text: from __future__ import absolute_import
  
  from collections import OrderedDict, defaultdict, Iterable
  
  try:
      import itertools.izip as zip
  except ImportError:
      pass
  
  
  import numpy as np
  array_types = (np.ndarray,)
  
  try:
      import dask.array as da
      array_types += (da.Array,)
  except ImportError:
      da = None
  
  def is_dask(array):
      return da and isinstance(array, da.Array)
  
  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, sorted_context
  from .. import util
  
  
  
  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 = [d.name 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 = OrderedDict([(d, []) for d in dimensions])
          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.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')
  
          for dim in kdims+vdims:
              name = dim.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], array_types):
                  data[name] = np.array(data[name])
  
          kdim_names = [d.name if isinstance(d, Dimension) else d for d in kdims]
          vdim_names = [d.name if isinstance(d, Dimension) else 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 (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((s!=e and (s+1)!=e) for s, e in zip(shape, valid_shape)):
                  raise error('Key dimension values and value array %s '
                              'shapes do not match. Expected shape %s, '
                              'actual shape: %s' % (vdim, valid_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, v.data if isinstance(v, Dataset) else v)
                                         for k, v in datasets.data.items()])
          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[dim.name] = np.array(values)
          for vdim in vdims:
              arrays = [grid[vdim.name] for grid in grids]
              shapes = set(arr.shape for arr in arrays)
              if len(shapes) > 1:
                  raise DataError('When concatenating gridded data the shape '
                                  'of arrays must match. %s found that arrays '
                                  'along the %s dimension do not match.' %
                                  (cls.__name__, vdim.name))
              stack = np.stack if any(is_dask(arr) for arr in arrays) else da.stack
              new_data[vdim.name] = stack(arrays, -1)
          return new_data
  
  
      @classmethod
      def irregular(cls, dataset, dim):
          return dataset.data[dim.name if isinstance(dim, Dimension) else 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):
          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):
          shape = dataset.data[dataset.vdims[0].name].shape
          if gridded:
              return shape
          else:
              return (np.product(shape), 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)
          deltas = 0.5 * np.diff(coord, axis=axis)
          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)
  
  
      @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 = dataset.data[dim.name]
              else:
                  data = util.expand_grid_coords(dataset, dim)
              if edges and data.shape == dataset.data[dataset.vdims[0].name].shape:
                  data = cls._infer_interval_breaks(data, axis=1)
                  data = cls._infer_interval_breaks(data, axis=0)
              return data
  
          data = dataset.data[dim.name]
          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
  
  
      @classmethod
      def canonicalize(cls, dataset, data, data_coords=None, virtual_coords=[]):
          """
          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 data_coords is None:
              data_coords = dataset.dimensions('key', label='name')[::-1]
  
          # Reorient data
          invert = False
          slices = []
          for d in data_coords:
              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[tuple(slices)] if invert else data
  
          # Transpose data
          dims = [name for name in data_coords
                  if isinstance(cls.coords(dataset, name), array_types)]
          dropped = [dims.index(d) for d in dims
                     if d not in dataset.kdims+virtual_coords]
          if dropped:
              data = np.squeeze(data, axis=tuple(dropped))
  
          if not any(cls.irregular(dataset, d) for d in dataset.kdims):
              inds = [dims.index(kd.name) 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])
  
          # 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 i, (kd, ind) in enumerate(zip(dataset.kdims[::-1], indices)):
              coords = cls.coords(dataset, kd.name, True)
              if np.isscalar(ind):
                  ind = [ind]
              else:
                  all_scalar = False
              selected[kd.name] = coords[ind]
              adjusted_inds.append(ind)
          for kd in dataset.kdims:
              if kd.name not in selected:
                  coords = cls.coords(dataset, kd.name)
                  selected[kd.name] = 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[d.name] = arr[tuple(adjusted_inds)]
          return tuple(selected[d.name] for d in dataset.dimensions())
  
  
      @classmethod
      def values(cls, dataset, dim, expanded=True, flat=True, compute=True):
          dim = dataset.get_dimension(dim, strict=True)
          if dim in dataset.vdims or dataset.data[dim.name].ndim > 1:
              data = dataset.data[dim.name]
              data = cls.canonicalize(dataset, data)
              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, dim.name, expanded=True)
              return data.T.flatten() if flat else data
          else:
              return cls.coords(dataset, dim.name, 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]
          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 dataset.data[d.name].ndim > 1]
          if invalid:
              if len(invalid) == 1: invalid = "'%s'" % 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, d.name) 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 = dataset.select(**select)
                  group_data = group_data if np.isscalar(group_data) else group_data.columns()
              else:
                  group_data = cls.select(dataset, **select)
  
              if np.isscalar(group_data) or (isinstance(group_data, 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, array_types):
                      group_data = {dataset.vdims[0].name: group_data}
                  for vdim in dataset.vdims:
                      data = group_data[vdim.name]
                      data = data.transpose(transpose[::-1])
                      group_data[vdim.name] = 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 isinstance(ind, tuple):
              ind = slice(*ind)
          if isinstance(ind, 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):
          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(d.name, 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 "
                                       "sampled data. %s value along %s dimension."
                                       "must be a slice or 2D boolean mask."
                                       % (ind, dim))
                  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("Index %s less than lower bound "
                                           "of %s for %s dimension." % (ind, emin, dim))
                      elif ind >= emax:
                          raise IndexError("Index %s more than or equal to upper bound "
                                           "of %s for %s dimension." % (ind, emax, dim))
                      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[dim.name] = 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):
                  if da and isinstance(dataset.data[kdim.name], da.Array):
                      data[kdim.name] = dataset.data[kdim.name].vindex[index]
                  else:
                      data[kdim.name] = np.asarray(data[kdim.name])[index]
  
          for vdim in dataset.vdims:
              if da and isinstance(dataset.data[vdim.name], da.Array):
                  data[vdim.name] = dataset.data[vdim.name].vindex[index]
              else:
                  data[vdim.name] = np.asarray(dataset.data[vdim.name])[index]
  
          if indexed:
              if len(dataset.vdims) == 1:
                  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[vd.name])
                                   for vd in dataset.vdims])
          return data
  
  
      @classmethod
      def sample(cls, dataset, samples=[]):
          """
          Samples the gridded data into dataset of samples.
          """
          ndims = dataset.ndims
          dimensions = dataset.dimensions(label='name')
          arrays = [dataset.data[vdim.name] 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 = dataset.data[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)
                  if da and isinstance(array, da.Array):
                      data[vdim.name].append(array.flatten().vindex[tuple(flat_index)])
                  else:
                      data[vdim.name].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 = [kd.name if isinstance(kd, Dimension) else kd for kd in kdims]
          data = {kdim: dataset.data[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:
              values = dataset.data[vdim.name]
              atleast_1d = da.atleast_1d if is_dask(values) else np.atleast_1d
              data[vdim.name] = atleast_1d(function(values, 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, kd.name, expanded=False)
              if len(vals) == 1:
                  constant[kd.name] = vals[0]
          data = {k: values for k, values in dataset.data.items()
                  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[vdim.name]
                  if len(axes) > 1:
                      vdata = vdata.transpose(axes[::-1])
                  if dropped_axes:
                      vdata = np.squeeze(vdata, axis=dropped_axes)
                  data[vdim.name] = 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 if isinstance(dimension, Dimension) else dimension
          return dict(dataset.data, **{dim: values})
  
  
      @classmethod
      def sort(cls, dataset, by=[], reverse=False):
          if not by or by in [dataset.kdims, dataset.dimensions()]:
              return dataset.data
          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:
              if new_data 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):
          if dataset._binned and dimension in dataset.kdims:
              expanded = cls.irregular(dataset, dimension)
              column = cls.coords(dataset, dimension, expanded=expanded, edges=True)
          else:
              column = cls.values(dataset, dimension, flat=False)
          if column.dtype.kind == 'M':
              dmin, dmax = column.min(), column.max()
              if da and isinstance(column, da.Array):
                  return da.compute(dmin, dmax)
              return dmin, dmax
          elif len(column) == 0:
              return np.NaN, np.NaN
          else:
              try:
                  dmin, dmax = (np.nanmin(column), np.nanmax(column))
                  if da and isinstance(column, da.Array):
                      return da.compute(dmin, dmax)
                  return dmin, dmax
              except TypeError:
                  column.sort()
                  return column[0], column[-1]
  
  
  Interface.register(GridInterface)
  