def mahalanobis_covariance(x, y, diff_degree, amp, val, vec, symm=None):
"""
Converts x and y to a matrix of covariances. x and y are assumed to have
columns (long,lat,t). Parameters are:
- t_gam_fun: A function returning a matrix of variogram values.
Inputs will be the 't' columns of x and y, as well as kwds.
- amp: The MS amplitude of realizations.
- scale: Scales distance.
- inc, ecc: Anisotropy parameters.
- n_threads: Maximum number of threads available to function.
- symm: Flag indicating whether matrix will be symmetric (optional).
- kwds: Passed to t_gam_fun.
Output value should never drop below -1. This happens when:
-1 > -sf*c+k
"""
# Allocate
nx = x.shape[0]
ny = y.shape[0]
ndim = x.shape[1]
C = np.asmatrix(np.empty((nx, ny), order="F"))
# Figure out symmetry and threading
if symm is None:
symm = x is y
n_threads = min(pm.get_threadpool_size(), nx * ny / 2000)
if n_threads > 1:
if not symm:
bounds = np.linspace(0, ny, n_threads + 1)
else:
bounds = np.array(np.sqrt(np.linspace(0, ny * ny, n_threads + 1)), dtype=int)
# Target function for threads
def targ(C, x, y, symm, diff_degree, amp, val, vec, cmin, cmax):
mahal(C, x, y, symm, diff_degree, gamma(diff_degree), amp, val, vec, cmin, cmax)
# Dispatch threads
if n_threads <= 1:
mahal(C, x, y, symm, diff_degree, gamma(diff_degree), amp, val, vec, cmin=0, cmax=C.shape[1])
else:
thread_args = [(C, x, y, symm, diff_degree, amp, val, vec, bounds[i], bounds[i + 1]) for i in xrange(n_threads)]
pm.map_noreturn(targ, thread_args)
if symm:
pm.gp.symmetrize(C)
# eigs = np.linalg.eigh(C)
# val,vec = eigs
# if np.any(val<-1.e-3):
# raise RuntimeError, 'Negative eigenvalues: %s'%val[np.where(val<0)]
return C
def targ(C, x, y, symm, diff_degree, amp, val, vec, cmin, cmax):
mahal(C, x, y, symm, diff_degree, gamma(diff_degree), amp, val, vec, cmin, cmax)