from collections import OrderedDict, defaultdict, Iterable
try:
import itertools.izip as zip
except ImportError:
pass
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 = [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 = {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], np.ndarray):
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])
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.data[dataset.vdims[0].name].shape
else:
return (cls.length(dataset), len(dataset.dimensions()))
@classmethod
def length(cls, dataset):
return np.product([len(dataset.data[d.name]) 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 = dataset.data[dim.name]
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.name)for 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, 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 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[vd.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):
dim = dataset.get_dimension(dim, strict=True)
if dim in dataset.vdims:
data = dataset.data.get(dim.name)
data = cls.canonicalize(dataset, data)
return data.T.flatten() if flat else data
elif expanded:
data = cls.coords(dataset, dim.name, expanded=True)
return data.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]
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 = [dataset.data[d.name] 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 = 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):
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[vdim.name] = np.squeeze(group_data[vdim.name])
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(d.name, 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[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 vdim in dataset.vdims:
data[vdim.name] = dataset.data[vdim.name][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[vd.name])
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 = [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)
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:
data[vdim.name] = np.atleast_1d(function(dataset.data[vdim.name],
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 = vdata.squeeze(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(dataset.dimension_values(d)[rows])
if scalar:
return new_data[0][0]
return tuple(new_data)
Interface.register(GridInterface)
