"""Compressed Sparse Row matrix format"""

__docformat__ = "restructuredtext en"

__all__ = ['csr_array', 'csr_matrix', 'isspmatrix_csr']

import numpy as np

from ._matrix import spmatrix
from ._base import _spbase, sparray
from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks,
                           get_csr_submatrix)
from ._sputils import upcast

from ._compressed import _cs_matrix


class _csr_base(_cs_matrix):
    _format = 'csr'

    def transpose(self, axes=None, copy=False):
        if axes is not None and axes != (1, 0):
            raise ValueError("Sparse arrays/matrices do not support "
                              "an 'axes' parameter because swapping "
                              "dimensions is the only logical permutation.")

        M, N = self.shape
        return self._csc_container((self.data, self.indices,
                                    self.indptr), shape=(N, M), copy=copy)

    transpose.__doc__ = _spbase.transpose.__doc__

    def tolil(self, copy=False):
        lil = self._lil_container(self.shape, dtype=self.dtype)

        self.sum_duplicates()
        ptr,ind,dat = self.indptr,self.indices,self.data
        rows, data = lil.rows, lil.data

        for n in range(self.shape[0]):
            start = ptr[n]
            end = ptr[n+1]
            rows[n] = ind[start:end].tolist()
            data[n] = dat[start:end].tolist()

        return lil

    tolil.__doc__ = _spbase.tolil.__doc__

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

    tocsr.__doc__ = _spbase.tocsr.__doc__

    def tocsc(self, copy=False):
        idx_dtype = self._get_index_dtype((self.indptr, self.indices),
                                    maxval=max(self.nnz, self.shape[0]))
        indptr = np.empty(self.shape[1] + 1, dtype=idx_dtype)
        indices = np.empty(self.nnz, dtype=idx_dtype)
        data = np.empty(self.nnz, dtype=upcast(self.dtype))

        csr_tocsc(self.shape[0], self.shape[1],
                  self.indptr.astype(idx_dtype),
                  self.indices.astype(idx_dtype),
                  self.data,
                  indptr,
                  indices,
                  data)

        A = self._csc_container((data, indices, indptr), shape=self.shape)
        A.has_sorted_indices = True
        return A

    tocsc.__doc__ = _spbase.tocsc.__doc__

    def tobsr(self, blocksize=None, copy=True):
        if blocksize is None:
            from ._spfuncs import estimate_blocksize
            return self.tobsr(blocksize=estimate_blocksize(self))

        elif blocksize == (1,1):
            arg1 = (self.data.reshape(-1,1,1),self.indices,self.indptr)
            return self._bsr_container(arg1, shape=self.shape, copy=copy)

        else:
            R,C = blocksize
            M,N = self.shape

            if R < 1 or C < 1 or M % R != 0 or N % C != 0:
                raise ValueError('invalid blocksize %s' % blocksize)

            blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices)

            idx_dtype = self._get_index_dtype((self.indptr, self.indices),
                                        maxval=max(N//C, blks))
            indptr = np.empty(M//R+1, dtype=idx_dtype)
            indices = np.empty(blks, dtype=idx_dtype)
            data = np.zeros((blks,R,C), dtype=self.dtype)

            csr_tobsr(M, N, R, C,
                      self.indptr.astype(idx_dtype),
                      self.indices.astype(idx_dtype),
                      self.data,
                      indptr, indices, data.ravel())

            return self._bsr_container(
                (data, indices, indptr), shape=self.shape
            )

    tobsr.__doc__ = _spbase.tobsr.__doc__

    # these functions are used by the parent class (_cs_matrix)
    # to remove redundancy between csc_matrix and csr_array
    def _swap(self, x):
        """swap the members of x if this is a column-oriented matrix
        """
        return x

    def __iter__(self):
        indptr = np.zeros(2, dtype=self.indptr.dtype)
        shape = (1, self.shape[1])
        i0 = 0
        for i1 in self.indptr[1:]:
            indptr[1] = i1 - i0
            indices = self.indices[i0:i1]
            data = self.data[i0:i1]
            yield self.__class__(
                (data, indices, indptr), shape=shape, copy=True
            )
            i0 = i1

    def _getrow(self, i):
        """Returns a copy of row i of the matrix, as a (1 x n)
        CSR matrix (row vector).
        """
        M, N = self.shape
        i = int(i)
        if i < 0:
            i += M
        if i < 0 or i >= M:
            raise IndexError('index (%d) out of range' % i)
        indptr, indices, data = get_csr_submatrix(
            M, N, self.indptr, self.indices, self.data, i, i + 1, 0, N)
        return self.__class__((data, indices, indptr), shape=(1, N),
                              dtype=self.dtype, copy=False)

    def _getcol(self, i):
        """Returns a copy of column i of the matrix, as a (m x 1)
        CSR matrix (column vector).
        """
        M, N = self.shape
        i = int(i)
        if i < 0:
            i += N
        if i < 0 or i >= N:
            raise IndexError('index (%d) out of range' % i)
        indptr, indices, data = get_csr_submatrix(
            M, N, self.indptr, self.indices, self.data, 0, M, i, i + 1)
        return self.__class__((data, indices, indptr), shape=(M, 1),
                              dtype=self.dtype, copy=False)

    def _get_intXarray(self, row, col):
        return self._getrow(row)._minor_index_fancy(col)

    def _get_intXslice(self, row, col):
        if col.step in (1, None):
            return self._get_submatrix(row, col, copy=True)
        # TODO: uncomment this once it's faster:
        # return self._getrow(row)._minor_slice(col)

        M, N = self.shape
        start, stop, stride = col.indices(N)

        ii, jj = self.indptr[row:row+2]
        row_indices = self.indices[ii:jj]
        row_data = self.data[ii:jj]

        if stride > 0:
            ind = (row_indices >= start) & (row_indices < stop)
        else:
            ind = (row_indices <= start) & (row_indices > stop)

        if abs(stride) > 1:
            ind &= (row_indices - start) % stride == 0

        row_indices = (row_indices[ind] - start) // stride
        row_data = row_data[ind]
        row_indptr = np.array([0, len(row_indices)])

        if stride < 0:
            row_data = row_data[::-1]
            row_indices = abs(row_indices[::-1])

        shape = (1, max(0, int(np.ceil(float(stop - start) / stride))))
        return self.__class__((row_data, row_indices, row_indptr), shape=shape,
                              dtype=self.dtype, copy=False)

    def _get_sliceXint(self, row, col):
        if row.step in (1, None):
            return self._get_submatrix(row, col, copy=True)
        return self._major_slice(row)._get_submatrix(minor=col)

    def _get_sliceXarray(self, row, col):
        return self._major_slice(row)._minor_index_fancy(col)

    def _get_arrayXint(self, row, col):
        return self._major_index_fancy(row)._get_submatrix(minor=col)

    def _get_arrayXslice(self, row, col):
        if col.step not in (1, None):
            col = np.arange(*col.indices(self.shape[1]))
            return self._get_arrayXarray(row, col)
        return self._major_index_fancy(row)._get_submatrix(minor=col)


def isspmatrix_csr(x):
    """Is `x` of csr_matrix type?

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

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

    Examples
    --------
    >>> from scipy.sparse import csr_array, csr_matrix, coo_matrix, isspmatrix_csr
    >>> isspmatrix_csr(csr_matrix([[5]]))
    True
    >>> isspmatrix_csr(csr_array([[5]]))
    False
    >>> isspmatrix_csr(coo_matrix([[5]]))
    False
    """
    return isinstance(x, csr_matrix)


# This namespace class separates array from matrix with isinstance
class csr_array(_csr_base, sparray):
    """
    Compressed Sparse Row array.

    This can be instantiated in several ways:
        csr_array(D)
            where D is a 2-D ndarray

        csr_array(S)
            with another sparse array or matrix S (equivalent to S.tocsr())

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

        csr_array((data, (row_ind, col_ind)), [shape=(M, N)])
            where ``data``, ``row_ind`` and ``col_ind`` satisfy the
            relationship ``a[row_ind[k], col_ind[k]] = data[k]``.

        csr_array((data, indices, indptr), [shape=(M, N)])
            is the standard CSR representation where the column indices for
            row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their
            corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.
            If the shape parameter is not supplied, the array dimensions
            are inferred from the index arrays.

    Attributes
    ----------
    dtype : dtype
        Data type of the array
    shape : 2-tuple
        Shape of the array
    ndim : int
        Number of dimensions (this is always 2)
    nnz
    size
    data
        CSR format data array of the array
    indices
        CSR format index array of the array
    indptr
        CSR format index pointer array of the array
    has_sorted_indices
    has_canonical_format
    T

    Notes
    -----

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

    Advantages of the CSR format
      - efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
      - efficient row slicing
      - fast matrix vector products

    Disadvantages of the CSR format
      - slow column slicing operations (consider CSC)
      - changes to the sparsity structure are expensive (consider LIL or DOK)

    Canonical Format
        - Within each row, indices are sorted by column.
        - There are no duplicate entries.

    Examples
    --------

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

    >>> row = np.array([0, 0, 1, 2, 2, 2])
    >>> col = np.array([0, 2, 2, 0, 1, 2])
    >>> data = np.array([1, 2, 3, 4, 5, 6])
    >>> csr_array((data, (row, col)), shape=(3, 3)).toarray()
    array([[1, 0, 2],
           [0, 0, 3],
           [4, 5, 6]])

    >>> indptr = np.array([0, 2, 3, 6])
    >>> indices = np.array([0, 2, 2, 0, 1, 2])
    >>> data = np.array([1, 2, 3, 4, 5, 6])
    >>> csr_array((data, indices, indptr), shape=(3, 3)).toarray()
    array([[1, 0, 2],
           [0, 0, 3],
           [4, 5, 6]])

    Duplicate entries are summed together:

    >>> row = np.array([0, 1, 2, 0])
    >>> col = np.array([0, 1, 1, 0])
    >>> data = np.array([1, 2, 4, 8])
    >>> csr_array((data, (row, col)), shape=(3, 3)).toarray()
    array([[9, 0, 0],
           [0, 2, 0],
           [0, 4, 0]])

    As an example of how to construct a CSR array incrementally,
    the following snippet builds a term-document array from texts:

    >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]]
    >>> indptr = [0]
    >>> indices = []
    >>> data = []
    >>> vocabulary = {}
    >>> for d in docs:
    ...     for term in d:
    ...         index = vocabulary.setdefault(term, len(vocabulary))
    ...         indices.append(index)
    ...         data.append(1)
    ...     indptr.append(len(indices))
    ...
    >>> csr_array((data, indices, indptr), dtype=int).toarray()
    array([[2, 1, 0, 0],
           [0, 1, 1, 1]])

    """


class csr_matrix(spmatrix, _csr_base):
    """
    Compressed Sparse Row matrix.

    This can be instantiated in several ways:
        csr_matrix(D)
            where D is a 2-D ndarray

        csr_matrix(S)
            with another sparse array or matrix S (equivalent to S.tocsr())

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

        csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
            where ``data``, ``row_ind`` and ``col_ind`` satisfy the
            relationship ``a[row_ind[k], col_ind[k]] = data[k]``.

        csr_matrix((data, indices, indptr), [shape=(M, N)])
            is the standard CSR representation where the column indices for
            row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their
            corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.
            If the shape parameter is not supplied, the matrix dimensions
            are inferred from the index arrays.

    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
    size
    data
        CSR format data array of the matrix
    indices
        CSR format index array of the matrix
    indptr
        CSR format index pointer array of the matrix
    has_sorted_indices
    has_canonical_format
    T

    Notes
    -----

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

    Advantages of the CSR format
      - efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
      - efficient row slicing
      - fast matrix vector products

    Disadvantages of the CSR format
      - slow column slicing operations (consider CSC)
      - changes to the sparsity structure are expensive (consider LIL or DOK)

    Canonical Format
        - Within each row, indices are sorted by column.
        - There are no duplicate entries.

    Examples
    --------

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

    >>> row = np.array([0, 0, 1, 2, 2, 2])
    >>> col = np.array([0, 2, 2, 0, 1, 2])
    >>> data = np.array([1, 2, 3, 4, 5, 6])
    >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
    array([[1, 0, 2],
           [0, 0, 3],
           [4, 5, 6]])

    >>> indptr = np.array([0, 2, 3, 6])
    >>> indices = np.array([0, 2, 2, 0, 1, 2])
    >>> data = np.array([1, 2, 3, 4, 5, 6])
    >>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()
    array([[1, 0, 2],
           [0, 0, 3],
           [4, 5, 6]])

    Duplicate entries are summed together:

    >>> row = np.array([0, 1, 2, 0])
    >>> col = np.array([0, 1, 1, 0])
    >>> data = np.array([1, 2, 4, 8])
    >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
    array([[9, 0, 0],
           [0, 2, 0],
           [0, 4, 0]])

    As an example of how to construct a CSR matrix incrementally,
    the following snippet builds a term-document matrix from texts:

    >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]]
    >>> indptr = [0]
    >>> indices = []
    >>> data = []
    >>> vocabulary = {}
    >>> for d in docs:
    ...     for term in d:
    ...         index = vocabulary.setdefault(term, len(vocabulary))
    ...         indices.append(index)
    ...         data.append(1)
    ...     indptr.append(len(indices))
    ...
    >>> csr_matrix((data, indices, indptr), dtype=int).toarray()
    array([[2, 1, 0, 0],
           [0, 1, 1, 1]])

    """

