Source code for

from collections import OrderedDict, defaultdict, Iterable

    import itertools.izip as zip
except ImportError:

import numpy as np

from .dictionary import DictInterface
from .interface import Interface, DataError
from ..dimension import Dimension
from ..element import Element
from ..dimension import OrderedDict as cyODict
from ..ndmapping import NdMapping, item_check
from .. import util

[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, OrderedDict, cyODict) 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 = [ if isinstance(d, Dimension) else d for d in kdims + vdims] if isinstance(data, tuple): data = {d: v for d, v in zip(dimensions, data)} 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') for dim in kdims+vdims: name = if isinstance(dim, Dimension) else dim if name not in data: raise ValueError("Values for dimension %s not found" % dim) if not isinstance(data[name], np.ndarray): data[name] = np.array(data[name]) kdim_names = [ if isinstance(d, Dimension) else d for d in kdims] vdim_names = [ if isinstance(d, Dimension) else d for d in vdims] expected = tuple([len(data[kd]) for kd in kdim_names]) for vdim in vdim_names: shape = data[vdim].shape error = DataError if len(shape) > 1 else ValueError if shape != expected[::-1] and not (not expected and shape == (1,)): raise error('Key dimension values and value array %s ' 'shapes do not match. Expected shape %s, ' 'actual shape: %s' % (vdim, expected[::-1], shape), cls) return data, {'kdims':kdims, 'vdims':vdims}, {} @classmethod def isscalar(cls, dataset, dim): return np.unique(cls.values(dataset, dim, expanded=False)) == 1 @classmethod def validate(cls, dataset, vdims=True): Interface.validate(dataset, vdims) @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 shape(cls, dataset, gridded=False): if gridded: return[dataset.vdims[0].name].shape else: return (cls.length(dataset), len(dataset.dimensions())) @classmethod def length(cls, dataset): return np.product([len([]) for d in dataset.kdims])
[docs] @classmethod def coords(cls, dataset, dim, ordered=False, expanded=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) if expanded: return util.expand_grid_coords(dataset, dim) data =[] if ordered and np.all(data[1:] < data[:-1]): data = data[::-1] return data
[docs] @classmethod def canonicalize(cls, dataset, data, coord_dims=None): """ Canonicalize takes an array of values as input and reorients and transposes it to match the canonical format expected by plotting functions. In addition to the dataset and the particular array to apply transforms to a list of coord_dims may be supplied in case the array indexing does not match the key dimensions of the dataset. """ if coord_dims is None: coord_dims = dataset.dimensions('key', label='name')[::-1] # Reorient data invert = False slices = [] for d in coord_dims: coords = cls.coords(dataset, d) if np.all(coords[1:] < coords[:-1]): slices.append(slice(None, None, -1)) invert = True else: slices.append(slice(None)) data = data[slices] if invert else data # Transpose data dims = [name for name in coord_dims if isinstance(cls.coords(dataset, name), np.ndarray)] dropped = [dims.index(d) for d in dims if d not in dataset.kdims] inds = [dims.index( kd in dataset.kdims] inds = [i - sum([1 for d in dropped if i>=d]) for i in inds] if dropped: data = data.squeeze(axis=tuple(dropped)) if inds: data = data.transpose(inds[::-1]) # 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, 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 vd in dataset.vdims: arr = dataset.dimension_values(vd, flat=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()) @classmethod def values(cls, dataset, dim, expanded=True, flat=True): dim = dataset.get_dimension(dim, strict=True) if dim in dataset.vdims: data = data = cls.canonicalize(dataset, data) return data.T.flatten() if flat else data elif expanded: data = cls.coords(dataset,, expanded=True) return data.flatten() if flat else data else: return cls.coords(dataset,, ordered=True) @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] kdims = [kdim for kdim in dataset.kdims if kdim not in dimensions] # 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)) group_kwargs['kdims'] = 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 = [[] for d in dimensions] # 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): 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: for vdim in dataset.vdims: group_data[] = np.squeeze(group_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 isinstance(ind, tuple): ind = slice(*ind) if isinstance(ind, np.ndarray): 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 and np.sum(index_mask) == 0: data_index = np.argmin(np.abs(values - ind)) mask = np.zeros(len(dataset), dtype=np.bool) mask[data_index] = True else: mask = index_mask return mask @classmethod def select(cls, dataset, selection_mask=None, **selection): 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) selection = [(d, selection.get(, selection.get(d.label))) for d in dimensions] data = {} value_select = [] for dim, ind in selection: values = cls.values(dataset, dim, False) mask = cls.key_select_mask(dataset, values, ind) if mask is None: mask = np.ones(values.shape, dtype=bool) else: values = values[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 vdim in dataset.vdims: data[] =[][index] if indexed: if len(dataset.vdims) == 1: arr = np.squeeze(data[dataset.vdims[0].name]) return arr if np.isscalar(arr) else arr[()] else: return np.array([np.squeeze(data[]) for vd in dataset.vdims]) return data
[docs] @classmethod def sample(cls, dataset, samples=[]): """ Samples the gridded data into dataset of 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): flat_index = np.ravel_multi_index(tuple(int_inds)[::-1], array.shape) 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 = [ if isinstance(kd, Dimension) else 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) for vdim in dataset.vdims: data[] = np.atleast_1d(function([], axis=axes, **kwargs)) return data @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 = vdata.squeeze(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 = if isinstance(dimension, Dimension) else dimension return dict(, **{dim: values}) @classmethod def sort(cls, dataset, by=[], reverse=False): 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(dataset.dimension_values(d)[rows]) if scalar: return new_data[0][0] return tuple(new_data)