def prior(self, cube, ndim, nparams):
"""
compute the mapping between the unit cube and parameter cube (in-place)
:param cube: unit hypercube, sampled by the algorithm
:param ndim: number of sampled parameters
:param nparams: total number of parameters
"""
cube_py = self._multinest2python(cube, ndim)
if self.prior_type == 'gaussian':
utils.cube2args_gaussian(cube_py, self.lowers, self.uppers,
self.means, self.sigmas, self.n_dims)
elif self.prior_type == 'uniform':
utils.cube2args_uniform(cube_py, self.lowers, self.uppers, self.n_dims)
for i in range(self.n_dims):
cube[i] = cube_py[i]
def prior(self, u):
"""
compute the mapping between the unit cube and parameter cube
:param u: unit hypercube, sampled by the algorithm
:return: hypercube in parameter space
"""
if self.prior_type == 'gaussian':
p = utils.cube2args_gaussian(u,
self.lowers,
self.uppers,
self.means,
self.sigmas,
self.n_dims,
copy=True)
elif self.prior_type == 'uniform':
p = utils.cube2args_uniform(u,
self.lowers,
self.uppers,
self.n_dims,
copy=True)
else:
raise ValueError(
'prior type %s not supported! Chose "gaussian" or "uniform".')
return p
def test_cube2args_gaussian():
n_dims = 3
l, u = -5., 15.
m, s = 5., 1.
lowers, uppers = [l] * n_dims, [u] * n_dims
means, sigmas = [m] * n_dims, [s] * n_dims
truth = [l, m, u]
cube = [0, 0.5, 1]
sampling_util.cube2args_gaussian(cube,
lowers,
uppers,
means,
sigmas,
n_dims,
copy=False)
npt.assert_equal(cube, truth)
cube = [0, 0.5, 1]
sampling_util.cube2args_gaussian(cube,
lowers,
uppers,
means,
sigmas,
n_dims,
copy=True)
# they should NOT be equal because cube was not modified in-place
npt.assert_equal(np.any(np.not_equal(cube, truth)), True)
cube = sampling_util.cube2args_gaussian(cube,
lowers,
uppers,
means,
sigmas,
n_dims,
copy=True)
# here they should
npt.assert_equal(cube, truth)
def prior(self, cube):
"""
compute the mapping between the unit cube and parameter cube
'copy=True' below because cube can not be modified in-place (read-only)
:param cube: unit hypercube, sampled by the algorithm
:return: hypercube in parameter space
"""
if self.prior_type == 'gaussian':
p = utils.cube2args_gaussian(cube, self.lowers, self.uppers,
self.means, self.sigmas, self.n_dims,
copy=True)
elif self.prior_type == 'uniform':
p = utils.cube2args_uniform(cube, self.lowers, self.uppers,
self.n_dims, copy=True)
return p
