def get_rv(self): self.vel_full = np.zeros_like(self.times_full) # rv at oversampled times self.vel_full_each = np.zeros_like(self.times_full) # rv at oversampled times for each planet self.vel_all = np.zeros((len(self.times), self.nplanets)) # rv at sample times for each planet self.vel_each = np.zeros_like(self.times) # rv at sample times for each planet (temporary) self.vel_total = np.zeros_like(self.times) # rv at sample times # if self.nplanets == 0: # self.get_noise(self.types_noise) # return for planet in range(self.nplanets): if self.periods is None: P = np.random.rand()*998. + 2. # random period, U(2, 998) else: P = self.periods[planet] # user-provided period if self.eccentricities is None: ecc = np.random.beta(0.867, 3.03) # from Kipping 2013 else: ecc = self.eccentricities[planet] # user-provided eccentricity if self.semi_amplitudes is None: K = np.random.rand()*50. + 2. else: K = self.semi_amplitudes[planet] # user-provided semi-amplitude if self.omegas is None: omega = np.random.rand()*2.*np.pi else: omega = self.omegas[planet] # user-provided omega if self.times_periastron is None: # random starting at around 2010-01-01, ending 1000 days later time_periastron = np.random.randint(2455197, 2456197) else: time_periastron = self.times_periastron[planet] # user-provided time of periastron # log # temp.write(output % (planet+1, P, K, ecc, omega, 2452000)) # get RVs for this planet get_rvn(self.times, P, K, ecc, omega, time_periastron, 0., self.vel_each) # get RVs for this planet at oversampled times get_rvn(self.times_full, P, K, ecc, omega, time_periastron, 0., self.vel_full_each) # store self.vel_all[:,planet] = self.vel_each self.vel_total += self.vel_each self.vel_full += self.vel_full_each # self.get_noise(self.types_noise) self.vel_total += self.noise # add noise
vel = np.zeros_like(tt) ## best solution found best_ind = np.argmax(m.chains[1, :]) par_best = m.chains[2:, best_ind] f = plt.figure() gs = gridspec.GridSpec(2, 1, height_ratios=[2,1]) ax = [plt.subplot(gs[0])] ax.append(plt.subplot(gs[1], sharex=ax[0])) # , ax = plt.subplots(nrows=2, sharex=True) #### plot best solution args = [tt] + list(par_best) + [vel] get_rvn(*args) ax[0].plot(tt, (vel - par_best[5])*1e3, '-', lw=1.5, color='k') limits = ax[0].axis() #### plot posterior predictive # n_posterior_samples = 1000 # par_samples = sample(m.chains[2:, :].T, n_posterior_samples) # for p in par_samples: # # if abs(p[4] - 2493817.60) > 1000: continue # args = [tt] + list(p) + [vel] # get_rvn(*args) # ax[0].plot(tt, (vel - p[5])*1e3, '-', lw=1.5, alpha=0.05) # we have to do it again... args = [tt] + list(par_best) + [vel] get_rvn(*args)
