def Generalized_Ohm(sdfdata, axis, species=None):
grid = sdfdata.__dict__["Grid_Grid_mid"].data
# grid = sdfdata.__dict__["Grid_Grid"].data
x = grid[0]
y = grid[1]
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
p_pos = sdfdata.__dict__[get_varname("Grid_Particles", species)].data
vx = sdfdata.__dict__[get_varname("Particles_Vx", species)].data
vy = sdfdata.__dict__[get_varname("Particles_Vy", species)].data
vz = sdfdata.__dict__[get_varname("Particles_Vz", species)].data
w = sdfdata.__dict__[get_varname("Particles_Weight", species)].data
v_3 = (np.sqrt(vx**2 + vy**2 + vz**2))**3
v_5 = (np.sqrt(vx**2 + vy**2 + vz**2))**5
p_list = list(zip(p_pos[0], p_pos[1]))
v_3_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_3)
v_5_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_5)
const = -sc.m_e / (6 * sc.e)
grad_num = np.gradient(v_5_dist, dx, dy)[axis]
# grad_num_y = np.gradient(v_5_dist, dx, dy)[1]
return const * np.divide(grad_num, v_3_dist, where=v_3_dist!=0.0)
def Generalized_Ohm_radavg(sdfdata, species=None):
# grid = sdfdata.__dict__["Grid_Grid_mid"].data
grid = sdfdata.__dict__["Grid_Grid"].data
x = grid[0]
y = grid[1]
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
p_pos = sdfdata.__dict__[get_varname("Grid_Particles", species)].data
vx = sdfdata.__dict__[get_varname("Particles_Vx", species)].data
vy = sdfdata.__dict__[get_varname("Particles_Vy", species)].data
vz = sdfdata.__dict__[get_varname("Particles_Vz", species)].data
w = sdfdata.__dict__[get_varname("Particles_Weight", species)].data
v_3 = (np.sqrt(vx**2 + vy**2 + vz**2))**3
v_5 = (np.sqrt(vx**2 + vy**2 + vz**2))**5
p_list = list(zip(p_pos[0], p_pos[1]))
v_3_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_3)
v_5_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_5)
# ax = plt.subplot(1, 2, 1)
# im = ax.pcolormesh(v_3_dist, cmap=cm.coolwarm)
# ax = plt.subplot(1, 2, 2)
# im = ax.pcolormesh(v_5_dist, cmap=cm.coolwarm)
# plt.savefig('v5.png', dpi=600, bbox_inches="tight")
avg_3, dummy_r, dummy_t = reproject_image_into_polar(v_3_dist, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(avg_3, 0.)
avg_3 = np.average(o_ma, axis=1)
avg_5, dummy_r, dummy_t = reproject_image_into_polar(v_5_dist, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(avg_5, 0.)
avg_5 = np.average(o_ma, axis=1)
const = -sc.m_e / (6 * sc.e)
grad_num = np.gradient(avg_5, dx) #TODO: what to use for grid spacing radially
return const * np.divide(grad_num, avg_3, where=avg_3!=0.0)
def __map_particle_to_grid(sdfdata, p_var_name, species=None):
grid = sdfdata.__dict__["Grid_Grid_mid"].data
x = grid[0]
y = grid[1]
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
p_pos = sdfdata.__dict__[get_varname("Grid_Particles", species)].data
p_list = list(zip(p_pos[0], p_pos[1]))
w = sdfdata.__dict__[get_varname("Particles_Weight", species)].data
p_var = sdfdata.__dict__[p_var_name].data
return first_order_weight_2d(x, y, dx, dy, p_list,
values=p_var, weight=w)
def main():
species = "Electron"
path = "/scratch/lsa_flux/diiorios/2d_run/"
fnums = ["0200"]
fname = []
for n in fnums:
fname.append(path + n + ".sdf")
for i in range(len(fname)):
sdfdata = sdf.read(fname[i])
print(sdfdata.Header['time'])
grid = sdfdata.__dict__["Grid_Grid"].data
x = grid[0]
y = grid[1]
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
p_pos = sdfdata.__dict__[get_varname("Grid_Particles", species)].data
p_list = list(zip(p_pos[0], p_pos[1]))
p_list[:] = [p for p in p_list if in_domain(p, x, y)]
w = sdfdata.__dict__[get_varname("Particles_Weight", species)].data
v = sdfdata.__dict__[get_varname("Particles_Px", species)].data
grid = first_order_weight_2d(x, y, dx, dy, p_list, values=v, weight=w)
# p = (2 - (-2)) * np.random.rand(500,2) - 2#np.array([[0., 0.],[0.5,0.5],[-1,-1],[-2,-2],[-1.8,-1.5]])
# grid = first_order_weight_2d(x, y, dx, dy, p, values=None, weight=None)
cmap = shiftedColorMap(cm.coolwarm, np.amin(grid), np.amax(grid))
plt.figure()
plt.pcolormesh(grid, cmap=cmap)
cbar = plt.colorbar()
plt.savefig('w.png')
return
def main():
species = "Electron"
path = "/scratch/lsa_flux/diiorios/2d_run/"
fnums = ["0100"]
fname = []
for n in fnums:
fname.append(path + n + ".sdf")
x_axis_num = 0
y_axis_num = 1
z_axis_num = 2
fig, axarr = plt.subplots(len(fname), sharex=True, sharey=True)
# only have a single file to plot, so we so this little hack since
# axarr does not come out in an array in this case
if not isinstance(axarr, np.ndarray):
axarr = np.array([axarr])
axarr[0].set_title(species + " files " + str(fnums))
for i in range(len(fname)):
sdfdata = sdf.read(fname[i])
print(sdfdata.Header['time'])
grid = sdfdata.__dict__["Grid_Grid_mid"].data
# grid = sdfdata.__dict__["Grid_Grid"].data
x = grid[0]
y = grid[1]
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
p_pos = sdfdata.__dict__[get_varname("Grid_Particles", species)].data
vx = sdfdata.__dict__[get_varname("Particles_Vx", species)].data
vy = sdfdata.__dict__[get_varname("Particles_Vy", species)].data
vz = sdfdata.__dict__[get_varname("Particles_Vz", species)].data
w = sdfdata.__dict__[get_varname("Particles_Weight", species)].data
v_3 = (np.sqrt(vx**2 + vy**2 + vz**2))**3
v_5 = (np.sqrt(vx**2 + vy**2 + vz**2))**5
p_list = list(zip(p_pos[0], p_pos[1]))
# p_list = list(zip(p_pos[0], p_pos[1], w, vx, vy, vz)) # pack everything together so we remove all the right values
# p_list[:] = [p for p in p_list if in_domain(p, x, y)]
# x_pos, y_pos, w, vx, vy, vz = zip(*p_list)
# p_list = list(zip(x_pos, y_pos))
v_3_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_3)
# v_3_dist = np.divide(v_3_dist, ne, where=ne!=0.0)
v_5_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_5)
# v_5_dist = np.divide(v_5_dist, ne, where=ne!=0.0)
const = -sc.m_e / (6 * sc.e)
grad_num_x = np.gradient(v_5_dist, dx, dy)[0]
grad_num_y = np.gradient(v_5_dist, dx, dy)[1]
term_x = const * np.divide(grad_num_x, v_3_dist, where=v_3_dist!=0.0)
term_y = const * np.divide(grad_num_y, v_3_dist, where=v_3_dist!=0.0)
# limit = 1E10
plt.figure()
plt.pcolormesh(v_5_dist, cmap=cm.coolwarm)#, vmin=-limit, vmax=limit)
cbar = plt.colorbar()
plt.savefig('den.png')
return
def cart_higher_order_y(sdfdata, species=None):
# grid = sdfdata.__dict__["Grid_Grid_mid"].data
grid = sdfdata.__dict__["Grid_Grid"].data
x = grid[0]
y = grid[1]
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
p_pos = sdfdata.__dict__[get_varname("Grid_Particles", species)].data
vx = sdfdata.__dict__[get_varname("Particles_Vx", species)].data
vy = sdfdata.__dict__[get_varname("Particles_Vy", species)].data
vz = sdfdata.__dict__[get_varname("Particles_Vz", species)].data
w = sdfdata.__dict__[get_varname("Particles_Weight", species)].data
v_3 = (np.sqrt(vx**2 + vy**2 + vz**2))**3
p_list = list(zip(p_pos[0], p_pos[1]))
v_3_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_3)
vx_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=vx)
vy_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=vy)
limit = 1e108
term1 = np.multiply(vy_dist, vx_dist)
term1 = np.multiply(term1, v_3_dist)
fig4, ax4 = plt.subplots()
im4 = ax4.pcolormesh(term1, vmin=-1e102, vmax=1e102)
fig4.colorbar(im4)
fig4.savefig('term1_carty_before_grad.png', dpi=600, bbox_inches="tight")
plt.close(fig4)
term1 = np.gradient(term1, dx, axis=0)
fig5, ax5 = plt.subplots()
im5 = ax5.pcolormesh(term1, vmin=-limit, vmax=limit)
fig5.colorbar(im5)
fig5.savefig('term1_carty.png', dpi=600, bbox_inches="tight")
plt.close(fig5)
term2 = np.multiply(vy_dist, vy_dist)
term2 = np.multiply(term2, v_3_dist)
fig6, ax6 = plt.subplots()
im6 = ax6.pcolormesh(term2, vmin=-1e102, vmax=1e102)
fig6.colorbar(im6)
fig6.savefig('term2_carty_before_grad.png', dpi=600, bbox_inches="tight")
plt.close(fig6)
term2 = np.gradient(term2, dy, axis=1)
fig7, ax7 = plt.subplots()
im7 = ax7.pcolormesh(term2, vmin=-limit, vmax=limit)
fig7.colorbar(im7)
fig7.savefig('term2_carty.png', dpi=600, bbox_inches="tight")
plt.close(fig7)
div_num = term1 + term2
const = -sc.m_e / (2 * sc.e)
final = const * np.divide(div_num, v_3_dist, where=v_3_dist != 0.0)
fig8, ax8 = plt.subplots()
im8 = ax8.pcolormesh(final, vmin=-1e53, vmax=1e53)
fig8.colorbar(im8)
fig8.savefig('final_carty.png', dpi=600, bbox_inches="tight")
plt.close(fig8)
return final
def higher_order(sdfdata, species=None):
# grid = sdfdata.__dict__["Grid_Grid_mid"].data
grid = sdfdata.__dict__["Grid_Grid"].data
x = grid[0]
y = grid[1]
dx = grid[0][1] - grid[0][0]
dy = grid[1][1] - grid[1][0]
p_pos = sdfdata.__dict__[get_varname("Grid_Particles", species)].data
vx = sdfdata.__dict__[get_varname("Particles_Vx", species)].data
vy = sdfdata.__dict__[get_varname("Particles_Vy", species)].data
vz = sdfdata.__dict__[get_varname("Particles_Vz", species)].data
w = sdfdata.__dict__[get_varname("Particles_Weight", species)].data
p_list = list(zip(p_pos[0], p_pos[1]))
limit = 1e20
v_r = np.sqrt(vx**2 + vy**2)# + vz**2) #NOTE: might need to get rid of vz. can then use cart2polar for v_r and v_t
v_r_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_r)
avg_r, r, dummy_t = reproject_image_into_polar(v_r_dist, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(avg_r, 0.)
avg_r = np.average(o_ma, axis=1)
fig1, ax1 = plt.subplots(2, sharex=True)
ax1[0].plot(avg_r)
ax1[1].plot(np.clip(avg_r, -limit, limit))
fig1.savefig('vr.png', dpi=600, bbox_inches="tight")
plt.close(fig1)
v_t = np.arctan2(vx, vy)
v_t_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_t)
avg_t, r, t = reproject_image_into_polar(v_t_dist, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(avg_t, 0.)
avg_t = np.average(o_ma, axis=1)
fig2, ax2 = plt.subplots(2, sharex=True)
ax2[0].plot(avg_t)
ax2[1].plot(np.clip(avg_t, -limit, limit))
fig2.savefig('vt.png', dpi=600, bbox_inches="tight")
plt.close(fig2)
v_3 = (np.sqrt(vx**2 + vy**2 + vz**2))**3
v_3_dist = first_order_weight_2d(x, y, dx, dy, p_list, weight=w, values=v_3)
avg_3, r, t = reproject_image_into_polar(v_3_dist, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(avg_3, 0.)
avg_3 = np.average(o_ma, axis=1)
fig3, ax3 = plt.subplots(2, sharex=True)
ax3[0].plot(avg_3)
ax3[1].plot(np.clip(avg_3, -limit, limit))
fig3.savefig('avg_v3.png', dpi=600, bbox_inches="tight")
plt.close(fig3)
r = np.linspace(0.0, np.max(np.sqrt(x**2 + y**2)), num=avg_r.size)
num_1 = np.multiply(r, avg_3)
num_1 = np.multiply(avg_r, num_1)
num_1 = np.multiply(avg_r, num_1)
fig4, ax4 = plt.subplots(2, sharex=True)
ax4[0].plot(num_1)
ax4[1].plot(np.clip(num_1, -limit, limit))
fig4.savefig('num1_before_grad', dpi=600, bbox_inches="tight")
plt.close(fig4)
num_1 = np.gradient(num_1, dx) #TODO: what to use for grid spacing radially
fig5, ax5 = plt.subplots(2, sharex=True)
ax5[0].plot(num_1)
ax5[1].plot(np.clip(num_1, -limit, limit))
fig5.savefig('num1.png', dpi=600, bbox_inches="tight")
plt.close(fig5)
num_2 = np.multiply(avg_r, avg_t)
num_2 = np.multiply(avg_3, num_2)
fig6, ax6 = plt.subplots(2, sharex=True)
ax6[0].plot(num_2)
ax6[1].plot(np.clip(num_2, -limit, limit))
fig6.savefig('num2_before_grad.png', dpi=600, bbox_inches="tight")
plt.close(fig6)
num_2 = np.gradient(num_2, max(x.size, y.size)) #TODO: what to use for grid spacing theta
fig7, ax7 = plt.subplots(2, sharex=True)
ax7[0].plot(num_2)
ax7[1].plot(np.clip(num_2, -limit, limit))
fig7.savefig('num2.png', dpi=600, bbox_inches="tight")
plt.close(fig7)
# num = np.divide(num_1 + num_2, r, where=r!=0.0)
num = np.divide(num_1, r, where=r!=0.0)
fig8, ax8 = plt.subplots(2, sharex=True)
ax8[0].plot(num)
ax8[1].plot(np.clip(num, -limit, limit))
fig8.savefig('num.png', dpi=600, bbox_inches="tight")
plt.close(fig8)
const = -sc.m_e / (2 * sc.e)
final = const * np.divide(num, avg_3, where=avg_3!=0.0)
fig9, ax9 = plt.subplots(2, sharex=True)
ax9[0].plot(final)
ax9[1].plot(np.clip(final, -limit, limit))
fig9.savefig('final.png', dpi=600, bbox_inches="tight")
plt.close(fig9)
return final#const * np.divide(num, avg_3, where=avg_3!=0.0)