % (vdim, len(expected), len(shape))) elif any((e not in (s, s + 1)) for s, e in zip(shape, valid_shape)): raise error(f'Key dimension values and value array {vdim} ' f'shapes do not match. Expected shape {valid_shape}, ' f'actual shape: {shape}', cls) return data, {'kdims':kdims, 'vdims':vdims}, {} @classmethod def concat(cls, datasets, dimensions, vdims): from . import Dataset with sorted_context(False): datasets = NdMapping(datasets, kdims=dimensions) datasets = datasets.clone([(k, if isinstance(v, Dataset) else v) for k, v in]) if len(datasets.kdims) > 1: items = datasets.groupby(datasets.kdims[:-1]).data.items() return cls.concat([(k, cls.concat(v, v.kdims, vdims=vdims)) for k, v in items], datasets.kdims[:-1], vdims) return cls.concat_dim(datasets, datasets.kdims[0], vdims) @classmethod def concat_dim(cls, datasets, dim, vdims): values, grids = zip(*datasets.items()) new_data = {k: v for k, v in grids[0].items() if k not in vdims} new_data[] = np.array(values) for vdim in vdims: arrays = [grid[] for grid in grids] shapes = {arr.shape for arr in arrays} if len(shapes) > 1: raise DataError('When concatenating gridded data the shape ' f'of arrays must match. {cls.__name__} found that arrays ' f'along the {} dimension do not match.') stack = dask_array_module().stack if any(is_dask(arr) for arr in arrays) else np.stack new_data[] = stack(arrays, -1) return new_data @classmethod def irregular(cls, dataset, dim): return[dimension_name(dim)].ndim > 1 @classmethod def isscalar(cls, dataset, dim): values = cls.values(dataset, dim, expanded=False) return values.shape in ((), (1,)) or len(np.unique(values)) == 1 @classmethod def validate(cls, dataset, vdims=True): dims = 'all' if vdims else 'key' not_found = [d for d in dataset.dimensions(dims, label='name') if d not in] if not_found and tuple(not_found) not in raise DataError("Supplied data does not contain specified " "dimensions, the following dimensions were " "not found: %s" % repr(not_found), cls) @classmethod def dimension_type(cls, dataset, dim): if dim in dataset.dimensions(): arr = cls.values(dataset, dim, False, False) else: return None return arr.dtype.type @classmethod def packed(cls, dataset): vdim_tuple = tuple( for vd in dataset.vdims) return vdim_tuple if vdim_tuple in else False @classmethod def dtype(cls, dataset, dimension): name = dataset.get_dimension(dimension, strict=True).name vdim_tuple = cls.packed(dataset) if vdim_tuple and name in vdim_tuple: data =[vdim_tuple][..., vdim_tuple.index(name)] else: data =[name] if util.isscalar(data): return np.array([data]).dtype else: return data.dtype @classmethod def shape(cls, dataset, gridded=False): vdim_tuple = cls.packed(dataset) if vdim_tuple: shape =[vdim_tuple].shape[:-1] else: shape =[dataset.vdims[0].name].shape if gridded: return shape else: return (, dtype=np.intp), len(dataset.dimensions())) @classmethod def length(cls, dataset): return cls.shape(dataset)[0] @classmethod def _infer_interval_breaks(cls, coord, axis=0): """ >>> GridInterface._infer_interval_breaks(np.arange(5)) array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5]) >>> GridInterface._infer_interval_breaks([[0, 1], [3, 4]], axis=1) array([[-0.5, 0.5, 1.5], [ 2.5, 3.5, 4.5]]) """ coord = np.asarray(coord) if coord.shape[axis] == 0: return np.array([], dtype=coord.dtype) if coord.shape[axis] > 1: deltas = 0.5 * np.diff(coord, axis=axis) else: deltas = np.array([0.5]) first = np.take(coord, [0], axis=axis) - np.take(deltas, [0], axis=axis) last = np.take(coord, [-1], axis=axis) + np.take(deltas, [-1], axis=axis) trim_last = tuple(slice(None, -1) if n == axis else slice(None) for n in range(coord.ndim)) return np.concatenate([first, coord[trim_last] + deltas, last], axis=axis)
[docs] @classmethod def coords(cls, dataset, dim, ordered=False, expanded=False, edges=False): """ Returns the coordinates along a dimension. Ordered ensures coordinates are in ascending order and expanded creates ND-array matching the dimensionality of the dataset. """ dim = dataset.get_dimension(dim, strict=True) irregular = cls.irregular(dataset, dim) if irregular or expanded: if irregular: data =[] else: data = util.expand_grid_coords(dataset, dim) if edges and data.shape ==[dataset.vdims[0].name].shape: data = cls._infer_interval_breaks(data, axis=1) data = cls._infer_interval_breaks(data, axis=0) return data data =[] if ordered and np.all(data[1:] < data[:-1]): data = data[::-1] shape = cls.shape(dataset, True) if dim in dataset.kdims: idx = dataset.get_dimension_index(dim) isedges = (dim in dataset.kdims and len(shape) == dataset.ndims and len(data) == (shape[dataset.ndims-idx-1]+1)) else: isedges = False if edges and not isedges: data = cls._infer_interval_breaks(data) elif not edges and isedges: data = data[:-1] + np.diff(data)/2. return data
[docs] @classmethod def canonicalize(cls, dataset, data, data_coords=None, virtual_coords=None): """ Canonicalize takes an array of values as input and reorients and transposes it to match the canonical format expected by plotting functions. In certain cases the dimensions defined via the kdims of an Element may not match the dimensions of the underlying data. A set of data_coords may be passed in to define the dimensionality of the data, which can then be used to np.squeeze the data to remove any constant dimensions. If the data is also irregular, i.e. contains multi-dimensional coordinates, a set of virtual_coords can be supplied, required by some interfaces (e.g. xarray) to index irregular datasets with a virtual integer index. This ensures these coordinates are not simply dropped. """ if virtual_coords is None: virtual_coords = [] if data_coords is None: data_coords = dataset.dimensions('key', label='name')[::-1] # Transpose data dims = [name for name in data_coords if isinstance(cls.coords(dataset, name), get_array_types())] dropped = [dims.index(d) for d in dims if d not in dataset.kdims+virtual_coords] if dropped: if len(dropped) == data.ndim: data = data.flatten() else: data = np.squeeze(data, axis=tuple(dropped)) if not any(cls.irregular(dataset, d) for d in dataset.kdims): inds = [dims.index( for kd in dataset.kdims] inds = [i - sum([1 for d in dropped if i>=d]) for i in inds] if inds: data = data.transpose(inds[::-1]) # Reorient data invert = False slices = [] for d in dataset.kdims[::-1]: coords = cls.coords(dataset, d) if np.all(coords[1:] < coords[:-1]) and not coords.ndim > 1: slices.append(slice(None, None, -1)) invert = True else: slices.append(slice(None)) data = data[tuple(slices)] if invert else data # Allow lower dimensional views into data if len(dataset.kdims) < 2: data = data.flatten() return data
@classmethod def invert_index(cls, index, length): if np.isscalar(index): return length - index elif isinstance(index, slice): start, stop = index.start, index.stop new_start, new_stop = None, None if start is not None: new_stop = length - start if stop is not None: new_start = length - stop return slice(new_start-1, new_stop-1) elif isinstance(index, util.Iterable): new_index = [] for ind in index: new_index.append(length-ind) return new_index @classmethod def ndloc(cls, dataset, indices): selected = {} adjusted_inds = [] all_scalar = True for kd, ind in zip(dataset.kdims[::-1], indices): coords = cls.coords(dataset,, True) if np.isscalar(ind): ind = [ind] else: all_scalar = False selected[] = coords[ind] adjusted_inds.append(ind) for kd in dataset.kdims: if not in selected: coords = cls.coords(dataset, selected[] = coords all_scalar = False for d in dataset.dimensions(): if d in dataset.kdims and not cls.irregular(dataset, d): continue arr = cls.values(dataset, d, flat=False, compute=False) if all_scalar and len(dataset.vdims) == 1: return arr[tuple(ind[0] for ind in adjusted_inds)] selected[] = arr[tuple(adjusted_inds)] return tuple(selected[] for d in dataset.dimensions())
[docs] @classmethod def persist(cls, dataset): da = dask_array_module() return {k: v.persist() if da and isinstance(v, da.Array) else v for k, v in}
[docs] @classmethod def compute(cls, dataset): da = dask_array_module() return {k: v.compute() if da and isinstance(v, da.Array) else v for k, v in}
@classmethod def values(cls, dataset, dim, expanded=True, flat=True, compute=True, keep_index=False, canonicalize=True): dim = dataset.get_dimension(dim, strict=True) if dim in dataset.vdims or[].ndim > 1: vdim_tuple = cls.packed(dataset) if vdim_tuple: data =[vdim_tuple][..., dataset.vdims.index(dim)] else: data =[] if canonicalize: data = cls.canonicalize(dataset, data) da = dask_array_module() if compute and da and isinstance(data, da.Array): data = data.compute() return data.T.flatten() if flat else data elif expanded: data = cls.coords(dataset,, expanded=True, ordered=canonicalize) return data.T.flatten() if flat else data else: return cls.coords(dataset,, ordered=canonicalize) @classmethod def groupby(cls, dataset, dim_names, container_type, group_type, **kwargs): # Get dimensions information dimensions = [dataset.get_dimension(d, strict=True) for d in dim_names] if 'kdims' in kwargs: kdims = kwargs['kdims'] else: kdims = [kdim for kdim in dataset.kdims if kdim not in dimensions] kwargs['kdims'] = kdims invalid = [d for d in dimensions if[].ndim > 1] if invalid: if len(invalid) == 1: invalid = f"'{invalid[0]}'" raise ValueError("Cannot groupby irregularly sampled dimension(s) %s." % invalid) # Update the kwargs appropriately for Element group types group_kwargs = {} group_type = dict if group_type == 'raw' else group_type if issubclass(group_type, Element): group_kwargs.update(util.get_param_values(dataset)) else: kwargs.pop('kdims') group_kwargs.update(kwargs) drop_dim = any(d not in group_kwargs['kdims'] for d in kdims) # Find all the keys along supplied dimensions keys = [cls.coords(dataset, for d in dimensions] transpose = [dataset.ndims-dataset.kdims.index(kd)-1 for kd in kdims] transpose += [i for i in range(dataset.ndims) if i not in transpose] # Iterate over the unique entries applying selection masks grouped_data = [] for unique_key in zip(*util.cartesian_product(keys)): select = dict(zip(dim_names, unique_key)) if drop_dim: group_data =**select) group_data = group_data if np.isscalar(group_data) else group_data.columns() else: group_data =, **select) if np.isscalar(group_data) or (isinstance(group_data, get_array_types()) and group_data.shape == ()): group_data = {dataset.vdims[0].name: np.atleast_1d(group_data)} for dim, v in zip(dim_names, unique_key): group_data[dim] = np.atleast_1d(v) elif not drop_dim: if isinstance(group_data, get_array_types()): group_data = {dataset.vdims[0].name: group_data} for vdim in dataset.vdims: data = group_data[] data = data.transpose(transpose[::-1]) group_data[] = np.squeeze(data) group_data = group_type(group_data, **group_kwargs) grouped_data.append((tuple(unique_key), group_data)) if issubclass(container_type, NdMapping): with item_check(False): return container_type(grouped_data, kdims=dimensions) else: return container_type(grouped_data) @classmethod def key_select_mask(cls, dataset, values, ind): if values.dtype.kind == 'M': ind = util.parse_datetime_selection(ind) if isinstance(ind, tuple): ind = slice(*ind) if isinstance(ind, get_array_types()): mask = ind elif isinstance(ind, slice): mask = True if ind.start is not None: mask &= ind.start <= values if ind.stop is not None: mask &= values < ind.stop # Expand empty mask if mask is True: mask = np.ones(values.shape, dtype=np.bool_) elif isinstance(ind, (set, list)): iter_slcs = [] for ik in ind: iter_slcs.append(values == ik) mask = np.logical_or.reduce(iter_slcs) elif callable(ind): mask = ind(values) elif ind is None: mask = None else: index_mask = values == ind if (dataset.ndims == 1 or dataset._binned) and np.sum(index_mask) == 0: data_index = np.argmin(np.abs(values - ind)) mask = np.zeros(len(values), dtype=np.bool_) mask[data_index] = True else: mask = index_mask if mask is None: mask = np.ones(values.shape, dtype=bool) return mask @classmethod def select(cls, dataset, selection_mask=None, **selection): if selection_mask is not None: raise ValueError(f"Masked selections currently not supported for {cls.__name__}.") dimensions = dataset.kdims val_dims = [vdim for vdim in dataset.vdims if vdim in selection] if val_dims: raise IndexError('Cannot slice value dimensions in compressed format, ' 'convert to expanded format before slicing.') indexed = cls.indexed(dataset, selection) full_selection = [(d, selection.get(, selection.get(d.label))) for d in dimensions] data = {} value_select = [] for i, (dim, ind) in enumerate(full_selection): irregular = cls.irregular(dataset, dim) values = cls.coords(dataset, dim, irregular) mask = cls.key_select_mask(dataset, values, ind) if irregular: if np.isscalar(ind) or isinstance(ind, (set, list)): raise IndexError("Indexing not supported for irregularly " f"sampled data. {ind} value along {dim} dimension." "must be a slice or 2D boolean mask.") mask = mask.max(axis=i) elif dataset._binned: edges = cls.coords(dataset, dim, False, edges=True) inds = np.argwhere(mask) if np.isscalar(ind): emin, emax = edges.min(), edges.max() if ind < emin: raise IndexError(f"Index {ind} less than lower bound " f"of {emin} for {dim} dimension.") elif ind >= emax: raise IndexError(f"Index {ind} more than or equal to upper bound " f"of {emax} for {dim} dimension.") idx = max([np.digitize([ind], edges)[0]-1, 0]) mask = np.zeros(len(values), dtype=np.bool_) mask[idx] = True values = edges[idx:idx+2] elif len(inds): values = edges[inds.min(): inds.max()+2] else: values = edges[0:0] else: values = values[mask] values, mask = np.asarray(values), np.asarray(mask) value_select.append(mask) data[] = np.array([values]) if np.isscalar(values) else values int_inds = [np.argwhere(v) for v in value_select][::-1] index = np.ix_(*[np.atleast_1d(np.squeeze(ind)) if ind.ndim > 1 else np.atleast_1d(ind) for ind in int_inds]) for kdim in dataset.kdims: if cls.irregular(dataset, dim): da = dask_array_module() if da and isinstance([], da.Array): data[] =[].vindex[index] else: data[] = np.asarray(data[])[index] for vdim in dataset.vdims: da = dask_array_module() if da and isinstance([], da.Array): data[] =[].vindex[index] else: data[] = np.asarray([])[index] if indexed: if len(dataset.vdims) == 1: da = dask_array_module() arr = np.squeeze(data[dataset.vdims[0].name]) if da and isinstance(arr, da.Array): arr = arr.compute() return arr if np.isscalar(arr) else arr[()] else: return np.array([np.squeeze(data[]) for vd in dataset.vdims]) return data @classmethod def mask(cls, dataset, mask, mask_val=np.nan): mask = cls.canonicalize(dataset, mask) packed = cls.packed(dataset) masked = dict( if packed: masked =[packed].copy() try: masked[mask] = mask_val except ValueError: masked = masked.astype('float') masked[mask] = mask_val else: for vd in dataset.vdims: masked[] = marr = masked[].copy() try: marr[mask] = mask_val except ValueError: masked[] = marr = marr.astype('float') marr[mask] = mask_val return masked
[docs] @classmethod def sample(cls, dataset, samples=None): """ Samples the gridded data into dataset of samples. """ if samples is None: samples = [] ndims = dataset.ndims dimensions = dataset.dimensions(label='name') arrays = [[] for vdim in dataset.vdims] data = defaultdict(list) for sample in samples: if np.isscalar(sample): sample = [sample] if len(sample) != ndims: sample = [sample[i] if i < len(sample) else None for i in range(ndims)] sampled, int_inds = [], [] for d, ind in zip(dimensions, sample): cdata =[d] mask = cls.key_select_mask(dataset, cdata, ind) inds = np.arange(len(cdata)) if mask is None else np.argwhere(mask) int_inds.append(inds) sampled.append(cdata[mask]) for d, arr in zip(dimensions, np.meshgrid(*sampled)): data[d].append(arr) for vdim, array in zip(dataset.vdims, arrays): da = dask_array_module() flat_index = np.ravel_multi_index(tuple(int_inds)[::-1], array.shape) if da and isinstance(array, da.Array): data[].append(array.flatten().vindex[tuple(flat_index)]) else: data[].append(array.flat[flat_index]) concatenated = {d: np.concatenate(arrays).flatten() for d, arrays in data.items()} return concatenated
@classmethod def aggregate(cls, dataset, kdims, function, **kwargs): kdims = [dimension_name(kd) for kd in kdims] data = {kdim:[kdim] for kdim in kdims} axes = tuple(dataset.ndims-dataset.get_dimension_index(kdim)-1 for kdim in dataset.kdims if kdim not in kdims) da = dask_array_module() dropped = [] vdim_tuple = cls.packed(dataset) if vdim_tuple: values =[vdim_tuple] if axes: data[vdim_tuple] = function(values, axis=axes, **kwargs) else: data[vdim_tuple] = values else: for vdim in dataset.vdims: values =[] atleast_1d = da.atleast_1d if is_dask(values) else np.atleast_1d try: data[] = atleast_1d(function(values, axis=axes, **kwargs)) except TypeError: dropped.append(vdim) return data, dropped @classmethod def reindex(cls, dataset, kdims, vdims): dropped_kdims = [kd for kd in dataset.kdims if kd not in kdims] dropped_vdims = ([vdim for vdim in dataset.vdims if vdim not in vdims] if vdims else []) constant = {} for kd in dropped_kdims: vals = cls.values(dataset,, expanded=False) if len(vals) == 1: constant[] = vals[0] data = {k: values for k, values in if k not in dropped_kdims+dropped_vdims} if len(constant) == len(dropped_kdims): joined_dims = kdims+dropped_kdims axes = tuple(dataset.ndims-dataset.kdims.index(d)-1 for d in joined_dims) dropped_axes = tuple(dataset.ndims-joined_dims.index(d)-1 for d in dropped_kdims) for vdim in vdims: vdata = data[] if len(axes) > 1: vdata = vdata.transpose(axes[::-1]) if dropped_axes: vdata = np.squeeze(vdata, axis=dropped_axes) data[] = vdata return data elif dropped_kdims: return tuple(dataset.columns(kdims+vdims).values()) return data @classmethod def add_dimension(cls, dataset, dimension, dim_pos, values, vdim): if not vdim: raise Exception("Cannot add key dimension to a dense representation.") dim = dimension_name(dimension) return dict(, **{dim: values}) @classmethod def sort(cls, dataset, by=None, reverse=False): if by is None: by = [] if not by or by in [dataset.kdims, dataset.dimensions()]: return else: raise Exception('Compressed format cannot be sorted, either instantiate ' 'in the desired order or use the expanded format.') @classmethod def iloc(cls, dataset, index): rows, cols = index scalar = False if np.isscalar(cols): scalar = np.isscalar(rows) cols = [dataset.get_dimension(cols, strict=True)] elif isinstance(cols, slice): cols = dataset.dimensions()[cols] else: cols = [dataset.get_dimension(d, strict=True) for d in cols] if np.isscalar(rows): rows = [rows] new_data = [] for d in cols: new_data.append(cls.values(dataset, d, compute=False)[rows]) if scalar: da = dask_array_module() if new_data and (da and isinstance(new_data[0], da.Array)): return new_data[0].compute()[0] return new_data[0][0] return tuple(new_data) @classmethod def range(cls, dataset, dimension): dimension = dataset.get_dimension(dimension, strict=True) if dataset._binned and dimension in dataset.kdims: expanded = cls.irregular(dataset, dimension) array = cls.coords(dataset, dimension, expanded=expanded, edges=True) else: array = cls.values(dataset, dimension, expanded=False, flat=False) if dimension.nodata is not None: array = cls.replace_value(array, dimension.nodata) da = dask_array_module() if len(array) == 0: return np.nan, np.nan if array.dtype.kind == 'M': dmin, dmax = array.min(), array.max() else: try: dmin, dmax = (np.nanmin(array), np.nanmax(array)) except TypeError: return np.nan, np.nan if da and isinstance(array, da.Array): return finite_range(array, *da.compute(dmin, dmax)) return finite_range(array, dmin, dmax) @classmethod def assign(cls, dataset, new_data): data = dict( for k, v in new_data.items(): if k in dataset.kdims: coords = cls.coords(dataset, k) if not coords.ndim > 1 and np.all(coords[1:] < coords[:-1]): v = v[::-1] data[k] = v else: data[k] = cls.canonicalize(dataset, v) return data