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indexing.py
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indexing.py
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# -*- coding: utf-8 -*-
import collections
import itertools
import math
import numbers
import numpy as np
from zarr.errors import (err_boundscheck, err_negative_step,
err_too_many_indices, err_vindex_invalid_selection)
def is_integer(x):
return isinstance(x, numbers.Integral)
def is_integer_array(x, ndim=None):
t = hasattr(x, 'shape') and hasattr(x, 'dtype') and x.dtype.kind in 'ui'
if ndim is not None:
t = t and len(x.shape) == ndim
return t
def is_bool_array(x, ndim=None):
t = hasattr(x, 'shape') and hasattr(x, 'dtype') and x.dtype == bool
if ndim is not None:
t = t and len(x.shape) == ndim
return t
def is_scalar(value, dtype):
if np.isscalar(value):
return True
if isinstance(value, tuple) and dtype.names and len(value) == len(dtype.names):
return True
return False
def normalize_integer_selection(dim_sel, dim_len):
# normalize type to int
dim_sel = int(dim_sel)
# handle wraparound
if dim_sel < 0:
dim_sel = dim_len + dim_sel
# handle out of bounds
if dim_sel >= dim_len or dim_sel < 0:
err_boundscheck(dim_len)
return dim_sel
ChunkDimProjection = collections.namedtuple(
'ChunkDimProjection',
('dim_chunk_ix', 'dim_chunk_sel', 'dim_out_sel')
)
"""A mapping from chunk to output array for a single dimension.
Parameters
----------
dim_chunk_ix
Index of chunk.
dim_chunk_sel
Selection of items from chunk array.
dim_out_sel
Selection of items in target (output) array.
"""
class IntDimIndexer(object):
def __init__(self, dim_sel, dim_len, dim_chunk_len):
# normalize
dim_sel = normalize_integer_selection(dim_sel, dim_len)
# store attributes
self.dim_sel = dim_sel
self.dim_len = dim_len
self.dim_chunk_len = dim_chunk_len
self.nitems = 1
def __iter__(self):
dim_chunk_ix = self.dim_sel // self.dim_chunk_len
dim_offset = dim_chunk_ix * self.dim_chunk_len
dim_chunk_sel = self.dim_sel - dim_offset
dim_out_sel = None
yield ChunkDimProjection(dim_chunk_ix, dim_chunk_sel, dim_out_sel)
def ceildiv(a, b):
return math.ceil(a / b)
class SliceDimIndexer(object):
def __init__(self, dim_sel, dim_len, dim_chunk_len):
# normalize
self.start, self.stop, self.step = dim_sel.indices(dim_len)
if self.step < 1:
err_negative_step()
# store attributes
self.dim_len = dim_len
self.dim_chunk_len = dim_chunk_len
self.nitems = max(0, ceildiv((self.stop - self.start), self.step))
self.nchunks = ceildiv(self.dim_len, self.dim_chunk_len)
def __iter__(self):
# figure out the range of chunks we need to visit
dim_chunk_ix_from = self.start // self.dim_chunk_len
dim_chunk_ix_to = ceildiv(self.stop, self.dim_chunk_len)
# iterate over chunks in range
for dim_chunk_ix in range(dim_chunk_ix_from, dim_chunk_ix_to):
# compute offsets for chunk within overall array
dim_offset = dim_chunk_ix * self.dim_chunk_len
dim_limit = min(self.dim_len, (dim_chunk_ix + 1) * self.dim_chunk_len)
# determine chunk length, accounting for trailing chunk
dim_chunk_len = dim_limit - dim_offset
if self.start < dim_offset:
# selection starts before current chunk
dim_chunk_sel_start = 0
remainder = (dim_offset - self.start) % self.step
if remainder:
dim_chunk_sel_start += self.step - remainder
# compute number of previous items, provides offset into output array
dim_out_offset = ceildiv((dim_offset - self.start), self.step)
else:
# selection starts within current chunk
dim_chunk_sel_start = self.start - dim_offset
dim_out_offset = 0
if self.stop > dim_limit:
# selection ends after current chunk
dim_chunk_sel_stop = dim_chunk_len
else:
# selection ends within current chunk
dim_chunk_sel_stop = self.stop - dim_offset
dim_chunk_sel = slice(dim_chunk_sel_start, dim_chunk_sel_stop, self.step)
dim_chunk_nitems = ceildiv((dim_chunk_sel_stop - dim_chunk_sel_start),
self.step)
dim_out_sel = slice(dim_out_offset, dim_out_offset + dim_chunk_nitems)
yield ChunkDimProjection(dim_chunk_ix, dim_chunk_sel, dim_out_sel)
def check_selection_length(selection, shape):
if len(selection) > len(shape):
err_too_many_indices(selection, shape)
def replace_ellipsis(selection, shape):
selection = ensure_tuple(selection)
# count number of ellipsis present
n_ellipsis = sum(1 for i in selection if i is Ellipsis)
if n_ellipsis > 1:
# more than 1 is an error
raise IndexError("an index can only have a single ellipsis ('...')")
elif n_ellipsis == 1:
# locate the ellipsis, count how many items to left and right
n_items_l = selection.index(Ellipsis) # items to left of ellipsis
n_items_r = len(selection) - (n_items_l + 1) # items to right of ellipsis
n_items = len(selection) - 1 # all non-ellipsis items
if n_items >= len(shape):
# ellipsis does nothing, just remove it
selection = tuple(i for i in selection if i != Ellipsis)
else:
# replace ellipsis with as many slices are needed for number of dims
new_item = selection[:n_items_l] + ((slice(None),) * (len(shape) - n_items))
if n_items_r:
new_item += selection[-n_items_r:]
selection = new_item
# fill out selection if not completely specified
if len(selection) < len(shape):
selection += (slice(None),) * (len(shape) - len(selection))
# check selection not too long
check_selection_length(selection, shape)
return selection
def replace_lists(selection):
return tuple(
np.asarray(dim_sel) if isinstance(dim_sel, list) else dim_sel
for dim_sel in selection
)
def ensure_tuple(v):
if not isinstance(v, tuple):
v = (v,)
return v
ChunkProjection = collections.namedtuple(
'ChunkProjection',
('chunk_coords', 'chunk_selection', 'out_selection')
)
"""A mapping of items from chunk to output array. Can be used to extract items from the
chunk array for loading into an output array. Can also be used to extract items from a
value array for setting/updating in a chunk array.
Parameters
----------
chunk_coords
Indices of chunk.
chunk_selection
Selection of items from chunk array.
out_selection
Selection of items in target (output) array.
"""
def is_slice(s):
return isinstance(s, slice)
def is_contiguous_slice(s):
return is_slice(s) and (s.step is None or s.step == 1)
def is_positive_slice(s):
return is_slice(s) and (s.step is None or s.step >= 1)
def is_contiguous_selection(selection):
selection = ensure_tuple(selection)
return all([
(is_integer_array(s) or is_contiguous_slice(s) or s == Ellipsis)
for s in selection
])
def is_basic_selection(selection):
selection = ensure_tuple(selection)
return all([is_integer(s) or is_positive_slice(s) for s in selection])
# noinspection PyProtectedMember
class BasicIndexer(object):
def __init__(self, selection, array):
# handle ellipsis
selection = replace_ellipsis(selection, array._shape)
# setup per-dimension indexers
dim_indexers = []
for dim_sel, dim_len, dim_chunk_len in \
zip(selection, array._shape, array._chunks):
if is_integer(dim_sel):
dim_indexer = IntDimIndexer(dim_sel, dim_len, dim_chunk_len)
elif is_slice(dim_sel):
dim_indexer = SliceDimIndexer(dim_sel, dim_len, dim_chunk_len)
else:
raise IndexError('unsupported selection item for basic indexing; '
'expected integer or slice, got {!r}'
.format(type(dim_sel)))
dim_indexers.append(dim_indexer)
self.dim_indexers = dim_indexers
self.shape = tuple(s.nitems for s in self.dim_indexers
if not isinstance(s, IntDimIndexer))
self.drop_axes = None
def __iter__(self):
for dim_projections in itertools.product(*self.dim_indexers):
chunk_coords = tuple(p.dim_chunk_ix for p in dim_projections)
chunk_selection = tuple(p.dim_chunk_sel for p in dim_projections)
out_selection = tuple(p.dim_out_sel for p in dim_projections
if p.dim_out_sel is not None)
yield ChunkProjection(chunk_coords, chunk_selection, out_selection)
class BoolArrayDimIndexer(object):
def __init__(self, dim_sel, dim_len, dim_chunk_len):
# check number of dimensions
if not is_bool_array(dim_sel, 1):
raise IndexError('Boolean arrays in an orthogonal selection must '
'be 1-dimensional only')
# check shape
if dim_sel.shape[0] != dim_len:
raise IndexError('Boolean array has the wrong length for dimension; '
'expected {}, got {}'.format(dim_len, dim_sel.shape[0]))
# store attributes
self.dim_sel = dim_sel
self.dim_len = dim_len
self.dim_chunk_len = dim_chunk_len
self.nchunks = ceildiv(self.dim_len, self.dim_chunk_len)
# precompute number of selected items for each chunk
self.chunk_nitems = np.zeros(self.nchunks, dtype='i8')
for dim_chunk_ix in range(self.nchunks):
dim_offset = dim_chunk_ix * self.dim_chunk_len
self.chunk_nitems[dim_chunk_ix] = np.count_nonzero(
self.dim_sel[dim_offset:dim_offset + self.dim_chunk_len]
)
self.chunk_nitems_cumsum = np.cumsum(self.chunk_nitems)
self.nitems = self.chunk_nitems_cumsum[-1]
self.dim_chunk_ixs = np.nonzero(self.chunk_nitems)[0]
def __iter__(self):
# iterate over chunks with at least one item
for dim_chunk_ix in self.dim_chunk_ixs:
# find region in chunk
dim_offset = dim_chunk_ix * self.dim_chunk_len
dim_chunk_sel = self.dim_sel[dim_offset:dim_offset + self.dim_chunk_len]
# pad out if final chunk
if dim_chunk_sel.shape[0] < self.dim_chunk_len:
tmp = np.zeros(self.dim_chunk_len, dtype=bool)
tmp[:dim_chunk_sel.shape[0]] = dim_chunk_sel
dim_chunk_sel = tmp
# find region in output
if dim_chunk_ix == 0:
start = 0
else:
start = self.chunk_nitems_cumsum[dim_chunk_ix - 1]
stop = self.chunk_nitems_cumsum[dim_chunk_ix]
dim_out_sel = slice(start, stop)
yield ChunkDimProjection(dim_chunk_ix, dim_chunk_sel, dim_out_sel)
class Order:
UNKNOWN = 0
INCREASING = 1
DECREASING = 2
UNORDERED = 3
@staticmethod
def check(a):
diff = np.diff(a)
diff_positive = diff >= 0
n_diff_positive = np.count_nonzero(diff_positive)
all_increasing = n_diff_positive == len(diff_positive)
any_increasing = n_diff_positive > 0
if all_increasing:
order = Order.INCREASING
elif any_increasing:
order = Order.UNORDERED
else:
order = Order.DECREASING
return order
def wraparound_indices(x, dim_len):
loc_neg = x < 0
if np.any(loc_neg):
x[loc_neg] = x[loc_neg] + dim_len
def boundscheck_indices(x, dim_len):
if np.any(x < 0) or np.any(x >= dim_len):
err_boundscheck(dim_len)
class IntArrayDimIndexer(object):
"""Integer array selection against a single dimension."""
def __init__(self, dim_sel, dim_len, dim_chunk_len, wraparound=True, boundscheck=True,
order=Order.UNKNOWN):
# ensure 1d array
dim_sel = np.asanyarray(dim_sel)
if not is_integer_array(dim_sel, 1):
raise IndexError('integer arrays in an orthogonal selection must be '
'1-dimensional only')
# handle wraparound
if wraparound:
wraparound_indices(dim_sel, dim_len)
# handle out of bounds
if boundscheck:
boundscheck_indices(dim_sel, dim_len)
# store attributes
self.dim_len = dim_len
self.dim_chunk_len = dim_chunk_len
self.nchunks = ceildiv(self.dim_len, self.dim_chunk_len)
self.nitems = len(dim_sel)
# determine which chunk is needed for each selection item
# note: for dense integer selections, the division operation here is the
# bottleneck
dim_sel_chunk = dim_sel // dim_chunk_len
# determine order of indices
if order == Order.UNKNOWN:
order = Order.check(dim_sel)
self.order = order
if self.order == Order.INCREASING:
self.dim_sel = dim_sel
self.dim_out_sel = None
elif self.order == Order.DECREASING:
self.dim_sel = dim_sel[::-1]
# TODO should be possible to do this without creating an arange
self.dim_out_sel = np.arange(self.nitems - 1, -1, -1)
else:
# sort indices to group by chunk
self.dim_out_sel = np.argsort(dim_sel_chunk)
self.dim_sel = np.take(dim_sel, self.dim_out_sel)
# precompute number of selected items for each chunk
self.chunk_nitems = np.bincount(dim_sel_chunk, minlength=self.nchunks)
# find chunks that we need to visit
self.dim_chunk_ixs = np.nonzero(self.chunk_nitems)[0]
# compute offsets into the output array
self.chunk_nitems_cumsum = np.cumsum(self.chunk_nitems)
def __iter__(self):
for dim_chunk_ix in self.dim_chunk_ixs:
# find region in output
if dim_chunk_ix == 0:
start = 0
else:
start = self.chunk_nitems_cumsum[dim_chunk_ix - 1]
stop = self.chunk_nitems_cumsum[dim_chunk_ix]
if self.order == Order.INCREASING:
dim_out_sel = slice(start, stop)
else:
dim_out_sel = self.dim_out_sel[start:stop]
# find region in chunk
dim_offset = dim_chunk_ix * self.dim_chunk_len
dim_chunk_sel = self.dim_sel[start:stop] - dim_offset
yield ChunkDimProjection(dim_chunk_ix, dim_chunk_sel, dim_out_sel)
def slice_to_range(s, l):
return range(*s.indices(l))
def ix_(selection, shape):
"""Convert an orthogonal selection to a numpy advanced (fancy) selection, like numpy.ix_
but with support for slices and single ints."""
# normalisation
selection = replace_ellipsis(selection, shape)
# replace slice and int as these are not supported by numpy.ix_
selection = [slice_to_range(dim_sel, dim_len) if isinstance(dim_sel, slice)
else [dim_sel] if is_integer(dim_sel)
else dim_sel
for dim_sel, dim_len in zip(selection, shape)]
# now get numpy to convert to a coordinate selection
selection = np.ix_(*selection)
return selection
def oindex(a, selection):
"""Implementation of orthogonal indexing with slices and ints."""
selection = replace_ellipsis(selection, a.shape)
drop_axes = tuple([i for i, s in enumerate(selection) if is_integer(s)])
selection = ix_(selection, a.shape)
result = a[selection]
if drop_axes:
result = result.squeeze(axis=drop_axes)
return result
def oindex_set(a, selection, value):
selection = replace_ellipsis(selection, a.shape)
drop_axes = tuple([i for i, s in enumerate(selection) if is_integer(s)])
selection = ix_(selection, a.shape)
if not np.isscalar(value) and drop_axes:
value = np.asanyarray(value)
value_selection = [slice(None)] * len(a.shape)
for i in drop_axes:
value_selection[i] = np.newaxis
value_selection = tuple(value_selection)
value = value[value_selection]
a[selection] = value
# noinspection PyProtectedMember
class OrthogonalIndexer(object):
def __init__(self, selection, array):
# handle ellipsis
selection = replace_ellipsis(selection, array._shape)
# normalize list to array
selection = replace_lists(selection)
# setup per-dimension indexers
dim_indexers = []
for dim_sel, dim_len, dim_chunk_len in \
zip(selection, array._shape, array._chunks):
if is_integer(dim_sel):
dim_indexer = IntDimIndexer(dim_sel, dim_len, dim_chunk_len)
elif isinstance(dim_sel, slice):
dim_indexer = SliceDimIndexer(dim_sel, dim_len, dim_chunk_len)
elif is_integer_array(dim_sel):
dim_indexer = IntArrayDimIndexer(dim_sel, dim_len, dim_chunk_len)
elif is_bool_array(dim_sel):
dim_indexer = BoolArrayDimIndexer(dim_sel, dim_len, dim_chunk_len)
else:
raise IndexError('unsupported selection item for orthogonal indexing; '
'expected integer, slice, integer array or Boolean '
'array, got {!r}'
.format(type(dim_sel)))
dim_indexers.append(dim_indexer)
self.array = array
self.dim_indexers = dim_indexers
self.shape = tuple(s.nitems for s in self.dim_indexers
if not isinstance(s, IntDimIndexer))
self.is_advanced = not is_basic_selection(selection)
if self.is_advanced:
self.drop_axes = tuple([i for i, dim_indexer in enumerate(self.dim_indexers)
if isinstance(dim_indexer, IntDimIndexer)])
else:
self.drop_axes = None
def __iter__(self):
for dim_projections in itertools.product(*self.dim_indexers):
chunk_coords = tuple(p.dim_chunk_ix for p in dim_projections)
chunk_selection = tuple(p.dim_chunk_sel for p in dim_projections)
out_selection = tuple(p.dim_out_sel for p in dim_projections
if p.dim_out_sel is not None)
# handle advanced indexing arrays orthogonally
if self.is_advanced:
# N.B., numpy doesn't support orthogonal indexing directly as yet,
# so need to work around via np.ix_. Also np.ix_ does not support a
# mixture of arrays and slices or integers, so need to convert slices
# and integers into ranges.
chunk_selection = ix_(chunk_selection, self.array._chunks)
# special case for non-monotonic indices
if not is_basic_selection(out_selection):
out_selection = ix_(out_selection, self.shape)
yield ChunkProjection(chunk_coords, chunk_selection, out_selection)
class OIndex(object):
def __init__(self, array):
self.array = array
def __getitem__(self, selection):
fields, selection = pop_fields(selection)
selection = ensure_tuple(selection)
selection = replace_lists(selection)
return self.array.get_orthogonal_selection(selection, fields=fields)
def __setitem__(self, selection, value):
fields, selection = pop_fields(selection)
selection = ensure_tuple(selection)
selection = replace_lists(selection)
return self.array.set_orthogonal_selection(selection, value, fields=fields)
# noinspection PyProtectedMember
def is_coordinate_selection(selection, array):
return (
(len(selection) == len(array._shape)) and
all([is_integer(dim_sel) or is_integer_array(dim_sel)
for dim_sel in selection])
)
# noinspection PyProtectedMember
def is_mask_selection(selection, array):
return (
len(selection) == 1 and
is_bool_array(selection[0]) and
selection[0].shape == array._shape
)
# noinspection PyProtectedMember
class CoordinateIndexer(object):
def __init__(self, selection, array):
# some initial normalization
selection = ensure_tuple(selection)
selection = tuple([i] if is_integer(i) else i for i in selection)
selection = replace_lists(selection)
# validation
if not is_coordinate_selection(selection, array):
raise IndexError('invalid coordinate selection; expected one integer '
'(coordinate) array per dimension of the target array, '
'got {!r}'.format(selection))
# handle wraparound, boundscheck
for dim_sel, dim_len in zip(selection, array.shape):
# handle wraparound
wraparound_indices(dim_sel, dim_len)
# handle out of bounds
boundscheck_indices(dim_sel, dim_len)
# compute chunk index for each point in the selection
chunks_multi_index = tuple(
dim_sel // dim_chunk_len
for (dim_sel, dim_chunk_len) in zip(selection, array._chunks)
)
# broadcast selection - this will raise error if array dimensions don't match
selection = np.broadcast_arrays(*selection)
chunks_multi_index = np.broadcast_arrays(*chunks_multi_index)
# remember shape of selection, because we will flatten indices for processing
self.sel_shape = selection[0].shape if selection[0].shape else (1,)
# flatten selection
selection = [dim_sel.reshape(-1) for dim_sel in selection]
chunks_multi_index = [dim_chunks.reshape(-1) for dim_chunks in chunks_multi_index]
# ravel chunk indices
chunks_raveled_indices = np.ravel_multi_index(chunks_multi_index,
dims=array._cdata_shape)
# group points by chunk
if np.any(np.diff(chunks_raveled_indices) < 0):
# optimisation, only sort if needed
sel_sort = np.argsort(chunks_raveled_indices)
selection = tuple(dim_sel[sel_sort] for dim_sel in selection)
else:
sel_sort = None
# store attributes
self.selection = selection
self.sel_sort = sel_sort
self.shape = selection[0].shape if selection[0].shape else (1,)
self.drop_axes = None
self.array = array
# precompute number of selected items for each chunk
self.chunk_nitems = np.bincount(chunks_raveled_indices, minlength=array.nchunks)
self.chunk_nitems_cumsum = np.cumsum(self.chunk_nitems)
# locate the chunks we need to process
self.chunk_rixs = np.nonzero(self.chunk_nitems)[0]
# unravel chunk indices
self.chunk_mixs = np.unravel_index(self.chunk_rixs, array._cdata_shape)
def __iter__(self):
# iterate over chunks
for i, chunk_rix in enumerate(self.chunk_rixs):
chunk_coords = tuple(m[i] for m in self.chunk_mixs)
if chunk_rix == 0:
start = 0
else:
start = self.chunk_nitems_cumsum[chunk_rix - 1]
stop = self.chunk_nitems_cumsum[chunk_rix]
if self.sel_sort is None:
out_selection = slice(start, stop)
else:
out_selection = self.sel_sort[start:stop]
chunk_offsets = tuple(
dim_chunk_ix * dim_chunk_len
for dim_chunk_ix, dim_chunk_len in zip(chunk_coords, self.array._chunks)
)
chunk_selection = tuple(
dim_sel[start:stop] - dim_chunk_offset
for (dim_sel, dim_chunk_offset) in zip(self.selection, chunk_offsets)
)
yield ChunkProjection(chunk_coords, chunk_selection, out_selection)
# noinspection PyProtectedMember
class MaskIndexer(CoordinateIndexer):
def __init__(self, selection, array):
# some initial normalization
selection = ensure_tuple(selection)
selection = replace_lists(selection)
# validation
if not is_mask_selection(selection, array):
raise IndexError('invalid mask selection; expected one Boolean (mask)'
'array with the same shape as the target array, got {!r}'
.format(selection))
# convert to indices
selection = np.nonzero(selection[0])
# delegate the rest to superclass
super(MaskIndexer, self).__init__(selection, array)
class VIndex(object):
def __init__(self, array):
self.array = array
def __getitem__(self, selection):
fields, selection = pop_fields(selection)
selection = ensure_tuple(selection)
selection = replace_lists(selection)
if is_coordinate_selection(selection, self.array):
return self.array.get_coordinate_selection(selection, fields=fields)
elif is_mask_selection(selection, self.array):
return self.array.get_mask_selection(selection, fields=fields)
else:
err_vindex_invalid_selection(selection)
def __setitem__(self, selection, value):
fields, selection = pop_fields(selection)
selection = ensure_tuple(selection)
selection = replace_lists(selection)
if is_coordinate_selection(selection, self.array):
self.array.set_coordinate_selection(selection, value, fields=fields)
elif is_mask_selection(selection, self.array):
self.array.set_mask_selection(selection, value, fields=fields)
else:
err_vindex_invalid_selection(selection)
def check_fields(fields, dtype):
# early out
if fields is None:
return dtype
# check type
if not isinstance(fields, (str, list, tuple)):
raise IndexError("'fields' argument must be a string or list of strings; found "
"{!r}".format(type(fields)))
if fields:
if dtype.names is None:
raise IndexError("invalid 'fields' argument, array does not have any fields")
try:
if isinstance(fields, str):
# single field selection
out_dtype = dtype[fields]
else:
# multiple field selection
out_dtype = np.dtype([(f, dtype[f]) for f in fields])
except KeyError as e:
raise IndexError("invalid 'fields' argument, field not found: {!r}".format(e))
else:
return out_dtype
else:
return dtype
def check_no_multi_fields(fields):
if isinstance(fields, list):
if len(fields) == 1:
return fields[0]
elif len(fields) > 1:
raise IndexError('multiple fields are not supported for this operation')
return fields
def pop_fields(selection):
if isinstance(selection, str):
# single field selection
fields = selection
selection = ()
elif not isinstance(selection, tuple):
# single selection item, no fields
fields = None
# leave selection as-is
else:
# multiple items, split fields from selection items
fields = [f for f in selection if isinstance(f, str)]
fields = fields[0] if len(fields) == 1 else fields
selection = tuple(s for s in selection if not isinstance(s, str))
selection = selection[0] if len(selection) == 1 else selection
return fields, selection