def _compute_intensity(self, tau, Te, bpar, omega1, sigma1, kappa1,
bperp2):
# Bad hack, but we get NaNs if we don't do something like this
small_beta = np.abs(bpar) < 1.0e-20
bpar[small_beta] = 1.0e-20
comm = communication_system.communicators[-1]
nx, ny = self.nx, self.nx
signal = np.zeros((self.num_freqs, nx, ny))
xo = np.zeros(self.num_freqs)
k = int(0)
start_i = comm.rank * nx // comm.size
end_i = (comm.rank + 1) * nx // comm.size
pbar = get_pbar("Computing SZ signal.", nx * nx)
for i in range(start_i, end_i):
for j in range(ny):
xo[:] = self.xinit[:]
SZpack.compute_combo_means(xo, tau[i, j], Te[i, j], bpar[i, j],
omega1[i, j], sigma1[i, j],
kappa1[i, j], bperp2[i, j])
signal[:, i, j] = xo[:]
pbar.update(k)
k += 1
signal = comm.mpi_allreduce(signal)
pbar.finish()
for i, field in enumerate(self.freq_fields):
self.data[field] = I0 * self.xinit[i]**3 * signal[i, :, :]
self.data["Tau"] = self.ds.arr(tau, "dimensionless")
self.data["TeSZ"] = self.ds.arr(Te, "keV")
def _compute_intensity(self, tau, Te, bpar, omega1, sigma1, kappa1, bperp2):
# Bad hack, but we get NaNs if we don't do something like this
small_beta = np.abs(bpar) < 1.0e-20
bpar[small_beta] = 1.0e-20
comm = communication_system.communicators[-1]
nx, ny = self.nx,self.nx
signal = np.zeros((self.num_freqs,nx,ny))
xo = np.zeros(self.num_freqs)
k = int(0)
start_i = comm.rank*nx//comm.size
end_i = (comm.rank+1)*nx//comm.size
pbar = get_pbar("Computing SZ signal.", nx*nx)
for i in range(start_i, end_i):
for j in range(ny):
xo[:] = self.xinit[:]
SZpack.compute_combo_means(xo, tau[i,j], Te[i,j],
bpar[i,j], omega1[i,j],
sigma1[i,j], kappa1[i,j], bperp2[i,j])
signal[:,i,j] = xo[:]
pbar.update(k)
k += 1
signal = comm.mpi_allreduce(signal)
pbar.finish()
for i, field in enumerate(self.freq_fields):
self.data[field] = I0*self.xinit[i]**3*signal[i,:,:]
self.data["Tau"] = self.ds.arr(tau, "dimensionless")
self.data["TeSZ"] = self.ds.arr(Te, "keV")
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))
