Vehicle-cpp/hkpc/software/scipy/sparse/_dia.py

"""Sparse DIAgonal format"""

__docformat__ = "restructuredtext en"

__all__ = ['dia_matrix', 'isspmatrix_dia']

import numpy as np

from ._base import isspmatrix, _formats, spmatrix
from ._data import _data_matrix
from ._sputils import (isshape, upcast_char, getdtype, get_index_dtype,
                       get_sum_dtype, validateaxis, check_shape)
from ._sparsetools import dia_matvec


class dia_matrix(_data_matrix):
    """Sparse matrix with DIAgonal storage

    This can be instantiated in several ways:
        dia_matrix(D)
            with a dense matrix

        dia_matrix(S)
            with another sparse matrix S (equivalent to S.todia())

        dia_matrix((M, N), [dtype])
            to construct an empty matrix with shape (M, N),
            dtype is optional, defaulting to dtype='d'.

        dia_matrix((data, offsets), shape=(M, N))
            where the ``data[k,:]`` stores the diagonal entries for
            diagonal ``offsets[k]`` (See example below)

    Attributes
    ----------
    dtype : dtype
        Data type of the matrix
    shape : 2-tuple
        Shape of the matrix
    ndim : int
        Number of dimensions (this is always 2)
    nnz
        Number of stored values, including explicit zeros
    data
        DIA format data array of the matrix
    offsets
        DIA format offset array of the matrix

    Notes
    -----

    Sparse matrices can be used in arithmetic operations: they support
    addition, subtraction, multiplication, division, and matrix power.

    Examples
    --------

    >>> import numpy as np
    >>> from scipy.sparse import dia_matrix
    >>> dia_matrix((3, 4), dtype=np.int8).toarray()
    array([[0, 0, 0, 0],
           [0, 0, 0, 0],
           [0, 0, 0, 0]], dtype=int8)

    >>> data = np.array([[1, 2, 3, 4]]).repeat(3, axis=0)
    >>> offsets = np.array([0, -1, 2])
    >>> dia_matrix((data, offsets), shape=(4, 4)).toarray()
    array([[1, 0, 3, 0],
           [1, 2, 0, 4],
           [0, 2, 3, 0],
           [0, 0, 3, 4]])

    >>> from scipy.sparse import dia_matrix
    >>> n = 10
    >>> ex = np.ones(n)
    >>> data = np.array([ex, 2 * ex, ex])
    >>> offsets = np.array([-1, 0, 1])
    >>> dia_matrix((data, offsets), shape=(n, n)).toarray()
    array([[2., 1., 0., ..., 0., 0., 0.],
           [1., 2., 1., ..., 0., 0., 0.],
           [0., 1., 2., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 2., 1., 0.],
           [0., 0., 0., ..., 1., 2., 1.],
           [0., 0., 0., ..., 0., 1., 2.]])
    """
    format = 'dia'

    def __init__(self, arg1, shape=None, dtype=None, copy=False):
        _data_matrix.__init__(self)

        if isspmatrix_dia(arg1):
            if copy:
                arg1 = arg1.copy()
            self.data = arg1.data
            self.offsets = arg1.offsets
            self._shape = check_shape(arg1.shape)
        elif isspmatrix(arg1):
            if isspmatrix_dia(arg1) and copy:
                A = arg1.copy()
            else:
                A = arg1.todia()
            self.data = A.data
            self.offsets = A.offsets
            self._shape = check_shape(A.shape)
        elif isinstance(arg1, tuple):
            if isshape(arg1):
                # It's a tuple of matrix dimensions (M, N)
                # create empty matrix
                self._shape = check_shape(arg1)
                self.data = np.zeros((0,0), getdtype(dtype, default=float))
                idx_dtype = get_index_dtype(maxval=max(self.shape))
                self.offsets = np.zeros((0), dtype=idx_dtype)
            else:
                try:
                    # Try interpreting it as (data, offsets)
                    data, offsets = arg1
                except Exception as e:
                    raise ValueError('unrecognized form for dia_matrix constructor') from e
                else:
                    if shape is None:
                        raise ValueError('expected a shape argument')
                    self.data = np.atleast_2d(np.array(arg1[0], dtype=dtype, copy=copy))
                    self.offsets = np.atleast_1d(np.array(arg1[1],
                                                          dtype=get_index_dtype(maxval=max(shape)),
                                                          copy=copy))
                    self._shape = check_shape(shape)
        else:
            #must be dense, convert to COO first, then to DIA
            try:
                arg1 = np.asarray(arg1)
            except Exception as e:
                raise ValueError("unrecognized form for"
                        " %s_matrix constructor" % self.format) from e
            A = self._coo_container(arg1, dtype=dtype, shape=shape).todia()
            self.data = A.data
            self.offsets = A.offsets
            self._shape = check_shape(A.shape)

        if dtype is not None:
            self.data = self.data.astype(dtype)

        #check format
        if self.offsets.ndim != 1:
            raise ValueError('offsets array must have rank 1')

        if self.data.ndim != 2:
            raise ValueError('data array must have rank 2')

        if self.data.shape[0] != len(self.offsets):
            raise ValueError('number of diagonals (%d) '
                    'does not match the number of offsets (%d)'
                    % (self.data.shape[0], len(self.offsets)))

        if len(np.unique(self.offsets)) != len(self.offsets):
            raise ValueError('offset array contains duplicate values')

    def __repr__(self):
        format = _formats[self.getformat()][1]
        return "<%dx%d sparse matrix of type '%s'\n" \
               "\twith %d stored elements (%d diagonals) in %s format>" % \
               (self.shape + (self.dtype.type, self.nnz, self.data.shape[0],
                              format))

    def _data_mask(self):
        """Returns a mask of the same shape as self.data, where
        mask[i,j] is True when data[i,j] corresponds to a stored element."""
        num_rows, num_cols = self.shape
        offset_inds = np.arange(self.data.shape[1])
        row = offset_inds - self.offsets[:,None]
        mask = (row >= 0)
        mask &= (row < num_rows)
        mask &= (offset_inds < num_cols)
        return mask

    def count_nonzero(self):
        mask = self._data_mask()
        return np.count_nonzero(self.data[mask])

    def getnnz(self, axis=None):
        if axis is not None:
            raise NotImplementedError("getnnz over an axis is not implemented "
                                      "for DIA format")
        M,N = self.shape
        nnz = 0
        for k in self.offsets:
            if k > 0:
                nnz += min(M,N-k)
            else:
                nnz += min(M+k,N)
        return int(nnz)

    getnnz.__doc__ = spmatrix.getnnz.__doc__
    count_nonzero.__doc__ = spmatrix.count_nonzero.__doc__

    def sum(self, axis=None, dtype=None, out=None):
        validateaxis(axis)

        if axis is not None and axis < 0:
            axis += 2

        res_dtype = get_sum_dtype(self.dtype)
        num_rows, num_cols = self.shape
        ret = None

        if axis == 0:
            mask = self._data_mask()
            x = (self.data * mask).sum(axis=0)
            if x.shape[0] == num_cols:
                res = x
            else:
                res = np.zeros(num_cols, dtype=x.dtype)
                res[:x.shape[0]] = x
            ret = self._ascontainer(res, dtype=res_dtype)

        else:
            row_sums = np.zeros((num_rows, 1), dtype=res_dtype)
            one = np.ones(num_cols, dtype=res_dtype)
            dia_matvec(num_rows, num_cols, len(self.offsets),
                       self.data.shape[1], self.offsets, self.data, one, row_sums)

            row_sums = self._ascontainer(row_sums)

            if axis is None:
                return row_sums.sum(dtype=dtype, out=out)

            ret = self._ascontainer(row_sums.sum(axis=axis))

        if out is not None and out.shape != ret.shape:
            raise ValueError("dimensions do not match")

        return ret.sum(axis=(), dtype=dtype, out=out)

    sum.__doc__ = spmatrix.sum.__doc__

    def _add_sparse(self, other):

        # Check if other is also of type dia_matrix
        if not isinstance(other, type(self)):
            # If other is not of type dia_matrix, default to
            # converting to csr_matrix, as is done in the _add_sparse
            # method of parent class spmatrix
            return self.tocsr()._add_sparse(other)

        # The task is to compute m = self + other
        # Start by making a copy of self, of the datatype
        # that should result from adding self and other
        dtype = np.promote_types(self.dtype, other.dtype)
        m = self.astype(dtype, copy=True)

        # Then, add all the stored diagonals of other.
        for d in other.offsets:
            # Check if the diagonal has already been added.
            if d in m.offsets:
                # If the diagonal is already there, we need to take
                # the sum of the existing and the new
                m.setdiag(m.diagonal(d) + other.diagonal(d), d)
            else:
                m.setdiag(other.diagonal(d), d)
        return m

    def _mul_vector(self, other):
        x = other

        y = np.zeros(self.shape[0], dtype=upcast_char(self.dtype.char,
                                                       x.dtype.char))

        L = self.data.shape[1]

        M,N = self.shape

        dia_matvec(M,N, len(self.offsets), L, self.offsets, self.data, x.ravel(), y.ravel())

        return y

    def _mul_multimatrix(self, other):
        return np.hstack([self._mul_vector(col).reshape(-1,1) for col in other.T])

    def _setdiag(self, values, k=0):
        M, N = self.shape

        if values.ndim == 0:
            # broadcast
            values_n = np.inf
        else:
            values_n = len(values)

        if k < 0:
            n = min(M + k, N, values_n)
            min_index = 0
            max_index = n
        else:
            n = min(M, N - k, values_n)
            min_index = k
            max_index = k + n

        if values.ndim != 0:
            # allow also longer sequences
            values = values[:n]

        data_rows, data_cols = self.data.shape
        if k in self.offsets:
            if max_index > data_cols:
                data = np.zeros((data_rows, max_index), dtype=self.data.dtype)
                data[:, :data_cols] = self.data
                self.data = data
            self.data[self.offsets == k, min_index:max_index] = values
        else:
            self.offsets = np.append(self.offsets, self.offsets.dtype.type(k))
            m = max(max_index, data_cols)
            data = np.zeros((data_rows + 1, m), dtype=self.data.dtype)
            data[:-1, :data_cols] = self.data
            data[-1, min_index:max_index] = values
            self.data = data

    def todia(self, copy=False):
        if copy:
            return self.copy()
        else:
            return self

    todia.__doc__ = spmatrix.todia.__doc__

    def transpose(self, axes=None, copy=False):
        if axes is not None:
            raise ValueError(("Sparse matrices do not support "
                              "an 'axes' parameter because swapping "
                              "dimensions is the only logical permutation."))

        num_rows, num_cols = self.shape
        max_dim = max(self.shape)

        # flip diagonal offsets
        offsets = -self.offsets

        # re-align the data matrix
        r = np.arange(len(offsets), dtype=np.intc)[:, None]
        c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]
        pad_amount = max(0, max_dim-self.data.shape[1])
        data = np.hstack((self.data, np.zeros((self.data.shape[0], pad_amount),
                                              dtype=self.data.dtype)))
        data = data[r, c]
        return self._dia_container((data, offsets), shape=(
            num_cols, num_rows), copy=copy)

    transpose.__doc__ = spmatrix.transpose.__doc__

    def diagonal(self, k=0):
        rows, cols = self.shape
        if k <= -rows or k >= cols:
            return np.empty(0, dtype=self.data.dtype)
        idx, = np.nonzero(self.offsets == k)
        first_col = max(0, k)
        last_col = min(rows + k, cols)
        result_size = last_col - first_col
        if idx.size == 0:
            return np.zeros(result_size, dtype=self.data.dtype)
        result = self.data[idx[0], first_col:last_col]
        padding = result_size - len(result)
        if padding > 0:
            result = np.pad(result, (0, padding), mode='constant')
        return result

    diagonal.__doc__ = spmatrix.diagonal.__doc__

    def tocsc(self, copy=False):
        if self.nnz == 0:
            return self._csc_container(self.shape, dtype=self.dtype)

        num_rows, num_cols = self.shape
        num_offsets, offset_len = self.data.shape
        offset_inds = np.arange(offset_len)

        row = offset_inds - self.offsets[:,None]
        mask = (row >= 0)
        mask &= (row < num_rows)
        mask &= (offset_inds < num_cols)
        mask &= (self.data != 0)

        idx_dtype = get_index_dtype(maxval=max(self.shape))
        indptr = np.zeros(num_cols + 1, dtype=idx_dtype)
        indptr[1:offset_len+1] = np.cumsum(mask.sum(axis=0)[:num_cols])
        if offset_len < num_cols:
            indptr[offset_len+1:] = indptr[offset_len]
        indices = row.T[mask.T].astype(idx_dtype, copy=False)
        data = self.data.T[mask.T]
        return self._csc_container((data, indices, indptr), shape=self.shape,
                                   dtype=self.dtype)

    tocsc.__doc__ = spmatrix.tocsc.__doc__

    def tocoo(self, copy=False):
        num_rows, num_cols = self.shape
        num_offsets, offset_len = self.data.shape
        offset_inds = np.arange(offset_len)

        row = offset_inds - self.offsets[:,None]
        mask = (row >= 0)
        mask &= (row < num_rows)
        mask &= (offset_inds < num_cols)
        mask &= (self.data != 0)
        row = row[mask]
        col = np.tile(offset_inds, num_offsets)[mask.ravel()]
        data = self.data[mask]

        A = self._coo_container(
            (data, (row, col)), shape=self.shape, dtype=self.dtype
        )
        A.has_canonical_format = True
        return A

    tocoo.__doc__ = spmatrix.tocoo.__doc__

    # needed by _data_matrix
    def _with_data(self, data, copy=True):
        """Returns a matrix with the same sparsity structure as self,
        but with different data.  By default the structure arrays are copied.
        """
        if copy:
            return self._dia_container(
                (data, self.offsets.copy()), shape=self.shape
            )
        else:
            return self._dia_container(
                (data, self.offsets), shape=self.shape
            )

    def resize(self, *shape):
        shape = check_shape(shape)
        M, N = shape
        # we do not need to handle the case of expanding N
        self.data = self.data[:, :N]

        if (M > self.shape[0] and
                np.any(self.offsets + self.shape[0] < self.data.shape[1])):
            # explicitly clear values that were previously hidden
            mask = (self.offsets[:, None] + self.shape[0] <=
                    np.arange(self.data.shape[1]))
            self.data[mask] = 0

        self._shape = shape

    resize.__doc__ = spmatrix.resize.__doc__


def isspmatrix_dia(x):
    """Is x of dia_matrix type?

    Parameters
    ----------
    x
        object to check for being a dia matrix

    Returns
    -------
    bool
        True if x is a dia matrix, False otherwise

    Examples
    --------
    >>> from scipy.sparse import dia_matrix, isspmatrix_dia
    >>> isspmatrix_dia(dia_matrix([[5]]))
    True

    >>> from scipy.sparse import dia_matrix, csr_matrix, isspmatrix_dia
    >>> isspmatrix_dia(csr_matrix([[5]]))
    False
    """
    from ._arrays import dia_array
    return isinstance(x, dia_matrix) or isinstance(x, dia_array)