def as_matrix(self):
"""
Returns the l array as an lm-indexed diagonal matrix, with repeated elements for
each m in [-l, l].
EXAMPLES::
>>> ClArray([1,2], 1, 2).as_matrix()
8 by 8 diagonal matrix of float64:
[ 1. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 1. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 2. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 2. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 2. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 2. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 2.]
"""
return isotropic_real_covar(self.astype(np.double))
def load_covariance(self, lmin, lmax, dtype=np.double):
Cl = self._power_spectrum.by_l[lmin:lmax + 1].astype(dtype)
return isotropic_real_covar(Cl)
