def test_per_particle(self):
_c = minimum_config.format(potential_params=pot_params,
particles_params="",
satellite_params=sat_params)
config = io.read_config(_c)
model = si.StreamModel.from_config(config)
model.sample_priors()
test_path = os.path.join(output_path, "model")
if not os.path.exists(test_path):
os.mkdir(test_path)
# likelihood args
t1, t2, dt = model.lnpargs
p_gc = model.true_particles.to_frame(galactocentric)._X
s_gc = model.true_satellite.to_frame(galactocentric)._X
logmass = model.satellite.logmass.truth
logmdot = model.satellite.logmdot.truth
#true_alpha = model.satellite.alpha.truth
true_alpha = 1.4
beta = model.particles.beta.truth
tub = model.particles.tub.truth
truth_dict = model._decompose_vector(model.truths)
group = truth_dict['potential']
for param_name,truths in group.items():
print(param_name)
param = model.parameters['potential'][param_name]
vals = np.linspace(0.9,1.1,Nfine)*truths
pparams = dict()
Ls = []
for val in vals:
pparams[param_name] = val
potential = model._potential_class(**pparams)
ln_like = back_integration_likelihood(t1, t2, dt,
potential, p_gc, s_gc,
logmass, logmdot,
beta, true_alpha, tub)
Ls.append(ln_like)
Ls = np.array(Ls).T
fig,ax = plt.subplots(1,1,figsize=(8,8))
for ii,Lvec in enumerate(Ls):
ax.plot(vals,Lvec,marker=None,linestyle='-',
label=str(ii), alpha=0.5)
if param_name == "v_halo":
ax.set_ylim(-300,50)
ax.axvline(truths)
ax.legend(loc='lower right', fontsize=14)
fig.savefig(os.path.join(test_path, "per_particle_{}.png".format(param_name)))
#########################
# alpha
param = model.parameters['satellite']['alpha']
vals = np.linspace(0.5,2.5,Nfine)
potential = model._potential_class()
Ls = []
for val in vals:
ln_like = back_integration_likelihood(t1, t2, dt,
potential, p_gc, s_gc,
logmass, logmdot,
beta, val, tub)
Ls.append(ln_like)
Ls = np.array(Ls).T
fig,ax = plt.subplots(1,1,figsize=(8,8))
for ii,Lvec in enumerate(Ls):
ax.plot(vals,Lvec,marker=None,linestyle='-',
label=str(ii), alpha=0.5)
ax.axvline(true_alpha)
ax.legend(loc='lower right', fontsize=14)
fig.savefig(os.path.join(test_path, "per_particle_alpha.png"))
plt.close('all')
a = time.time()
integrator = LeapfrogIntegrator(potential._acceleration_at,
np.array(gc[:, :3]),
np.array(gc[:, 3:]),
args=(gc.shape[0], acc))
t, rs, vs = integrator.run(t1=6200, t2=0, dt=-1)
times.append(time.time() - a)
print(np.min(times), "seconds per integration")
times = []
for ii in range(10):
a = time.time()
back_integration_likelihood(6200, 0, -1, potential, p_gc, s_gc, 2.5e8,
0.01, particles.tub, 1.5,
np.array([-1] * nparticles))
times.append(time.time() - a)
print(np.min(times), "seconds per likelihood call")
_config = """
name: test
data_file: data/observed_particles/2.5e8.hdf5
nparticles: {}
potential:
class_name: LawMajewski2010
parameters: [q1, qz, phi, v_halo]
particles:
for ii in range(10):
a = time.time()
integrator = LeapfrogIntegrator(potential._acceleration_at,
np.array(gc[:,:3]), np.array(gc[:,3:]),
args=(gc.shape[0], acc))
t, rs, vs = integrator.run(t1=6200, t2=0, dt=-1)
times.append(time.time()-a)
print(np.min(times), "seconds per integration")
times = []
for ii in range(10):
a = time.time()
back_integration_likelihood(6200, 0, -1, potential, p_gc, s_gc,
2.5e8, 0.01, particles.tub, 1.5,
np.array([-1]*nparticles))
times.append(time.time()-a)
print(np.min(times), "seconds per likelihood call")
_config = """
name: test
data_file: data/observed_particles/2.5e8.hdf5
nparticles: {}
potential:
class_name: LawMajewski2010
parameters: [q1, qz, phi, v_halo]
particles:
def test_per_particle(self):
_c = minimum_config.format(potential_params=pot_params,
particles_params="",
satellite_params=sat_params)
config = io.read_config(_c)
model = si.StreamModel.from_config(config)
model.sample_priors()
test_path = os.path.join(output_path, "model")
if not os.path.exists(test_path):
os.mkdir(test_path)
# likelihood args
t1, t2, dt = model.lnpargs
p_gc = model.true_particles.to_frame(galactocentric)._X
s_gc = model.true_satellite.to_frame(galactocentric)._X
logmass = model.satellite.logmass.truth
logmdot = model.satellite.logmdot.truth
#true_alpha = model.satellite.alpha.truth
true_alpha = 1.4
beta = model.particles.beta.truth
tub = model.particles.tub.truth
truth_dict = model._decompose_vector(model.truths)
group = truth_dict['potential']
for param_name, truths in group.items():
print(param_name)
param = model.parameters['potential'][param_name]
vals = np.linspace(0.9, 1.1, Nfine) * truths
pparams = dict()
Ls = []
for val in vals:
pparams[param_name] = val
potential = model._potential_class(**pparams)
ln_like = back_integration_likelihood(t1, t2, dt, potential,
p_gc, s_gc, logmass,
logmdot, beta,
true_alpha, tub)
Ls.append(ln_like)
Ls = np.array(Ls).T
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
for ii, Lvec in enumerate(Ls):
ax.plot(vals,
Lvec,
marker=None,
linestyle='-',
label=str(ii),
alpha=0.5)
if param_name == "v_halo":
ax.set_ylim(-300, 50)
ax.axvline(truths)
ax.legend(loc='lower right', fontsize=14)
fig.savefig(
os.path.join(test_path,
"per_particle_{}.png".format(param_name)))
#########################
# alpha
param = model.parameters['satellite']['alpha']
vals = np.linspace(0.5, 2.5, Nfine)
potential = model._potential_class()
Ls = []
for val in vals:
ln_like = back_integration_likelihood(t1, t2, dt, potential, p_gc,
s_gc, logmass, logmdot, beta,
val, tub)
Ls.append(ln_like)
Ls = np.array(Ls).T
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
for ii, Lvec in enumerate(Ls):
ax.plot(vals,
Lvec,
marker=None,
linestyle='-',
label=str(ii),
alpha=0.5)
ax.axvline(true_alpha)
ax.legend(loc='lower right', fontsize=14)
fig.savefig(os.path.join(test_path, "per_particle_alpha.png"))
plt.close('all')
