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183 lines (149 loc) · 5.63 KB
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from typing import Callable, Sequence, Tuple, Any
from numpy import dtype, ndarray, ix_
import numpy
import biocutils
import copy
from .DelayedOp import DelayedOp
from .SparseNdarray import SparseNdarray
from ._subset import _sanitize_subset, _is_single_subset_noop
from .extract_dense_array import extract_dense_array
from .extract_sparse_array import extract_sparse_array
from .create_dask_array import create_dask_array
from .chunk_grid import chunk_grid
from .is_sparse import is_sparse
from .is_masked import is_masked
__author__ = "ltla"
__copyright__ = "ltla"
__license__ = "MIT"
class Subset(DelayedOp):
"""Delayed subset operation, based on Bioconductor's ``DelayedArray::DelayedSubset`` class.
This will slice the array along one or more dimensions, equivalent to the outer product of subset indices.
This class is intended for developers to construct new :py:class:`~delayedarray.DelayedArray.DelayedArray`
instances. In general, end users should not be interacting with ``Subset`` objects directly.
"""
def __init__(self, seed, subset: Tuple[Sequence[int], ...]):
"""
Args:
seed:
Any object that satisfies the seed contract, see
:py:class:`~delayedarray.DelayedArray.DelayedArray` for
details.
subset:
Tuple of length equal to the dimensionality of ``seed``,
containing the subsetted elements for each dimension. Each
entry should be a vector of integer indices specifying the
elements of the corresponding dimension to retain, where each
integer is non-negative and less than the extent of the
dimension. Unsorted and/or duplicate indices are allowed.
"""
self._seed = seed
if len(subset) != len(seed.shape):
raise ValueError(
"Dimensionality of 'seed' and 'subset' should be the same."
)
self._subset = subset
final_shape = []
for idx in subset:
final_shape.append(len(idx))
self._shape = (*final_shape,)
@property
def shape(self) -> Tuple[int, ...]:
"""
Returns:
Tuple of integers specifying the extent of each dimension of the
subsetted object.
"""
return self._shape
@property
def dtype(self) -> dtype:
"""
Returns:
NumPy type for the subsetted contents, same as ``seed``.
"""
return self._seed.dtype
@property
def seed(self):
"""
Returns:
The seed object.
"""
return self._seed
@property
def subset(self) -> Tuple[Sequence[int], ...]:
"""
Returns:
Subset sequences to be applied to each dimension of the seed.
"""
return self._subset
def _simplify_subset(x: Subset) -> Any:
seed = x.seed
if not type(seed) is Subset:
# Don't use isinstance, we don't want to collapse for Subset
# subclasses that might be doing god knows what.
return x
all_subsets = []
noop = True
for i, sub in enumerate(x.subset):
seed_sub = seed.subset[i]
new_sub = biocutils.subset_sequence(seed_sub, sub)
if noop and not _is_single_subset_noop(seed.seed.shape[i], new_sub):
noop = False
all_subsets.append(new_sub)
if noop:
return seed.seed
new_x = copy.copy(x)
new_x._seed = seed.seed
new_x._subset = (*all_subsets,)
return new_x
def _extract_array(x: Subset, subset: Tuple[Sequence[int], ...], f: Callable):
newsub = list(subset)
expanded = []
is_safe = 0
for i, s in enumerate(newsub):
cursub = x._subset[i]
replacement = biocutils.subset_sequence(cursub, s)
san_sub, san_remap = _sanitize_subset(replacement)
newsub[i] = san_sub
if san_remap is None:
is_safe += 1
san_remap = range(len(san_sub))
expanded.append(san_remap)
raw = f(x._seed, (*newsub,))
if is_safe != len(subset):
raw = raw[ix_(*expanded)]
return raw
@extract_dense_array.register
def extract_dense_array_Subset(x: Subset, subset: Tuple[Sequence[int], ...]) -> numpy.ndarray:
"""See :py:meth:`~delayedarray.extract_dense_array.extract_dense_array`."""
return _extract_array(x, subset, extract_dense_array)
@extract_sparse_array.register
def extract_sparse_array_Subset(x: Subset, subset: Tuple[Sequence[int], ...]) -> SparseNdarray:
"""See :py:meth:`~delayedarray.extract_sparse_array.extract_sparse_array`."""
return _extract_array(x, subset, extract_sparse_array)
@create_dask_array.register
def create_dask_array_Subset(x: Subset):
"""See :py:meth:`~delayedarray.create_dask_array.create_dask_array`."""
target = create_dask_array(x._seed)
# Oh god, this is horrible. But dask doesn't support ix_ yet.
ndim = len(target.shape)
for i in range(ndim):
replacement = x._subset[i]
if isinstance(replacement, range):
replacement = list(replacement)
current = [slice(None)] * ndim
current[i] = replacement
target = target[(..., *current)]
return target
@chunk_grid.register
def chunk_grid_Subset(x: Subset):
"""See :py:meth:`~delayedarray.chunk_grid.chunk_grid`."""
chunk = chunk_grid(x._seed)
return chunk.subset(x._subset)
@is_sparse.register
def is_sparse_Subset(x: Subset):
"""See :py:meth:`~delayedarray.is_sparse.is_sparse`."""
return is_sparse(x._seed)
@is_masked.register
def is_masked_Subset(x: Subset):
"""See :py:meth:`~delayedarray.is_masked.is_masked`."""
return is_masked(x._seed)