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 asmatrix : ?kwargs:(string * Py.Object.t) list -> Py.Object.t list -> Py.Object.t

None

val bmat : ?kwargs:(string * Py.Object.t) list -> Py.Object.t list -> Py.Object.t

None

val check_reshape_kwargs : Py.Object.t -> Py.Object.t

Unpack keyword arguments for reshape function.

This is useful because keyword arguments after star arguments are not allowed in Python 2, but star keyword arguments are. This function unpacks 'order' and 'copy' from the star keyword arguments (with defaults) and throws an error for any remaining.

val check_shape : ?current_shape:Py.Object.t -> args:Py.Object.t -> unit -> Py.Object.t

Imitate numpy.matrix handling of shape arguments

val downcast_intp_index : Py.Object.t -> Py.Object.t

Down-cast index array to np.intp dtype if it is of a larger dtype.

Raise an error if the array contains a value that is too large for intp.

val get_index_dtype : ?arrays:Py.Object.t -> ?maxval:float -> ?check_contents:bool -> unit -> Np.Dtype.t

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)

val get_sum_dtype : Py.Object.t -> Py.Object.t

Mimic numpy's casting for np.sum

val getdtype : ?a:Py.Object.t -> ?default:Py.Object.t -> dtype:Py.Object.t -> unit -> Py.Object.t

Function used to simplify argument processing. If 'dtype' is not specified (is None), returns a.dtype; otherwise returns a np.dtype object created from the specified dtype argument. If 'dtype' and 'a' are both None, construct a data type out of the 'default' parameter. Furthermore, 'dtype' must be in 'allowed' set.

val is_pydata_spmatrix : Py.Object.t -> Py.Object.t

Check whether object is pydata/sparse matrix, avoiding importing the module.

val isdense : Py.Object.t -> Py.Object.t

None

val isintlike : Py.Object.t -> Py.Object.t

Is x appropriate as an index into a sparse matrix? Returns True if it can be cast safely to a machine int.

val ismatrix : Py.Object.t -> Py.Object.t

None

val isscalarlike : Py.Object.t -> Py.Object.t

Is x either a scalar, an array scalar, or a 0-dim array?

val issequence : Py.Object.t -> Py.Object.t

None

val isshape : ?nonneg:Py.Object.t -> x:Py.Object.t -> unit -> Py.Object.t

Is x a valid 2-tuple of dimensions?

If nonneg, also checks that the dimensions are non-negative.

val matrix : ?kwargs:(string * Py.Object.t) list -> Py.Object.t list -> Py.Object.t

None

val to_native : Py.Object.t -> Py.Object.t

None

val upcast : Py.Object.t list -> Py.Object.t

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'>

val upcast_char : Py.Object.t list -> Py.Object.t

Same as `upcast` but taking dtype.char as input (faster).

val upcast_scalar : dtype:Py.Object.t -> scalar:Py.Object.t -> unit -> Py.Object.t

Determine data type for binary operation between an array of type `dtype` and a scalar.

val validateaxis : Py.Object.t -> Py.Object.t

None

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