package scipy

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

Based on input (integer) arrays `a`, determine a suitable index data type that can hold the data in the arrays.

Parameters ---------- arrays : tuple of array_like Input arrays whose types/contents to check maxval : float, optional Maximum value needed check_contents : bool, optional Whether to check the values in the arrays and not just their types. Default: False (check only the types)

Returns ------- dtype : dtype Suitable index data type (int32 or int64)

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_matrix, isspmatrix_csr >>> isspmatrix_csr(csr_matrix([5])) True

>>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc >>> isspmatrix_csr(csc_matrix([5])) False

Returns the nearest supported sparse dtype for the combination of one or more types.

upcast(t0, t1, ..., tn) -> T where T is a supported dtype

Examples --------

>>> upcast('int32') <type 'numpy.int32'> >>> upcast('bool') <type 'numpy.bool_'> >>> upcast('int32','float32') <type 'numpy.float64'> >>> upcast('bool',complex,float) <type 'numpy.complex128'>