def diags_cp_cm(x, y):
dd.read_parameters()
diagdir = '/scratch/01658/drhatch/dna_out'
par['diagdir'] = "\'" + diagdir + "\'"
time = dd.get_time_from_gout()
kx, ky, kz, hermiteNumbers = dd.get_grids()
print 'kx =', np.shape(kx)
print 'ky =', np.shape(ky)
print 'kz =', np.shape(kz)
print 'n =', np.shape(hermiteNumbers)
g_in = dd.read_time_step_g(len(time) - 1)
g_in = np.reshape(g_in,
(par['nkx0'], par['nky0'], par['nkz0'], par['nv0']),
order='F')
x = x
y = y
dist = g_in[x, y, :, :] #distribution function: 2d slice of 4d g_in
#make a new 2d array with the same dimensions as dist_
shape = np.shape(dist)
dist_t = np.empty(shape)
for i in range(len(kz)):
for j in range(len(hermiteNumbers)):
dist_t[i,
j] = (1j * np.sign(kz[i]))**hermiteNumbers[j] * dist[i, j]
dist_tp = np.empty(shape)
dist_tm = np.empty(shape)
for i in range(len(kz)):
for j in range(len(hermiteNumbers) + 1):
if j < (len(hermiteNumbers) - 1):
dist_tp[i, j] = (dist_t[i, j] + dist_t[i, j + 1]) / 2
dist_tm[i, j] = (dist_t[i, j] - dist_t[i, j + 1]) / 2
c_p = np.empty(np.shape(dist_tp))
c_m = np.empty(np.shape(dist_tm))
for i in range(len(kz)):
for j in range(len(kz)):
c_p[i, j] = dist_tp[i, j] * np.conjugate(dist_tp[i, j])
c_m[i, j] = dist_tm[i, j] * np.conjugate(dist_tm[i, j])
c_ps = np.sum(c_p, axis=0)
c_ms = np.sum(c_m, axis=0)
xf = kx[x]
yf = ky[y]
return c_ps, c_ms, xf, yf
def calculate_g(kx, ky, kz, nmax, nu, gam0):
"""calculates g using eigenvector g"""
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dd.read_parameters()
diagdir = '/scratch/01658/drhatch/dna_out'
par['diagdir'] = "\'" + diagdir + "\'"
time = dd.get_time_from_gout()
kx, ky, kz, herm_grid = dd.get_grids()
nmax = 36
mat.set_nmax(nmax)
Gam0 = mat.get_gamma0()
par['nu'] = 0.002222
print 'nu = ', par['nu']
par['omn'] = 0
par['omt'] = 0
par['hyp_x'] = 0
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~``
omega, freq, growth, evec = mat.get_spectrum(kx, ky, kz, Gam0, nu)
g_max, gi_max = max_g(growth)
g_0 = evec[:, gi_max]
nu_bar = nu / kz
g_calc = np.empty(nmax, dtype=complex)
for n in range(nmax):
if n == 0 or n == 1:
g_calc[0] = g_0[0]
g_calc[1] = g_0[1]
else:
g_calc[n] = ((omega[gi_max] / kz + 1j * nu_bar *
(n - 1)) * g_0[n - 1] -
(n - 1)**(.5) * g_0[n - 2]) / ((n)**(.5))
err = find_error(g_0, g_calc, nmax)
plot_eig_vec(g_calc, g_0, herm_grid, nmax)
plot_error(err, herm_grid, nmax)
return g_calc, g_0
from scipy.stats import linregress from config import * import new_dd as dd from ev_diags import * import time as t2 from spectra import * from nlt_diags import * #from landau_tests import * import os import matrix_maker as mat dd.read_parameters() diagdir = '/scratch/01658/drhatch/dna_out' par['diagdir'] = "\'" + diagdir + "\'" time = dd.get_time_from_gout() kx, ky, kz, herm_grid = dd.get_grids() nmax = 40 mat.set_nmax(nmax) par['nu'] = 0.05 print 'nu = ', par['nu'] print 'kz = ', kz par['omn'] = 0 par['omt'] = 0 par['hyp_x'] = 0 par['hyp_y'] = 0 par['hyp_v'] = 0 par['nuno_closure'] = False
