numpy.matrix.ravel¶

method

matrix.ravel(order='C')[source]¶

Return a flattened matrix.

Refer to numpy.ravel for more documentation.

Parameters:

Parameters:	order : {‘C’, ‘F’, ‘A’, ‘K’}, optional The elements of m are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. ‘A’ means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.
Returns:	ret : matrix Return the matrix flattened to shape (1, N) where N is the number of elements in the original matrix. A copy is made only if necessary.

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional: The elements of m are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. ‘A’ means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.

Returns:

ret : matrix: Return the matrix flattened to shape (1, N) where N is the number of elements in the original matrix. A copy is made only if necessary.