Kafka Notices via SCiMMA

In this section, we’ll show you how to register for a SCiMMA account and then set up credentials to receive Avro-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://hop.scimma.org. Note that if you are not affiliated with any options in the identity provider list, you can use a GitHub or Google account.

  2. Create credentials at https://my.hop.scimma.org/. Take care to save your credential password when you do so.


    You will not be able to access the password for your newly created credentials again after this step. If you lose it you will need to create new credentials.

  3. Subscribe to the igwn.gwalert topic. You will find the list of topics available for subscribing by clicking the “Manage” button next to the credential you created at https://my.hop.scimma.org/


    It can take up to approximately an hour for a newly added subscription to register and become usable.

  4. Finally, add your credentials to your environment with

    hop auth add

    This will prompt you for your HOPSKOTCH credentials username and password.

Receiving and Parsing Notices

Once you’re authenticated to receive LIGO/Virgo/KAGRA notices, 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 io import BytesIO
from pprint import pprint

from astropy.table import Table
import astropy_healpix as ah
from hop import stream
import numpy as np

def parse_notice(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        print(record['superevent_id'], 'was retracted')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_bytes = record.get('event', {}).pop('skymap')
    if skymap_bytes:
        # Parse skymap directly and print most probable sky location
        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.

with stream.open('kafka://kafka.scimma.org/igwn.gwalert', 'r') as s:
    for message in s:

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.avro

Now you can parse the Avro packet using the code we wrote above.

import fastavro

# Read the file in bytes mode and then parse it
with open('MS181101ab-preliminary.avro', 'rb') as fo:
    reader = fastavro.reader(fo)
    # LIGO/Virgo/KAGRA notices will only ever contain one record
    record = next(reader)


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 Avro alerts that can be used for testing purposes.