def full_szpack3d(ds, xo):
data = ds.index.grids[0]
dz = ds.index.get_smallest_dx().in_units("cm")
nx,ny,nz = data["density"].shape
dn = np.zeros((nx,ny,nz))
Dtau = np.array(sigma_thompson*data["density"]/(mh*mue)*dz)
Te = data["kT"].ndarray_view()
betac = np.array(data["velocity_z"]/clight)
pbar = get_pbar("Computing 3-D cell-by-cell S-Z signal for comparison.", nx)
for i in range(nx):
pbar.update(i)
for j in range(ny):
for k in range(nz):
dn[i,j,k] = SZpack.compute_3d(xo, Dtau[i,j,k],
Te[i,j,k], betac[i,j,k],
1.0, 0.0, 0.0, 1.0e-5)
pbar.finish()
return np.array(I0*xo**3*np.sum(dn, axis=2))
def full_szpack3d(ds, xo):
data = ds.index.grids[0]
dz = ds.index.get_smallest_dx().in_units("cm")
nx,ny,nz = data["density"].shape
dn = np.zeros((nx,ny,nz))
Dtau = np.array(sigma_thompson*data["density"]/(mh*mue)*dz)
Te = data["kT"].ndarray_view()
betac = np.array(data["velocity_z"]/clight)
pbar = get_pbar("Computing 3-D cell-by-cell S-Z signal for comparison.", nx)
for i in range(nx):
pbar.update(i)
for j in range(ny):
for k in range(nz):
dn[i,j,k] = SZpack.compute_3d(xo, Dtau[i,j,k],
Te[i,j,k], betac[i,j,k],
1.0, 0.0, 0.0, 1.0e-5)
pbar.finish()
return np.array(I0*xo**3*np.sum(dn, axis=2))
