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# scipy.sparse.dia_matrix

class `scipy.sparse.``dia_matrix`(arg1, shape=None, dtype=None, copy=False)

  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)

  Examples

      >>> from scipy.sparse import *
      >>> from scipy import *
      >>> dia_matrix( (3,4), dtype=int8).todense()
      matrix([[0, 0, 0, 0],
              [0, 0, 0, 0],
              [0, 0, 0, 0]], dtype=int8)

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

  Methods

  `asformat` |
  `asfptype` |
  `astype` |
  `conj` |
  `conjugate` |
  `copy` | Generic (shallow and deep) copying operations.
  `diagonal` |
  `dot` |
  `getH` |
  `get_shape` |
  `getcol` |
  `getformat` |
  `getmaxprint` |
  `getnnz` |
  `getrow` |
  `mean` |
  `multiply` |
  `nonzero` |
  `reshape` |
  `set_shape` |
  `setdiag` |
  `sum` |
  `toarray` |
  `tobsr` |
  `tocoo` |
  `tocsc` |
  `tocsr` |
  `todense` |
  `todia` |
  `todok` |
  `tolil` |
  `transpose` |

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