def different_time_kernel():
mu = 1.
st = 1.
t = np.linspace(-3, 5, 500)
k1 = k_models.kernels.MultivariateNormal([mu], [st])
k2 = k_models.kernels.SpaceTimeNormalReflective([mu], [st])
z1 = k1.pdf(t)
z2 = k2.pdf(t)
t_refl = t[t<0][::-1]
z_refl = k1.pdf(t_refl)
import seaborn as sns
sns.set_context("paper", font_scale=2.0)
plt.figure(figsize=(8, 6))
plt.plot(t, z1, 'k')
plt.plot(t, z2, 'r-')
plt.plot(-t_refl, z_refl, 'r--')
plt.legend(('Normal', 'Reflective'))
plt.xlabel('Time (days)')
plt.ylabel('Density')
c = simulate.MohlerSimulation()
c.run()
data = c.data
max_delta_t = 100
max_delta_d = 1.
init_est_params = {
'ct': 10,
'cd': .05,
'frac_bg': 0.5,
}
ra = pp_models.SeppStochasticNn(data=data, max_delta_d=max_delta_d, max_delta_t=max_delta_t)
rb = pp_models.SeppStochasticNnReflected(data=data, max_delta_d=max_delta_d, max_delta_t=max_delta_t)
ra.train(niter=20)
rb.train(niter=20)
t = np.linspace(-10, 60, 500)
za = ra.trigger_kde.marginal_pdf(t, dim=0, normed=False) / float(ra.ndata)
zb = rb.trigger_kde.marginal_pdf(t, dim=0, normed=False) / float(rb.ndata)
w = c.trigger_params['time_decay']
th = c.trigger_params['intensity']
ztrue = th * w * np.exp(-w * t)
ztrue[t<0] = 0
plt.figure(figsize=(8, 6))
plt.plot(t, za, 'k-')
plt.plot(t, zb, 'r-')
plt.plot(t, ztrue, 'k--')
plt.legend(('Inferred, normal', 'Inferred, reflective', 'True'))
plt.xlabel('Time (days)')
plt.ylabel('Density')
def simulation_mohler():
c = simulate.MohlerSimulation()
c.run()
data = c.data
max_delta_t = 100.
max_delta_d = 1.
initial_est = lambda x, y: estimation.estimator_exp_gaussian(x, y, ct=0.1, cd=0.1)
r = pp_models.SeppStochasticNn(data=data,
max_delta_d=max_delta_d,
max_delta_t=max_delta_t,
estimation_function=initial_est)
r.train(niter=50)
pp_plotting.multiplots(r, c)
def load_results():
n = len(sigmas)
sepp = np.empty((n, n), dtype=object)
sims = np.empty((n, n), dtype=object)
for i in range(n):
for j in range(i, n):
fn = os.path.join(OUT_DIR,
'%.2f_%.2f.pickle' % (sigmas[i], sigmas[j]))
sepp[i, j] = models.SeppStochasticNn.from_pickle(fn)
c = simulate.MohlerSimulation()
c.t_total = 1500
c.bg_params[0]['sigma'] = [1., 1.]
c.bg_params[0]['intensity'] = 5
c.trigger_params['sigma'] = [sigmas[i], sigmas[j]]
c.run()
sims[i, j] = copy.deepcopy(c)
return sepp, sims
trigger_kde_kwargs = {
'strict': False,
}
s0, s1 = np.meshgrid(sigmas, sigmas)
try:
sepp_obj
except NameError:
sepp_obj = {} # already started
for i in range(s0.size):
tt = sorted([s0.flat[i], s1.flat[i]])
if tuple(tt) in sepp_obj:
continue
c = simulate.MohlerSimulation()
c.t_total = t_total
c.bg_params[0]['sigma'] = [1., 1.]
c.bg_params[0]['intensity'] = 5
c.trigger_sigma = list(tt)
init_est = lambda d, t: estimation.estimator_exp_gaussian(
d, t, ct=0.1, cd=np.mean(tt))
c.run()
data = c.data
r = models.SeppStochasticNn(data=data,
max_delta_d=max_delta_d,
max_delta_t=max_delta_t,
estimation_function=init_est,
seed=42,
bg_kde_kwargs=bg_kde_kwargs,
trigger_kde_kwargs=trigger_kde_kwargs)