def compute_a(a, num_terms, mid):
"""
Compute the rank-2 tensor a. a is a
num_terms x num_terms matrix.
a -- the destination matrix
num_terms -- the number of terms to take into account
when computing a.
"""
mid = num_terms / 2
for i in xrange(num_terms):
for j in xrange(mid, num_terms):
a[i][j] = random.gaussian(0,1) + 1j*
random.gaussian(0,1)
a[2*mid-i][2*mid-j] = a[i][j].conjugate
def z(self):
return gaussian(self.mean, self.sigma)
Compute the rank-2 tensor a. a is a
num_terms x num_terms matrix.
a -- the destination matrix
num_terms -- the number of terms to take into account
when computing a.
"""
mid = num_terms / 2
for i in xrange(num_terms):
for j in xrange(mid, num_terms):
a[i][j] = random.gaussian(0,1) + 1j*
random.gaussian(0,1)
a[2*mid-i][2*mid-j] = a[i][j].conjugate
a[mid][mid] = random.gaussian(0,1)
def turb_field(x, a, num_terms, mid, sigma):
"""
The scalar field f that simulate turbulence
(using matrix a) for a particle at position x.
"""
def gaussian(self):
return gaussian(0., 1.)