code: """Compressed Sparse Row matrix format"""
  
  from __future__ import division, print_function, absolute_import
  
  __docformat__ = "restructuredtext en"
  
  __all__ = ['csr_matrix', 'isspmatrix_csr']
  
  
  import numpy as np
  from scipy._lib.six import xrange
  
  from .base import spmatrix
  
  from ._sparsetools import csr_tocsc, csr_tobsr, csr_count_blocks, \
          get_csr_submatrix, csr_sample_values
  from .sputils import (upcast, isintlike, IndexMixin, issequence,
                        get_index_dtype, ismatrix)
  
  from .compressed import _cs_matrix
  
  
  class csr_matrix(_cs_matrix, IndexMixin):
      """
      Compressed Sparse Row matrix
  
      This can be instantiated in several ways:
          csr_matrix(D)
              with a dense matrix or rank-2 ndarray D
  
          csr_matrix(S)
              with another sparse 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
          Number of nonzero elements
      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
          Whether indices are sorted
  
      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)
  
      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]])
  
      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]])
  
      """
      format = 'csr'
  
      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."))
  
          M, N = self.shape
  
          from .csc import csc_matrix
          return csc_matrix((self.data, self.indices,
                             self.indptr), shape=(N, M), copy=copy)
  
      transpose.__doc__ = spmatrix.transpose.__doc__
  
      def tolil(self, copy=False):
          from .lil import lil_matrix
          lil = lil_matrix(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 xrange(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__ = spmatrix.tolil.__doc__
  
      def tocsr(self, copy=False):
          if copy:
              return self.copy()
          else:
              return self
  
      tocsr.__doc__ = spmatrix.tocsr.__doc__
  
      def tocsc(self, copy=False):
          idx_dtype = 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)
  
          from .csc import csc_matrix
          A = csc_matrix((data, indices, indptr), shape=self.shape)
          A.has_sorted_indices = True
          return A
  
      tocsr.__doc__ = spmatrix.tocsr.__doc__
  
      def tobsr(self, blocksize=None, copy=True):
          from .bsr import bsr_matrix
  
          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 bsr_matrix(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 = 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 bsr_matrix((data,indices,indptr), shape=self.shape)
  
      tobsr.__doc__ = spmatrix.tobsr.__doc__
  
      # these functions are used by the parent class (_cs_matrix)
      # to remove redudancy between csc_matrix and csr_matrix
      def _swap(self,x):
          """swap the members of x if this is a column-oriented matrix
          """
          return (x[0],x[1])
  
      def __getitem__(self, key):
          def asindices(x):
              try:
                  x = np.asarray(x)
  
                  # Check index contents, to avoid creating 64-bit arrays needlessly
                  idx_dtype = get_index_dtype((x,), check_contents=True)
                  if idx_dtype != x.dtype:
                      x = x.astype(idx_dtype)
              except:
                  raise IndexError('invalid index')
              else:
                  return x
  
          def check_bounds(indices, N):
              if indices.size == 0:
                  return (0, 0)
  
              max_indx = indices.max()
              if max_indx >= N:
                  raise IndexError('index (%d) out of range' % max_indx)
  
              min_indx = indices.min()
              if min_indx < -N:
                  raise IndexError('index (%d) out of range' % (N + min_indx))
  
              return (min_indx,max_indx)
  
          def extractor(indices,N):
              """Return a sparse matrix P so that P*self implements
              slicing of the form self[[1,2,3],:]
              """
              indices = asindices(indices)
  
              (min_indx,max_indx) = check_bounds(indices,N)
  
              if min_indx < 0:
                  indices = indices.copy()
                  indices[indices < 0] += N
  
              indptr = np.arange(len(indices)+1, dtype=indices.dtype)
              data = np.ones(len(indices), dtype=self.dtype)
              shape = (len(indices),N)
  
              return csr_matrix((data,indices,indptr), shape=shape)
  
          row, col = self._unpack_index(key)
  
          # First attempt to use original row optimized methods
          # [1, ?]
          if isintlike(row):
              # [i, j]
              if isintlike(col):
                  return self._get_single_element(row, col)
              # [i, 1:2]
              elif isinstance(col, slice):
                  return self._get_row_slice(row, col)
              # [i, [1, 2]]
              elif issequence(col):
                  P = extractor(col,self.shape[1]).T
                  return self[row, :] * P
          elif isinstance(row, slice):
              # [1:2,??]
              if ((isintlike(col) and row.step in (1, None)) or
                      (isinstance(col, slice) and
                       col.step in (1, None) and
                       row.step in (1, None))):
                  # col is int or slice with step 1, row is slice with step 1.
                  return self._get_submatrix(row, col)
              elif issequence(col):
                  # row is slice, col is sequence.
                  P = extractor(col,self.shape[1]).T        # [1:2,[1,2]]
                  sliced = self
                  if row != slice(None, None, None):
                      sliced = sliced[row,:]
                  return sliced * P
  
          elif issequence(row):
              # [[1,2],??]
              if isintlike(col) or isinstance(col,slice):
                  P = extractor(row, self.shape[0])     # [[1,2],j] or [[1,2],1:2]
                  extracted = P * self
                  if col == slice(None, None, None):
                      return extracted
                  else:
                      return extracted[:,col]
  
          elif ismatrix(row) and issequence(col):
              if len(row[0]) == 1 and isintlike(row[0][0]):
                  # [[[1],[2]], [1,2]], outer indexing
                  row = asindices(row)
                  P_row = extractor(row[:,0], self.shape[0])
                  P_col = extractor(col, self.shape[1]).T
                  return P_row * self * P_col
  
          if not (issequence(col) and issequence(row)):
              # Sample elementwise
              row, col = self._index_to_arrays(row, col)
  
          row = asindices(row)
          col = asindices(col)
          if row.shape != col.shape:
              raise IndexError('number of row and column indices differ')
          assert row.ndim <= 2
  
          num_samples = np.size(row)
          if num_samples == 0:
              return csr_matrix(np.atleast_2d(row).shape, dtype=self.dtype)
          check_bounds(row, self.shape[0])
          check_bounds(col, self.shape[1])
  
          val = np.empty(num_samples, dtype=self.dtype)
          csr_sample_values(self.shape[0], self.shape[1],
                            self.indptr, self.indices, self.data,
                            num_samples, row.ravel(), col.ravel(), val)
          if row.ndim == 1:
              # row and col are 1d
              return np.asmatrix(val)
          return self.__class__(val.reshape(row.shape))
  
      def getrow(self, i):
          """Returns a copy of row i of the matrix, as a (1 x n)
          CSR matrix (row vector).
          """
          return self._get_submatrix(i, slice(None))
  
      def getcol(self, i):
          """Returns a copy of column i of the matrix, as a (m x 1)
          CSR matrix (column vector).
          """
          return self._get_submatrix(slice(None), i)
  
      def _get_row_slice(self, i, cslice):
          """Returns a copy of row self[i, cslice]
          """
          if i < 0:
              i += self.shape[0]
  
          if i < 0 or i >= self.shape[0]:
              raise IndexError('index (%d) out of range' % i)
  
          start, stop, stride = cslice.indices(self.shape[1])
  
          if stride == 1:
              # for stride == 1, _get_submatrix is ~30% faster than below
              row_slice = self._get_submatrix(i, cslice)
  
          else:
              # other strides need new code
              row_indices = self.indices[self.indptr[i]:self.indptr[i + 1]]
              row_data = self.data[self.indptr[i]:self.indptr[i + 1]]
  
              if stride > 0:
                  ind = (row_indices >= start) & (row_indices < stop)
              elif stride < 0:
                  ind = (row_indices <= start) & (row_indices > stop)
  
              if abs(stride) > 1:
                  ind = 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, int(np.ceil(float(stop - start) / stride)))
  
              row_slice = csr_matrix((row_data, row_indices, row_indptr),
                                     shape=shape)
  
          return row_slice
  
      def _get_submatrix(self, row_slice, col_slice):
          """Return a submatrix of this matrix (new matrix is created)."""
  
          M,N = self.shape
  
          def process_slice(sl, num):
              if isinstance(sl, slice):
                  if sl.step not in (1, None):
                      raise ValueError('slicing with step != 1 not supported')
                  i0, i1 = sl.start, sl.stop
                  if i0 is None:
                      i0 = 0
                  elif i0 < 0:
                      i0 = num + i0
  
                  if i1 is None:
                      i1 = num
                  elif i1 < 0:
                      i1 = num + i1
                  return i0, i1
  
              elif isintlike(sl):
                  if sl < 0:
                      sl += num
                  return sl, sl + 1
              else:
                  raise TypeError('expected slice or scalar')
  
          def check_bounds(i0, i1, num):
              if not (0 <= i0 <= num) or not (0 <= i1 <= num) or not (i0 <= i1):
                  raise IndexError(
                        "index out of bounds: 0 <= %d <= %d, 0 <= %d <= %d,"
                        " %d <= %d" % (i0, num, i1, num, i0, i1))
  
          i0, i1 = process_slice(row_slice, M)
          j0, j1 = process_slice(col_slice, N)
          check_bounds(i0, i1, M)
          check_bounds(j0, j1, N)
  
          indptr, indices, data = get_csr_submatrix(M, N,
                  self.indptr, self.indices, self.data,
                  int(i0), int(i1), int(j0), int(j1))
  
          shape = (i1 - i0, j1 - j0)
  
          return self.__class__((data,indices,indptr), shape=shape)
  
  def isspmatrix_csr(x):
      return isinstance(x, csr_matrix)
  
apis: numpy.atleast_2d numpy.empty numpy.zeros scipy._lib.six.xrange numpy.asarray numpy.size numpy.asmatrix