def get_pressureEfield_classic(grid, rho_vyx, Bz_vyx, ne, Te_vyx):
'''
----- should get the kinetic biermann term ----->
BierE_x,BierE_y = get_pressureEfield_classic(grid,rho_vyx,Bz_vyx,ne,Te_vyx)
'''
# --- construct fo maxwellian
v_grid = grid['v_grid']
nv, ny, nx = np.shape(Bz_vyx)
v = extend_grid_v_to_vxy(nv, ny, nx, v_grid)
#Te_vyx = extend_grid_xy_to_vxy(nv,ny,nx,Te)
vte = (2.0 * Te_vyx)**0.5
fo_unnorm = ((np.pi * (vte**2))**(-1.5)) * np.exp(-(v / vte)**2)
sum_array = np.sum(fo_unnorm, axis=0)
sum_vyx = np.zeros((np.shape(Te_vyx)))
for iv in range(nv):
sum_vyx[iv, :, :] = sum_array
fo = ne * ((np.pi * (vte**2))**(-1.5)) * np.exp(-(v / vte)**2) / sum_vyx
# note IMPACT normalisation of fo different to Epperleins
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
BierE_x = (1.0 / Eta) * (I1_x + (omega_yx**2) * (I4 / I2) * I3_x)
BierE_y = (1.0 / Eta) * (I1_y + (omega_yx**2) * (I4 / I2) * I3_y)
return BierE_x, BierE_y
def get_hallfield(grid, rho_vyx, Bz_vyx, jx_vyx, jy_vyx, fo):
'''
# kinetic nernst
E_x,E_y = get_hallfield(grid,rho_vyx,Bz_vyx,jx_vyx,jy_vyx,fo)
'''
v_grid = grid['v_grid']
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
jx_yx = jx_vyx[0, :, :]
jy_yx = jy_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
hallcoeff = (1.0 / Eta) * (I4 / I2)
# E = -jxomega * hallcoeff
##E = omega x v
# | i j k | | -j_y B_z |
#-1* | jx jy 0 | | j_x B_z |
# | 0 0 Bz| = | 0 |
E_x = -Bz_yx * jy_yx * hallcoeff
E_y = Bz_yx * jx_yx * hallcoeff
return E_x, E_y
def get_kinetic_and_classicB(path, fprefix, time, xlim=-1):
'''
classic_biermann,kinetic_biermann = get_kinetic_and_classicB(path,fprefix,time)
'''
dict = cf.load_data_all_1D(path, fpre(path), time)
ne = dict['ne']
Te = dict['Te']
qx = dict['qx']
qy = dict['qy']
jx = dict['jx']
jy = dict['jy']
dxT = dict['dxT']
dyT = dict['dyT']
Bz = dict['Bz']
x_grid = dict['x_grid']
y_grid = dict['y_grid']
Z2ni = dict['Z2ni']
prof_Z = dict['Z']
fo = dict['fo']
nv = dict['nv']
ny = dict['ny']
nx = dict['nx']
rA = (Z2ni)**-1
dict_fo = cf.load_dict_1D(path, fpre(path), 'fo', time)
fo = dict_fo['mat']
time_col = dict_fo['time'] * tstep_factor
rA = (Z2ni)**-1
#===============
grid = dict['grid']
dxfo, dyfo = cf.get_grad_3d_varZ(grid, fo)
omega = Bz * rA
rho_vyx = extend_grid_xy_to_vxy(nv, ny, nx, rA)
Bz_vyx = extend_grid_xy_to_vxy(nv, ny, nx, Bz)
omega_vyx = extend_grid_xy_to_vxy(nv, ny, nx, omega)
Te_vyx = extend_grid_xy_to_vxy(nv, ny, nx, Te)
ne_vyx = extend_grid_xy_to_vxy(nv, ny, nx, ne)
#------ NERNST -----------------------------------------------------
print '\n\n--------- time ==================== ', time
#v_nx,v_ny = get_v_N(grid,rho_vyx,Bz_vyx,fo)
print(' bier init= ', np.shape(rho_vyx), np.shape(Bz_vyx),
np.shape(ne_vyx), np.shape(Te_vyx))
classic_biermann = get_Biermann_classic(grid, rho_vyx, Bz_vyx, ne_vyx,
Te_vyx)
kinetic_biermann = get_Biermann(grid, rho_vyx, Bz_vyx, fo)
classic_biermann = np.transpose(classic_biermann)
kinetic_biermann = np.transpose(kinetic_biermann)
return classic_biermann, kinetic_biermann
def get_Nernst_abs(path, time):
'''
data_x,data_y = get_Nernst_ratio(path,time)
'''
dict = cf.load_data_all_1D(path, fpre(path), time)
ne = dict['ne']
Te = dict['Te']
qx = dict['qx']
qy = dict['qy']
jx = dict['jx']
jy = dict['jy']
dxT = dict['dxT']
dyT = dict['dyT']
Bz = dict['Bz']
x_grid = dict['x_grid']
y_grid = dict['y_grid']
Z2ni = dict['Z2ni']
prof_Z = dict['Z']
fo = dict['fo']
nv = dict['nv']
ny = dict['ny']
nx = dict['nx']
rA = (Z2ni)**-1
dict_fo = cf.load_dict_1D(path, fpre(path), 'fo', time)
fo = dict_fo['mat']
rA = (Z2ni)**-1
#===============
grid = dict['grid']
dxfo, dyfo = cf.get_grad_3d_varZ(grid, fo)
rho = rA
omega = Bz * rA
rho_vyx = extend_grid_xy_to_vxy(nv, ny, nx, rA)
Bz_vyx = extend_grid_xy_to_vxy(nv, ny, nx, Bz)
omega_vyx = extend_grid_xy_to_vxy(nv, ny, nx, omega)
#------ NERNST -----------------------------------------------------
v_nx, v_ny = get_v_N(grid, rho_vyx, Bz_vyx, fo)
v_nx_classical, v_ny_classical, v_nx_hf, v_ny_hf = get_vN_from_path(
path, fpre(path), time)
v_nx_c = np.transpose(v_nx_classical)
v_ny_c = np.transpose(
v_ny_classical) #np.transpoe(v_ny_classical)#[:,::-1]
v_nx_k = np.transpose(v_nx)
v_ny_k = np.transpose(v_ny) #np.transpose(v_ny)#[:,::-1]
return v_nx_k, v_ny_k, v_nx_c, v_ny_c
def get_kinetic_heatflow_b(path, time):
'''
dict = get_kinetic_heatflow_b(path,time)
var_list = ['q_SH_x','q_SH_y','q_RL_x','q_RL_y','q_TE_x','q_TE_y','q_E_x','q_E_y']
for var in range(len(dict.keys())):
var_name = var_list[var]
ax = fig.add_subplot(ny_plots,nx_plots,var+1)
plot_2D(ax,dict[var_name]['data'],dict[var_name]['name'])
'''
dict = cf.load_data_all_1D(path, fpre(path), time)
ne = dict['ne']
Te = dict['Te']
qx = dict['qx']
qy = dict['qy']
jx = dict['jx']
jy = dict['jy']
dxT = dict['dxT']
dyT = dict['dyT']
Bz = dict['Bz']
x_grid = dict['x_grid']
y_grid = dict['y_grid']
Z2ni = dict['Z2ni']
prof_Z = dict['Z']
fo = dict['fo']
nv = dict['nv']
ny = dict['ny']
nx = dict['nx']
rA = (Z2ni)**-1
dict_fo = cf.load_dict_1D(path, fpre(path), 'fo', time)
#===============
grid = dict['grid']
dxfo, dyfo = cf.get_grad_3d_varZ(grid, fo)
rho = rA
omega = Bz * rA
rho_vyx = extend_grid_xy_to_vxy(nv, ny, nx, rA)
Bz_vyx = extend_grid_xy_to_vxy(nv, ny, nx, Bz)
omega_vyx = extend_grid_xy_to_vxy(nv, ny, nx, omega)
dict = get_qSH_kinetic(grid, rho_vyx, Bz_vyx, jx, jy, fo)
dict['x_grid'] = dict_fo['x_grid']
dict['y_grid'] = dict_fo['y_grid']
dict['v_grid'] = dict_fo['v_grid']
dict['time'] = dict_fo['time']
return dict
def get_Biermann_classic(grid, rho_vyx, Bz_vyx, ne, Te_vyx):
'''
----- should get the kinetic biermann term ----->
kinetic_biermann = get_Biermann(grid,rho_vyx,Bz_vyx,fo)
'''
# --- construct fo maxwellian
v_grid = grid['v_grid']
nv, ny, nx = np.shape(Bz_vyx)
v = extend_grid_v_to_vxy(nv, ny, nx, v_grid)
vte = (2.0 * Te_vyx)**0.5
fo_unnorm = ((np.pi * (vte**2))**(-1.5)) * np.exp(-(v / vte)**2)
sum_array = np.sum(fo_unnorm, axis=0)
sum_vyx = np.zeros((np.shape(Te_vyx)))
for iv in range(nv):
sum_vyx[iv, :, :] = sum_array
fo = ne * ((np.pi * (vte**2))**(-1.5)) * np.exp(-(v / vte)**2) / sum_vyx
# note IMPACT normalisation of fo different to Epperleins
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
BierE_x = (1.0 / Eta) * (I1_x + (omega_yx**2) * (I4 / I2) * I3_x)
BierE_y = (1.0 / Eta) * (I1_y + (omega_yx**2) * (I4 / I2) * I3_y)
#---- now take the curl?
# --- get_grad assumes [x,y] indices
x_grid_yx, xY = np.meshgrid(grid['x_grid'], np.ones(len(grid['y_grid'])))
yY, y_grid_yx = np.meshgrid(np.ones(len(grid['x_grid'])), grid['y_grid'])
dxBierE_y = (BierE_y[:, 2:] - BierE_y[:, :-2]) / (x_grid_yx[:, 2:] -
x_grid_yx[:, :-2])
dyBierE_x = (BierE_x[2:, :] - BierE_x[:-2, :]) / (y_grid_yx[2:, :] -
y_grid_yx[:-2, :])
kinetic_biermann_z = -1.0 * (dxBierE_y[1:-1, :] - dyBierE_x[:, 1:-1])
return kinetic_biermann_z
def get_alpha_perp_kinetic(grid, rho_vyx, Bz_vyx, fo):
'''
alpha_perp_kin = get_alpha_perp_kinetic(grid,rho_vyx,Bz_vyx,fo)
'''
v_grid = grid['v_grid']
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
alpha_perp_kin = -(1.0 / Eta)
return alpha_perp_kin
def get_pressureEfield_kinetic(grid, rho_vyx, Bz_vyx, fo):
'''
----- should get the kinetic biermann term ----->
BierE_x,BierE_y = get_pressureEfield_kinetic(grid,rho_vyx,Bz_vyx,fo)
'''
#--
v_grid = grid['v_grid']
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
BierE_x = (1.0 / Eta) * (I1_x + (omega_yx**2) * (I4 / I2) * I3_x)
BierE_y = (1.0 / Eta) * (I1_y + (omega_yx**2) * (I4 / I2) * I3_y)
#---- now take the curl?
return BierE_x, BierE_y
def get_v_N(grid, rho_vyx, Bz_vyx, fo):
'''
# kinetic nernst
v_nx,v_ny = get_v_N(grid,rho_vyx,Bz_vyx,fo)
'''
v_grid = grid['v_grid']
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
v_nx = (1.0 / Eta) * (I3_x - (I4 / I2) * I1_x)
v_ny = (1.0 / Eta) * (I3_y - (I4 / I2) * I1_y)
return v_nx, v_ny
def get_Biermann(grid, rho_vyx, Bz_vyx, fo):
'''
----- should get the kinetic biermann term ----->
kinetic_biermann = get_Biermann(grid,rho_vyx,Bz_vyx,fo)
'''
#--
v_grid = grid['v_grid']
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
BierE_x = (1.0 / Eta) * (I1_x + (omega_yx**2) * (I4 / I2) * I3_x)
BierE_y = (1.0 / Eta) * (I1_y + (omega_yx**2) * (I4 / I2) * I3_y)
#---- now take the curl?
# --- get_grad assumes [x,y] indices
x_grid_yx, xY = np.meshgrid(grid['x_grid'], np.ones(len(grid['y_grid'])))
yY, y_grid_yx = np.meshgrid(np.ones(len(grid['x_grid'])), grid['y_grid'])
dxBierE_y = (BierE_y[:, 2:] - BierE_y[:, :-2]) / (x_grid_yx[:, 2:] -
x_grid_yx[:, :-2])
dyBierE_x = (BierE_x[2:, :] - BierE_x[:-2, :]) / (y_grid_yx[2:, :] -
y_grid_yx[:-2, :])
kinetic_biermann_z = -1.0 * (dxBierE_y[1:-1, :] - dyBierE_x[:, 1:-1])
return kinetic_biermann_z
def get_q_abs(path, time):
'''
dict_ratio = get_q_ratio(path,time)
'''
time_int = int(time)
dict = cf.load_data_all_1D(path, fpre(path), time)
ne = dict['ne']
Te = dict['Te']
qx = dict['qx']
qy = dict['qy']
jx = dict['jx']
jy = dict['jy']
dxT = dict['dxT']
dyT = dict['dyT']
Bz = dict['Bz']
U = dict['U']
x_grid = dict['x_grid']
y_grid = dict['y_grid']
Z2ni = dict['Z2ni']
prof_Z = dict['Z']
fo = dict['fo']
nv = dict['nv']
ny = dict['ny']
nx = dict['nx']
rA = (Z2ni)**-1
dict_fo = cf.load_dict_1D(path, fpre(path), 'fo', time)
fo = dict_fo['mat']
rA = (Z2ni)**-1
#===============
grid = dict['grid']
dxfo, dyfo = cf.get_grad_3d_varZ(grid, fo)
rho = rA
omega = Bz * rA
rho_vyx = extend_grid_xy_to_vxy(nv, ny, nx, rA)
Bz_vyx = extend_grid_xy_to_vxy(nv, ny, nx, Bz)
omega_vyx = extend_grid_xy_to_vxy(nv, ny, nx, omega)
#heat flow ratios
#--- kinetic
dict = get_qSH_kinetic(grid, rho_vyx, Bz_vyx, jx, jy, fo)
name_list = [
'q_SH_x', 'q_SH_y', 'q_RL_x', 'q_RL_y', 'q_TE_x', 'q_TE_y', 'q_E_x',
'q_E_y'
]
for name in name_list:
dict[name]['data'] = np.transpose(dict[name]['data'])[:]
q_KSH_x, q_KSH_y, q_KRL_x, q_KRL_y, q_KTE_x, q_KTE_y, q_KE_x, q_KE_y = dict[
'q_SH_x']['data'], dict['q_SH_y']['data'], dict['q_RL_x'][
'data'], dict['q_RL_y']['data'], dict['q_TE_x']['data'], dict[
'q_TE_y']['data'], dict['q_E_x']['data'], dict['q_E_y']['data']
#--- classical
q_dir = path + '/'
q_SH_x = np.load(q_dir + 'q_SH_x.txt.npy')
q_SH_y = np.load(q_dir + 'q_SH_y.txt.npy')
q_RL_x = np.load(q_dir + 'q_RL_x.txt.npy')
q_RL_y = np.load(q_dir + 'q_RL_y.txt.npy')
q_E_x = np.load(q_dir + 'q_E_x.txt.npy')
q_E_y = np.load(q_dir + 'q_E_y.txt.npy')
q_xX = np.load(q_dir + 'q_xX.txt.npy')
q_yY = np.load(q_dir + 'q_yY.txt.npy')
#nt,ny,nx
q_x_VFP = (q_xX[:, 1:, :] + q_xX[:, :-1, :]) * 0.5
q_y_VFP = (q_yY[:, :, 1:] + q_yY[:, :, :-1]) * 0.5
q_x_B = q_RL_x + q_E_x + q_SH_x
q_y_B = q_RL_y + q_E_y + q_SH_y
dict_c = {}
dict_c['tot x'] = q_x_B[time_int, :, :] #q_xc
dict_c['tot y'] = q_y_B[time_int, :, :]
dict_c['SH x'] = q_SH_x[time_int, :, :] #q_SH_x[time_int,:x_lim,:]
dict_c['SH y'] = q_SH_y[time_int, :, :] #q_SH_y[time_int,:x_lim,:]
dict_c['RL x'] = q_RL_x[time_int, :, :]
dict_c['RL y'] = q_RL_y[time_int, :, :]
dict_c['E x'] = q_E_x[time_int, :, :]
dict_c['E y'] = q_E_y[time_int, :, :]
dict_k = {}
dict_k['tot x'] = q_x_VFP[time_int, :, :]
dict_k['tot y'] = q_y_VFP[time_int, :, :]
dict_k['SH x'] = q_KSH_x #q_SH_x[time_int,:x_lim,:]
dict_k['SH y'] = q_KSH_y #q_SH_y[time_int,:x_lim,:]
dict_k['RL x'] = q_KRL_x
dict_k['RL y'] = q_KRL_y
dict_k['E x'] = q_KTE_x + q_KE_x
dict_k['E y'] = q_KTE_y + q_KE_y
dict_k['time'] = dict_fo['time']
dict_c['x_grid'] = x_grid[1:-1]
dict_c['y_grid'] = y_grid
dict_k['x_grid'] = x_grid[1:-1]
dict_k['y_grid'] = y_grid
dict_k['ne'] = np.transpose(ne)
dict_k['Te'] = np.transpose(Te)
dict_k['jx'] = np.transpose(jx)
dict_k['jy'] = np.transpose(jy)
dict_k['dxT'] = np.transpose(dxT)
dict_k['dyT'] = np.transpose(dyT)
dict_k['Bz'] = np.transpose(Bz)
dict_k['Z2ni'] = np.transpose(Z2ni)
dict_k['U'] = np.transpose(U)
return dict_c, dict_k
def get_qSH_kinetic(grid, rho_vyx, Bz_vyx, jx, jy, fo):
'''
dict = get_qSH_kinetic(grid,rho_vyx,Bz_vyx,jx,jy,fo)
'''
v_grid = grid['v_grid']
omega_vyx = Bz_vyx * rho_vyx
gradfo_x, gradfo_y = cf.get_grad_3d_varZ(grid, fo)
I1_x, I1_y = get_I1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I3_x, I3_y = get_I3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
I2 = get_I2_scalar(rho_vyx, omega_vyx, fo, v_grid)
I4 = get_I4_scalar(rho_vyx, omega_vyx, fo, v_grid)
#========
K2 = get_K2_scalar(rho_vyx, omega_vyx, fo, v_grid)
K4 = get_K4_scalar(rho_vyx, omega_vyx, fo, v_grid)
K1_x, K1_y = get_K1_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
K3_x, K3_y = get_K3_vec(rho_vyx, omega_vyx, gradfo_x, gradfo_y, v_grid)
omega_yx = omega_vyx[0, :, :]
Bz_yx = Bz_vyx[0, :, :]
Eta = (I2 + (omega_yx**2) * ((I4**2) / I2))
v_nx = -(1.0 / Eta) * (I3_x - (I4 / I2) * I1_x)
v_ny = -(1.0 / Eta) * (I3_y - (I4 / I2) * I1_y)
#--- Spitzer
q_SH_x = K1_x - (K2 / Eta) * I1_x - (K2 / Eta) * (I4 / I2) * (
Bz_yx**2) * I3_x + (K4 / Eta) * (Bz_yx**2) * (I3_x - I1_x * (I4 / I2))
q_SH_y = K1_y - (K2 / Eta) * I1_y - (K2 / Eta) * (I4 / I2) * (
Bz_yx**2) * I3_y + (K4 / Eta) * (Bz_yx**2) * (I3_y - I1_y * (I4 / I2))
#---- RL
kappa_w_dxT = -(K2 / Eta) * (I3_x - I1_x * (I4 / I2)) + K3_x - (
K4 / Eta) * I1_x - (Bz_yx**2) * (K4 / Eta) * (I4 / I2) * I3_x
kappa_w_dyT = -(K2 / Eta) * (I3_y - I1_y * (I4 / I2)) + K3_y - (
K4 / Eta) * I1_y - (Bz_yx**2) * (K4 / Eta) * (I4 / I2) * I3_y
q_RL_x = -Bz_yx * kappa_w_dyT
q_RL_y = Bz_yx * kappa_w_dxT
#--- Thermo electric perp
beta_perp = K2 / Eta + (Bz_yx**2) * (K4 * I4) / (Eta * I2)
q_TE_x = beta_perp * jx
q_TE_y = beta_perp * jy
#--- Thermo electric wedge
beta_wedge = (-(K2 * I4 / (Eta * I2)) + K4 / Eta)
q_E_x = -Bz_yx * beta_wedge * jy
q_E_y = Bz_yx * beta_wedge * jx
dict = {}
dict['q_SH_x'] = data_var(-1.0 * q_SH_x, 'q_x SH')
dict['q_SH_y'] = data_var(-1.0 * q_SH_y, 'q_y SH')
dict['q_RL_x'] = data_var(-1.0 * q_RL_x, 'q_x RL')
dict['q_RL_y'] = data_var(-1.0 * q_RL_y, 'q_y RL')
dict['q_TE_x'] = data_var(-1.0 * q_TE_x, 'q_x thermoelectric')
dict['q_TE_y'] = data_var(-1.0 * q_TE_y, 'q_y thermoelectric')
dict['q_E_x'] = data_var(-1.0 * q_E_x, 'q_x Ettinghausen')
dict['q_E_y'] = data_var(-1.0 * q_E_y, 'q_y Ettinghausen')
return dict
