def electric_radial_average(sdf_data):
ex = sdf_data.__dict__['Electric_Field_Ex']
ey = sdf_data.__dict__['Electric_Field_Ey']
x_grid = ex.grid_mid.data[0]
y_grid = ex.grid_mid.data[1]
ex = ex.data
ey = ey.data
X, Y = np.meshgrid(x_grid, y_grid)
R, T = cart2polar(X, Y)
er = np.multiply(ex, np.cos(T)) + np.multiply(ey, np.sin(T))
et = np.multiply(ey, np.cos(T)) - np.multiply(ex, np.sin(T))
o, r, t = reproject_image_into_polar(er,
origin=None,
Jacobian=False,
dr=1,
dt=None)
o_ma = np.ma.masked_equal(o, 0.)
avg = np.average(o_ma, axis=1)
return avg, R, T
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 __radial_average(x_grid, y_grid, field_x, field_y):
X, Y = np.meshgrid(x_grid, y_grid)
R, T = cart2polar(X, Y)
er = np.multiply(field_x, np.cos(T)) + np.multiply(field_y, np.sin(T))
dummy_et = np.multiply(field_y, np.cos(T)) - np.multiply(field_x, np.sin(T))
o, dummy_r, dummy_t = reproject_image_into_polar(er, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(o, 0.)
avg = np.average(o_ma, axis=1)
return avg, R, T
def density_radial_average(sdf_data, varname):
dens = sdf_data.__dict__[varname]
x_grid = dens.grid_mid.data[0]
y_grid = dens.grid_mid.data[1]
dens = dens.data
X, Y = np.meshgrid(x_grid, y_grid)
R, T = cart2polar(X, Y)
o, r, t = reproject_image_into_polar(dens, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(o, 0.)
avg = np.average(o_ma, axis=1)
return avg, R, T
def main():
path = "/Users/stephendiiorio/Desktop/"
fnums = ["restart0010"]
fname = []
for n in fnums:
fname.append(path + n + ".sdf")
for i in range(len(fname)):
sdfdata = sdf.read(fname[i])
ex = sdfdata.__dict__['Electric_Field_Ex'].data
ey = sdfdata.__dict__['Electric_Field_Ey'].data
x_grid = sdfdata.__dict__['Electric_Field_Ex'].grid_mid.data[0]
y_grid = sdfdata.__dict__['Electric_Field_Ex'].grid_mid.data[1]
# See https://www.mathworks.com/matlabcentral/answers/95758-how-can-i-convert-vector-fields-from-cartesian-to-polar-coordinate-system-in-matlab-7-10-r2010a for a discussion on how to create a radial vector field from a cartesian one.
X, Y = np.meshgrid(x_grid, y_grid)
R, T = cart2polar(X, Y)
print(np.max(R), np.min(R))
er = np.multiply(ex, np.cos(T)) + np.multiply(ey, np.sin(T))
et = np.multiply(ey, np.cos(T)) - np.multiply(ex, np.sin(T))
# See https://stackoverflow.com/questions/2164570/reprojecting-polar-to-cartesian-grid and the python package Ansel, where these functions are taken from, to see how to transform a cartesian image into polar coordinates.
o, r, t = reproject_image_into_polar(er, origin=None, Jacobian=False, dr=1, dt=None)
o_ma = np.ma.masked_equal(o, 0.)
avg = np.average(o_ma, axis=1)
fig, ax = plt.subplots()
# im = ax.pcolormesh(o, cmap=cm.coolwarm, vmin=-8e8, vmax=8e8)
# cb = fig.colorbar(im)
im = ax.plot(avg)
plt.ylim(-8e8, 8e8)
plt.savefig('asdf.png', dpi=600, bbox_inches="tight")
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)