def rm_all():
rmid_list = get_total_rmid_list()
for each in rmid_list:
try:
rm(each)
except Exception as reason:
print("Failed because of: " + str(reason))
def inte_all():
rmid_list = get_total_rmid_list()
for each in rmid_list:
try:
line_integration(each)
except Exception as reason:
print("Failed because of: " + str(reason))
def error_estimation_all():
rmid_list = get_total_rmid_list()
for each in rmid_list:
try:
mc_ee(each)
except Exception as reason:
print("Failed because of: " + str(reason))
def plot_lum_vs_lag():
rmid_list = get_total_rmid_list()
zfinal = pickle.load(open(Location.project_loca +
"/info_database/zfinal.pkl"))
lag_list = list()
lag_err_list = list()
lum_list = list()
lum_err_list = list()
fig = plt.figure()
for each in rmid_list:
try:
lag, lag_err = get_lag(each, zfinal)
lum, lum_err = get_lum(each, zfinal)
if lag < 3.0 * lag_err or lum < 3.0 * lum_err:
raise Exception
lag_list.append(lag)
lag_err_list.append(lag_err)
lum_list.append(lum)
lum_err_list.append(lum_err)
except Exception:
continue
# plt.errorbar([lum], [lag])
# fig.text(np.log10(lum), np.log10(lag), str(each))
lag = np.log10(np.array(lag_list))
lum = np.log10(np.array(lum_list))
lag_err = np.array(lag_err_list) / np.array(lag_list)
lum_err = np.array(lum_err_list) / np.array(lum_list)
theory = models.Linear1D(0.5, -10.0, fixed={"slope": True})
obs = models.Const1D(0.0)
fitter = fitting.LinearLSQFitter()
theory_fit = fitter(theory, lum, lag, weights = lag_err ** 2.0)
print(theory_fit.parameters)
obs_fit = fitter(obs, lum, lag, weights = lag_err ** 2.0)
print(obs_fit.parameters)
rcs = np.sum((obs_fit(lum) - lag) ** 2.0) ** 0.5 / (len(rmid_list) - 1.0)
print(rcs)
print(len(lum))
plt.errorbar(lum, lag, xerr=lum_err, yerr=lag_err, fmt='o')
# plt.plot(lum, theory_fit(lum))
plt.plot(lum, obs_fit(lum))
plt.show()
[wave, flux, error] = read_raw_data(rmid, mjd)
[wave, flux,
error] = extract_fit_part(wave, flux, error, hb[0] - 2.0 * hb[1],
hb[0] + 2.0 * hb[1])
[wave, flux, error] = mask_points(wave, flux, error)
flux = flux - cont_func(wave)
up_wave = wave[0:flux.argmax()]
up_flux = flux[0:flux.argmax()]
down_flux = flux[flux.argmax():-1]
down_wave = wave[flux.argmax():-1]
wave_min = find_wave(up_wave, up_flux, 0.5 * np.amax(flux))
wave_max = find_wave(down_wave, down_flux, 0.5 * np.amax(flux))
return (wave_max - wave_min) / hb[1]
rmid_list = get_total_rmid_list()
fe = list()
fe_e = list()
hb = list()
hb_e = list()
for each in rmid_list:
try:
a, b, e, f = get_fe_hb(each)
mjd_list = map(
int, os.listdir(Location.project_loca + "data/raw/" + str(each)))
hb_fwhm = list()
for each_day in mjd_list:
hb_fwhm.append(get_fwhm_hb(each, each_day))
except ValueError as reason:
continue
except IOError as reason:
def hist_fit_all():
rmid_list = get_total_rmid_list()
for each in rmid_list:
hist_fit(each)
def rescale_all():
rmid_list = get_total_rmid_list()
for each in rmid_list:
error_scaling(each)
def bin_all():
rmid_list = get_total_rmid_list()
for each in rmid_list:
binning(each)
def fit_all():
rmid_list = get_total_rmid_list()
for i in range(0, len(rmid_list)):
fe_fitter(rmid_list[i])
print(str(i + 1) + "out of " + str(len(rmid_list)))
print("\n\n")
mean_b = np.mean([a[x] * b[x] for x in res_date])
mean_c = np.mean([c[x] for x in res_date])
mean_d = np.mean([c[x] * d[x] for x in res_date])
mean_e = np.mean([e[x] for x in res_date])
mean_f = np.mean([e[x] * f[x] for x in res_date])
return [mean_a, mean_b, mean_c, mean_d, mean_e, mean_f]
def get_fe_hb(rmid):
fe, fe_e = get_flux(rmid, "Fe2")
hb, hb_e = get_flux(rmid, "Hbetab")
o3, o3_e = get_flux(rmid, "O3")
return intersect(fe, fe_e, o3, o3_e, hb, hb_e)
rmid_list = get_total_rmid_list()
fe = list()
fe_e = list()
hb = list()
hb_e = list()
for each in rmid_list:
a, b, c, d, e, f = get_fe_hb(each)
fe.append(a / e)
fe_e.append((e * b - f * a) / (e * e))
hb.append(c / e)
hb_e.append((e * d - f * c) / (e * e))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel("Fe / Hbeta")
ax.set_ylabel("OIII / Hbeta")
plt.scatter(fe, hb)