def __init__(self, uniform=True, deriv_accuracy_radius=5, tileheight = 32, tilewidth = 16):
"""
Defines parameters in constructed class instance.
"""
self.uniform = uniform
self.deriv_accuracy_radius = deriv_accuracy_radius
# cache radii, intpoints, and inverses
self._rad = None
self._intpoints = None
self._Omega = None
self._Yinv = None
self._T = None
self._Tinv = None
self._prec = 1e-8
if (gpu_capable):
self.parRiemann = RiemannThetaCuda(tileheight, tilewidth)
def riemanntheta_high_dim(X, Yinv, T, z, g, rad, max_points=10000000):
parRiemann = RiemannThetaCuda(1, 512)
#initialize parRiemann
parRiemann.compile(g)
parRiemann.cache_omega_real(X)
parRiemann.cache_omega_imag(Yinv, T)
#compile the box_points program
point_finder = func1()
R = get_rad(T, rad)
print R
num_int_points = (2 * R + 1)**g
num_partitions = num_int_points // max_points
num_final_partition = num_int_points - num_partitions * max_points
osc_part = 0 + 0 * 1.j
if (num_partitions > 0):
S = gpuarray.zeros(np.int(max_points * g), dtype=np.double)
print "Required number of iterations"
print num_partitions
print
for p in range(num_partitions):
print p
print
S = box_points(point_finder, max_points * p, max_points * (p + 1), g,
R, S)
parRiemann.cache_intpoints(S, gpu_already=True)
osc_part += parRiemann.compute_v_without_derivs(np.array([z]))
S = gpuarray.zeros(np.int(
(num_int_points - num_partitions * max_points) * g),
dtype=np.double)
print num_partitions * max_points, num_int_points
S = box_points(point_finder, num_partitions * max_points, num_int_points,
g, R, S)
parRiemann.cache_intpoints(S, gpu_already=True)
osc_part += parRiemann.compute_v_without_derivs(np.array([z]))
print osc_part
return osc_part
