def from_function(cls, function: Function):
dy = function.dy
if dy is None:
return function
value = numpy.random.normal(function.eval().real, dy.eval().real)
if function.is_complex():
value = value + 1j * numpy.random.normal(function.eval().imag, dy.eval().imag)
return Function.to_function(function.get_dom(), value)
def get_kernel(cls, fun: Function, **kwargs):
r"""
Returns the gaussian kernel, centered at \mu = 0 and variance = sigma
:param fun: Function to apply the gaussian kernel (needed for spacing purposes)
:param kwargs: possible variable: sigma
:return:
"""
sigma = kwargs.get('sigma', 1.0)
dx = Domain.get_dx(fun.get_dom())
width = numpy.arange(-5 * sigma, 5 * sigma, dx)
kernel = 1.0 / numpy.sqrt(2 * numpy.pi * sigma**2) * numpy.exp(
-numpy.square(width / sigma) / 2.0) * dx
return kernel
