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([(d.name, 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([(d.name, np.array([])) for d in kdims])
data[vdims[0].name] = array
elif data.shape == (0, 0, len(vdims)):
array = data
data = dict([(d.name, 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, 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 = {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 {vdim.name} dimension do not match.')
stack = dask_array_module().stack if any(is_dask(arr) for arr in arrays) else np.stack
new_data[vdim.name] = stack(arrays, -1)
return new_data
@classmethod
def irregular(cls, dataset, dim):
return dataset.data[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 dataset.data]
if not_found and tuple(not_found) not in dataset.data:
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(vd.name for vd in dataset.vdims)
return vdim_tuple if vdim_tuple in dataset.data 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 = dataset.data[vdim_tuple][..., vdim_tuple.index(name)]
else:
data = dataset.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 = dataset.data[vdim_tuple].shape[:-1]
else:
shape = dataset.data[dataset.vdims[0].name].shape
if gridded:
return shape
else:
return (np.prod(shape, 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 = 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
[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(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])
# 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, 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())
[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 dataset.data.items()}
[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 dataset.data.items()}
@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 dataset.data[dim.name].ndim > 1:
vdim_tuple = cls.packed(dataset)
if vdim_tuple:
data = dataset.data[vdim_tuple][..., dataset.vdims.index(dim)]
else:
data = dataset.data[dim.name]
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, dim.name, expanded=True, ordered=canonicalize)
return data.T.flatten() if flat else data
else:
return cls.coords(dataset, dim.name, 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 dataset.data[d.name].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, 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, 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[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 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(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 "
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[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):
da = dask_array_module()
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:
da = dask_array_module()
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:
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[vd.name])
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(dataset.data)
if packed:
masked = dataset.data[packed].copy()
try:
masked[mask] = mask_val
except ValueError:
masked = masked.astype('float')
masked[mask] = mask_val
else:
for vd in dataset.vdims:
masked[vd.name] = marr = masked[vd.name].copy()
try:
marr[mask] = mask_val
except ValueError:
masked[vd.name] = 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 = [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):
da = dask_array_module()
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 = [dimension_name(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)
da = dask_array_module()
dropped = []
vdim_tuple = cls.packed(dataset)
if vdim_tuple:
values = dataset.data[vdim_tuple]
if axes:
data[vdim_tuple] = function(values, axis=axes, **kwargs)
else:
data[vdim_tuple] = values
else:
for vdim in dataset.vdims:
values = dataset.data[vdim.name]
atleast_1d = da.atleast_1d if is_dask(values) else np.atleast_1d
try:
data[vdim.name] = 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, 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(dimension)
return dict(dataset.data, **{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 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:
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(dataset.data)
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
Interface.register(GridInterface)