np.save('fit_outputs/hat11_1_bestps.npy', bestp_hat11_1)
np.save('fit_outputs/hat11_1_error_upper.npy', hat11_1_errors_upper)
np.save('fit_outputs/hat11_1_error_lower.npy', hat11_1_errors_lower)
# np.save('fit_outputs/hat11_2_bestps.npy', bestp_hat11_2)
# np.save('fit_outputs/hat11_2_error_upper.npy', hat11_2_errors_upper)
# np.save('fit_outputs/hat11_2_error_lower.npy', hat11_2_errors_lower)
import corner
test_lats = np.linspace(-60, 60, 500)
plt.figure()
plt.hist(significant_latitudes, 30, normed=True,
alpha=0.5, histtype='stepfilled', color='k')
plt.plot(test_lats, model(bestp_hat11_1, test_lats), ls='--', lw=2)
plt.legend(loc='upper left')
plt.xlabel('Latitude $\ell$ [deg]', fontsize=15)
plt.ylabel('$p(\ell)$', fontsize=18)
plt.savefig('plots/hat11_1_hist.png', bbox_inches='tight')
plt.close()
fig = corner.corner(samples_hat11_1, labels=labels)
fig.savefig('plots/hat11_1_triangle.png', bbox_inches='tight')
#plt.show()
plt.close()
# plt.figure()
# plt.hist(second_half_lats, 30, normed=True,
# alpha=0.5, histtype='stepfilled', color='k')
# for i in range(len(year_bins)):
start = Time(datetime.datetime(year_bins[i], 1, 1))
end = start + 4 * u.year
in_time_bin = (table["jd"] > start.jd) & (table["jd"] < end.jd)
solar_lats = table["latitude_day_1"][in_time_bin]
solar_lats_error = np.ones_like(table["latitude_day_1"][in_time_bin])
print("begin mcmc {0}".format(i))
samples_i, bestp_i = run_emcee(initp_sun, solar_lats, solar_lats_error, n_steps=2000, burnin=1000)
plt.figure()
plt.hist(solar_lats, 30, normed=True, alpha=0.5, histtype="stepfilled", color="k")
plt.plot(test_lats, model(bestp_i, test_lats), ls="--", lw=2)
plt.legend(loc="upper left")
plt.xlabel("Latitude $\ell$ [deg]", fontsize=15)
plt.ylabel("$p(\ell)$", fontsize=18)
plt.savefig("plots/{0:03d}_hist.png".format(i), bbox_inches="tight")
plt.close()
fig = corner.corner(samples_i, labels=labels)
fig.savefig("plots/{0:03d}_triangle.png".format(i), bbox_inches="tight")
# plt.show()
plt.close()
mt_wilson_bestps = np.median(samples_i, axis=0)
mt_wilson_error_upper = np.diff(np.percentile(samples_i, [50, 84], axis=0), axis=0)
mt_wilson_error_lower = np.diff(np.percentile(samples_i, [50, 16], axis=0), axis=0)
