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"""Indexing mixin for sparse array/matrix classes.
"""
import numpy as np
from ._sputils import isintlike
from ._base import sparray, issparse
INT_TYPES = (int, np.integer)
def _broadcast_arrays(a, b):
"""
Same as np.broadcast_arrays(a, b) but old writeability rules.
NumPy >= 1.17.0 transitions broadcast_arrays to return
read-only arrays. Set writeability explicitly to avoid warnings.
Retain the old writeability rules, as our Cython code assumes
the old behavior.
"""
x, y = np.broadcast_arrays(a, b)
x.flags.writeable = a.flags.writeable
y.flags.writeable = b.flags.writeable
return x, y
class IndexMixin:
"""
This class provides common dispatching and validation logic for indexing.
"""
def __getitem__(self, key):
index, new_shape = self._validate_indices(key)
# 1D array
if len(index) == 1:
idx = index[0]
if isinstance(idx, np.ndarray):
if idx.shape == ():
idx = idx.item()
if isinstance(idx, INT_TYPES):
res = self._get_int(idx)
elif isinstance(idx, slice):
res = self._get_slice(idx)
else: # assume array idx
res = self._get_array(idx)
# package the result and return
if not isinstance(self, sparray):
return res
# handle np.newaxis in idx when result would otherwise be a scalar
if res.shape == () and new_shape != ():
if len(new_shape) == 1:
return self.__class__([res], shape=new_shape, dtype=self.dtype)
if len(new_shape) == 2:
return self.__class__([[res]], shape=new_shape, dtype=self.dtype)
return res.reshape(new_shape)
# 2D array
row, col = index
# Dispatch to specialized methods.
if isinstance(row, INT_TYPES):
if isinstance(col, INT_TYPES):
res = self._get_intXint(row, col)
elif isinstance(col, slice):
res = self._get_intXslice(row, col)
elif col.ndim == 1:
res = self._get_intXarray(row, col)
elif col.ndim == 2:
res = self._get_intXarray(row, col)
else:
raise IndexError('index results in >2 dimensions')
elif isinstance(row, slice):
if isinstance(col, INT_TYPES):
res = self._get_sliceXint(row, col)
elif isinstance(col, slice):
if row == slice(None) and row == col:
res = self.copy()
else:
res = self._get_sliceXslice(row, col)
elif col.ndim == 1:
res = self._get_sliceXarray(row, col)
else:
raise IndexError('index results in >2 dimensions')
else:
if isinstance(col, INT_TYPES):
res = self._get_arrayXint(row, col)
elif isinstance(col, slice):
res = self._get_arrayXslice(row, col)
# arrayXarray preprocess
elif (row.ndim == 2 and row.shape[1] == 1
and (col.ndim == 1 or col.shape[0] == 1)):
# outer indexing
res = self._get_columnXarray(row[:, 0], col.ravel())
else:
# inner indexing
row, col = _broadcast_arrays(row, col)
if row.shape != col.shape:
raise IndexError('number of row and column indices differ')
if row.size == 0:
res = self.__class__(np.atleast_2d(row).shape, dtype=self.dtype)
else:
res = self._get_arrayXarray(row, col)
# handle spmatrix (must be 2d, dont let 1d new_shape start reshape)
if not isinstance(self, sparray):
if new_shape == () or (len(new_shape) == 1 and res.ndim != 0):
# res handles cases not inflated by None
return res
if len(new_shape) == 1:
# shape inflated to 1D by None in index. Make 2D
new_shape = (1,) + new_shape
# reshape if needed (when None changes shape, e.g. A[1,:,None])
return res if new_shape == res.shape else res.reshape(new_shape)
# package the result and return
if res.shape != new_shape:
# handle formats that support indexing but not 1D (lil for now)
if self.format == "lil" and len(new_shape) != 2:
if res.shape == ():
return self._coo_container([res], shape = new_shape)
return res.tocoo().reshape(new_shape)
return res.reshape(new_shape)
return res
def __setitem__(self, key, x):
index, _ = self._validate_indices(key)
# 1D array
if len(index) == 1:
idx = index[0]
if issparse(x):
x = x.toarray()
else:
x = np.asarray(x, dtype=self.dtype)
if isinstance(idx, INT_TYPES):
if x.size != 1:
raise ValueError('Trying to assign a sequence to an item')
self._set_int(idx, x.flat[0])
return
if isinstance(idx, slice):
# check for simple case of slice that gives 1 item
# Note: Python `range` does not use lots of memory
idx_range = range(*idx.indices(self.shape[0]))
N = len(idx_range)
if N == 1 and x.size == 1:
self._set_int(idx_range[0], x.flat[0])
return
idx = np.arange(*idx.indices(self.shape[0]))
idx_shape = idx.shape
else:
idx_shape = idx.squeeze().shape
# broadcast scalar to full 1d
if x.squeeze().shape != idx_shape:
x = np.broadcast_to(x, idx.shape)
if x.size != 0:
self._set_array(idx, x)
return
# 2D array
row, col = index
if isinstance(row, INT_TYPES) and isinstance(col, INT_TYPES):
x = np.asarray(x, dtype=self.dtype)
if x.size != 1:
raise ValueError('Trying to assign a sequence to an item')
self._set_intXint(row, col, x.flat[0])
return
if isinstance(row, slice):
row = np.arange(*row.indices(self.shape[0]))[:, None]
else:
row = np.atleast_1d(row)
if isinstance(col, slice):
col = np.arange(*col.indices(self.shape[1]))[None, :]
if row.ndim == 1:
row = row[:, None]
else:
col = np.atleast_1d(col)
i, j = _broadcast_arrays(row, col)
if i.shape != j.shape:
raise IndexError('number of row and column indices differ')
if issparse(x):
if 0 in x.shape:
return
if i.ndim == 1:
# Inner indexing, so treat them like row vectors.
i = i[None]
j = j[None]
x = x.tocoo(copy=False).reshape(x._shape_as_2d, copy=True)
broadcast_row = x.shape[0] == 1 and i.shape[0] != 1
broadcast_col = x.shape[1] == 1 and i.shape[1] != 1
if not ((broadcast_row or x.shape[0] == i.shape[0]) and
(broadcast_col or x.shape[1] == i.shape[1])):
raise ValueError('shape mismatch in assignment')
x.sum_duplicates()
self._set_arrayXarray_sparse(i, j, x)
else:
# Make x and i into the same shape
x = np.asarray(x, dtype=self.dtype)
if x.squeeze().shape != i.squeeze().shape:
x = np.broadcast_to(x, i.shape)
if x.size == 0:
return
x = x.reshape(i.shape)
self._set_arrayXarray(i, j, x)
def _validate_indices(self, key):
"""Returns two tuples: (index tuple, requested shape tuple)"""
# single ellipsis
if key is Ellipsis:
return (slice(None),) * self.ndim, self.shape
if not isinstance(key, tuple):
key = [key]
ellps_pos = None
index_1st = []
prelim_ndim = 0
for i, idx in enumerate(key):
if idx is Ellipsis:
if ellps_pos is not None:
raise IndexError('an index can only have a single ellipsis')
ellps_pos = i
elif idx is None:
index_1st.append(idx)
elif isinstance(idx, slice) or isintlike(idx):
index_1st.append(idx)
prelim_ndim += 1
elif (ix := _compatible_boolean_index(idx, self.ndim)) is not None:
index_1st.append(ix)
prelim_ndim += ix.ndim
elif issparse(idx):
# TODO: make sparse matrix indexing work for sparray
raise IndexError(
'Indexing with sparse matrices is not supported '
'except boolean indexing where matrix and index '
'are equal shapes.')
else: # dense array
index_1st.append(np.asarray(idx))
prelim_ndim += 1
ellip_slices = (self.ndim - prelim_ndim) * [slice(None)]
if ellip_slices:
if ellps_pos is None:
index_1st.extend(ellip_slices)
else:
index_1st = index_1st[:ellps_pos] + ellip_slices + index_1st[ellps_pos:]
# second pass (have processed ellipsis and preprocessed arrays)
idx_shape = []
index_ndim = 0
index = []
array_indices = []
for i, idx in enumerate(index_1st):
if idx is None:
idx_shape.append(1)
elif isinstance(idx, slice):
index.append(idx)
Ms = self._shape[index_ndim]
len_slice = len(range(*idx.indices(Ms)))
idx_shape.append(len_slice)
index_ndim += 1
elif isintlike(idx):
N = self._shape[index_ndim]
if not (-N <= idx < N):
raise IndexError(f'index ({idx}) out of range')
idx = int(idx + N if idx < 0 else idx)
index.append(idx)
index_ndim += 1
# bool array (checked in first pass)
elif idx.dtype.kind == 'b':
ix = idx
tmp_ndim = index_ndim + ix.ndim
mid_shape = self._shape[index_ndim:tmp_ndim]
if ix.shape != mid_shape:
raise IndexError(
f"bool index {i} has shape {mid_shape} instead of {ix.shape}"
)
index.extend(ix.nonzero())
array_indices.extend(range(index_ndim, tmp_ndim))
index_ndim = tmp_ndim
else: # dense array
N = self._shape[index_ndim]
idx = self._asindices(idx, N)
index.append(idx)
array_indices.append(index_ndim)
index_ndim += 1
if index_ndim > self.ndim:
raise IndexError(
f'invalid index ndim. Array is {self.ndim}D. Index needs {index_ndim}D'
)
if len(array_indices) > 1:
idx_arrays = _broadcast_arrays(*(index[i] for i in array_indices))
if any(idx_arrays[0].shape != ix.shape for ix in idx_arrays[1:]):
shapes = " ".join(str(ix.shape) for ix in idx_arrays)
msg = (f'shape mismatch: indexing arrays could not be broadcast '
f'together with shapes {shapes}')
raise IndexError(msg)
# TODO: handle this for nD (adjacent arrays stay, separated move to start)
idx_shape = list(idx_arrays[0].shape) + idx_shape
elif len(array_indices) == 1:
arr_index = array_indices[0]
arr_shape = list(index[arr_index].shape)
idx_shape = idx_shape[:arr_index] + arr_shape + idx_shape[arr_index:]
if (ndim := len(idx_shape)) > 2:
raise IndexError(f'Only 1D or 2D arrays allowed. Index makes {ndim}D')
return tuple(index), tuple(idx_shape)
def _asindices(self, idx, length):
"""Convert `idx` to a valid index for an axis with a given length.
Subclasses that need special validation can override this method.
"""
try:
x = np.asarray(idx)
except (ValueError, TypeError, MemoryError) as e:
raise IndexError('invalid index') from e
if x.ndim not in (1, 2):
raise IndexError('Index dimension must be 1 or 2')
if x.size == 0:
return x
# Check bounds
max_indx = x.max()
if max_indx >= length:
raise IndexError('index (%d) out of range' % max_indx)
min_indx = x.min()
if min_indx < 0:
if min_indx < -length:
raise IndexError('index (%d) out of range' % min_indx)
if x is idx or not x.flags.owndata:
x = x.copy()
x[x < 0] += length
return x
def _getrow(self, i):
"""Return a copy of row i of the matrix, as a (1 x n) row vector.
"""
M, N = self.shape
i = int(i)
if i < -M or i >= M:
raise IndexError('index (%d) out of range' % i)
if i < 0:
i += M
return self._get_intXslice(i, slice(None))
def _getcol(self, i):
"""Return a copy of column i of the matrix, as a (m x 1) column vector.
"""
M, N = self.shape
i = int(i)
if i < -N or i >= N:
raise IndexError('index (%d) out of range' % i)
if i < 0:
i += N
return self._get_sliceXint(slice(None), i)
def _get_int(self, idx):
raise NotImplementedError()
def _get_slice(self, idx):
raise NotImplementedError()
def _get_array(self, idx):
raise NotImplementedError()
def _get_intXint(self, row, col):
raise NotImplementedError()
def _get_intXarray(self, row, col):
raise NotImplementedError()
def _get_intXslice(self, row, col):
raise NotImplementedError()
def _get_sliceXint(self, row, col):
raise NotImplementedError()
def _get_sliceXslice(self, row, col):
raise NotImplementedError()
def _get_sliceXarray(self, row, col):
raise NotImplementedError()
def _get_arrayXint(self, row, col):
raise NotImplementedError()
def _get_arrayXslice(self, row, col):
raise NotImplementedError()
def _get_columnXarray(self, row, col):
raise NotImplementedError()
def _get_arrayXarray(self, row, col):
raise NotImplementedError()
def _set_int(self, idx, x):
raise NotImplementedError()
def _set_array(self, idx, x):
raise NotImplementedError()
def _set_intXint(self, row, col, x):
raise NotImplementedError()
def _set_arrayXarray(self, row, col, x):
raise NotImplementedError()
def _set_arrayXarray_sparse(self, row, col, x):
# Fall back to densifying x
x = np.asarray(x.toarray(), dtype=self.dtype)
x, _ = _broadcast_arrays(x, row)
self._set_arrayXarray(row, col, x)
def _compatible_boolean_index(idx, desired_ndim):
"""Check for boolean array or array-like. peek before asarray for array-like"""
# use attribute ndim to indicate a compatible array and check dtype
# if not, look at 1st element as quick rejection of bool, else slower asanyarray
if not hasattr(idx, 'ndim'):
# is first element boolean?
try:
ix = next(iter(idx), None)
for _ in range(desired_ndim):
if isinstance(ix, bool):
break
ix = next(iter(ix), None)
else:
return None
except TypeError:
return None
# since first is boolean, construct array and check all elements
idx = np.asanyarray(idx)
if idx.dtype.kind == 'b':
return idx
return None
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