code: """Base class for sparse matrix formats using compressed storage."""
  from __future__ import division, print_function, absolute_import
  
  __all__ = []
  
  from warnings import warn
  import operator
  
  import numpy as np
  from scipy._lib._util import _prune_array
  
  from .base import spmatrix, isspmatrix, SparseEfficiencyWarning
  from .data import _data_matrix, _minmax_mixin
  from .dia import dia_matrix
  from . import _sparsetools
  from ._sparsetools import (get_csr_submatrix, csr_sample_offsets, csr_todense,
                             csr_sample_values, csr_row_index, csr_row_slice,
                             csr_column_index1, csr_column_index2)
  from ._index import IndexMixin
  from .sputils import (upcast, upcast_char, to_native, isdense, isshape,
                        getdtype, isscalarlike, isintlike, get_index_dtype,
                        downcast_intp_index, get_sum_dtype, check_shape,
                        matrix, asmatrix, is_pydata_spmatrix)
  
  
  class _cs_matrix(_data_matrix, _minmax_mixin, IndexMixin):
      """base matrix class for compressed row- and column-oriented matrices"""
  
      def __init__(self, arg1, shape=None, dtype=None, copy=False):
          _data_matrix.__init__(self)
  
          if isspmatrix(arg1):
              if arg1.format == self.format and copy:
                  arg1 = arg1.copy()
              else:
                  arg1 = arg1.asformat(self.format)
              self._set_self(arg1)
  
          elif isinstance(arg1, tuple):
              if isshape(arg1):
                  # It's a tuple of matrix dimensions (M, N)
                  # create empty matrix
                  self._shape = check_shape(arg1)
                  M, N = self.shape
                  # Select index dtype large enough to pass array and
                  # scalar parameters to sparsetools
                  idx_dtype = get_index_dtype(maxval=max(M, N))
                  self.data = np.zeros(0, getdtype(dtype, default=float))
                  self.indices = np.zeros(0, idx_dtype)
                  self.indptr = np.zeros(self._swap((M, N))[0] + 1,
                                         dtype=idx_dtype)
              else:
                  if len(arg1) == 2:
                      # (data, ij) format
                      from .coo import coo_matrix
                      other = self.__class__(coo_matrix(arg1, shape=shape))
                      self._set_self(other)
                  elif len(arg1) == 3:
                      # (data, indices, indptr) format
                      (data, indices, indptr) = arg1
  
                      # Select index dtype large enough to pass array and
                      # scalar parameters to sparsetools
                      maxval = None
                      if shape is not None:
                          maxval = max(shape)
                      idx_dtype = get_index_dtype((indices, indptr),
                                                  maxval=maxval,
                                                  check_contents=True)
  
                      self.indices = np.array(indices, copy=copy,
                                              dtype=idx_dtype)
                      self.indptr = np.array(indptr, copy=copy, dtype=idx_dtype)
                      self.data = np.array(data, copy=copy, dtype=dtype)
                  else:
                      raise ValueError("unrecognized {}_matrix "
                                       "constructor usage".format(self.format))
  
          else:
              # must be dense
              try:
                  arg1 = np.asarray(arg1)
              except Exception:
                  raise ValueError("unrecognized {}_matrix constructor usage"
                                   "".format(self.format))
              from .coo import coo_matrix
              self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
  
          # Read matrix dimensions given, if any
          if shape is not None:
              self._shape = check_shape(shape)
          else:
              if self.shape is None:
                  # shape not already set, try to infer dimensions
                  try:
                      major_dim = len(self.indptr) - 1
                      minor_dim = self.indices.max() + 1
                  except Exception:
                      raise ValueError('unable to infer matrix dimensions')
                  else:
                      self._shape = check_shape(self._swap((major_dim,
                                                            minor_dim)))
  
          if dtype is not None:
              self.data = self.data.astype(dtype, copy=False)
  
          self.check_format(full_check=False)
  
      def getnnz(self, axis=None):
          if axis is None:
              return int(self.indptr[-1])
          else:
              if axis < 0:
                  axis += 2
              axis, _ = self._swap((axis, 1 - axis))
              _, N = self._swap(self.shape)
              if axis == 0:
                  return np.bincount(downcast_intp_index(self.indices),
                                     minlength=N)
              elif axis == 1:
                  return np.diff(self.indptr)
              raise ValueError('axis out of bounds')
  
      getnnz.__doc__ = spmatrix.getnnz.__doc__
  
      def _set_self(self, other, copy=False):
          """take the member variables of other and assign them to self"""
  
          if copy:
              other = other.copy()
  
          self.data = other.data
          self.indices = other.indices
          self.indptr = other.indptr
          self._shape = check_shape(other.shape)
  
      def check_format(self, full_check=True):
          """check whether the matrix format is valid
  
          Parameters
          ----------
          full_check : bool, optional
              If `True`, rigorous check, O(N) operations. Otherwise
              basic check, O(1) operations (default True).
          """
          # use _swap to determine proper bounds
          major_name, minor_name = self._swap(('row', 'column'))
          major_dim, minor_dim = self._swap(self.shape)
  
          # index arrays should have integer data types
          if self.indptr.dtype.kind != 'i':
              warn("indptr array has non-integer dtype ({})"
                   "".format(self.indptr.dtype.name), stacklevel=3)
          if self.indices.dtype.kind != 'i':
              warn("indices array has non-integer dtype ({})"
                   "".format(self.indices.dtype.name), stacklevel=3)
  
          idx_dtype = get_index_dtype((self.indptr, self.indices))
          self.indptr = np.asarray(self.indptr, dtype=idx_dtype)
          self.indices = np.asarray(self.indices, dtype=idx_dtype)
          self.data = to_native(self.data)
  
          # check array shapes
          for x in [self.data.ndim, self.indices.ndim, self.indptr.ndim]:
              if x != 1:
                  raise ValueError('data, indices, and indptr should be 1-D')
  
          # check index pointer
          if (len(self.indptr) != major_dim + 1):
              raise ValueError("index pointer size ({}) should be ({})"
                               "".format(len(self.indptr), major_dim + 1))
          if (self.indptr[0] != 0):
              raise ValueError("index pointer should start with 0")
  
          # check index and data arrays
          if (len(self.indices) != len(self.data)):
              raise ValueError("indices and data should have the same size")
          if (self.indptr[-1] > len(self.indices)):
              raise ValueError("Last value of index pointer should be less than "
                               "the size of index and data arrays")
  
          self.prune()
  
          if full_check:
              # check format validity (more expensive)
              if self.nnz > 0:
                  if self.indices.max() >= minor_dim:
                      raise ValueError("{} index values must be < {}"
                                       "".format(minor_name, minor_dim))
                  if self.indices.min() < 0:
                      raise ValueError("{} index values must be >= 0"
                                       "".format(minor_name))
                  if np.diff(self.indptr).min() < 0:
                      raise ValueError("index pointer values must form a "
                                       "non-decreasing sequence")
  
          # if not self.has_sorted_indices():
          #    warn('Indices were not in sorted order.  Sorting indices.')
          #    self.sort_indices()
          #    assert(self.has_sorted_indices())
          # TODO check for duplicates?
  
      #######################
      # Boolean comparisons #
      #######################
  
      def _scalar_binopt(self, other, op):
          """Scalar version of self._binopt, for cases in which no new nonzeros
          are added. Produces a new spmatrix in canonical form.
          """
          self.sum_duplicates()
          res = self._with_data(op(self.data, other), copy=True)
          res.eliminate_zeros()
          return res
  
      def __eq__(self, other):
          # Scalar other.
          if isscalarlike(other):
              if np.isnan(other):
                  return self.__class__(self.shape, dtype=np.bool_)
  
              if other == 0:
                  warn("Comparing a sparse matrix with 0 using == is inefficient"
                       ", try using != instead.", SparseEfficiencyWarning,
                       stacklevel=3)
                  all_true = self.__class__(np.ones(self.shape, dtype=np.bool_))
                  inv = self._scalar_binopt(other, operator.ne)
                  return all_true - inv
              else:
                  return self._scalar_binopt(other, operator.eq)
          # Dense other.
          elif isdense(other):
              return self.todense() == other
          # Pydata sparse other.
          elif is_pydata_spmatrix(other):
              return NotImplemented
          # Sparse other.
          elif isspmatrix(other):
              warn("Comparing sparse matrices using == is inefficient, try using"
                   " != instead.", SparseEfficiencyWarning, stacklevel=3)
              # TODO sparse broadcasting
              if self.shape != other.shape:
                  return False
              elif self.format != other.format:
                  other = other.asformat(self.format)
              res = self._binopt(other, '_ne_')
              all_true = self.__class__(np.ones(self.shape, dtype=np.bool_))
              return all_true - res
          else:
              return False
  
      def __ne__(self, other):
          # Scalar other.
          if isscalarlike(other):
              if np.isnan(other):
                  warn("Comparing a sparse matrix with nan using != is"
                       " inefficient", SparseEfficiencyWarning, stacklevel=3)
                  all_true = self.__class__(np.ones(self.shape, dtype=np.bool_))
                  return all_true
              elif other != 0:
                  warn("Comparing a sparse matrix with a nonzero scalar using !="
                       " is inefficient, try using == instead.",
                       SparseEfficiencyWarning, stacklevel=3)
                  all_true = self.__class__(np.ones(self.shape), dtype=np.bool_)
                  inv = self._scalar_binopt(other, operator.eq)
                  return all_true - inv
              else:
                  return self._scalar_binopt(other, operator.ne)
          # Dense other.
          elif isdense(other):
              return self.todense() != other
          # Pydata sparse other.
          elif is_pydata_spmatrix(other):
              return NotImplemented
          # Sparse other.
          elif isspmatrix(other):
              # TODO sparse broadcasting
              if self.shape != other.shape:
                  return True
              elif self.format != other.format:
                  other = other.asformat(self.format)
              return self._binopt(other, '_ne_')
          else:
              return True
  
      def _inequality(self, other, op, op_name, bad_scalar_msg):
          # Scalar other.
          if isscalarlike(other):
              if 0 == other and op_name in ('_le_', '_ge_'):
                  raise NotImplementedError(" >= and <= don't work with 0.")
              elif op(0, other):
                  warn(bad_scalar_msg, SparseEfficiencyWarning)
                  other_arr = np.empty(self.shape, dtype=np.result_type(other))
                  other_arr.fill(other)
                  other_arr = self.__class__(other_arr)
                  return self._binopt(other_arr, op_name)
              else:
                  return self._scalar_binopt(other, op)
          # Dense other.
          elif isdense(other):
              return op(self.todense(), other)
          # Sparse other.
          elif isspmatrix(other):
              # TODO sparse broadcasting
              if self.shape != other.shape:
                  raise ValueError("inconsistent shapes")
              elif self.format != other.format:
                  other = other.asformat(self.format)
              if op_name not in ('_ge_', '_le_'):
                  return self._binopt(other, op_name)
  
              warn("Comparing sparse matrices using >= and <= is inefficient, "
                   "using <, >, or !=, instead.", SparseEfficiencyWarning)
              all_true = self.__class__(np.ones(self.shape, dtype=np.bool_))
              res = self._binopt(other, '_gt_' if op_name == '_le_' else '_lt_')
              return all_true - res
          else:
              raise ValueError("Operands could not be compared.")
  
      def __lt__(self, other):
          return self._inequality(other, operator.lt, '_lt_',
                                  "Comparing a sparse matrix with a scalar "
                                  "greater than zero using < is inefficient, "
                                  "try using >= instead.")
  
      def __gt__(self, other):
          return self._inequality(other, operator.gt, '_gt_',
                                  "Comparing a sparse matrix with a scalar "
                                  "less than zero using > is inefficient, "
                                  "try using <= instead.")
  
      def __le__(self, other):
          return self._inequality(other, operator.le, '_le_',
                                  "Comparing a sparse matrix with a scalar "
                                  "greater than zero using <= is inefficient, "
                                  "try using > instead.")
  
      def __ge__(self, other):
          return self._inequality(other, operator.ge, '_ge_',
                                  "Comparing a sparse matrix with a scalar "
                                  "less than zero using >= is inefficient, "
                                  "try using < instead.")
  
      #################################
      # Arithmetic operator overrides #
      #################################
  
      def _add_dense(self, other):
          if other.shape != self.shape:
              raise ValueError('Incompatible shapes.')
          dtype = upcast_char(self.dtype.char, other.dtype.char)
          order = self._swap('CF')[0]
          result = np.array(other, dtype=dtype, order=order, copy=True)
          M, N = self._swap(self.shape)
          y = result if result.flags.c_contiguous else result.T
          csr_todense(M, N, self.indptr, self.indices, self.data, y)
          return matrix(result, copy=False)
  
      def _add_sparse(self, other):
          return self._binopt(other, '_plus_')
  
      def _sub_sparse(self, other):
          return self._binopt(other, '_minus_')
  
      def multiply(self, other):
          """Point-wise multiplication by another matrix, vector, or
          scalar.
          """
          # Scalar multiplication.
          if isscalarlike(other):
              return self._mul_scalar(other)
          # Sparse matrix or vector.
          if isspmatrix(other):
              if self.shape == other.shape:
                  other = self.__class__(other)
                  return self._binopt(other, '_elmul_')
              # Single element.
              elif other.shape == (1, 1):
                  return self._mul_scalar(other.toarray()[0, 0])
              elif self.shape == (1, 1):
                  return other._mul_scalar(self.toarray()[0, 0])
              # A row times a column.
              elif self.shape[1] == 1 and other.shape[0] == 1:
                  return self._mul_sparse_matrix(other.tocsc())
              elif self.shape[0] == 1 and other.shape[1] == 1:
                  return other._mul_sparse_matrix(self.tocsc())
              # Row vector times matrix. other is a row.
              elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
                  other = dia_matrix((other.toarray().ravel(), [0]),
                                     shape=(other.shape[1], other.shape[1]))
                  return self._mul_sparse_matrix(other)
              # self is a row.
              elif self.shape[0] == 1 and self.shape[1] == other.shape[1]:
                  copy = dia_matrix((self.toarray().ravel(), [0]),
                                    shape=(self.shape[1], self.shape[1]))
                  return other._mul_sparse_matrix(copy)
              # Column vector times matrix. other is a column.
              elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
                  other = dia_matrix((other.toarray().ravel(), [0]),
                                     shape=(other.shape[0], other.shape[0]))
                  return other._mul_sparse_matrix(self)
              # self is a column.
              elif self.shape[1] == 1 and self.shape[0] == other.shape[0]:
                  copy = dia_matrix((self.toarray().ravel(), [0]),
                                    shape=(self.shape[0], self.shape[0]))
                  return copy._mul_sparse_matrix(other)
              else:
                  raise ValueError("inconsistent shapes")
  
          # Assume other is a dense matrix/array, which produces a single-item
          # object array if other isn't convertible to ndarray.
          other = np.atleast_2d(other)
  
          if other.ndim != 2:
              return np.multiply(self.toarray(), other)
          # Single element / wrapped object.
          if other.size == 1:
              return self._mul_scalar(other.flat[0])
          # Fast case for trivial sparse matrix.
          elif self.shape == (1, 1):
              return np.multiply(self.toarray()[0, 0], other)
  
          from .coo import coo_matrix
          ret = self.tocoo()
          # Matching shapes.
          if self.shape == other.shape:
              data = np.multiply(ret.data, other[ret.row, ret.col])
          # Sparse row vector times...
          elif self.shape[0] == 1:
              if other.shape[1] == 1:  # Dense column vector.
                  data = np.multiply(ret.data, other)
              elif other.shape[1] == self.shape[1]:  # Dense matrix.
                  data = np.multiply(ret.data, other[:, ret.col])
              else:
                  raise ValueError("inconsistent shapes")
              row = np.repeat(np.arange(other.shape[0]), len(ret.row))
              col = np.tile(ret.col, other.shape[0])
              return coo_matrix((data.view(np.ndarray).ravel(), (row, col)),
                                shape=(other.shape[0], self.shape[1]),
                                copy=False)
          # Sparse column vector times...
          elif self.shape[1] == 1:
              if other.shape[0] == 1:  # Dense row vector.
                  data = np.multiply(ret.data[:, None], other)
              elif other.shape[0] == self.shape[0]:  # Dense matrix.
                  data = np.multiply(ret.data[:, None], other[ret.row])
              else:
                  raise ValueError("inconsistent shapes")
              row = np.repeat(ret.row, other.shape[1])
              col = np.tile(np.arange(other.shape[1]), len(ret.col))
              return coo_matrix((data.view(np.ndarray).ravel(), (row, col)),
                                shape=(self.shape[0], other.shape[1]),
                                copy=False)
          # Sparse matrix times dense row vector.
          elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
              data = np.multiply(ret.data, other[:, ret.col].ravel())
          # Sparse matrix times dense column vector.
          elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
              data = np.multiply(ret.data, other[ret.row].ravel())
          else:
              raise ValueError("inconsistent shapes")
          ret.data = data.view(np.ndarray).ravel()
          return ret
  
      ###########################
      # Multiplication handlers #
      ###########################
  
      def _mul_vector(self, other):
          M, N = self.shape
  
          # output array
          result = np.zeros(M, dtype=upcast_char(self.dtype.char,
                                                 other.dtype.char))
  
          # csr_matvec or csc_matvec
          fn = getattr(_sparsetools, self.format + '_matvec')
          fn(M, N, self.indptr, self.indices, self.data, other, result)
  
          return result
  
      def _mul_multivector(self, other):
          M, N = self.shape
          n_vecs = other.shape[1]  # number of column vectors
  
          result = np.zeros((M, n_vecs),
                            dtype=upcast_char(self.dtype.char, other.dtype.char))
  
          # csr_matvecs or csc_matvecs
          fn = getattr(_sparsetools, self.format + '_matvecs')
          fn(M, N, n_vecs, self.indptr, self.indices, self.data,
             other.ravel(), result.ravel())
  
          return result
  
      def _mul_sparse_matrix(self, other):
          M, K1 = self.shape
          K2, N = other.shape
  
          major_axis = self._swap((M, N))[0]
          other = self.__class__(other)  # convert to this format
  
          idx_dtype = get_index_dtype((self.indptr, self.indices,
                                       other.indptr, other.indices))
  
          fn = getattr(_sparsetools, self.format + '_matmat_maxnnz')
          nnz = fn(M, N,
                   np.asarray(self.indptr, dtype=idx_dtype),
                   np.asarray(self.indices, dtype=idx_dtype),
                   np.asarray(other.indptr, dtype=idx_dtype),
                   np.asarray(other.indices, dtype=idx_dtype))
  
          idx_dtype = get_index_dtype((self.indptr, self.indices,
                                       other.indptr, other.indices),
                                      maxval=nnz)
  
          indptr = np.empty(major_axis + 1, dtype=idx_dtype)
          indices = np.empty(nnz, dtype=idx_dtype)
          data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype))
  
          fn = getattr(_sparsetools, self.format + '_matmat')
          fn(M, N, np.asarray(self.indptr, dtype=idx_dtype),
             np.asarray(self.indices, dtype=idx_dtype),
             self.data,
             np.asarray(other.indptr, dtype=idx_dtype),
             np.asarray(other.indices, dtype=idx_dtype),
             other.data,
             indptr, indices, data)
  
          return self.__class__((data, indices, indptr), shape=(M, N))
  
      def diagonal(self, k=0):
          rows, cols = self.shape
          if k <= -rows or k >= cols:
              raise ValueError("k exceeds matrix dimensions")
          fn = getattr(_sparsetools, self.format + "_diagonal")
          y = np.empty(min(rows + min(k, 0), cols - max(k, 0)),
                       dtype=upcast(self.dtype))
          fn(k, self.shape[0], self.shape[1], self.indptr, self.indices,
             self.data, y)
          return y
  
      diagonal.__doc__ = spmatrix.diagonal.__doc__
  
      #####################
      # Other binary ops  #
      #####################
  
      def _maximum_minimum(self, other, npop, op_name, dense_check):
          if isscalarlike(other):
              if dense_check(other):
                  warn("Taking maximum (minimum) with > 0 (< 0) number results"
                       " to a dense matrix.", SparseEfficiencyWarning,
                       stacklevel=3)
                  other_arr = np.empty(self.shape, dtype=np.asarray(other).dtype)
                  other_arr.fill(other)
                  other_arr = self.__class__(other_arr)
                  return self._binopt(other_arr, op_name)
              else:
                  self.sum_duplicates()
                  new_data = npop(self.data, np.asarray(other))
                  mat = self.__class__((new_data, self.indices, self.indptr),
                                       dtype=new_data.dtype, shape=self.shape)
                  return mat
          elif isdense(other):
              return npop(self.todense(), other)
          elif isspmatrix(other):
              return self._binopt(other, op_name)
          else:
              raise ValueError("Operands not compatible.")
  
      def maximum(self, other):
          return self._maximum_minimum(other, np.maximum,
                                       '_maximum_', lambda x: np.asarray(x) > 0)
  
      maximum.__doc__ = spmatrix.maximum.__doc__
  
      def minimum(self, other):
          return self._maximum_minimum(other, np.minimum,
                                       '_minimum_', lambda x: np.asarray(x) < 0)
  
      minimum.__doc__ = spmatrix.minimum.__doc__
  
      #####################
      # Reduce operations #
      #####################
  
      def sum(self, axis=None, dtype=None, out=None):
          """Sum the matrix over the given axis.  If the axis is None, sum
          over both rows and columns, returning a scalar.
          """
          # The spmatrix base class already does axis=0 and axis=1 efficiently
          # so we only do the case axis=None here
          if (not hasattr(self, 'blocksize') and
                  axis in self._swap(((1, -1), (0, 2)))[0]):
              # faster than multiplication for large minor axis in CSC/CSR
              res_dtype = get_sum_dtype(self.dtype)
              ret = np.zeros(len(self.indptr) - 1, dtype=res_dtype)
  
              major_index, value = self._minor_reduce(np.add)
              ret[major_index] = value
              ret = asmatrix(ret)
              if axis % 2 == 1:
                  ret = ret.T
  
              if out is not None and out.shape != ret.shape:
                  raise ValueError('dimensions do not match')
  
              return ret.sum(axis=(), dtype=dtype, out=out)
          # spmatrix will handle the remaining situations when axis
          # is in {None, -1, 0, 1}
          else:
              return spmatrix.sum(self, axis=axis, dtype=dtype, out=out)
  
      sum.__doc__ = spmatrix.sum.__doc__
  
      def _minor_reduce(self, ufunc, data=None):
          """Reduce nonzeros with a ufunc over the minor axis when non-empty
  
          Can be applied to a function of self.data by supplying data parameter.
  
          Warning: this does not call sum_duplicates()
  
          Returns
          -------
          major_index : array of ints
              Major indices where nonzero
  
          value : array of self.dtype
              Reduce result for nonzeros in each major_index
          """
          if data is None:
              data = self.data
          major_index = np.flatnonzero(np.diff(self.indptr))
          value = ufunc.reduceat(data,
                                 downcast_intp_index(self.indptr[major_index]))
          return major_index, value
  
      #######################
      # Getting and Setting #
      #######################
  
      def _get_intXint(self, row, col):
          M, N = self._swap(self.shape)
          major, minor = self._swap((row, col))
          indptr, indices, data = get_csr_submatrix(
              M, N, self.indptr, self.indices, self.data,
              major, major + 1, minor, minor + 1)
          return data.sum(dtype=self.dtype)
  
      def _get_sliceXslice(self, row, col):
          major, minor = self._swap((row, col))
          if major.step in (1, None) and minor.step in (1, None):
              return self._get_submatrix(major, minor, copy=True)
          return self._major_slice(major)._minor_slice(minor)
  
      def _get_arrayXarray(self, row, col):
          # inner indexing
          idx_dtype = self.indices.dtype
          M, N = self._swap(self.shape)
          major, minor = self._swap((row, col))
          major = np.asarray(major, dtype=idx_dtype)
          minor = np.asarray(minor, dtype=idx_dtype)
  
          val = np.empty(major.size, dtype=self.dtype)
          csr_sample_values(M, N, self.indptr, self.indices, self.data,
                            major.size, major.ravel(), minor.ravel(), val)
          if major.ndim == 1:
              return asmatrix(val)
          return self.__class__(val.reshape(major.shape))
  
      def _get_columnXarray(self, row, col):
          # outer indexing
          major, minor = self._swap((row, col))
          return self._major_index_fancy(major)._minor_index_fancy(minor)
  
      def _major_index_fancy(self, idx):
          """Index along the major axis where idx is an array of ints.
          """
          idx_dtype = self.indices.dtype
          indices = np.asarray(idx, dtype=idx_dtype).ravel()
  
          _, N = self._swap(self.shape)
          M = len(indices)
          new_shape = self._swap((M, N))
          if M == 0:
              return self.__class__(new_shape)
  
          row_nnz = np.diff(self.indptr)
          idx_dtype = self.indices.dtype
          res_indptr = np.zeros(M+1, dtype=idx_dtype)
          np.cumsum(row_nnz[idx], out=res_indptr[1:])
  
          nnz = res_indptr[-1]
          res_indices = np.empty(nnz, dtype=idx_dtype)
          res_data = np.empty(nnz, dtype=self.dtype)
          csr_row_index(M, indices, self.indptr, self.indices, self.data,
                        res_indices, res_data)
  
          return self.__class__((res_data, res_indices, res_indptr),
                                shape=new_shape, copy=False)
  
      def _major_slice(self, idx, copy=False):
          """Index along the major axis where idx is a slice object.
          """
          if idx == slice(None):
              return self.copy() if copy else self
  
          M, N = self._swap(self.shape)
          start, stop, step = idx.indices(M)
          M = len(range(start, stop, step))
          new_shape = self._swap((M, N))
          if M == 0:
              return self.__class__(new_shape)
  
          row_nnz = np.diff(self.indptr)
          idx_dtype = self.indices.dtype
          res_indptr = np.zeros(M+1, dtype=idx_dtype)
          np.cumsum(row_nnz[idx], out=res_indptr[1:])
  
          if step == 1:
              all_idx = slice(self.indptr[start], self.indptr[stop])
              res_indices = np.array(self.indices[all_idx], copy=copy)
              res_data = np.array(self.data[all_idx], copy=copy)
          else:
              nnz = res_indptr[-1]
              res_indices = np.empty(nnz, dtype=idx_dtype)
              res_data = np.empty(nnz, dtype=self.dtype)
              csr_row_slice(start, stop, step, self.indptr, self.indices,
                            self.data, res_indices, res_data)
  
          return self.__class__((res_data, res_indices, res_indptr),
                                shape=new_shape, copy=False)
  
      def _minor_index_fancy(self, idx):
          """Index along the minor axis where idx is an array of ints.
          """
          idx_dtype = self.indices.dtype
          idx = np.asarray(idx, dtype=idx_dtype).ravel()
  
          M, N = self._swap(self.shape)
          k = len(idx)
          new_shape = self._swap((M, k))
          if k == 0:
              return self.__class__(new_shape)
  
          # pass 1: count idx entries and compute new indptr
          col_offsets = np.zeros(N, dtype=idx_dtype)
          res_indptr = np.empty_like(self.indptr)
          csr_column_index1(k, idx, M, N, self.indptr, self.indices,
                            col_offsets, res_indptr)
  
          # pass 2: copy indices/data for selected idxs
          col_order = np.argsort(idx).astype(idx_dtype, copy=False)
          nnz = res_indptr[-1]
          res_indices = np.empty(nnz, dtype=idx_dtype)
          res_data = np.empty(nnz, dtype=self.dtype)
          csr_column_index2(col_order, col_offsets, len(self.indices),
                            self.indices, self.data, res_indices, res_data)
          return self.__class__((res_data, res_indices, res_indptr),
                                shape=new_shape, copy=False)
  
      def _minor_slice(self, idx, copy=False):
          """Index along the minor axis where idx is a slice object.
          """
          if idx == slice(None):
              return self.copy() if copy else self
  
          M, N = self._swap(self.shape)
          start, stop, step = idx.indices(N)
          N = len(range(start, stop, step))
          if N == 0:
              return self.__class__(self._swap((M, N)))
          if step == 1:
              return self._get_submatrix(minor=idx, copy=copy)
          # TODO: don't fall back to fancy indexing here
          return self._minor_index_fancy(np.arange(start, stop, step))
  
      def _get_submatrix(self, major=None, minor=None, copy=False):
          """Return a submatrix of this matrix.
  
          major, minor: None, int, or slice with step 1
          """
          M, N = self._swap(self.shape)
          i0, i1 = _process_slice(major, M)
          j0, j1 = _process_slice(minor, N)
  
          if i0 == 0 and j0 == 0 and i1 == M and j1 == N:
              return self.copy() if copy else self
  
          indptr, indices, data = get_csr_submatrix(
              M, N, self.indptr, self.indices, self.data, i0, i1, j0, j1)
  
          shape = self._swap((i1 - i0, j1 - j0))
          return self.__class__((data, indices, indptr), shape=shape,
                                dtype=self.dtype, copy=False)
  
      def _set_intXint(self, row, col, x):
          i, j = self._swap((row, col))
          self._set_many(i, j, x)
  
      def _set_arrayXarray(self, row, col, x):
          i, j = self._swap((row, col))
          self._set_many(i, j, x)
  
      def _set_arrayXarray_sparse(self, row, col, x):
          # clear entries that will be overwritten
          self._zero_many(*self._swap((row, col)))
  
          M, N = row.shape  # matches col.shape
          broadcast_row = M != 1 and x.shape[0] == 1
          broadcast_col = N != 1 and x.shape[1] == 1
          r, c = x.row, x.col
          x = np.asarray(x.data, dtype=self.dtype)
          if broadcast_row:
              r = np.repeat(np.arange(M), len(r))
              c = np.tile(c, M)
              x = np.tile(x, M)
          if broadcast_col:
              r = np.repeat(r, N)
              c = np.tile(np.arange(N), len(c))
              x = np.repeat(x, N)
          # only assign entries in the new sparsity structure
          i, j = self._swap((row[r, c], col[r, c]))
          self._set_many(i, j, x)
  
      def _setdiag(self, values, k):
          if 0 in self.shape:
              return
  
          M, N = self.shape
          broadcast = (values.ndim == 0)
  
          if k < 0:
              if broadcast:
                  max_index = min(M + k, N)
              else:
                  max_index = min(M + k, N, len(values))
              i = np.arange(max_index, dtype=self.indices.dtype)
              j = np.arange(max_index, dtype=self.indices.dtype)
              i -= k
  
          else:
              if broadcast:
                  max_index = min(M, N - k)
              else:
                  max_index = min(M, N - k, len(values))
              i = np.arange(max_index, dtype=self.indices.dtype)
              j = np.arange(max_index, dtype=self.indices.dtype)
              j += k
  
          if not broadcast:
              values = values[:len(i)]
  
          self[i, j] = values
  
      def _prepare_indices(self, i, j):
          M, N = self._swap(self.shape)
  
          def check_bounds(indices, bound):
              idx = indices.max()
              if idx >= bound:
                  raise IndexError('index (%d) out of range (>= %d)' %
                                   (idx, bound))
              idx = indices.min()
              if idx < -bound:
                  raise IndexError('index (%d) out of range (< -%d)' %
                                   (idx, bound))
  
          i = np.array(i, dtype=self.indices.dtype, copy=False, ndmin=1).ravel()
          j = np.array(j, dtype=self.indices.dtype, copy=False, ndmin=1).ravel()
          check_bounds(i, M)
          check_bounds(j, N)
          return i, j, M, N
  
      def _set_many(self, i, j, x):
          """Sets value at each (i, j) to x
  
          Here (i,j) index major and minor respectively, and must not contain
          duplicate entries.
          """
          i, j, M, N = self._prepare_indices(i, j)
          x = np.array(x, dtype=self.dtype, copy=False, ndmin=1).ravel()
  
          n_samples = x.size
          offsets = np.empty(n_samples, dtype=self.indices.dtype)
          ret = csr_sample_offsets(M, N, self.indptr, self.indices, n_samples,
                                   i, j, offsets)
          if ret == 1:
              # rinse and repeat
              self.sum_duplicates()
              csr_sample_offsets(M, N, self.indptr, self.indices, n_samples,
                                 i, j, offsets)
  
          if -1 not in offsets:
              # only affects existing non-zero cells
              self.data[offsets] = x
              return
  
          else:
              warn("Changing the sparsity structure of a {}_matrix is expensive."
                   " lil_matrix is more efficient.".format(self.format),
                   SparseEfficiencyWarning, stacklevel=3)
              # replace where possible
              mask = offsets > -1
              self.data[offsets[mask]] = x[mask]
              # only insertions remain
              mask = ~mask
              i = i[mask]
              i[i < 0] += M
              j = j[mask]
              j[j < 0] += N
              self._insert_many(i, j, x[mask])
  
      def _zero_many(self, i, j):
          """Sets value at each (i, j) to zero, preserving sparsity structure.
  
          Here (i,j) index major and minor respectively.
          """
          i, j, M, N = self._prepare_indices(i, j)
  
          n_samples = len(i)
          offsets = np.empty(n_samples, dtype=self.indices.dtype)
          ret = csr_sample_offsets(M, N, self.indptr, self.indices, n_samples,
                                   i, j, offsets)
          if ret == 1:
              # rinse and repeat
              self.sum_duplicates()
              csr_sample_offsets(M, N, self.indptr, self.indices, n_samples,
                                 i, j, offsets)
  
          # only assign zeros to the existing sparsity structure
          self.data[offsets[offsets > -1]] = 0
  
      def _insert_many(self, i, j, x):
          """Inserts new nonzero at each (i, j) with value x
  
          Here (i,j) index major and minor respectively.
          i, j and x must be non-empty, 1d arrays.
          Inserts each major group (e.g. all entries per row) at a time.
          Maintains has_sorted_indices property.
          Modifies i, j, x in place.
          """
          order = np.argsort(i, kind='mergesort')  # stable for duplicates
          i = i.take(order, mode='clip')
          j = j.take(order, mode='clip')
          x = x.take(order, mode='clip')
  
          do_sort = self.has_sorted_indices
  
          # Update index data type
          idx_dtype = get_index_dtype((self.indices, self.indptr),
                                      maxval=(self.indptr[-1] + x.size))
          self.indptr = np.asarray(self.indptr, dtype=idx_dtype)
          self.indices = np.asarray(self.indices, dtype=idx_dtype)
          i = np.asarray(i, dtype=idx_dtype)
          j = np.asarray(j, dtype=idx_dtype)
  
          # Collate old and new in chunks by major index
          indices_parts = []
          data_parts = []
          ui, ui_indptr = np.unique(i, return_index=True)
          ui_indptr = np.append(ui_indptr, len(j))
          new_nnzs = np.diff(ui_indptr)
          prev = 0
          for c, (ii, js, je) in enumerate(zip(ui, ui_indptr, ui_indptr[1:])):
              # old entries
              start = self.indptr[prev]
              stop = self.indptr[ii]
              indices_parts.append(self.indices[start:stop])
              data_parts.append(self.data[start:stop])
  
              # handle duplicate j: keep last setting
              uj, uj_indptr = np.unique(j[js:je][::-1], return_index=True)
              if len(uj) == je - js:
                  indices_parts.append(j[js:je])
                  data_parts.append(x[js:je])
              else:
                  indices_parts.append(j[js:je][::-1][uj_indptr])
                  data_parts.append(x[js:je][::-1][uj_indptr])
                  new_nnzs[c] = len(uj)
  
              prev = ii
  
          # remaining old entries
          start = self.indptr[ii]
          indices_parts.append(self.indices[start:])
          data_parts.append(self.data[start:])
  
          # update attributes
          self.indices = np.concatenate(indices_parts)
          self.data = np.concatenate(data_parts)
          nnzs = np.empty(self.indptr.shape, dtype=idx_dtype)
          nnzs[0] = idx_dtype(0)
          indptr_diff = np.diff(self.indptr)
          indptr_diff[ui] += new_nnzs
          nnzs[1:] = indptr_diff
          self.indptr = np.cumsum(nnzs, out=nnzs)
  
          if do_sort:
              # TODO: only sort where necessary
              self.has_sorted_indices = False
              self.sort_indices()
  
          self.check_format(full_check=False)
  
      ######################
      # Conversion methods #
      ######################
  
      def tocoo(self, copy=True):
          major_dim, minor_dim = self._swap(self.shape)
          minor_indices = self.indices
          major_indices = np.empty(len(minor_indices), dtype=self.indices.dtype)
          _sparsetools.expandptr(major_dim, self.indptr, major_indices)
          row, col = self._swap((major_indices, minor_indices))
  
          from .coo import coo_matrix
          return coo_matrix((self.data, (row, col)), self.shape, copy=copy,
                            dtype=self.dtype)
  
      tocoo.__doc__ = spmatrix.tocoo.__doc__
  
      def toarray(self, order=None, out=None):
          if out is None and order is None:
              order = self._swap('cf')[0]
          out = self._process_toarray_args(order, out)
          if not (out.flags.c_contiguous or out.flags.f_contiguous):
              raise ValueError('Output array must be C or F contiguous')
          # align ideal order with output array order
          if out.flags.c_contiguous:
              x = self.tocsr()
              y = out
          else:
              x = self.tocsc()
              y = out.T
          M, N = x._swap(x.shape)
          csr_todense(M, N, x.indptr, x.indices, x.data, y)
          return out
  
      toarray.__doc__ = spmatrix.toarray.__doc__
  
      ##############################################################
      # methods that examine or modify the internal data structure #
      ##############################################################
  
      def eliminate_zeros(self):
          """Remove zero entries from the matrix
  
          This is an *in place* operation
          """
          M, N = self._swap(self.shape)
          _sparsetools.csr_eliminate_zeros(M, N, self.indptr, self.indices,
                                           self.data)
          self.prune()  # nnz may have changed
  
      def __get_has_canonical_format(self):
          """Determine whether the matrix has sorted indices and no duplicates
  
          Returns
              - True: if the above applies
              - False: otherwise
  
          has_canonical_format implies has_sorted_indices, so if the latter flag
          is False, so will the former be; if the former is found True, the
          latter flag is also set.
          """
  
          # first check to see if result was cached
          if not getattr(self, '_has_sorted_indices', True):
              # not sorted => not canonical
              self._has_canonical_format = False
          elif not hasattr(self, '_has_canonical_format'):
              self.has_canonical_format = _sparsetools.csr_has_canonical_format(
                  len(self.indptr) - 1, self.indptr, self.indices)
          return self._has_canonical_format
  
      def __set_has_canonical_format(self, val):
          self._has_canonical_format = bool(val)
          if val:
              self.has_sorted_indices = True
  
      has_canonical_format = property(fget=__get_has_canonical_format,
                                      fset=__set_has_canonical_format)
  
      def sum_duplicates(self):
          """Eliminate duplicate matrix entries by adding them together
  
          The is an *in place* operation
          """
          if self.has_canonical_format:
              return
          self.sort_indices()
  
          M, N = self._swap(self.shape)
          _sparsetools.csr_sum_duplicates(M, N, self.indptr, self.indices,
                                          self.data)
  
          self.prune()  # nnz may have changed
          self.has_canonical_format = True
  
      def __get_sorted(self):
          """Determine whether the matrix has sorted indices
  
          Returns
              - True: if the indices of the matrix are in sorted order
              - False: otherwise
  
          """
  
          # first check to see if result was cached
          if not hasattr(self, '_has_sorted_indices'):
              self._has_sorted_indices = _sparsetools.csr_has_sorted_indices(
                  len(self.indptr) - 1, self.indptr, self.indices)
          return self._has_sorted_indices
  
      def __set_sorted(self, val):
          self._has_sorted_indices = bool(val)
  
      has_sorted_indices = property(fget=__get_sorted, fset=__set_sorted)
  
      def sorted_indices(self):
          """Return a copy of this matrix with sorted indices
          """
          A = self.copy()
          A.sort_indices()
          return A
  
          # an alternative that has linear complexity is the following
          # although the previous option is typically faster
          # return self.toother().toother()
  
      def sort_indices(self):
          """Sort the indices of this matrix *in place*
          """
  
          if not self.has_sorted_indices:
              _sparsetools.csr_sort_indices(len(self.indptr) - 1, self.indptr,
                                            self.indices, self.data)
              self.has_sorted_indices = True
  
      def prune(self):
          """Remove empty space after all non-zero elements.
          """
          major_dim = self._swap(self.shape)[0]
  
          if len(self.indptr) != major_dim + 1:
              raise ValueError('index pointer has invalid length')
          if len(self.indices) < self.nnz:
              raise ValueError('indices array has fewer than nnz elements')
          if len(self.data) < self.nnz:
              raise ValueError('data array has fewer than nnz elements')
  
          self.indices = _prune_array(self.indices[:self.nnz])
          self.data = _prune_array(self.data[:self.nnz])
  
      def resize(self, *shape):
          shape = check_shape(shape)
          if hasattr(self, 'blocksize'):
              bm, bn = self.blocksize
              new_M, rm = divmod(shape[0], bm)
              new_N, rn = divmod(shape[1], bn)
              if rm or rn:
                  raise ValueError("shape must be divisible into %s blocks. "
                                   "Got %s" % (self.blocksize, shape))
              M, N = self.shape[0] // bm, self.shape[1] // bn
          else:
              new_M, new_N = self._swap(shape)
              M, N = self._swap(self.shape)
  
          if new_M < M:
              self.indices = self.indices[:self.indptr[new_M]]
              self.data = self.data[:self.indptr[new_M]]
              self.indptr = self.indptr[:new_M + 1]
          elif new_M > M:
              self.indptr = np.resize(self.indptr, new_M + 1)
              self.indptr[M + 1:].fill(self.indptr[M])
  
          if new_N < N:
              mask = self.indices < new_N
              if not np.all(mask):
                  self.indices = self.indices[mask]
                  self.data = self.data[mask]
                  major_index, val = self._minor_reduce(np.add, mask)
                  self.indptr.fill(0)
                  self.indptr[1:][major_index] = val
                  np.cumsum(self.indptr, out=self.indptr)
  
          self._shape = shape
  
      resize.__doc__ = spmatrix.resize.__doc__
  
      ###################
      # utility methods #
      ###################
  
      # 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
          (i.e. .indptr and .indices) are copied.
          """
          if copy:
              return self.__class__((data, self.indices.copy(),
                                     self.indptr.copy()),
                                    shape=self.shape,
                                    dtype=data.dtype)
          else:
              return self.__class__((data, self.indices, self.indptr),
                                    shape=self.shape, dtype=data.dtype)
  
      def _binopt(self, other, op):
          """apply the binary operation fn to two sparse matrices."""
          other = self.__class__(other)
  
          # e.g. csr_plus_csr, csr_minus_csr, etc.
          fn = getattr(_sparsetools, self.format + op + self.format)
  
          maxnnz = self.nnz + other.nnz
          idx_dtype = get_index_dtype((self.indptr, self.indices,
                                       other.indptr, other.indices),
                                      maxval=maxnnz)
          indptr = np.empty(self.indptr.shape, dtype=idx_dtype)
          indices = np.empty(maxnnz, dtype=idx_dtype)
  
          bool_ops = ['_ne_', '_lt_', '_gt_', '_le_', '_ge_']
          if op in bool_ops:
              data = np.empty(maxnnz, dtype=np.bool_)
          else:
              data = np.empty(maxnnz, dtype=upcast(self.dtype, other.dtype))
  
          fn(self.shape[0], self.shape[1],
             np.asarray(self.indptr, dtype=idx_dtype),
             np.asarray(self.indices, dtype=idx_dtype),
             self.data,
             np.asarray(other.indptr, dtype=idx_dtype),
             np.asarray(other.indices, dtype=idx_dtype),
             other.data,
             indptr, indices, data)
  
          A = self.__class__((data, indices, indptr), shape=self.shape)
          A.prune()
  
          return A
  
      def _divide_sparse(self, other):
          """
          Divide this matrix by a second sparse matrix.
          """
          if other.shape != self.shape:
              raise ValueError('inconsistent shapes')
  
          r = self._binopt(other, '_eldiv_')
  
          if np.issubdtype(r.dtype, np.inexact):
              # Eldiv leaves entries outside the combined sparsity
              # pattern empty, so they must be filled manually.
              # Everything outside of other's sparsity is NaN, and everything
              # inside it is either zero or defined by eldiv.
              out = np.empty(self.shape, dtype=self.dtype)
              out.fill(np.nan)
              row, col = other.nonzero()
              out[row, col] = 0
              r = r.tocoo()
              out[r.row, r.col] = r.data
              out = matrix(out)
          else:
              # integers types go with nan <-> 0
              out = r
  
          return out
  
  
  def _process_slice(sl, num):
      if sl is None:
          i0, i1 = 0, num
      elif isinstance(sl, slice):
          i0, i1, stride = sl.indices(num)
          if stride != 1:
              raise ValueError('slicing with step != 1 not supported')
          i0 = min(i0, i1)  # give an empty slice when i0 > i1
      elif isintlike(sl):
          if sl < 0:
              sl += num
          i0, i1 = sl, sl + 1
          if i0 < 0 or i1 > num:
              raise IndexError('index out of bounds: 0 <= %d < %d <= %d' %
                               (i0, i1, num))
      else:
          raise TypeError('expected slice or scalar')
  
      return i0, i1
  
apis: numpy.ones numpy.multiply numpy.diff numpy.issubdtype numpy.argsort numpy.asarray scipy._lib._util._prune_array numpy.empty_like numpy.isnan numpy.unique numpy.tile numpy.atleast_2d numpy.zeros numpy.repeat numpy.arange numpy.all numpy.resize numpy.cumsum numpy.empty numpy.array numpy.result_type numpy.concatenate