def f(z0):
z0 = np.array([z0])
x0 = np.array([0.01])
r = np.array([z0, x0])
X = p.X(r)
Y = mf.Yabs(r)
Nz0 = np.sqrt(Y / (1 + Y)) - 0.001
print "Nz_opt", Nz0
Nx0 = coldNx_Om(X, Y, Nz0)
data = X, Y, p.gamma(10), Nz0
Nx0 = fsolve(hot_disp_rel_wrapp, Nx0, args=data)
print Nx0
N = np.array([Nz0, Nx0])
nz, nx = ri.nz(r, N), ri.nx(r, N)
init_cond = np.array([z0, x0, Nz0, Nx0])
print init_cond
soln = hot.get_ray(init_cond, ncycle)
#make gamma in the config!
soln = {
"init_cond": init_cond,
"soln": soln,
"ncycle": ncycle,
"gamma": p.gamma(10)
}
#make file exist check
#np.save(os.path.abspath("results/mirror/simple/Obeam/plasma_bvp/gamma10/soln_"+str(z0[0])),soln)
return soln
def f(Y):
params.Y = Y
X = p.X(r)
Nz0 = np.array([np.sqrt(Y / (1 + Y))])
Nx0 = -coldNx_Om(
X, Y,
Nz0) #Om should be called "+" mode. we actually above the O-cutoff
data = X, Y, p.gamma(Te), Nz0
Nx0 = fsolve(hot_disp_rel_wrapp, Nx0, args=data)
N = np.array([Nz0, Nx0])
nz, nx = ri.nz(r, N), ri.nx(r, N)
init_cond = np.array([z0, x0, Nz0, Nx0])
print mf.Yabs(np.array([z0, x0])), init_cond
soln = hot.get_ray(init_cond, ncycle)
soln = {
"init_cond": init_cond,
"soln": soln,
"Y": params.Y,
"ncycle": ncycle
}
def f(delta_Nz0):
z0 = np.array([0])
x0 = np.array([1.05])
r = np.array([z0, x0])
X = p.X(r)
Y = mf.Yabs(r)
Nz0 = np.sqrt(Y / (1 + Y)) + delta_Nz0
print "Nz_opt", X, Y
Nx0 = coldNx_Om(X, Y, Nz0)
data = X, Y, p.gamma(10), Nz0
Nx0 = fsolve(hot_disp_rel_wrapp, Nx0, args=data)
print Nx0
N = np.array([Nz0, Nx0])
nz, nx = ri.nz(r, N), ri.nx(r, N)
init_cond = np.array([z0, x0, Nz0, Nx0])
print init_cond
soln = hot.get_ray(init_cond, ncycle)
#make gamma in the config!
print(init_cond)
soln = {
"init_cond": init_cond,
"soln": soln,
"ncycle": ncycle,
"gamma": p.gamma(10)
}
#make file exist check
return soln
def start_Xm(Y, Nz):
X = np.linspace(1.0001, 3, 1000)
Nx0_Xm = coldNx_Xm(X, Y, Nz)
Nx0_Om = coldNx_Om(X, Y, Nz)
arrNan = np.where(np.isnan(Nx0_Xm) == True)[0]
Nx_turnP = coldNx_Om(X[arrNan[0] - 1], Y, Nz)
ind1 = np.where(Nx0_Om < Nx_turnP)[0]
plus_mode_ind = ind1
if plus_mode_ind.size != 0:
return X[plus_mode_ind[0]]
else:
return X[arrNan[0]]
def f(Y):
params.Y = Y
Nz0_opt = np.array([np.sqrt(Y / (1 + Y))])
allSolns = []
for Nz0 in np.linspace(Nz0_opt + 0.001, Nz0_opt + 0.001, 1):
Nz0 = np.array([Nz0])
#TODO: use the cold dispersion relation to find which refractive index to use, becasue I want to do the angular scan. We need to choose the proper branch
if (Nz0 < Nz0_opt):
#get the starting point of a ray
x0 = np.array([brentq(cold_C_wrapp, 1.001, 40, args=(Y, Nz0))
]) + 0.001
print "x0", x0
else:
x0 = np.array([1.001]) #more or less arbitrary
z0 = np.array([0])
r = np.array([z0, x0])
X = p.X(r)
print r, X
Nx0 = -coldNx_Om(X, Y, Nz0)
print Nx0
data = X, Y, p.gamma(10), Nz0
Nx0 = fsolve(hot_disp_rel_wrapp, Nx0, args=data)
N = np.array([Nz0, Nx0])
nz, nx = ri.nz(r, N), ri.nx(r, N)
init_cond = np.array([z0, x0, Nz0, Nx0])
print mf.Yabs(np.array([z0, x0])), init_cond
soln = hot.get_ray(init_cond, ncycle)
soln = {
"init_cond": init_cond,
"soln": soln,
"Y": params.Y,
"ncycle": ncycle
}
allSolns.append(soln)
#Idea: investigate vg_perp/v_parallel of as function of Nz in the slabb model. We willl find the that the dependy is weak! (hopefully) -> EBW is not affected much the injection angle
#np.save(os.path.abspath("results/slab/Obeam/harm1/gamma10/NzScan/soln_"+str(params.Y)),allSolns)
return np.array(allSolns)
plus_mode_ind = ind1
if plus_mode_ind.size != 0:
return X[plus_mode_ind[0]]
else:
return X[arrNan[0]]
plot = True
if plot == True:
z0 = np.zeros(1000)
x0 = np.linspace(0, 2, 1000)
r = np.array([z0, x0])
Y = mf.Yabs(r)
X = p.X(r)
import params
Y = params.Y * np.ones(1000)
Nz0 = u.Nz_opt(Y[0]) + 0.1
Nx0_Xm = coldNx_Xm(X, Y, Nz0)
Nx0_Om = coldNx_Om(X, Y, Nz0)
plt.plot(X, Nx0_Om)
plt.plot(X, Nx0_Xm)
plt.scatter(start_Xm(Y[0], Nz0), 0)
]
return A(Nz, dielTen), B(Nz, dielTen), C(Nz, dielTen)
coldNxXm = np.sqrt(coldNx_Xm(p.X([0, 1.139]), p.Yabs([0, 1.139]), 0.6))
A, B, C = get_ABC(p.X([0, 1.139]), p.Yabs([0, 1.139]), p.gamma(1), 0.6,
coldNxXm)
print coldNxXm
print B**2 - 4 * A * C
i = 0
for x in np.linspace(0, 5, 1000):
Nx_Xm = coldNx_Xm(p.X([0, x]), p.Yabs([0, x]), 0.01)
Nx_Om = coldNx_Om(p.X([0, x]), p.Yabs([0, x]), 0.01)
print Nx_Om, Nx_Xm
#plt.scatter(p.X([0,x]),Nx_Xm)
#plt.scatter(p.X([0,x]),Nx_Om)
plt.scatter(x, Nx_Xm)
plt.scatter(x, Nx_Om)
i += 1
x = -0.18
Nx_Xm = coldNx_Xm(p.X([0, x]), p.Yabs([0, x]), 0.1)**2
Nx_Om = coldNx_Om(p.X([0, x]), p.Yabs([0, x]), 0.1)**2
#plt.ylim([0,1000])