venv/lib/python3.10/site-packages/scipy/sparse/_index.py

"""Indexing mixin for sparse array/matrix classes.
"""
from __future__ import annotations

from typing import TYPE_CHECKING

import numpy as np
from ._sputils import isintlike

if TYPE_CHECKING:
    import numpy.typing as npt

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 _raise_on_1d_array_slice(self):
        """We do not currently support 1D sparse arrays.

        This function is called each time that a 1D array would
        result, raising an error instead.

        Once 1D sparse arrays are implemented, it should be removed.
        """
        from scipy.sparse import sparray

        if isinstance(self, sparray):
            raise NotImplementedError(
                'We have not yet implemented 1D sparse slices; '
                'please index using explicit indices, e.g. `x[:, [0]]`'
            )

    def __getitem__(self, key):
        row, col = self._validate_indices(key)

        # Dispatch to specialized methods.
        if isinstance(row, INT_TYPES):
            if isinstance(col, INT_TYPES):
                return self._get_intXint(row, col)
            elif isinstance(col, slice):
                self._raise_on_1d_array_slice()
                return self._get_intXslice(row, col)
            elif col.ndim == 1:
                self._raise_on_1d_array_slice()
                return self._get_intXarray(row, col)
            elif col.ndim == 2:
                return self._get_intXarray(row, col)
            raise IndexError('index results in >2 dimensions')
        elif isinstance(row, slice):
            if isinstance(col, INT_TYPES):
                self._raise_on_1d_array_slice()
                return self._get_sliceXint(row, col)
            elif isinstance(col, slice):
                if row == slice(None) and row == col:
                    return self.copy()
                return self._get_sliceXslice(row, col)
            elif col.ndim == 1:
                return self._get_sliceXarray(row, col)
            raise IndexError('index results in >2 dimensions')
        elif row.ndim == 1:
            if isinstance(col, INT_TYPES):
                self._raise_on_1d_array_slice()
                return self._get_arrayXint(row, col)
            elif isinstance(col, slice):
                return self._get_arrayXslice(row, col)
        else:  # row.ndim == 2
            if isinstance(col, INT_TYPES):
                return self._get_arrayXint(row, col)
            elif isinstance(col, slice):
                raise IndexError('index results in >2 dimensions')
            elif row.shape[1] == 1 and (col.ndim == 1 or col.shape[0] == 1):
                # special case for outer indexing
                return self._get_columnXarray(row[:,0], col.ravel())

        # The only remaining case is inner (fancy) 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:
            return self.__class__(np.atleast_2d(row).shape, dtype=self.dtype)
        return self._get_arrayXarray(row, col)

    def __setitem__(self, key, x):
        row, col = self._validate_indices(key)

        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')

        from ._base import issparse
        if issparse(x):
            if i.ndim == 1:
                # Inner indexing, so treat them like row vectors.
                i = i[None]
                j = j[None]
            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')
            if x.shape[0] == 0 or x.shape[1] == 0:
                return
            x = x.tocoo(copy=True)
            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):
        # First, check if indexing with single boolean matrix.
        from ._base import _spbase
        if (isinstance(key, (_spbase, np.ndarray)) and
                key.ndim == 2 and key.dtype.kind == 'b'):
            if key.shape != self.shape:
                raise IndexError('boolean index shape does not match array shape')
            row, col = key.nonzero()
        else:
            row, col = _unpack_index(key)
        M, N = self.shape

        def _validate_bool_idx(
            idx: npt.NDArray[np.bool_],
            axis_size: int,
            axis_name: str
        ) -> npt.NDArray[np.int_]:
            if len(idx) != axis_size:
                raise IndexError(
                    f"boolean {axis_name} index has incorrect length: {len(idx)} "
                    f"instead of {axis_size}"
                )
            return _boolean_index_to_array(idx)

        if isintlike(row):
            row = int(row)
            if row < -M or row >= M:
                raise IndexError('row index (%d) out of range' % row)
            if row < 0:
                row += M
        elif (bool_row := _compatible_boolean_index(row)) is not None:
            row = _validate_bool_idx(bool_row, M, "row")
        elif not isinstance(row, slice):
            row = self._asindices(row, M)

        if isintlike(col):
            col = int(col)
            if col < -N or col >= N:
                raise IndexError('column index (%d) out of range' % col)
            if col < 0:
                col += N
        elif (bool_col := _compatible_boolean_index(col)) is not None:
            col = _validate_bool_idx(bool_col, N, "column")
        elif not isinstance(col, slice):
            col = self._asindices(col, N)

        return row, col

    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_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_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 _unpack_index(index) -> tuple[
    int | slice | npt.NDArray[np.bool_ | np.int_],
    int | slice | npt.NDArray[np.bool_ | np.int_]
]:
    """ Parse index. Always return a tuple of the form (row, col).
    Valid type for row/col is integer, slice, array of bool, or array of integers.
    """
    # Parse any ellipses.
    index = _check_ellipsis(index)

    # Next, parse the tuple or object
    if isinstance(index, tuple):
        if len(index) == 2:
            row, col = index
        elif len(index) == 1:
            row, col = index[0], slice(None)
        else:
            raise IndexError('invalid number of indices')
    else:
        idx = _compatible_boolean_index(index)
        if idx is None:
            row, col = index, slice(None)
        elif idx.ndim < 2:
            return idx, slice(None)
        elif idx.ndim == 2:
            return idx.nonzero()
    # Next, check for validity and transform the index as needed.
    from ._base import issparse
    if issparse(row) or issparse(col):
        # Supporting sparse boolean indexing with both row and col does
        # not work because spmatrix.ndim is always 2.
        raise IndexError(
            'Indexing with sparse matrices is not supported '
            'except boolean indexing where matrix and index '
            'are equal shapes.')
    return row, col


def _check_ellipsis(index):
    """Process indices with Ellipsis. Returns modified index."""
    if index is Ellipsis:
        return (slice(None), slice(None))

    if not isinstance(index, tuple):
        return index

    # Find any Ellipsis objects.
    ellipsis_indices = [i for i, v in enumerate(index) if v is Ellipsis]
    if not ellipsis_indices:
        return index
    if len(ellipsis_indices) > 1:
        raise IndexError("an index can only have a single ellipsis ('...')")

    # Replace the Ellipsis object with 0, 1, or 2 null-slices as needed.
    i, = ellipsis_indices
    num_slices = max(0, 3 - len(index))
    return index[:i] + (slice(None),) * num_slices + index[i + 1:]


def _maybe_bool_ndarray(idx):
    """Returns a compatible array if elements are boolean.
    """
    idx = np.asanyarray(idx)
    if idx.dtype.kind == 'b':
        return idx
    return None


def _first_element_bool(idx, max_dim=2):
    """Returns True if first element of the incompatible
    array type is boolean.
    """
    if max_dim < 1:
        return None
    try:
        first = next(iter(idx), None)
    except TypeError:
        return None
    if isinstance(first, bool):
        return True
    return _first_element_bool(first, max_dim-1)


def _compatible_boolean_index(idx):
    """Returns a boolean index array that can be converted to
    integer array. Returns None if no such array exists.
    """
    # Presence of attribute `ndim` indicates a compatible array type.
    if hasattr(idx, 'ndim') or _first_element_bool(idx):
        return _maybe_bool_ndarray(idx)
    return None


def _boolean_index_to_array(idx):
    if idx.ndim > 1:
        raise IndexError('invalid index shape')
    return np.where(idx)[0]