Kafka Notices via GCN

In this section, we’ll show you how to register for a GCN account and then set up credentials to receive JSON-serialized notices over Kafka. We will then write a script to receive and parse these notices. Finally, we’ll download and parse an example notice.

Account Creation and Credential Generation

  1. Sign up for an account at https://gcn.nasa.gov/quickstart.

  2. Create new credentials by following the guide from the previous step. Select gcn.nasa.gov/kafka-public-consumer from the Scope drown-down menu (this should already be selected by default).

  3. Select a notice format and notice type(s) for the alerts you would like to subscribe to in the Customize Alerts section.

  4. Record the client_id and client_secret values in the generated code on the next section.


    You can retrieve your client_id and client_secret at a later time by signing in at https://gcn.nasa.gov and navigating to the credentials page. This page can be found in the drop-down menu underneath your sign-in name in the top menu-bar.

Receiving and Parsing Notices

Once you are authenticated to receive JSON-serialized LIGO/Virgo/KAGRA notices from GCN, we can write a function to parse them.


Note that mock or ‘test’ observations have superevent IDs that begin with ‘M’, while real observations have superevent IDs that begin with ‘S’. Mock events also list the search that found them as ‘MDC’, however this field is not present in retraction alerts so it is best to check the first character of the superevent ID to distinguish between the two.

from base64 import b64decode
from io import BytesIO
import json
from pprint import pprint

from astropy.table import Table
import astropy_healpix as ah
from gcn_kafka import Consumer
import numpy as np

def parse_notice(record):
    record = json.loads(record)

    # Only respond to mock events. Real events have GraceDB IDs like
    # S1234567, mock events have GraceDB IDs like M1234567.
    # NOTE NOTE NOTE replace the conditional below with this commented out
    # conditional to only parse real events.
    # if record['superevent_id'][0] != 'S':
    #    return
    if record['superevent_id'][0] != 'M':

    if record['alert_type'] == 'RETRACTION':
        print(record['superevent_id'], 'was retracted')

    # Respond only to 'CBC' events. Change 'CBC' to 'Burst' to respond to
    # only unmodeled burst events.
    if record['event']['group'] != 'CBC':

    # Parse sky map
    skymap_str = record.get('event', {}).pop('skymap')
    if skymap_str:
        # Decode, parse skymap, and print most probable sky location
        skymap_bytes = b64decode(skymap_str)
        skymap = Table.read(BytesIO(skymap_bytes))

        level, ipix = ah.uniq_to_level_ipix(
        ra, dec = ah.healpix_to_lonlat(ipix, ah.level_to_nside(level),
        print(f'Most probable sky location (RA, Dec) = ({ra.deg}, {dec.deg})')

        # Print some information from FITS header
        print(f'Distance = {skymap.meta["DISTMEAN"]} +/- {skymap.meta["DISTSTD"]}')

    # Print remaining fields

The final step is to set up a Kafka consumer that calls our function whenever a notice is received.

consumer = Consumer(client_id='fill me in', client_secret='fill me in')

while True:
    for message in consumer.consume():

When you run this script you should receive a sample LIGO/Virgo/KAGRA notice every hour. The output will be the same as the output in the Offline Testing section below.

Offline Testing

Sample files are available to download at any time for testing responses to notices without needing to wait for the one-per-hour example.

$ curl -O https://emfollow.docs.ligo.org/userguide/_static/MS181101ab-preliminary.json

This file can be parsed as follows:

# Read the file and then parse it
with open('MS181101ab-preliminary.json', 'r') as f:
    record = f.read()


Running this should produce the following output:

Most probable sky location (RA, Dec) = (194.30419921874997, -17.856895095545468)
Distance = 39.76999609489013 +/- 8.308435058808886
{'alert_type': 'PRELIMINARY',
 'event': {'central_frequency': None,
           'classification': {'BBH': 0.03,
                              'BNS': 0.95,
                              'NSBH': 0.01,
                              'Terrestrial': 0.01},
           'duration': None,
           'far': 9.11069936486e-14,
           'group': 'CBC',
           'instruments': ['H1', 'L1', 'V1'],
           'pipeline': 'gstlal',
           'properties': {'HasMassGap': 0.01,
                          'HasNS': 0.95,
                          'HasRemnant': 0.91},
           'search': 'MDC',
           'significant': True,
           'time': '2018-11-01T22:22:46.654Z'},
 'external_coinc': None,
 'superevent_id': 'MS181101ab',
 'time_created': '2018-11-01T22:34:49Z',
 'urls': {'gracedb': 'https://example.org/superevents/MS181101ab/view/'}}


Below are some sample JSON alerts that can be used for testing purposes.