dra_ns, ddec_ns = residual_calc02(dra, ddec, ra_rad, dec_rad, w2 / 1.e3)
# Now re-calculate the normalized difference
sep = nor_sep_calc(dra_ns, dra_err, ddec_ns, ddec_err,
dra_ddec_cov / dra_err / ddec_err)
X = np.array(sep[-1])
return X
# ----------------------------- MAIN -----------------------------
# ======================================================================
# Read the catalog
# ======================================================================
# Read Gaia DR2 IERS quasar data
gdr2 = read_dr2_iers()
# Read ICRF1 catalog
icrf1 = read_icrf1()
# Read ICRF2 catalog
icrf2 = read_icrf2()
# Read ICRF3 S/X catalog
icrf3sx = read_icrf3(wv="sx")
# Read ICRF3 K catalog
icrf3k = read_icrf3(wv="k")
# Read ICRF3 S/X catalog
icrf3xka = read_icrf3(wv="xka")
# Median error me_ra_i3k = np.median(icrf3k["ra_err"]) * 1.e3 me_dec_i3k = np.median(icrf3k["dec_err"]) * 1.e3 me_pos_i3k = np.median(icrf3k["pos_err"]) * 1.e3 # ICRF3 X/Ka catalog icrf3xka = read_icrf3(wv="xka") # Median error me_ra_i3xka = np.median(icrf3xka["ra_err"]) * 1.e3 me_dec_i3xka = np.median(icrf3xka["dec_err"]) * 1.e3 me_pos_i3xka = np.median(icrf3xka["pos_err"]) * 1.e3 # Gaia DR2 aux_iers catalog gaiadr2 = read_dr2_iers() # Bright sample gaiadr2 = gaiadr2[gaiadr2["phot_g_mean_mag"] < 18.7] # Median error me_ra_g2 = np.median(gaiadr2["ra_err"]) * 1.e3 me_dec_g2 = np.median(gaiadr2["dec_err"]) * 1.e3 me_pos_g2 = np.median(gaiadr2["pos_err"]) * 1.e3 # Plot for median error fig, ax = plt.subplots() barwidth = 0.25 # data catalogs = ["GAIA DR2", "ICRF1", "ICRF2", "ICRF3 S/X", "ICRF3 K", "ICRF3 X/KA"] ra_pos = np.arange(len(catalogs)) - barwidth