def scf_density_grid_fast(x,
y,
z,
Smw,
Tmw,
Slmc,
Tlmc,
nbins,
rs_mw,
rs_lmc,
lmc_com,
quantity,
G=1):
q_all = np.zeros((nbins, nbins, nbins))
xyz_lmc = np.array([x - lmc_com[0], y - lmc_com[1], z - lmc_com[2]]).T
xyz = np.array([x, y, z]).T
print(len(xyz_lmc[:, 0]))
print(xyz_lmc[:, 0])
if quantity == "density":
q_all = biff.density(np.ascontiguousarray(xyz_lmc), Slmc, Tlmc, M=1, r_s=rs_lmc) \
+ biff.density(np.ascontiguousarray(xyz), Smw, Tmw, M=1, r_s=rs_mw)
if quantity == "potential":
q_all = biff.potential(np.ascontiguousarray(xyz_lmc), Slmc, Tlmc, M=1, r_s=rs_lmc, G=G) \
+ biff.potential(np.ascontiguousarray(xyz), Smw, Tmw, M=1, r_s=rs_mw, G=G)
if quantity == "acceleration":
almc = biff.gradient(xyz_lmc, Slmc, Tlmc, M=1, r_s=rs_lmc, G=G)
amw = biff.gradient(xyz, Smw, Tmw, M=1, r_s=rs_mw, G=G)
q_all = np.sqrt(np.sum(almc**2, axis=1)) + np.sqrt(
np.sum(amw**2, axis=1))
return q_all
def a_bfe_all(self, S, T, S_mw, T_mw, S_mw_wake, T_mw_wake, S_lmc, T_lmc,
S_mwlmc, T_mwlmc):
xyz, xyz_shift = self.grid_cartessian()
a_mon = biff.gradient(xyz, S, T, M=1, r_s=self.rs, G=self.G_gadget)
a_mwwake = biff.gradient(xyz,
S_mw,
T_mw,
G=self.G_gadget,
M=1,
r_s=self.rs)
a_wake = biff.gradient(xyz,
S_mw_wake,
T_mw_wake,
M=1,
r_s=self.rs,
G=self.G_gadget)
a_mwlmc = biff.gradient(xyz,
S_mwlmc,
T_mwlmc,
M=1,
G=self.G_gadget,
r_s=self.rs)
# shift
a_lmcshift = biff.gradient(xyz_shift,
S_lmc,
T_lmc,
M=1,
G=self.G_gadget,
r_s=self.rs_sat)
#a_lmcshift = biff.gradient(xyz, S_lmc, T_lmc, M=1, G=self.G_gadget, r_s=self.rs_sat)
return a_mon, a_mwwake, a_wake, a_lmcshift, a_mwlmc
def acceleration_plot(S, T, SS, TT, ST, figname, cbar_name):
sn = [0, 1, 2, 3, 4, 5]
fig = plt.figure(figsize=(10, 14))
for i in range(len(sn)):
S_smooth, T_smooth, N_smooth = coefficients_smoothing.smooth_coeff_matrix(
S, T, SS, TT, ST, mass, 20, 20, 20, sn[i])
a_biff = biff.gradient(np.ascontiguousarray(xyz),
S_smooth,
T_smooth,
M=1,
r_s=40.85,
G=1)
a_biff_all = np.sqrt(a_biff[:, 0]**2 + a_biff[:, 1]**2 +
a_biff[:, 2]**2)
a_biff_0 = biff.gradient(np.ascontiguousarray(xyz),
np.array([[[S_smooth[0, 0, 0]], [0], [0]]]).T,
np.array([[[T_smooth[0, 0, 0]], [0], [0]]]).T,
M=1,
r_s=40.85,
G=1)
a_biff_all_0 = np.sqrt(a_biff_0[:, 0]**2 + a_biff_0[:, 1]**2 +
a_biff_0[:, 2]**2)
plt.subplot(3, 2, i + 1)
#levels = np.arange(-0.03, 0.12, 0.005)
im = plt.contourf(y_grid,
z_grid, ((a_biff_all / a_biff_all_0) - 1).reshape(
bins, bins),
40,
origin='lower',
cmap='viridis')
plt.text(-180, 160, 'Ncoeff={}'.format(N_smooth), color='w')
plt.text(-180, 130, 'S/N={}'.format(sn[i]), color='w')
plt.xlim(-200, 200)
plt.ylim(-200, 200)
if ((i == 4) | (i == 5)):
plt.xlabel('y[kpc]')
if (i + 1) % 2 == 1:
plt.ylabel('z[kpc]')
cb_ax = fig.add_axes([0.93, 0.1, 0.02, 0.8])
cbar = plt.colorbar(im, cax=cb_ax)
fig.suptitle(
'Relative acceleration of MW + LMC unbound particles {}'.format(
cbar_name),
y=0.93)
cbar.set_label('$\Delta |a|$')
plt.savefig(figname + '.pdf', bbox_inches='tight')
plt.savefig(figname + '.png', bbox_inches='tight')
return 0
def combine_bfe_a(S1, T1, S2, T2, y_grid, z_grid, lmc_com, nbins):
a_mwlmc = np.zeros((nbins, nbins))
for i in range(nbins):
for j in range(nbins):
a = biff.gradient(np.array([[0-lmc_com[0]],
[y_grid[0][i]-lmc_com[1]],
[z_grid[j,0]-lmc_com[2]]]).T,
S2, T2, M=1,r_s=10, G=1) + \
biff.gradient(np.array([[0], [y_grid[0][i]], [z_grid[j,0]]]).T,
S1, T1, M=1, r_s=40.85, G=1)
a_mwlmc[i][j] = (a[:,0]**2 + a[:,1]**2 + a[:,2]**2)**0.5
return a_mwlmc
def bfe_a_grid(S1, T1, y_grid, z_grid, nbins, rs):
xyz = np.ascontiguousarray(np.array([np.zeros(len(y_grid.flatten())),
y_grid.flatten(),
z_grid.flatten()]).T)
a = biff.gradient(xyz, S1, T1, M=1, r_s=rs, G=1)
a_mw = (a[:,0]**2 + a[:,1]**2 + a[:,2]**2)**0.5
return a_mw
def _ln_potential_prior(self, pars):
lp = 0.
lp += -(pars['Snlm']**2).sum()
grad = biff.gradient(self._xyz,
Snlm=pars['Snlm'],
Tnlm=pars['Tnlm'],
nmax=self.nmax,
lmax=0,
G=_G,
M=pars['m'],
r_s=pars['r_s'])
if np.any(grad < 0.):
return -np.inf
dens = biff.density(self._xyz,
Snlm=pars['Snlm'],
Tnlm=pars['Tnlm'],
nmax=self.nmax,
lmax=0,
M=pars['m'],
r_s=pars['r_s'])
if np.any(dens < 0.):
return -np.inf
return lp
def scf_density_grid(x,
y,
z,
Smw,
Tmw,
Slmc,
Tlmc,
nbins,
rs_mw,
rs_lmc,
lmc_com,
quantity,
G=1):
q_all = np.zeros((nbins, nbins, nbins))
for i in range(nbins):
for j in range(nbins):
for k in range(nbins):
# These line is flipping x with y!
xyz_lmc = np.array([[x[0, i, 0] - lmc_com[0]],
[y[j, 0, 0] - lmc_com[1]],
[z[0, 0, k] - lmc_com[2]]]).T
xyz = np.array([[x[0, i, 0]], [y[j, 0, 0]], [z[0, 0, k]]]).T
if quantity == "density":
q_all[i][j][k] = \
biff.density(xyz_lmc, Slmc, Tlmc, M=1, r_s=rs_lmc) \
+ biff.density(xyz, Smw, Tmw, M=1, r_s=rs_mw)
if quantity == "potential":
q_all[i][j][k] = \
biff.potential(np.ascontiguousarray(xyz_lmc), Slmc, Tlmc, M=1, r_s=rs_lmc,
G=G) \
+ biff.potential(np.ascontiguousarray(xyz), Smw, Tmw, M=1, r_s=rs_mw, G=G)
if quantity == "acceleration":
almc = biff.gradient(xyz_lmc,
Slmc,
Tlmc,
M=1,
r_s=rs_lmc,
G=G)
amw = biff.gradient(xyz, Smw, Tmw, M=1, r_s=rs_mw, G=G)
q_all[i][j][k] = np.sqrt(np.sum(almc**2)) \
+ np.sqrt(np.sum(amw**2))
return q_all.flatten()
def _ln_potential_prior(self, pars):
lp = 0.
lp += -(pars['Snlm']**2).sum()
grad = biff.gradient(self._xyz, Snlm=pars['Snlm'], Tnlm=pars['Tnlm'],
nmax=self.nmax, lmax=0, G=_G, M=pars['m'], r_s=pars['r_s'])
if np.any(grad < 0.):
return -np.inf
dens = biff.density(self._xyz, Snlm=pars['Snlm'], Tnlm=pars['Tnlm'],
nmax=self.nmax, lmax=0, M=pars['m'], r_s=pars['r_s'])
if np.any(dens < 0.):
return -np.inf
return lp