<jupyter_start><jupyter_text>Plot the KN<jupyter_code>kn = lightcurves.iloc[-1]  # the last one is the KN
plt.figure(figsize=(14, 6))

pkmjd_kn = kn.pkmjd
mjd_r_kn = kn.mjd_r - pkmjd_kn
mjd_g_kn = kn.mjd_g - pkmjd_kn

plt.errorbar(mjd_r_kn, kn.mag_r, yerr=kn.magerr_r, color='red',
             marker='*', fmt='o', label='R')
plt.errorbar(mjd_g_kn, kn.mag_g, yerr=kn.magerr_g, color='green',
             marker='^', fmt='o', label='g')
plt.ylim(plt.ylim()[::-1])
plt.legend(fontsize=14)
plt.xlabel('Time (days)', fontsize=14)<jupyter_output><empty_output><jupyter_text>Plot a few other objects<jupyter_code>plt.figure(figsize=(14, 6))

for idx, row in lightcurves.iterrows():
    if 'SALT' not in row.SIM_MODEL_NAME:  # plot SALT2 Ia; try a few others from list above
        continue
    pkmjd = row.pkmjd
    mjd_r = row.mjd_r - pkmjd
    mjd_g = row.mjd_g - pkmjd
    plt.errorbar(mjd_r, row.mag_r, yerr=row.magerr_r, color='red',
             marker='*', fmt='o', label='R')
    plt.errorbar(mjd_g, row.mag_g, yerr=row.magerr_g, color='green',
             marker='^', fmt='o', label='g')
plt.ylim(plt.ylim()[::-1])
plt.legend(fontsize=14)
plt.xlabel('Time (days)', fontsize=14)<jupyter_output><empty_output><jupyter_text>Associated skymap<jupyter_code>import healpy as hp
from ligo.skymap.io import read_sky_map
skymap, *h = read_sky_map('data/skymap.fits')
plt.figure(figsize=(15, 10))
hp.mollview(skymap, fig=0, cmap='YlOrBr', cbar=False, hold=True,
            title="Mock LVK skymap associated with KN")

# plot locations of all objects
for idx, row in lightcurves.iterrows():
    hp.visufunc.projplot(
        row.SIM_RA, row.SIM_DEC, lonlat=True, marker='x',
        c='r' if row.true_label else 'b',
        markersize=15 if row.true_label else 5
    )
hp.graticule()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1590281612368857, 'fraction_numerical': 0.01435670900055218, 'mean_word_length': 4.267441860465116, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '540598', 'license': 'MIT', 'n_tokens_mistral': 801, 'n_tokens_neox': 758, 'n_words': 142, 'path': 'ANTARES/lsst-broker-workshop-mma-demo.ipynb', 'repo_name': 'LSSTDESC/tutorials', 'size': '1811'}