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

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

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

None

val npfunc : ?out: [ `Ndarray of [> `Ndarray ] Np.Obj.t | `Tuple_of_ndarray_and_None of Py.Object.t ] -> ?where:[> `Ndarray ] Np.Obj.t -> x:[> `Ndarray ] Np.Obj.t -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

expm1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Calculate ``exp(x) - 1`` for all elements in the array.

Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Element-wise exponential minus one: ``out = exp(x) - 1``. This is a scalar if `x` is a scalar.

See Also -------- log1p : ``log(1 + x)``, the inverse of expm1.

Notes ----- This function provides greater precision than ``exp(x) - 1`` for small values of ``x``.

Examples -------- The true value of ``exp(1e-10) - 1`` is ``1.00000000005e-10`` to about 32 significant digits. This example shows the superiority of expm1 in this case.

>>> np.expm1(1e-10) 1.00000000005e-10 >>> np.exp(1e-10) - 1 1.000000082740371e-10

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

None

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