package scipy

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package scipy
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val get_py : string -> Py.Object.t

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

val crotg : a:Py.Object.t -> b:Py.Object.t -> unit -> Py.Object.t

c,s = crotg(a,b)

Wrapper for ``crotg``.

Parameters ---------- a : input complex b : input complex

Returns ------- c : complex s : complex

val drotg : a:Py.Object.t -> b:Py.Object.t -> unit -> float

c,s = drotg(a,b)

Wrapper for ``drotg``.

Parameters ---------- a : input float b : input float

Returns ------- c : float s : float

val find_best_blas_type : ?arrays:[> `Ndarray ] Np.Obj.t list -> ?dtype:[ `S of string | `Dtype of Np.Dtype.t ] -> unit -> string * Np.Dtype.t * bool

Find best-matching BLAS/LAPACK type.

Arrays are used to determine the optimal prefix of BLAS routines.

Parameters ---------- arrays : sequence of ndarrays, optional Arrays can be given to determine optimal prefix of BLAS routines. If not given, double-precision routines will be used, otherwise the most generic type in arrays will be used. dtype : str or dtype, optional Data-type specifier. Not used if `arrays` is non-empty.

Returns ------- prefix : str BLAS/LAPACK prefix character. dtype : dtype Inferred Numpy data type. prefer_fortran : bool Whether to prefer Fortran order routines over C order.

Examples -------- >>> import scipy.linalg.blas as bla >>> a = np.random.rand(10,15) >>> b = np.asfortranarray(a) # Change the memory layout order >>> bla.find_best_blas_type((a,)) ('d', dtype('float64'), False) >>> bla.find_best_blas_type((a*1j,)) ('z', dtype('complex128'), False) >>> bla.find_best_blas_type((b,)) ('d', dtype('float64'), True)

val get_blas_funcs : ?arrays:[> `Ndarray ] Np.Obj.t list -> ?dtype:[ `S of string | `Dtype of Np.Dtype.t ] -> names:[ `Sequence_of_str of Py.Object.t | `S of string ] -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Return available BLAS function objects from names.

Arrays are used to determine the optimal prefix of BLAS routines.

Parameters ---------- names : str or sequence of str Name(s) of BLAS functions without type prefix.

arrays : sequence of ndarrays, optional Arrays can be given to determine optimal prefix of BLAS routines. If not given, double-precision routines will be used, otherwise the most generic type in arrays will be used.

dtype : str or dtype, optional Data-type specifier. Not used if `arrays` is non-empty.

Returns ------- funcs : list List containing the found function(s).

Notes ----- This routine automatically chooses between Fortran/C interfaces. Fortran code is used whenever possible for arrays with column major order. In all other cases, C code is preferred.

In BLAS, the naming convention is that all functions start with a type prefix, which depends on the type of the principal matrix. These can be one of 's', 'd', 'c', 'z' for the NumPy types float32, float64, complex64, complex128 respectively. The code and the dtype are stored in attributes `typecode` and `dtype` of the returned functions.

Examples -------- >>> import scipy.linalg as LA >>> a = np.random.rand(3,2) >>> x_gemv = LA.get_blas_funcs('gemv', (a,)) >>> x_gemv.typecode 'd' >>> x_gemv = LA.get_blas_funcs('gemv',(a*1j,)) >>> x_gemv.typecode 'z'

val srotg : a:Py.Object.t -> b:Py.Object.t -> unit -> float

c,s = srotg(a,b)

Wrapper for ``srotg``.

Parameters ---------- a : input float b : input float

Returns ------- c : float s : float

val zrotg : a:Py.Object.t -> b:Py.Object.t -> unit -> Py.Object.t

c,s = zrotg(a,b)

Wrapper for ``zrotg``.

Parameters ---------- a : input complex b : input complex

Returns ------- c : complex s : complex