GWCelery¶

GWCelery is a simple and reliable package for annotating and orchestrating LIGO/Virgo alerts, built from widely used open source components. It is built on the Celery distributed task queue (hence the name). This is the design and reference manual for GWCelery.

GWCelery’s responsibilities include:

Merging related candidates from multiple online LIGO/Virgo transient searches into “superevents”
Correlating LIGO/Virgo events with gamma-ray bursts, neutrinos, and supernovae
Launching automated follow-up analyses including data quality checks, rapid sky localization, automated parameter estimation, and source classification
Generating and sending preliminary machine-readable GCN notices
Sending updated GCN notices after awaiting human input
Automatically composing GCN Circulars

Note

If you are a scientist, student, educator, or astronomy enthusiast looking for information about LIGO/Virgo alerts and low-latency data products, then please see our LIGO/Virgo Public Alerts User Guide.

Quick start¶

These instructions are suitable for installing GWCelery for development and testing on any machine.

To install¶

Important

GWCelery requires Python 3.6; it does not work with Python 3.7 right now.

There is a bug in the Celery 4.2.1 that makes it incompatible with Python 3.7. It has already been fixed upstream, but it will continue to affect us until Celery 4.3 is released.

The easiest way to install it is with venv and pip:

$ python -m venv --system-site-packages ~/gwcelery
$ source ~/gwcelery/bin/activate
$ pip install gwcelery

Hint

Note: GWCelery requires a fairly new version of setuptools. If you get an error message that looks like this:

pkg_resources.VersionConflict: (setuptools 0.9.8
(/usr/lib/python2.7/site-packages),
Requirement.parse('setuptools>=30.3.0'))

then run pip install --upgrade setuptools and try again.

To test¶

With setup.py:

$ python setup.py test

To start¶

Before starting GWCelery, you need to authenticate for access to GraceDb and LVAlert and make sure that you have a Redis server running. Once you have completed those steps, you can start each of the GWCelery manually.

Authentication¶

To authenticate for GraceDb, obtain grid credentials from the LSC DataGrid Client by running ligo-proxy-init:

$ ligo-proxy-init albert.einstein

To authenticate for LVAlert, first complete the LVAlert Account Activation form once for each LVAlert server that you intend to use (generally you only need “Playground” for development purposes). Make a note of the passwords and store them in your ~/.netrc file with appropriate file permissions:

$ echo > ~/.netrc
$ chmod 0600 ~/.netrc
$ echo machine lvalert.cgca.uwm.edu login albert.einstein password password-for-production >> ~/.netrc
$ echo machine lvalert-playground.cgca.uwm.edu login albert.einstein password password-for-playground >> ~/.netrc
$ echo machine lvalert-test.cgca.uwm.edu login albert.einstein password password-for-test >> ~/.netrc

Redis¶

GWCelery requires a Redis database server for task bookkeeping. Your operating system’s package manager may be able to install, configure, and automatically launch a suitable Redis server for you.

Debian, Ubuntu, apt

Debian or Ubuntu users can install and start Redis using apt-get:

$ sudo apt-get install redis

macOS, MacPorts

Mac users with MacPorts can install Redis using port install:

$ sudo port install redis

Use port load to start the server:

$ sudo port load redis

From source

If none of the above options are available, then you can follow the Redis Quick Start instructions to build redis from source and start a server:

$ wget http://download.redis.io/redis-stable.tar.gz
$ tar xvzf redis-stable.tar.gz
$ cd redis-stable
$ make -j
$ src/redis-server

Start GWCelery components manually¶

GWCelery itself consists of five Celery workers and one Flask web application. Start them all by running each of the following commands:

$ gwcelery worker -l info -n gwcelery-worker -Q celery -B
$ gwcelery worker -l info -n gwcelery-exttrig-worker -Q exttrig -c 1
$ gwcelery worker -l info -n gwcelery-openmp-worker -Q openmp -c 1
$ gwcelery worker -l info -n gwcelery-superevent-worker -Q superevent -c 1
$ gwcelery worker -l info -n gwcelery-voevent-worker -Q voevent -P solo
$ gwcelery flask run

Hint

With these arguments, each of the commands above will run until you type Control-C. You may want to run each of them in a separate terminal, or in the background using screen or nohup.

Design and anatomy of GWCelery¶

Conceptual overview¶

Several online gravitational-wave transient search pipelines (currently Gstlal, PyCBC, cWB, and oLIB) upload candidates in real time to GraceDb, the central database and web portal for low-latency LIGO/Virgo analyses. Whenever an event is uploaded or altered, GraceDb pushes machine-readable notifications through LVAlert, a pubsub system based on XMPP.

The business logic for selecting and sending alerts to astronomers resides not in GraceDb itself but in GWCelery. The role of GWCelery in the LIGO/Virgo alert infrastructure is to drive the workflow of aggregating and annotating gravitational-wave candidates and sending GCN Notices to astronomers.

GWCelery interacts with GraceDb by listening for LVAlert messages and making REST API requests through the GraceDb client. GWCelery interacts with GCN by listening for and sending GCN Notices using the Comet VOEvent broker.

The major subsystems of GWCelery are:

the LVAlert listener
the GraceDb client
the GCN listener
the GCN broker
the Superevent Manager, which clusters and merges related candidates into “superevents”
the External Trigger Manager, which correlates gravitational-wave events with GRB, neutrino, and supernova events
the Orchestrator, which executes the per-event annotation workflow

Block diagram¶

Below is a diagram illustrating the conceptual relationships of these subsystems. Nodes in the graph are hyperlinks to the relevant API documentation.

Processes¶

A complete deployment of GWCelery (whether launched from the shell or from HTCondor) consists of several processes:

Message Broker

Routes and distributes Celery task messages and stores results of tasks for later retrieval. See Choosing a Broker in the Celery manual for more details. For technical reasons, we use a Redis broker.
Celery Beat

Scheduler for periodic tasks (the Celery equivalent of cron jobs). For more information, see Periodic Tasks in the Celery manual.
Monitoring Console (optional)

You can optionally run Flower, a web monitoring console for Celery.
OpenMP Worker

A Celery worker that has been configured to accept only computationally intensive tasks that use OpenMP parallelism. To route a task to the OpenMP worker, pass the keyword argument queue='openmp' to the @app.task decorator when you declare it.

There are two tasks that run in the OpenMP queue:
- gwcelery.tasks.bayestar.localize()
- gwcelery.tasks.skymaps.plot_volume()
Superevent Worker

A Celery worker that is dedicated to serially process triggers from low latency pipelines and create/modify superevents in GraceDb. There is only one task that runs on the Superevent queue:
- gwcelery.tasks.superevents.handle()
External Trigger Worker

A Celery worker that is dedicated to serially process external triggers from GRB alerts received from Fermi, Swift and neutrino alerts received from SNEWS and create/modify external trigger events in GraceDb:
- gwcelery.tasks.external_triggers.handle_gcn()
VOEvent Worker

A Celery worker that is dedicated to sending and receiving VOEvents. It runs an embedded instance of the Comet VOEvent broker, which is started and stopped using a set of custom Celery bootsteps. Note that the VOEvent worker must be started with the --pool=solo option so that tasks are executed in the same Python process that is running the VOEvent broker.
General-Purpose Worker

A Celery worker that accepts all other tasks.
Flask Web Application

A web application that provides forms to manually initiate certain tasks, including sending an update alert or creating a mock event.

Eternal tasks¶

GWCelery has a few long-running tasks that do not return because they have to keep open a persistent connection with some external service. These tasks are subclasses of celery_eternal.EternalTask or celery_eternal.EternalProcessTask.

gwcelery.tasks.lvalert.listen()

These tasks run inside the general-purpose worker process described above, and are automatically started (and restarted as necessary) by Celery Beat.

Handlers¶

A recurring pattern in GWCelery is that an eternal task listens continuously to a remote connection, receives packets of data over that connection, and dispatches further handling to other tasks based on packet type.

A decorator is provided to register a function as a Celery task and also plug it in as a handler for one or more packet types. This pattern is used for both GCN notices and LVAlert message handlers.

GCN notices¶

GCN notice handler tasks are declared using the gwcelery.tasks.gcn.handler() decorator:

import lxml.etree
from gwcelery.tasks import gcn

@gcn.handler(gcn.NoticeType.FERMI_GBM_GND_POS,
             gcn.NoticeType.FERMI_GBM_FIN_POS)
def handle_fermi(payload):
    root = lxml.etree.fromstring(payload)
    # do work here...

LVAlert messages¶

LVAlert message handler tasks are declared using the gwcelery.tasks.lvalert.handler() decorator:

from gwcelery.tasks import lvalert

@lvalert.handler('cbc_gstlal',
                 'cbc_spiir',
                 'cbc_pycbc',
                 'cbc_mbtaonline')
def handle_cbc(alert):
    # do work here...

Configuration¶

Like any Celery application, GWCelery’s configuration options are stored at run time in a global configuration object, gwcelery.app.conf. There are options for Celery itself such as options that affect the task and result backends; these options are documented in the Configuration and defaults section of the Celery manual.

The configuration object also holds all of the options that are specific to GWCelery and affect the behavior of individual GWCelery tasks; examples include the GraceDb and LVAlert service URLs, GCN hostnames, and frame file types and channel names. For a list of all GWCelery-specific options, see the API documentation for the gwcelery.conf module.

GWCelery provides four preset configurations, one for each GraceDb server instance (production, deployment, testing, or playground). The default configuration preset is for the playground server, gracedb-playground.ligo.org. The recommended way to select a different preset is to set the CELERY_CONFIG_MODULE environment variable before starting the workers. For example, to configure GWCelery for production:

$ export CELERY_CONFIG_MODULE=gwcelery.conf.production

Authentication¶

There are a few files that must be present in order to provide authentication tokens for GraceDb and LValert.

GraceDb

You must provide valid LSC DataGrid credentials in order for requests to the GraceDb REST API to work. During development and testing, you can use your personal credentials obtained from the LSC DataGrid Client by running ligo-proxy-init. However, credentials obtained this way expire after a few days or whenever your machine’s temporary directory is wiped (e.g., at system restart).

For production deployment, you should obtain a robot certificate and store it in a location such as ~/.globus/userkey.pem and ~/.globus/usercert.pem.

LVAlert

You must provide a valid username and password for LVAlert. You can request an account using the LVAlert Account Activation form. The LVAlert username and password should be stored in your netrc file.

Running under HTCondor¶

The recommended way to start and stop GWCelery on the LIGO Data Grid cluster is using HTCondor. See the example HTCondor submit file gwcelery.sub. This submit file will start up Redis, the worker processes, the Flask web application, and Flower. It will create some log files and a Unix domain socket, so you should first navigate to a directory where you want these files to go. For example:

$ mkdir -p ~/gwcelery/var && cd ~/gwcelery/var

Then run the submit file as follows:

$ gwcelery.sub
Submitting job(s)........
8 job(s) submitted to cluster 293497.

To stop GWCelery, run the condor_hold command:

$ condor_hold -constraint 'JobBatchName == "gwcelery"'
All jobs matching constraint (JobBatchName == "gwcelery") have been held

To restart GWCelery, run condor_release:

$ condor_release -constraint 'JobBatchName == "gwcelery"'
All jobs matching constraint (JobBatchName == "gwcelery") have been released

Note that there is normally no need to re-submit GWCelery if the machine is rebooted, because the jobs will persist in the HTCondor queue.

Shortcuts¶

The following commands are provided as shortcuts for the above operations:

$ gwcelery condor submit
$ gwcelery condor rm
$ gwcelery condor q
$ gwcelery condor hold
$ gwcelery condor release

The following command is a shortcut for gwcelery condor rm; gwcelery condor submit:

$ gwcelery condor resubmit

Managing multiple deployments¶

There should generally be at most one full deployment of GWCelery per GraceDb server running at one time. The gwcelery condor shortcut command is designed to protect you from accidentally starting multiple deployments of GWCelery by inspecting the HTCondor job queue before submitting new jobs. If you try to start GWCelery a second time on the same host in the same directory, you will get the following error message:

$ gwcelery condor submit
error: GWCelery jobs are already running in this directory.
You must first remove exist jobs with "gwcelery condor rm".
To see the status of those jobs, run "gwcelery condor q".

However, there are situations where you may actually want to run multiple instances of GWCelery on the same machine. For example, you may want to run one instance for the ‘production’ GraceDb server and one for the ‘playground’ server. To accomplish this, just start the two instances of gwcelery in different directories. Here is an example:

$ mkdir -p production
$ pushd production
$ CELERY_CONFIG_MODULE=gwcelery.conf.production gwcelery condor submit
$ popd
$ mkdir -p playground
$ pushd playground
$ CELERY_CONFIG_MODULE=gwcelery.conf.playground gwcelery condor submit
$ popd

Monitoring and Management¶

GWCelery supports a rich selection of management and monitoring tools. Here is an introduction to a few of them. For more Celery monitoring solutions, see the Celery monitoring and management guide.

Flower¶

Flower is a dashboard for monitoring Celery tasks. To start Flower for monitoring during local development, run the following command and then navigate to http://localhost:5555/ in your browser:

$ gwcelery flower

Sentry¶

All warnings, errors, exceptions, and tasks failures are both logged to disk and uploaded instantly to Sentry, an error monitoring and reporting platform. The Sentry instance is installed on premise at Caltech. Sentry notifies GWCelery contributors by email when a new bug occurs.

For details about the Sentry logging configuration, see the gwcelery.sentry module or the Celery integration module in the Sentry SDK docs.

Flask¶

GWCelery includes a Flask web application that provides forms to manually initiate certain tasks.

To start Flask for monitoring during local development, run the following command and then navigate to http://localhost:5000/ in your browser:

$ gwcelery flask run

Nagios¶

Note

The GWCelery Nagios plugin is tailored to GWCelery and is not sufficiently general to use with other Celery applications.

The dashboard.ligo.org and monitor.ligo.org services use Nagios to monitor and report on the health of all of the components of the low-latency analysis infrastructure.

GWCelery provides the command gwcelery nagios to check the status of the application and provide a report in the format that Nagios expects.

You can run it manually from the command line:

$ gwcelery nagios
OK: GWCelery is running normally

To configure Nagios itself, see the Nagios configuration overview, or if GWCelery and Nagios are running on different hosts, the Nagios Remote Plugin Executor (NRPE) documentation.

Command-Line Tools¶

All Celery application provide command-line monitoring and management utilities, including the following:

gwcelery shell: Start an interactive Python or IPython interpreter for interacting with Celery. All tasks as well as the app application instance are automatically imported and available as globals. Example:

$ gwcelery shell
Python 3.6.6 (default, Jun 28 2018, 05:43:53)
Type 'copyright', 'credits' or 'license' for more information
IPython 6.5.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: download.s('coinc.xml', 'M6757').delay().get()

gwcelery call: Call a task from the command line by passing it arguments in JSON format. The output is the unique identifier of the result. Example:
```
$ gwcelery call gwcelery.tasks.gracedb.download --args='["coinc.xml", "M6757"]'
d11099e7-75e5-4aa3-800b-b122b667757c
```

gwcelery result: Get the result of a previously called task. Example:

$ gwcelery result ab4aa6d7-9f21-420c-8401-cbe6863cf7dc
(b'<?xml version=\'1.0\' encoding=\'utf-8\'?>\n<!DOCTYPE LIGO_LW SYSTEM "htt'
 b'p://ldas-sw.ligo.caltech.edu/doc/ligolwAPI/html/ligolw_dtd.txt">\n<LIGO_L'
 ...
 b'\t</Stream>\n\t</Table>\n</LIGO_LW>\n')

gwcelery events: A text UI monitoring tool that offers functionality similar to Flower. Example:
```
$ gwcelery events
```

API Reference¶

Celery application initialization.

gwcelery.app = <Celery gwcelery>¶: Celery application object.

gwcelery.conf module¶

GWCelery application configuration.

This module defines configuration variables and default values, including both generic options for Celery as well as options that control the behavior of specific GWCelery tasks.

To override the configuration, define the CELERY_CONFIG_MODULE environment variable to the fully qualified name of any Python module that can be located in sys.path, including any of the following presets:

gwcelery.conf.development

gwcelery.conf.playground (the default)

gwcelery.conf.production

gwcelery.conf.test

gwcelery.conf.lvalert_host = 'lvalert-playground.cgca.uwm.edu'¶: LVAlert host.

gwcelery.conf.gracedb_host = 'gracedb-playground.ligo.org'¶: GraceDb host.

gwcelery.conf.voevent_broadcaster_address = ':5342'¶: The VOEvent broker will bind to this address to send GCNs. This should be a string of the form host:port. If host is empty, then listen on all available interfaces.

gwcelery.conf.voevent_broadcaster_whitelist = []¶: List of hosts from which the broker will accept connections. If empty, then completely disable the broker’s broadcast capability.

gwcelery.conf.voevent_receiver_address = '68.169.57.253:8099'¶: The VOEvent listener will connect to this address to receive GCNs. If empty, then completely disable the GCN listener.

gwcelery.conf.superevent_d_t_start = {'gstlal': 1.0, 'mbtaonline': 1.0, 'pycbc': 1.0, 'spiir': 1.0}¶: Pipeline based lower extent of superevent segments. For cwb and lib this is decided from extra attributes.

gwcelery.conf.superevent_d_t_end = {'gstlal': 1.0, 'mbtaonline': 1.0, 'pycbc': 1.0, 'spiir': 1.0}¶: Pipeline based upper extent of superevent segments For cwb and lib this is decided from extra attributes.

gwcelery.conf.superevent_query_d_t_start = 100.0¶: Lower extent of superevents query

gwcelery.conf.superevent_query_d_t_end = 100.0¶: Upper extent of superevents query

gwcelery.conf.superevent_default_d_t_start = 1.0¶: Default lower extent of superevent segments

gwcelery.conf.superevent_default_d_t_end = 1.0¶: Default upper extent for superevent segments

gwcelery.conf.superevent_far_threshold = 0.0002777777777777778¶: Maximum false alarm rate to consider events superevents.

gwcelery.conf.preliminary_alert_far_threshold = {'burst': 3.1709791983764586e-08, 'cbc': 1.9290123456790122e-07, 'test': 3.8580246913580245e-07}¶: Group specific maximum false alarm rate to consider sending preliminary alerts.

gwcelery.conf.preliminary_alert_trials_factor = {'burst': 4.0, 'cbc': 5.0}¶: Trials factor corresponding to trigger categories. For CBC and Burst, trials factor is the number of pipelines. CBC pipelines are gstlal, pycbc, mbtaonline, spiir-highmass, spiir-lowmass. Burst searches are cwb.allsky, cwb.bbh, cwb.imbh and olib.allsky.

gwcelery.conf.orchestrator_timeout = 15.0¶: The orchestrator will wait this many seconds from the time of the creation of a new superevent to the time that annotations begin, in order to let the superevent manager’s decision on the preferred event stabilize.

gwcelery.conf.pe_timeout = 60.0¶: The orchestrator will wait this many seconds from the time of the creation of a new superevent to the time that parameter estimation begins, in case the preferred event is updated with high latency.

gwcelery.conf.check_vector_prepost = {'CWB': [0.5, 0.5], 'Fermi': [2, 2], 'HardwareInjection': [2, 2], 'LIB': [0.5, 0.5], 'MBTAOnline': [2, 2], 'SNEWS': [10, 10], 'Swift': [2, 2], 'gstlal': [2, 2], 'oLIB': [0.5, 0.5], 'pycbc': [2, 2], 'spiir': [2, 2]}¶: Seconds before and after the superevent start and end times which the DQ vector check will include in its check. Pipeline dependent.

gwcelery.conf.uses_gatedhoft = {'CWB': True, 'Fermi': False, 'HardwareInjection': False, 'LIB': False, 'MBTAOnline': True, 'SNEWS': False, 'Swift': False, 'gstlal': True, 'oLIB': False, 'pycbc': True, 'spiir': True}¶: Whether or not a pipeline uses gated h(t). Determines whether or not the DMT-DQ_VECTOR will be analyzed for data quality.

gwcelery.conf.llhoft_glob = '/dev/shm/kafka/{detector}_O2/*.gwf'¶: File glob for playground low-latency h(t) frames. Currently points to O2 replay data.

gwcelery.conf.llhoft_channels = {'H1:DMT-DQ_VECTOR': 'dmt_dq_vector_bits', 'H1:GDS-CALIB_STATE_VECTOR': 'ligo_state_vector_bits', 'L1:DMT-DQ_VECTOR': 'dmt_dq_vector_bits', 'L1:GDS-CALIB_STATE_VECTOR': 'ligo_state_vector_bits', 'V1:DQ_ANALYSIS_STATE_VECTOR': 'virgo_state_vector_bits'}¶: Low-latency h(t) state vector configuration. This is a dictionary consisting of a channel and its bitmask, as defined in gwcelery.tasks.detchar.

gwcelery.conf.idq_channels = ['H1:IDQ-PGLITCH_OVL_16_4096', 'L1:IDQ-PGLITCH_OVL_16_4096']¶: Low-latency iDQ p(glitch) channel names

gwcelery.conf.idq_pglitch_thresh = 0.95¶: If P(Glitch) is above this threshold, and idq_veto for the pipeline is true, DQV will be labeled for the event.

gwcelery.conf.idq_veto = {'CWB': False, 'Fermi': False, 'HardwareInjection': False, 'LIB': False, 'MBTAOnline': False, 'SNEWS': False, 'Swift': False, 'gstlal': False, 'oLIB': False, 'pycbc': False, 'spiir': False}¶: If true for a pipeline, iDQ values above the threshold defined in thres will cause DQV to be labeled. Currently all False, pending iDQ review (should be done before O3).

gwcelery.conf.p_astro_gstlal_ln_likelihood_threshold = 6¶: log likelihood threshold

gwcelery.conf.p_astro_url = 'http://emfollow.ldas.cit/data/H1L1V1-mean_counts-1126051217-61603201.json'¶: URL for mean values of Poisson counts using which p_astro is computed. (Used by gwcelery.tasks.p_astro_gstlal and gwcelery.tasks.p_astro_other)

gwcelery.conf.p_astro_weights_url = 'http://emfollow.ldas.cit/data/H1L1V1-weights-bins_686-1126051217-61603201.json'¶: URL for template weights using which p_astro is computed. (Used by gwcelery.tasks.p_astro_gstlal)

gwcelery.conf.p_astro_thresh_url = 'http://emfollow.ldas.cit/data/H1L1V1-pipeline-far_snr-thresholds.json'¶: URL for pipeline thresholds on FAR and SNR. (Used by gwcelery.tasks.p_astro_other)

gwcelery.conf.em_bright_url = 'http://emfollow.ldas.cit/data/em_bright_classifier.pickle'¶: URL for trained RandomForestClassifier based on which em_bright classification is conducted. (Used by gwcelery.tasks.em_bright)

gwcelery.conf.low_latency_frame_types = {'H1': 'H1_O2_llhoft', 'L1': 'L1_O2_llhoft', 'V1': 'V1_O2_llhoft'}¶: Types of frames used in Parameter Estimation with LALInference (see gwcelery.tasks.lalinference)

gwcelery.conf.high_latency_frame_types = {'H1': 'None', 'L1': 'None', 'V1': 'None'}¶: Types of nonllhoft-frames used in Parameter Estimation with LALInference. They do not exist for O2Replay data. (see gwcelery.tasks.lalinference)

gwcelery.conf.strain_channel_names = {'H1': 'H1:GDS-CALIB_STRAIN_O2Replay', 'L1': 'L1:GDS-CALIB_STRAIN_O2Replay', 'V1': 'V1:Hrec_hoft_16384Hz_O2Replay'}¶: Names of h(t) channels used in Parameter Estimation with LALInference (see gwcelery.tasks.lalinference)

gwcelery.conf.state_vector_channel_names = {'H1': 'H1:GDS-CALIB_STATE_VECTOR', 'L1': 'L1:GDS-CALIB_STATE_VECTOR', 'V1': 'V1:DQ_ANALYSIS_STATE_VECTOR'}¶: Names of state vector channels used in Parameter Estimation with LALInference (see gwcelery.tasks.lalinference)

gwcelery.conf.pe_threshold = 8.267195767195768e-07¶: FAR threshold in Hz for Parameter Estimation. PE group now applies 1/(2 weeks) as a threshold. 86400 seconds = 1 day and 14 days = 2 weeks.

gwcelery.conf.pe_results_path = '/home/docs/public_html/online_pe'¶: Path to the results of Parameter Estimation (see gwcelery.tasks.lalinference)

gwcelery.conf.pe_results_url = 'https://ldas-jobs.ligo.caltech.edu/~docs/online_pe/'¶: URL of page where all the results of Parameter Estimation are outputted (see gwcelery.tasks.lalinference)

gwcelery.conf.development module¶

Application configuration for gracedb-dev1.ligo.org. Inherits all settings from gwcelery.conf.test, with the exceptions below.

gwcelery.conf.development.gracedb_host = 'gracedb-dev1.ligo.org'¶: GraceDb host.

gwcelery.conf.development.sentry_environment = 'development'¶: Record this environment tag <https://docs.sentry.io/enriching-error-data/environments/>)` in Sentry log messages.

gwcelery.conf.playground module¶

Application configuration for gracedb-playground.ligo.org.

gwcelery.conf.playground.sentry_environment = 'playground'¶: Record this environment tag <https://docs.sentry.io/enriching-error-data/environments/>)` in Sentry log messages.

gwcelery.conf.production module¶

Application configuration for gracedb.ligo.org. Inherits all settings from gwcelery.conf.playground, with the exceptions below.

gwcelery.conf.production.lvalert_host = 'lvalert.cgca.uwm.edu'¶: LVAlert host.

gwcelery.conf.production.gracedb_host = 'gracedb.ligo.org'¶: GraceDb host.

gwcelery.conf.production.voevent_broadcaster_address = ':5341'¶: The VOEvent broker will bind to this address to send GCNs. This should be a string of the form host:port. If host is empty, then listen on all available interfaces.

gwcelery.conf.production.voevent_broadcaster_whitelist = ['capella2.gsfc.nasa.gov']¶: List of hosts from which the broker will accept connections. If empty, then completely disable the broker’s broadcast capability.

gwcelery.conf.production.llhoft_glob = '/dev/shm/kafka/{detector}/*.gwf'¶: File glob for low-latency h(t) frames.

gwcelery.conf.production.low_latency_frame_types = {'H1': 'H1_llhoft', 'L1': 'L1_llhoft', 'V1': 'V1_llhoft'}¶: Types of frames used in Parameter Estimation with LALInference (see gwcelery.tasks.lalinference)

gwcelery.conf.production.high_latency_frame_types = {'H1': 'H1_HOFT_C00', 'L1': 'L1_HOFT_C00', 'V1': 'V1Online'}¶: Types of nonllhoft-frames used in Parameter Estimation with LALInference (see gwcelery.tasks.lalinference)

gwcelery.conf.production.strain_channel_names = {'H1': 'H1:GDS-CALIB_STRAIN_CLEAN', 'L1': 'L1:GDS-CALIB_STRAIN_CLEAN', 'V1': 'V1:Hrec_hoft_16384Hz'}¶: Names of h(t) channels used in Parameter Estimation with LALInference (see gwcelery.tasks.lalinference)

gwcelery.conf.production.sentry_environment = 'production'¶: Record this environment tag <https://docs.sentry.io/enriching-error-data/environments/>)` in Sentry log messages.

gwcelery.conf.test module¶

Application configuration for gracedb-test.ligo.org. Inherits all settings from gwcelery.conf.playground, with the exceptions below.

gwcelery.conf.test.lvalert_host = 'lvalert-test.cgca.uwm.edu'¶: LVAlert host.

gwcelery.conf.test.gracedb_host = 'gracedb-test.ligo.org'¶: GraceDb host.

gwcelery.conf.test.sentry_environment = 'test'¶: Record this environment tag <https://docs.sentry.io/enriching-error-data/environments/>)` in Sentry log messages.

gwcelery.sentry module¶

Integration of the Celery logging system with Sentry.

gwcelery.sentry.DSN = 'https://sentry.io/1425216'¶: Sentry data source name (DSN).

gwcelery.sentry.configure()[source]¶

Configure Sentry logging integration for Celery according to the official instructions.

Add the API key username/pasword pair to your netrc file.

gwcelery.tasks module¶

All Celery tasks are declared in submodules of this module.

gwcelery.tasks.bayestar module¶

Rapid sky localization with BAYESTAR.

(task)gwcelery.tasks.bayestar.localize(coinc_psd, graceid, filename='bayestar.fits.gz', disabled_detectors=None)[source]¶

Generate a rapid sky localization using BAYESTAR.

Parameters:	coinc_psd (tuple) – Tuple consisting of the byte contents of the input event’s `coinc.xml` and `psd.xml.gz` files. graceid (str) – The GraceDB ID, used for FITS metadata and recording log messages to GraceDb. filename (str, optional) – The name of the FITS file. disabled_detectors (list, optional) – List of detectors to disable.
Returns:	The byte contents of the finished FITS file.
Return type:	bytes

Notes

This task is adapted from the command-line tool bayestar-localize-lvalert.

It should execute in a special queue for computationally intensive, multithreaded, OpenMP tasks.

gwcelery.tasks.circulars module¶

Generate and upload automated circulars.

(task)gwcelery.tasks.circulars.create_initial_circular(graceid)[source]¶: Create and return circular txt.

(task)gwcelery.tasks.circulars.create_emcoinc_circular(graceid)[source]¶: Create and return the em_coinc circular txt.

(task)gwcelery.tasks.circulars.create_retraction_circular(graceid)[source]¶: Create and return retraction circular txt.

gwcelery.tasks.condor module¶

Submit and monitor HTCondor jobs [1].

Notes

Internally, we use the XML condor log format [2] for easier parsing.

References

[1]	http://research.cs.wisc.edu/htcondor/manual/latest/condor_submit.html

[2]	http://research.cs.wisc.edu/htcondor/classad/refman/node3.html

exception gwcelery.tasks.condor.JobAborted[source]¶

Bases: Exception

Raised if an HTCondor job was aborted (e.g. by condor_rm).

exception gwcelery.tasks.condor.JobRunning[source]¶

Bases: Exception

Raised if an HTCondor job is still running.

exception gwcelery.tasks.condor.JobFailed(returncode, cmd, output=None, stderr=None)[source]¶

Bases: subprocess.CalledProcessError

Raised if an HTCondor job fails.

(task)gwcelery.tasks.condor.submit(submit_file, log=None)[source]¶

Submit a job using HTCondor.

Parameters:	submit_file (str) – Path of the submit file. log (str) – Used internally to track job state. Caller should not set.
Raises:	`JobAborted` – If the job was aborted (e.g. by running `condor_rm`). `JobFailed` – If the job terminates and returns a nonzero exit code. `JobRunning` – If the job is still running. Causes the task to be re-queued until the job is complete.

Example

>>> submit.s('example.sub',
...          accounting_group='ligo.dev.o3.cbc.explore.test')

(task)gwcelery.tasks.condor.check_output(args, log=None, error=None, output=None, **kwargs)[source]¶

Call a process using HTCondor.

Call an external process using HTCondor, in a manner patterned after subprocess.check_output(). If successful, returns its output on stdout. On failure, raise an exception.

Parameters:	args (list) – Command line arguments, as if passed to `subprocess.check_call()`. error, output (log,) – Used internally to track job state. Caller should not set. **kwargs – Extra submit description file commands. See the documentation for `condor_submit` for possible values.
Returns:	Captured output from command.
Return type:	str
Raises:	`JobAborted` – If the job was aborted (e.g. by running `condor_rm`). `JobFailed` – If the job terminates and returns a nonzero exit code. `JobRunning` – If the job is still running. Causes the task to be re-queued until the job is complete.

Example

>>> check_output.s(['sleep', '10'],
...                accounting_group='ligo.dev.o3.cbc.explore.test')

gwcelery.tasks.detchar module¶

Flow Chart¶

The flow chart below shows the decision process for the application of DQOK and DQV labels.

Data quality and detector characterization tasks.

These tasks are mostly focused on checking interferometer state vectors. By design, the [LIGO] and [Virgo] state vectors share the same definitions for the first 8 fields.

LIGO also has a [DMT] DQ vector that provides some additional instrumental checks.

References

[LIGO]

https://wiki.ligo.org/Calibration/TDCalibReview

[Virgo]

https://dcc.ligo.org/G1801125/

[DMT]

https://wiki.ligo.org/DetChar/DmtDqVector

gwcelery.tasks.detchar.dmt_dq_vector_bits¶: DMT DQ vector bits (LIGO only).

gwcelery.tasks.detchar.ligo_state_vector_bits¶: State vector bitfield definitions for LIGO.

gwcelery.tasks.detchar.virgo_state_vector_bits¶: State vector bitfield definitions for Virgo.

gwcelery.tasks.detchar.create_cache(ifo, start, end)[source]¶

Find .gwf files and create cache. Will first look in the llhoft, and if the frames have expired from llhoft, will call gwdatafind.

Parameters:	ifo (str) – Interferometer name (e.g. `H1`). end (start,) – GPS start and end times desired.
Returns:
Return type:	`glue.lal.Cache`

Example

>>> create_cache('H1', 1198800018, 1198800618)
[<glue.lal.CacheEntry at 0x7fbae6b71278>,
  <glue.lal.CacheEntry at 0x7fbae6ae5b38>,
  <glue.lal.CacheEntry at 0x7fbae6ae5c50>,
 ...
  <glue.lal.CacheEntry at 0x7fbae6b15080>,
  <glue.lal.CacheEntry at 0x7fbae6b15828>]

gwcelery.tasks.detchar.generate_table(title, high_bit_list, low_bit_list, unknown_bit_list)[source]¶

Make a nice table which shows the status of the bits checked.

Parameters:	title (str) – Title of the table. high_bit_list (list) – List of bit names which are high. low_bit_list (list) – List of bit names which are low. unknown_bit_list (list) – List of bit names which are unknown.
Returns:	HTML string of the table.
Return type:	str

gwcelery.tasks.detchar.dqr_json(state, summary)[source]¶

Generate DQR-compatible json-ready dictionary from process results, as described in data-quality-report.design.

Parameters:	state ({'pass', 'fail'}) – State of the detchar checks. summary (str) – Summary of results from the process.
Returns:	Ready to be converted into json.
Return type:	dict

gwcelery.tasks.detchar.check_idq(cache, channel, start, end)[source]¶

Looks for iDQ frame and reads them.

Parameters:	cache (`glue.lal.Cache`) – Cache from which to check. channel (str) – which idq channel (pglitch) end (start,) – GPS start and end times desired.
Returns:	Tuple mapping iDQ channel to its maximum P(glitch).
Return type:	tuple

Example

>>> check_idq(cache, 'H1:IDQ-PGLITCH-OVL-100-1000',
              1216496260, 1216496262)
('H1:IDQ-PGLITCH-OVL-100-1000', 0.87)

gwcelery.tasks.detchar.check_vector(cache, channel, start, end, bits, logic_type='all')[source]¶

Check timeseries of decimals against a bitmask. This is inclusive of the start time and exclusive of the end time, i.e. [start, …, end).

Parameters:	cache (`glue.lal.Cache`) – Cache from which to check. channel (str) – Channel to look at, e.g. `H1:DMT-DQ_VECTOR`. end (start,) – GPS start and end times desired. bits (`gwpy.TimeSeries.Bits`) – Definitions of the bits in the channel. logic_type (str, optional) – Type of logic to apply for vetoing. If `all`, then all samples in the window must pass the bitmask. If `any`, then one or more samples in the window must pass.
Returns:	Maps each bit in channel to its state.
Return type:	dict

Example

>>> check_vector(cache, 'H1:GDS-CALIB_STATE_VECTOR', 1216496260,
                 1216496262, ligo_state_vector_bits)
{'H1:HOFT_OK': True,
 'H1:OBSERVATION_INTENT': True,
 'H1:NO_STOCH_HW_INJ': True,
 'H1:NO_CBC_HW_INJ': True,
 'H1:NO_BURST_HW_INJ': True,
 'H1:NO_DETCHAR_HW_INJ': True}

(task)gwcelery.tasks.detchar.check_vectors(event, graceid, start, end)[source]¶

Perform data quality checks for an event and labels/logs results to GraceDb.

Depending on the pipeline, a certain amount of time (specified in check_vector_prepost) is appended to either side of the superevent start and end time. This is to catch DQ issues slightly before and after the event, such as that appearing in L1 just before GW170817.

A cache is then created for H1, L1, and V1, regardless of the detectors involved in the event. Then, the bits and channels specified in the configuration file (llhoft_channels) are checked. If an injection is found in the active detectors, ‘INJ’ is labeled to GraceDb. If an injection is found in any detector, a message with the injection found is logged to GraceDb. If no injections are found across all detectors, this is logged to GraceDb.

A similar task is performed for the DQ states described in the DMT-DQ_VECTOR, LIGO GDS-CALIB_STATE_VECTOR, and Virgo DQ_ANALYSIS_STATE_VECTOR. If no DQ issues are found in active detectors, ‘DQOK’ is labeled to GraceDb. Otherwise, ‘DQV’ is labeled. In all cases, the DQ states of all the state vectors checked are logged to GraceDb.

This skips MDC events.

Parameters:	event (dict) – Details of event. graceid (str) – GraceID of event to which to log. end (start,) – GPS start and end times desired.

gwcelery.tasks.em_bright module¶

Qualitative source classification for CBC events.

(task)gwcelery.tasks.em_bright.classifier_other(args, graceid)[source]¶

Returns the boolean probability of having a NS component and the probability of having non-zero disk mass. This method is used for pipelines that do not provide the data products necessary for computation of the source properties probabilities.

Parameters:	args (tuple) – Tuple containing (m1, m2, spin1z, spin2z, snr) graceid (str) – The graceid of the event
Returns:	JSON formatted string storing `HasNS` and `HasRemnant` probabilities
Return type:	str

Example

>>> em_bright.classifier_other((2.0, 1.0, 0.0, 0.0, 10.), 'S123456')
'{"HasNS": 1.0, "HasRemnant": 1.0}'

(task)gwcelery.tasks.em_bright.classifier_gstlal(args, graceid)[source]¶

Returns the probability of having a NS component and the probability of having non-zero disk mass in the detected event. This method will be using the data products obtained from the weekly supervised learning runs for injections campaigns. The data products are in pickle formatted RandomForestClassifier objects. The method predict_proba of these objects provides us the probabilities of the coalesence being EM-Bright and existence of neutron star in the binary.

Parameters:	args (tuple) – Tuple containing (m1, m2, spin1z, spin2z, snr) graceid (str) – The graceid of the event
Returns:	JSON formatted string storing `HasNS` and `HasRemnant` probabilities
Return type:	str

Notes

This task would only work from within the CIT cluster.

gwcelery.tasks.external_triggers module¶

This module listens to the GCNs from SNEWS and the Fermi and Swift missions. It is also responsible for carrying out tasks related to external trigger-gravitational wave coincidences, including looking for temporal coincidences, creating combined GRB-GW sky localization probability maps, and computing their joint temporal and spatio-temporal false alarm rates.

There are two GCN and two LVAlert message handlers in the ~gwcelery.tasks.external_triggers module:

handle_sn_gcn() is called for each SNEWS GCN.
handle_grb_gcn() is called for each Fermi and Swift GCN.
handle_sn_lvalert() is called for each SNEWS external trigger and superevent LVAlert.
handle_grb_lvalert() is called for each Fermi and Swift external trigger and superevent LVAlert.

Flow Chart¶

Tasks¶

(task)gwcelery.tasks.external_triggers.handle_snews_gcn(payload)[source]¶: Handles the payload from SNEWS alerts. Prepares the alert to be sent to graceDB as ‘E’ events.

(task)gwcelery.tasks.external_triggers.handle_grb_gcn(payload)[source]¶: Handles the payload from Fermi and Swift alerts. Prepares the alert to be sent to graceDB as ‘E’ events.

(task)gwcelery.tasks.external_triggers.handle_grb_lvalert(alert)[source]¶

Parse an LVAlert message related to superevents/GRB external triggers and dispatch it to other tasks.

Notes

This LVAlert message handler is triggered by creating a new superevent or GRB external trigger event, or applying the EM_COINC label to any superevent:

Any new event triggers a coincidence search with gwcelery.tasks.raven.coincidence_search().
The EM_COINC label triggers the creation of a combined GW-GRB sky map using gwcelery.tasks.ligo_fermi_skymaps.create_combined_skymap().

(task)gwcelery.tasks.external_triggers.handle_snews_lvalert(alert)[source]¶

Parse an LVAlert message related to superevents/SN external triggers and dispatch it to other tasks.

Notes

This LVAlert message handler is triggered by creating a new superevent or SN external trigger event, or applying the EM_COINC label to any superevent:

Any new event triggers a coincidence search with gwcelery.tasks.raven.coincidence_search().

(task)gwcelery.tasks.external_triggers.handle_emcoinc_lvalert(alert)[source]¶

Parse an LVAlert message related to EM_COINC label application and upload circular. We need a separate handler to prevent doubles from occurring by adding the task to both the handle_snews_lvalert and handle_grb_lvalert handlers.

Notes

This LVAlert message handler is triggered by applying the EM_COINC label to any superevent:

Any EM_COINC label application triggers gwcelery.tasks.circulars.create_emcoinc_circular().

gwcelery.tasks.first2years module¶

Create mock events from the “First Two Years” paper.

(task)gwcelery.tasks.first2years.pick_coinc[source]¶: Pick a coincidence from the “First Two Years” paper.

(task)gwcelery.tasks.first2years._vet_event(superevents)[source]¶

(task)gwcelery.tasks.first2years.upload_event[source]¶

Upload a random event from the “First Two Years” paper.

After 2 minutes, randomly either retract or confirm the event to send a retraction or initial notice respectively.

gwcelery.tasks.ligo_fermi_skymaps module¶

Create and upload LVC-Fermi sky maps.

gwcelery.tasks.ligo_fermi_skymaps.create_combined_skymap(graceid)[source]¶: Creates and uploads the combined LVC-Fermi skymap. This also uploads the external trigger skymap to the external trigger GraceDb page.

(task)gwcelery.tasks.ligo_fermi_skymaps.get_preferred_skymap(graceid)[source]¶: Get the LVC skymap fits filename. If not available, will try again 10 seconds later, then 20, then 40, etc. until up to 10 minutes after initial attempt.

(task)gwcelery.tasks.ligo_fermi_skymaps.combine_skymaps(skymap1filebytes, skymap2filebytes)[source]¶: This task combines the two input skymaps, in this case the external trigger skymap and the LVC skymap and writes to a temporary output file. It then returns the contents of the file as a byte array.

(task)gwcelery.tasks.ligo_fermi_skymaps.external_trigger(graceid)[source]¶: Returns the associated external trigger GraceDB ID.

(task)gwcelery.tasks.ligo_fermi_skymaps.external_trigger_heasarc(external_id)[source]¶: Returns the HEASARC fits file link

(task)gwcelery.tasks.ligo_fermi_skymaps.get_external_skymap(heasarc_link)[source]¶: Download the Fermi sky map fits file and return the contents as a byte array. If not available, will try again 10 seconds later, then 20, then 40, etc. until up to 10 minutes after initial attempt.

gwcelery.tasks.gcn module¶

Tasks for sending, receiving, and processing Gamma-ray Coordinates Network [GCN] notices.

References

[GCN]

https://gcn.gsfc.nasa.gov

gwcelery.tasks.gcn.handler = {<sphinx.ext.autodoc.importer._MockObject object>: [<@task: gwcelery.tasks.gcn.validate of gwcelery>], <sphinx.ext.autodoc.importer._MockObject object>: [<@task: gwcelery.tasks.gcn.validate of gwcelery>], <sphinx.ext.autodoc.importer._MockObject object>: [<@task: gwcelery.tasks.gcn.validate of gwcelery>], <sphinx.ext.autodoc.importer._MockObject object>: [<@task: gwcelery.tasks.gcn.validate of gwcelery>], <sphinx.ext.autodoc.importer._MockObject object>: [<@task: gwcelery.tasks.external_triggers.handle_snews_gcn of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_grb_gcn of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_grb_gcn of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_grb_gcn of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_grb_gcn of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_grb_gcn of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_grb_gcn of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_grb_gcn of gwcelery>]}¶

Function decorator to register a handler callback for specified GCN notice types. The decorated function is turned into a Celery task, which will be automatically called whenever a matching GCN notice is received.

Parameters:	keys – List of GCN notice types to accept *kwargs – Additional keyword arguments for `celery.Celery.task()`.

Examples

Declare a new handler like this:

@gcn.handler(gcn.NoticeType.FERMI_GBM_GND_POS,
             gcn.NoticeType.FERMI_GBM_FIN_POS)
def handle_fermi(payload):
    root = lxml.etree.fromstring(payload)
    # do work here...

exception gwcelery.tasks.gcn.SendingError[source]¶

Bases: RuntimeError

A generic error associated with sending VOEvents.

(task)gwcelery.tasks.gcn.send(message)[source]¶

Send a VOEvent to GCN.

This task will be retried several times if the VOEvent cannot be sent. See the Raises section below for circumstances that cause a retry.

Parameters:	message (bytes) – The raw VOEvent file contents.
Raises:	`SendingError` – If the VOEvent could not be sent because there were no network peers connected to the VOEvent broadcaster.

(task)gwcelery.tasks.gcn.validate(payload)[source]¶

Check that the contents of a public LIGO/Virgo GCN matches the original VOEvent in GraceDB.

Notes

If the VOEvent broadcaster is disabled by setting voevent_broadcaster_whitelist to an empty list, then this task becomes a no-op.

gwcelery.tasks.gracedb module¶

Communication with GraceDB.

gwcelery.tasks.gracedb.task(*args, **kwargs)[source]¶

(task)gwcelery.tasks.gracedb.create_event(filecontents, search, pipeline, group)[source]¶: Create an event in GraceDb.

(task)gwcelery.tasks.gracedb.create_label(label, graceid)[source]¶: Create a label in GraceDb.

(task)gwcelery.tasks.gracedb.create_signoff(status, comment, signoff_type, graceid)[source]¶: Create a label in GraceDb.

(task)gwcelery.tasks.gracedb.create_tag(filename, tag, graceid)[source]¶: Create a tag in GraceDb.

(task)gwcelery.tasks.gracedb.create_voevent(graceid, voevent_type, **kwargs)[source]¶

Create a VOEvent.

Returns:	The filename of the new VOEvent.
Return type:	str

(task)gwcelery.tasks.gracedb.download(filename, graceid)[source]¶: Download a file from GraceDB.

(task)gwcelery.tasks.gracedb.expose(graceid)[source]¶: Expose an event to the public.

(task)gwcelery.tasks.gracedb.get_events(query=None, orderby=None, count=None, columns=None)[source]¶: Get events from GraceDb.

(task)gwcelery.tasks.gracedb.get_event(graceid)[source]¶: Retrieve an event from GraceDb.

(task)gwcelery.tasks.gracedb.get_labels(graceid)[source]¶: Get all labels for an event in GraceDb.

(task)gwcelery.tasks.gracedb.get_log(graceid)[source]¶: Get all log messages for an event in GraceDb.

(task)gwcelery.tasks.gracedb.get_instruments(gracedb_id)[source]¶

Get the set of instruments that contributed to the ranking statistic of a coincident event.

Parameters:	gracedb_id (str) – The GraceDB ID.
Returns:	The set of instruments that contributed to the ranking statistic for the event.
Return type:	set

Notes

The number of instruments that contributed data to an event is given by the instruments key of the GraceDB event JSON structure. However, some pipelines (e.g. gstlal) have a distinction between which instruments contributed data and which were considered in the ranking of the candidate. For such pipelines, we infer which pipelines contributed to the ranking by counting only the SingleInspiral records for which the chi squared field is non-empty.

(task)gwcelery.tasks.gracedb.get_superevent(graceid)[source]¶: Retrieve a superevent from GraceDb.

(task)gwcelery.tasks.gracedb.replace_event(graceid, payload)[source]¶: Get an event from GraceDb.

(task)gwcelery.tasks.gracedb.upload(filecontents, filename, graceid, message, tags=())[source]¶: Upload a file to GraceDB.

(task)gwcelery.tasks.gracedb.get_superevents(query)[source]¶

List matching superevents in gracedb.

Parameters:	query (str) – query to be passed to `superevents()`
Returns:	superevents – The list of the superevents.
Return type:	list

(task)gwcelery.tasks.gracedb.update_superevent(superevent_id, t_start=None, t_end=None, t_0=None, preferred_event=None)[source]¶

Update superevent information. Wrapper around updateSuperevent()

Parameters:	superevent_id (str) – superevent uid t_start (float) – start of superevent time window, unchanged if None t_end (float) – end of superevent time window, unchanged if None t_0 (float) – superevent t_0, unchanged if None preferred_event (str) – uid of the preferred event, unchanged if None

(task)gwcelery.tasks.gracedb.create_superevent(graceid, t0, d_t_start, d_t_end, category)[source]¶

Create new superevent in GraceDb with graceid

Parameters:	graceid (str) – graceid with which superevent is created. t0 (float) – t_0 parameter of superevent d_t_start (float) – superevent t_start = t0 - d_t_start d_t_end (float) – superevent t_end = t0 + t_end category (str) – superevent category

(task)gwcelery.tasks.gracedb.add_event_to_superevent(superevent_id, graceid)[source]¶: Add an event to a superevent in GraceDb.

gwcelery.tasks.lalinference module¶

Source Parameter Estimation with LALInference.

exception gwcelery.tasks.lalinference.NotEnoughData[source]¶

Bases: Exception

Raised if found data is not enough due to the latency of data transfer

(task)gwcelery.tasks.lalinference.query_data(trigtime)[source]¶: Continues to query data until it is found with gwdatafind and return frametypes for the data. If data is not found in 86400 seconds = 1 day, raise NotEnoughData.

(task)gwcelery.tasks.lalinference.upload_no_frame_files(request, exc, traceback, superevent_id)[source]¶

Upload notification when no frame files are found.

Parameters:	request (Context (placeholder)) – Task request variables exc (Exception) – Exception rased by condor.submit traceback (str (placeholder)) – Traceback message from a task superevent_id (str) – The GraceDb ID of a target superevent

(task)gwcelery.tasks.lalinference.prepare_ini(frametype_dict, event, superevent_id=None)[source]¶: Determine an appropriate PE settings for the target event and return ini file content

gwcelery.tasks.lalinference.pre_pe_tasks(event, superevent_id)[source]¶: Return canvas of tasks executed before parameter estimation starts

(task)gwcelery.tasks.lalinference.dag_prepare(coinc_contents, ini_contents, rundir, superevent_id)[source]¶

Create a Condor DAG to run LALInference on a given event.

Parameters:	coinc_contents (bytes) – The byte contents of `coinc.xml` ini_contents (str) – The content of online_pe.ini rundir (str) – The path to a run directory where the DAG file exits superevent_id (str) – The GraceDb ID of a target superevent
Returns:	submit_file – The path to the .sub file
Return type:	str

(task)gwcelery.tasks.lalinference.job_error_notification(request, exc, traceback, superevent_id, rundir)[source]¶

Upload notification when condor.submit terminates unexpectedly.

Parameters:	request (Context (placeholder)) – Task request variables exc (Exception) – Exception rased by condor.submit traceback (str (placeholder)) – Traceback message from a task superevent_id (str) – The GraceDb ID of a target superevent rundir (str) – The run directory for PE

(task)gwcelery.tasks.lalinference._upload_url(pe_results_path, graceid)[source]¶: Upload url of a page containing all of the plots.

(task)gwcelery.tasks.lalinference._get_result_contents(pe_results_path, filename)[source]¶: Return the contents of a PE results file by reading it from the local filesystem.

(task)gwcelery.tasks.lalinference.clean_up(rundir)[source]¶

Clean up a run directory.

Parameters:	rundir (str) – The path to a run directory where the DAG file exits

gwcelery.tasks.lalinference.dag_finished(rundir, preferred_event_id, superevent_id)[source]¶

Upload PE results and clean up run directory

Parameters:	rundir (str) – The path to a run directory where the DAG file exits preferred_event_id (str) – The GraceDb ID of a target preferred event superevent_id (str) – The GraceDb ID of a target superevent
Returns:	tasks – The work-flow for uploading PE results
Return type:	canvas

(task)gwcelery.tasks.lalinference.start_pe(ini_contents, preferred_event_id, superevent_id)[source]¶

Run LALInference on a given event.

Parameters:	ini_contents (str) – The content of online_pe.ini preferred_event_id (str) – The GraceDb ID of a target preferred event superevent_id (str) – The GraceDb ID of a target superevent

gwcelery.tasks.lvalert module¶

LVAlert client.

gwcelery.tasks.lvalert.handler = {'burst_cwb': [<@task: gwcelery.tasks.superevents.handle of gwcelery>], 'burst_olib': [<@task: gwcelery.tasks.superevents.handle of gwcelery>], 'cbc_gstlal': [<@task: gwcelery.tasks.orchestrator.handle_cbc_event of gwcelery>, <@task: gwcelery.tasks.superevents.handle of gwcelery>], 'cbc_mbtaonline': [<@task: gwcelery.tasks.orchestrator.handle_cbc_event of gwcelery>, <@task: gwcelery.tasks.superevents.handle of gwcelery>], 'cbc_pycbc': [<@task: gwcelery.tasks.orchestrator.handle_cbc_event of gwcelery>, <@task: gwcelery.tasks.superevents.handle of gwcelery>], 'cbc_spiir': [<@task: gwcelery.tasks.orchestrator.handle_cbc_event of gwcelery>, <@task: gwcelery.tasks.superevents.handle of gwcelery>], 'external_fermi': [<@task: gwcelery.tasks.external_triggers.handle_grb_lvalert of gwcelery>], 'external_fermi_grb': [<@task: gwcelery.tasks.external_triggers.handle_grb_lvalert of gwcelery>], 'external_grb': [<@task: gwcelery.tasks.external_triggers.handle_grb_lvalert of gwcelery>], 'external_snews': [<@task: gwcelery.tasks.external_triggers.handle_snews_lvalert of gwcelery>], 'external_snews_supernova': [<@task: gwcelery.tasks.external_triggers.handle_snews_lvalert of gwcelery>], 'external_swift': [<@task: gwcelery.tasks.external_triggers.handle_grb_lvalert of gwcelery>], 'mdc_superevent': [<@task: gwcelery.tasks.external_triggers.handle_grb_lvalert of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_snews_lvalert of gwcelery>, <@task: gwcelery.tasks.orchestrator.handle_superevent of gwcelery>, <@task: gwcelery.tasks.p_astro.handle of gwcelery>], 'superevent': [<@task: gwcelery.tasks.external_triggers.handle_grb_lvalert of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_snews_lvalert of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_emcoinc_lvalert of gwcelery>, <@task: gwcelery.tasks.orchestrator.handle_superevent of gwcelery>, <@task: gwcelery.tasks.p_astro.handle of gwcelery>], 'test_gstlal': [<@task: gwcelery.tasks.superevents.handle of gwcelery>], 'test_mbtaonline': [<@task: gwcelery.tasks.superevents.handle of gwcelery>], 'test_pycbc': [<@task: gwcelery.tasks.superevents.handle of gwcelery>], 'test_superevent': [<@task: gwcelery.tasks.external_triggers.handle_grb_lvalert of gwcelery>, <@task: gwcelery.tasks.external_triggers.handle_snews_lvalert of gwcelery>]}¶

Function decorator to register a handler callback for specified LVAlert message types. The decorated function is turned into a Celery task, which will be automatically called whenever a matching LVAlert message is received.

Parameters:	keys – List of LVAlert message types to accept *kwargs – Additional keyword arguments for `celery.Celery.task()`.

Examples

Declare a new handler like this:

@lvalert.handler('cbc_gstlal',
                 'cbc_spiir',
                 'cbc_pycbc',
                 'cbc_mbtaonline')
def handle_cbc(alert_content):
    # do work here...

(task)gwcelery.tasks.lvalert.listen[source]¶: Listen for LVAlert messages forever. LVAlert messages are dispatched asynchronously to tasks that have been registered with gwcelery.tasks.lvalert.handler().

gwcelery.tasks.orchestrator module¶

This module implements the alert orchestrator, which responsible for the vetting and annotation workflow to produce preliminary, initial, and update alerts for gravitational-wave event candidates.

The orchestrator consists of two LVAlert message handlers:

handle_superevent() is called for each superevent. It waits for a short duration of orchestrator_timeout seconds for the selection of the superevent by the superevent manager to stabilize, then performs data quality checks. If the data quality checks pass, then it calls preliminary_alert() to copy annotations from the preferred event and send the preliminary GCN notice.
handle_cbc_event() is called for each CBC event. It performs some CBC-specific annotations that depend closely on the CBC matched-filter parameters estimates and that might influence selection of the preferred event: rapid sky localization with BAYESTAR and rapid source classification.

Note that there is no equivalent of this task for burst events because both burst searches (cWB, LIB) have integrated source localization and have no other annotations.

Preliminary Alerts¶

The flow chart below illustrates the operation of these two tasks.

Initial and Update Alerts¶

The initial_alert() and update_alert() tasks create Initial and Update alerts respectively. At the moment, there is no handler or user interface to trigger these tasks, and they must be invoked manually (see Command-Line Tools). A flow chart for the initial alerts is shown below; the flow chart for update alerts is the same.

Retraction Alerts¶

Likewise, the retraction_alert() task creates Retraction alerts, and at the moment must be invoked manually. A flow chart is shown below.

Tasks¶

Tasks that comprise the alert orchestrator, which responsible for the vetting and annotation workflow to produce preliminary, initial, and update alerts for gravitational-wave event candidates.

(task)gwcelery.tasks.orchestrator.handle_superevent(alert)[source]¶

Schedule annotations for new superevents.

After waiting for a time specified by the orchestrator_timeout configuration variable for the choice of preferred event to settle down, this task peforms data quality checks with gwcelery.tasks.detchar.check_vectors() and calls preliminary_alert() to send a preliminary GCN notice.

(task)gwcelery.tasks.orchestrator.handle_cbc_event(alert)[source]¶

Peform annotations for CBC events that depend on pipeline-specific matched-filter parameter estimates.

Notes

This LVAlert message handler is triggered by updates that include the files psd.xml.gz and ranking_data.xml.gz. The table below lists which files are created as a result, and which tasks generate them.

File	Task
`bayestar.fits`	`gwcelery.tasks.bayestar.localize()`
`em_bright.json`	`gwcelery.tasks.em_bright.classifier()`
`p_astro.json`	`gwcelery.tasks.p_astro_gstlal.compute_p_astro()`

(task)gwcelery.tasks.orchestrator._download(*args, **kwargs)[source]¶

Download a file from GraceDb.

This works just like gwcelery.tasks.gracedb.download(), except that it is retried for both TimeoutError and URLError. In particular, it will be retried for 404 (not found) errors.

(task)gwcelery.tasks.orchestrator._update_if_dqok(superevent_id, event_id)[source]¶: Update preferred_event of superevent_id to event_id if DQOK label has been applied

(task)gwcelery.tasks.orchestrator._get_preferred_event(superevent_id)[source]¶

Determine preferred event for a superevent by querying GraceDb.

This works just like gwcelery.tasks.gracedb.get_superevent(), except that it returns only the preferred event, and not the entire GraceDb JSON response.

(task)gwcelery.tasks.orchestrator._create_voevent(classification, *args, **kwargs)[source]¶

Create a VOEvent record from an EM bright JSON file.

Parameters:	classification (tuple, None) – A collection of JSON strings, generated by `gwcelery.tasks.em_bright.classifier()` and `gwcelery.tasks.p_astro_gstlal.compute_p_astro()` respectively; or None args – Additional positional arguments passed to `gwcelery.tasks.gracedb.create_voevent()`. *kwargs – Additional keyword arguments passed to `gwcelery.tasks.gracedb.create_voevent()`.
Returns:	The filename of the newly created VOEvent.
Return type:	str

(task)gwcelery.tasks.orchestrator.preliminary_alert(event, superevent_id)[source]¶

Produce a preliminary alert by copying any sky maps.

This consists of the following steps:

Copy any sky maps and source classification from the preferred event to the superevent.
Create standard annotations for sky maps including all-sky plots by calling gwcelery.tasks.skymaps.annotate_fits().
Create a preliminary VOEvent.
Send the VOEvent to GCN.
Apply the GCN_PRELIM_SENT label to the superevent.
Create and upload a GCN Circular draft.

(task)gwcelery.tasks.orchestrator.parameter_estimation(event, superevent_id)[source]¶

Tasks for Parameter Estimation Followup with LALInference

This consists of the following steps:

Upload an ini file which is suitable for the target event.
Start Parameter Estimation if FAR is smaller than the PE threshold.

(task)gwcelery.tasks.orchestrator.initial_or_update_alert(superevent_id, alert_type, skymap_filename=None, em_bright_filename=None, p_astro_filename=None)[source]¶

Create and send initial or update GCN notice.

Parameters:

superevent_id (str) – The superevent ID.
alert_type ({'initial', 'update'}) – The alert type.
skymap_filename (str, optional) – The sky map to send. If None, then most recent public sky map is used.
em_bright_filename (str, optional) – The source classification file to use. If None, then most recent one is used.
p_astro_filename (str, optional) – The p_astro file to use. If None, then most recent one is used.

(task)gwcelery.tasks.orchestrator.initial_alert(superevent_id, skymap_filename=None, em_bright_filename=None, p_astro_filename=None)[source]¶

Produce an initial alert.

This does nothing more than call initial_or_update_alert() with alert_type='initial'.

Parameters:

superevent_id (str) – The superevent ID.
skymap_filename (str, optional) – The sky map to send. If None, then most recent public sky map is used.
em_bright_filename (str, optional) – The source classification file to use. If None, then most recent one is used.
p_astro_filename (str, optional) – The p_astro file to use. If None, then most recent one is used.

(task)gwcelery.tasks.orchestrator.update_alert(superevent_id, skymap_filename=None, em_bright_filename=None, p_astro_filename=None)[source]¶

Produce an update alert.

This does nothing more than call initial_or_update_alert() with alert_type='update'.

Parameters:

superevent_id (str) – The superevent ID.
skymap_filename (str, optional) – The sky map to send. If None, then most recent public sky map is used.
em_bright_filename (str, optional) – The source classification file to use. If None, then most recent one is used.
p_astro_filename (str, optional) – The p_astro file to use. If None, then most recent one is used.

(task)gwcelery.tasks.orchestrator.retraction_alert(superevent_id)[source]¶: Produce a retraction alert. This is currently just a stub and does nothing more than create and send a VOEvent.

gwcelery.tasks.p_astro module¶

Utilities related to p_astro.json source classification files.

(task)gwcelery.tasks.p_astro.plot(contents)[source]¶

Make a visualization of the source classification.

Parameters:	contents (str, bytes) – The contents of the `p_astro.json` file.
Returns:	png – The contents of a PNG file.
Return type:	bytes

Notes

The unusually small size of the plot (2.5 x 2 inches) is optimized for viewing in GraceDB’s image display widget.

Examples

>>> from gwcelery.tasks import p_astro
>>> contents = '''
... {"Terrestrial": 0.001, "BNS": 0.65, "NSBH": 0.20,
... "MassGap": 0.10, "BBH": 0.059}
... '''
>>> p_astro.plot(contents)

(Source code)

(task)gwcelery.tasks.p_astro.handle(alert)[source]¶: LVAlert handler to plot and upload a visualization of every p_astro.json that is added to a superevent.

gwcelery.tasks.p_astro_gstlal module¶

Computation of p_astro by source category. See Kapadia et al (2019), arXiv:1903.06881, for details.

(task)gwcelery.tasks.p_astro_gstlal.compute_p_astro(files)[source]¶

Task to compute p_astro by source category.

Parameters:	files (tuple) – Tuple of byte content from (coinc.xml, ranking_data.xml.gz)
Returns:	p_astros – JSON dump of the p_astro by source category
Return type:	str

Example

>>> p_astros = json.loads(compute_p_astro(files))
>>> p_astros
{'BNS': 0.999, 'BBH': 0.0, 'NSBH': 0.0, 'Terrestrial': 0.001}

gwcelery.tasks.p_astro_other module¶

Computation of p_astro by source category. See Kapadia et al (2019), arXiv:1903.06881, for details.

gwcelery.tasks.p_astro_other.p_astro_update(category, event_bayesfac_dict, mean_values_dict)[source]¶

Compute p_astro for a new event using mean values of Poisson expected counts constructed from all the previous events. Invoked with every new GraceDB entry.

Parameters:	category (string) – source category event_bayesfac_dict (dictionary) – event Bayes factors mean_values_dict (dictionary) – mean values of Poisson counts
Returns:	p_astro – p_astro by source category
Return type:	float

gwcelery.tasks.p_astro_other.evaluate_p_astro_from_bayesfac(astro_bayesfac, mean_values_dict, mass1, mass2, spin1z=None, spin2z=None, url_weights_key=None)[source]¶

Evaluates p_astro for a new event using Bayes factor, masses, and number of astrophysical categories. Invoked with every new GraceDB entry.

Parameters:	astro_bayesfac (float) – astrophysical Bayes factor mean_values_dict (dictionary) – mean values of Poisson counts mass1 (float) – event mass1 mass2 (float) – event mass2 spin1z (float) – event spin1z spin2z (float) – event spin2z url_weights_key (str) – url config key pointing to weights file
Returns:	p_astro – p_astro for all source categories
Return type:	dictionary

gwcelery.tasks.p_astro_other.read_mean_values()[source]¶

Reads the mean values in the file pointed to by a url.

Returns:	mean_values_dict – mean values read from url file
Return type:	dictionary

gwcelery.tasks.p_astro_other.make_weights_from_hardcuts(mass1, mass2)[source]¶

Construct binary weights from component masses based on cuts in component mass space that define astrophysical source categories. To be used for MBTA, PyCBC and SPIIR.

Parameters:	mass1 (float) – heavier component mass of the event mass2 (float) – lighter component mass of the event
Returns:	a_bns, a_bbh, a_nshb, a_mg – binary weights (i.e, 1 or 0)
Return type:	floats

gwcelery.tasks.p_astro_other.closest_template(params, params_list)[source]¶

Associate event’s template to a template in the template bank. The assumed bank is the one used by Gstlal. Hence, for Gstlal events, the association should be exact, up to rounding errors.

Parameters:	params (tuple of floats) – intrinsic params of event template params_list (list of strings) – list of template bank’s template params
Returns:	key – params of template in template bank matching event’s template
Return type:	string

gwcelery.tasks.p_astro_other.make_weights_from_histograms(url_weights_key, mass1, mass2, spin1z, spin2z)[source]¶

Construct binary weights from bin number provided by GstLAL, and a weights matrix pre-constructed and stored in a file, to be read from a url. The weights are keyed on template parameters of Gstlal’s template bank. If that doesn’t work, construct binary weights.

Parameters:	url_weights_key (string) – url config key pointing at location of weights file mass1 (float) – heavier component mass of the event mass2 (float) – lighter component mass of the event spin1z (float) – z component spin of heavier mass spin2z (float) – z component spin of lighter mass
Returns:	a_bns, a_bbh, a_nsbh, a_mg – mass-based template weights
Return type:	floats

gwcelery.tasks.p_astro_other.choose_snr(far, snr, pipeline, instruments)[source]¶

Given a pipeline and combination of instruments, return an SNR that does not exceed the SNR threshold for FARs below a FAR threshold. The SNR and FAR thresholds are read from a file containing these values keyed on pipelines and instrument combinations.

Parameters:	far (float) – false alarm rate of the event snr (float) – SNR of the event pipeline (string) – pipeline that posted the event instruments (set) – set of instruments that detected the event
Returns:	snr – limiting SNR value
Return type:	float

(task)gwcelery.tasks.p_astro_other.compute_p_astro(snr, far, mass1, mass2, pipeline, instruments)[source]¶

Task to compute p_astro by source category.

Parameters:	snr (float) – event’s SNR far (float) – event’s cfar mass1 (float) – event’s mass1 mass2 (float) – event’s mass2 instruments (set) – set of instruments that detected the event
Returns:	p_astros – JSON dump of the p_astro by source category
Return type:	str

Example

>>> p_astros = json.loads(compute_p_astro(files))
>>> p_astros
{'BNS': 0.999, 'BBH': 0.0, 'NSBH': 0.0, 'Terrestrial': 0.001}

gwcelery.tasks.raven module¶

Search for GRB-GW coincidences with ligo-raven.

gwcelery.tasks.raven.calculate_spacetime_coincidence_far(gracedb_id, group)[source]¶

Compute spatio-temporal coincidence FAR for GRB external trigger and superevent coincidence by calling ligo.raven.search.calc_signif_gracedb. Note: this will only run if skymaps from both triggers are available to download.

Parameters:	gracedb_id (str) – ID of the superevent trigger used by GraceDb group (str) – CBC or Burst; group of the preferred_event associated with the gracedb_id superevent

gwcelery.tasks.raven.calculate_coincidence_far(gracedb_id, group)[source]¶

Compute temporal coincidence FAR for external trigger and superevent coincidence by calling ligo.raven.search.calc_signif_gracedb.

Parameters:	gracedb_id (str) – ID of the superevent trigger used by GraceDb group (str) – CBC or Burst; group of the preferred_event associated with the gracedb_id superevent

(task)gwcelery.tasks.raven.calc_signif(se, exttrig, tl, th, incl_sky)[source]¶: Calculate FAR of GRB exttrig-GW coincidence

gwcelery.tasks.raven.coincidence_search(gracedb_id, alert_object, group=None, pipelines=[])[source]¶

Perform ligo-raven search for coincidences. The ligo.raven.search.search method applies EM_COINC label on its own.

Parameters:	gracedb_id (str) – ID of the trigger used by GraceDb alert_object (dict) – lvalert[‘object’] group (str) – Burst or CBC pipelines (list) – list of external trigger pipeline names

(task)gwcelery.tasks.raven.search(gracedb_id, alert_object, tl=-5, th=5, group=None, pipelines=[])[source]¶

Perform ligo-raven search for coincidences. The ligo.raven.search.search method applies EM_COINC label on its own.

Parameters:	gracedb_id (str) – ID of the trigger used by GraceDb alert_object (dict) – lvalert[‘object’] tl (int) – number of seconds to search before th (int) – number of seconds to search after group (str) – Burst or CBC pipelines (list) – list of external trigger pipelines for performing coincidence search against
Returns:
Return type:	list with the dictionaries of related gracedb events

(task)gwcelery.tasks.raven.add_exttrig_to_superevent(raven_search_results, gracedb_id)[source]¶

Add external trigger to the list of em_events after ligo.raven.search.search finds a coincidence

Parameters:	raven_search_results (list) – list of dictionaries of each related gracedb trigger gracedb_id (str) – ID of either a superevent or external trigger

gwcelery.tasks.skymaps module¶

Annotations for sky maps.

gwcelery.tasks.skymaps.annotate_fits(versioned_filename, graceid, tags)[source]¶

Perform annotations on a sky map.

This function downloads a FITS file and then generates and uploads all derived images as well as an HTML dump of the FITS header.

gwcelery.tasks.skymaps.is_3d_fits_file(filecontents)[source]¶: Determine if a FITS file has distance information.

(task)gwcelery.tasks.skymaps.annotate_fits_volume(filecontents, *args)[source]¶: Perform annotations that are specific to 3D sky maps.

(task)gwcelery.tasks.skymaps.fits_header(filecontents, filename)[source]¶: Dump FITS header to HTML.

(task)gwcelery.tasks.skymaps.plot_allsky(filecontents)[source]¶: Plot a Mollweide projection of a sky map using the command-line tool ligo-skymap-plot.

(task)gwcelery.tasks.skymaps.plot_volume(filecontents)[source]¶: Plot a 3D volume rendering of a sky map using the command-line tool ligo-skymap-plot-volume.

(task)gwcelery.tasks.skymaps.flatten(filecontents, filename)[source]¶: Convert a HEALPix FITS file from multi-resolution UNIQ indexing to the more common IMPLICIT indexing using the command-line tool ligo-skymap-flatten.

gwcelery.tasks.superevents module¶

Superevents are a new abstraction of gravitational-wave candidates introduced in the third LIGO/Virgo observing (O3). Each superevent is intended to represent a single astrophysical event. A superevent consists of one or more event candidates, possibly from different pipelines, that are neighbors in gpstime. One event belonging to the superevent is identified as the preferred event.

Flow Chart¶

The flow chart below illustrates the decision process for selection of the preferred event.

Tasks¶

Module containing the functionality for creation and management of superevents.

There is serial processing of triggers from low latency pipelines.

Dedicated superevent queue for this purpose.

Primary logic to respond to low latency triggers contained in handle() function.

(task)gwcelery.tasks.superevents.handle(payload)[source]¶: LVAlert handler for superevent manager. Recieves payload from test and production nodes and serially processes them to create/modify superevents

gwcelery.tools module¶

Custom Celery subcommands. The subcommand that is implemented in gwcelery.tools.submodule can be invoked from the command line as gwcelery submodule.

gwcelery.tools.condor module¶

Shortcuts for HTCondor commands to manage deployment of GWCelery on LIGO Data Grid clusters.

These commands apply to the GWCelery instance that is running in the current working directory.

gwcelery.tools.condor.get_constraints()[source]¶

gwcelery.tools.condor.run_exec(*args)[source]¶

gwcelery.tools.condor.running()[source]¶: Determine if GWCelery is already running under HTCondor.

gwcelery.tools.condor.submit()[source]¶: Submit all GWCelery jobs to HTCondor (if not already running).

gwcelery.tools.condor.resubmit()[source]¶: Remove any running GWCelery jobs and resubmit to HTCondor.

gwcelery.tools.condor.rm()[source]¶: Remove all GWCelery jobs.

gwcelery.tools.condor.hold()[source]¶: Put all GWCelery jobs on hold.

gwcelery.tools.condor.release()[source]¶: Release all GWCelery jobs from hold status.

gwcelery.tools.condor.q()[source]¶: Show status of all GWCelery jobs.

class gwcelery.tools.condor.CondorCommand(app=None, get_app=None, no_color=False, stdout=None, stderr=None, quiet=False, on_error=None, on_usage_error=None)[source]¶

Bases: celery.bin.base.Command

Shortcuts for HTCondor commands to manage deployment of GWCelery on LIGO Data Grid clusters.

These commands apply to the GWCelery instance that is running in the current working directory.

add_arguments(parser)[source]¶

run(func=None, **kwargs)[source]¶

gwcelery.tools.flask module¶

gwcelery.tools.nagios module¶

A Nagios plugin for monitoring GWCelery.

class gwcelery.tools.nagios.NagiosPluginStatus[source]¶

Bases: enum.IntEnum

Nagios plugin status codes.

OK = 0¶

WARNING = 1¶

CRITICAL = 2¶

UNKNOWN = 3¶

exception gwcelery.tools.nagios.NagiosCriticalError[source]¶

Bases: Exception

An exception that maps to a Nagios status of CRITICAL.

gwcelery.tools.nagios.get_active_queues(inspector)[source]¶

gwcelery.tools.nagios.get_active_tasks(inspector)[source]¶

gwcelery.tools.nagios.get_active_lvalert_nodes(app)[source]¶

gwcelery.tools.nagios.get_expected_queues(app)[source]¶

gwcelery.tools.nagios.get_expected_tasks(app)[source]¶

gwcelery.tools.nagios.get_expected_lvalert_nodes()[source]¶

gwcelery.tools.nagios.check_status(app)[source]¶

class gwcelery.tools.nagios.NagiosCommand(app=None, get_app=None, no_color=False, stdout=None, stderr=None, quiet=False, on_error=None, on_usage_error=None)[source]¶

Bases: celery.bin.base.Command

A Nagios plugin for monitoring GWCelery.

run(**kwargs)[source]¶

gwcelery.util module¶

Miscellaneous utilities that are useful inside many different tasks.

class gwcelery.util.PromiseProxy(*args, **kwargs)[source]¶: Bases: object

gwcelery.util.NamedTemporaryFile(content=None, **kwargs)[source]¶

Convenience wrapper for tempfile.NamedTemporaryFile() that writes some data to the file before handing it to the calling code.

Parameters:	content (str, bytes, None) – Initial contents of the file. **kwargs – Additional keyword arguments to pass to `tempfile.NamedTemporaryFile()`.

gwcelery.voevent module¶

Embed a Comet VOEvent broker and subscriber into a Celery worker by extending Celery with bootsteps.

gwcelery.voevent.install(app)[source]¶: Register the VOEvent subsystem in the application boot steps.

gwcelery.voevent.bootsteps module¶

class gwcelery.voevent.bootsteps.Reactor(consumer, **kwargs)[source]¶

Bases: gwcelery.voevent.bootsteps.VOEventBootStep

Run the global Twisted reactor in background thread.

The Twisted reactor is a global run loop that drives all Twisted services and operations. This boot step starts the Twisted reactor in a background thread when the Celery consumer starts, and stops the thread when the Consumer terminates.

name = 'Twisted reactor'¶

create(consumer)[source]¶: Create the step.

start(consumer)[source]¶

stop(consumer)[source]¶

class gwcelery.voevent.bootsteps.Broadcaster(consumer, **kwargs)[source]¶

Bases: gwcelery.voevent.bootsteps.TwistedService

Comet-based VOEvent broadcaster.

Run a Comet-based VOEvent broadcaster (comet.protocol.broadcaster.VOEventBroadcasterFactory). Starts after the Reactor bootstep.

A few configuration options are available:

voevent_broadcaster_address: The address to bind to, in host:port format.
voevent_broadcaster_whitelist: A list of hostnames, IP addresses, or

CIDR address ranges from which to accept connections.

The list of active connections is made available inspection with the gwcelery inspect stats command under the voevent-broker-peers key.

name = 'VOEvent broadcaster'¶

create_service(consumer)[source]¶

info(consumer)[source]¶

class gwcelery.voevent.bootsteps.Receiver(consumer, **kwargs)[source]¶

Bases: gwcelery.voevent.bootsteps.TwistedService

VOEvent receiver.

Run a Comet-based VOEvent receiver (comet.protocol.subscriber.VOEventSubscriberFactory). Starts after the Reactor bootstep.

A few configuration options are available:

voevent_receiver_address: The address to connect to, in host:port format.

The list of active connections is made available inspection with the gwcelery inspect stats command under the voevent-receiver-peers key.

name = 'VOEvent receiver'¶

requires = ('celery.worker.consumer:Connection', step:Twisted reactor{('celery.worker.consumer:Connection',)}, 'celery.worker.consumer.tasks:Tasks')¶

create_service(consumer)[source]¶

info(consumer)[source]¶

gwcelery.voevent.logging module¶

Integration between the Celery, Twisted, and Comet logging systems.

gwcelery.voevent.logging.after_setup_logger(logger, loglevel, **kwargs)[source]¶

Celery signal handler to set up capturing of all log messages from Comet and Twisted.

Celery uses the Python standard library’s logging module. Twisted has its own separate logging facility. Use Twisted’s PythonLoggingObserver to forward all Twisted log messages to the Python logging module.
Comet uses the Twisted logging facility, but has its own separate management of log severity level (e.g., info, debug). Set Comet’s log level to match Celery’s.

gwcelery.voevent.util module¶

VOEvent-related utilities.

gwcelery.voevent.util.get_host_port(address)[source]¶

Split a network address of the form host:port.

Parameters:	network (str) – The network address.
Returns:	host (str) – The hostname, or an empty string if missing. port (int, None) – The port number, or None if missing.

gwcelery.voevent.util.get_local_ivo(app)[source]¶

Create an IVOID to identify this application in VOEvent Transport Protocol packets.

Returns:	A local IVOID composed of the machine’s fully qualified domain name and the Celery application name (for example, ivo://emfollow.ligo.caltech.edu/gwcelery).
Return type:	str

gwcelery.voevent.util.get_network(address)[source]¶

Find the IP network prefix for a hostname or CIDR notation.

Parameters:	address (str) – A hostname, such as `ligo.org`, or an IP address prefix in CIDR notation, such as `127.0.0.0/8`.
Returns:	An object representing the IP address prefix.
Return type:	ipaddress.IPv4Network

gwcelery.voevent.signals module¶

Definitions of custom Celery signals related to VOEvents.

These signals allow us to keep the VOEvent broker code decoupled from any GCN-specific logic. Notably, it allows us to keep all of the details of the GCN-specific “Notice Type” concept out of gwcelery.voevent.

gwcelery.voevent.signals.voevent_received = <Signal: voevent_received providing_args={'xml_document'}>¶

Fired whenever a VOEvent is received.

Parameters:	xml_document (`comet.utility.xml.xml_document`) – The XML document that was received. The raw file contents are available as `xml_document.raw_bytes`. The `lxml.etree` representation of the document is available as `xml_document.element`.

Contributing¶

Contributors may familiarize themselves with Celery itself by going through the First Steps with Celery tutorial.

Development model¶

GWCelery operates on a fork-and-merge development model (see GitLab basics for an introduction).

To contribute to GWCelery development, follow these steps:

Create a personal fork of GWCelery.
Make your changes on a branch.
Open a merge request.

Note that GWCelery uses fast-forward merges.

Where new code should go¶

New code will generally consist of adding Celery tasks. Tasks are organized by functionality into submodules of gwcelery.tasks. If your new task does not match with one of the existing submodules, please create a new submodule.

Guidelines for tasks¶

Tasks should be short. When deciding where a new task should go, start from the following loose rules of thumb:
1. If it’s less than a screenful of code, and related to functionality in an existing module, then put the code in a new task in that module.
2. If it’s up to a few screenfuls of code, or not related to functionality in an existing module, then try to break it into a few smaller functions or tasks and put it in a new module.
3. If it’s more than a few screenfuls of code, or adds many additional dependencies, then it should go in a separate package.
See also the note on Granularity in the Celery manual’s Tips and Best Practices section.
Tasks should avoid saving files to disk. Output should be placed directly in GraceDb. Temporary files that are written in /tmp are OK but should be cleaned up promptly.

See also the Celery manual’s notes on Data locality and State.
Dependencies should be installable by pip. Dependencies of tasks should be listed in the requirements.txt file so that they are installed automatically when GWCelery is installed with pip.

There are two extra steps involved in making changes to the dependencies:
1. The Sphinx-generated documentation (that is to say, this manual) is generally built without most of the dependencies installed. Whenever you add a new package to requirements.txt, you should also add any modules that are imported from that package to the autodoc_mock_imports list in the Sphinx configuration file, doc/conf.py.
2. We use pipenv to make the precise versions of packages reproducible in our deployment. If you make changes to requirements.txt, then run pipenv update and commit the changes to Pipfile.lock.

Unit tests¶

Unit tests and code coverage measurement are run automatically for every branch and for every merge request. New code contributions must have 100% test coverage. Modifications to existing code must not decrease test coverage. To run the unit tests and measure code coverage, run the following commands in the top directory of your local source checkout:

$ pip install pytest-cov
$ python setup.py test --addopts='--cov --cov-report html'

This will save a coverage report that you can view in a web browser as htmlcov/index.html.

Code style¶

Code should be written in the PEP 8 style and must pass linting by Flake8. To check code style, run the following commands in the top of your source directory:

$ pip install flake8 pep8-naming
$ flake8 --show-source .

Documentation¶

Documentation strings should be written in the Numpydoc style.

Deployment¶

Continuous deployment¶

GWCelery is automatically deployed using GitLab’s continuous deployment features, configured through the project’s .gitlab-ci.yml file. Deployment can be managed through the GitLab project’s Environments page.

Python dependencies in the deployment environment are managed automatically using pipenv.

There are two instances of GWCelery that are running on the LIGO-Caltech computing cluster and that are managed in this manner:

Playground: The playground instance is re-deployed on every push to master that passes the unit tests. It uses the gwcelery.conf.playground configuration preset.
Production: The production instance is re-deployed only when manually triggered through GitLab. It uses the gwcelery.conf.production configuration preset.

When we observe that the Playground instance shows correct end-to-end behavior, we have the option of triggering a re-deployment to Production. Deployment to production should preferably occur at a release. The procedure for performing a release is described below.

Danger

It is possible to start an interactive session inside the GWCelery production environment by logging in to the LIGO-Caltech cluster, but this measure should be reserved for emergencies only.

Any manual changes to the environment may disrupt the logging and monitoring subsystems. Any files that are manually changed, added to, or removed from the deployment environment will not be captured in version control and may be rolled back without warning the next time that the continuous deployment is triggered.

Making a new release¶

We always prepare releases from the tip of the master branch. GitLab is configured through the project’s .gitlab-ci.yml file to automatically build and push any tagged release to the Python Package Index (PyPI). Follow these steps when issuing a release in order to maintain a consistent and orderly change log.

Check the pipeline status. Before you begin, first make sure that the unit tests, documentation, and packaging jobs are passing. Consult the project’s GitLab pipeline status to make sure that all of the continuous integration jobs are passing on master.

If necessary, fix any bugs that are preventing the pipeline from passing, push the changes to master, and repeat until all jobs pass.
Update the change log. The first subsection of the change log file, CHANGES.rst, should have the title MAJOR.MINOR.PATCH (unreleased), where MAJOR.MINOR.PATCH will be the version number of the new release. Review the git commit log.

Make any necessary changes to CHANGES.rst so that this subsection of the change log accurately summarizes all of the significant changes since the last release and is free of spelling, grammatical, or reStructuredText formatting errors.

Review the list of changes and make sure that the new version number is appropriate. We follow SemVer very loosely, and also generally bump at least the minor version number at the start of a new LSC/Virgo engineering or observing run.

Commit and push any corrections to CHANGES.rst to master.
Tag the release. Change the title of the first section of CHANGES.rst to MAJOR.MINOR.PATCH (YYYY-MM-DD) where YYYY-MM-DD is today’s date. Commit with the message Update changelog for version MAJOR.MINOR.PATCH.

Create a git tag to mark the release by running the following command:

$ git tag vMAJOR.MINOR.PATCH -m "Version MAJOR.MINOR.PATCH"
Create a change log section for the next release. Add a new section to CHANGES.rst with the title NEXT_MAJOR.NEXT_MINOR.NEXT_PATCH (unreleased), where NEXT_MAJOR.NEXT_MINOR.NEXT_PATCH is a provisional version number for the next release. Add a single list item with the text No changes yet. Commit with the message Back to development.
Push the new tag and updated change log. Push the new tag and updated change log:

git push && git push --tags
Wait a couple minutes, and then verify that the new release has been published on our PyPI project page, https://pypi.org/project/gwcelery/.
If desired, navigate to the GitLab project’s Environments page and trigger a deployment to production.

Changelog¶

0.5.0 (2019-04-12)¶

Give permissions to read the files under parameter estimation run directories to non-owner people so that rota people can check their progresses. The naming convention of the run directories changed.
EM-Bright ML classification requires review. Until then, give answer based on low-latency estimates.
Compute P_astro with mass-based template weighting. Template weights are now keyed on template parameters, rather than bin numbers. This should make P_astro immune to binning conventions.
Add form to manually send a preliminary GCN Notice.
Fix a typo in gwcelery.sub that caused the Flower dashboard to fail to start.
Round iDQ p(glitch) to 3 decimal places in GraceDb log message.
Switch log telemetry from the on-premise instance of Sentry at Caltech to a cloud-hosted subscription to sentry.io.
In the playground configuration, the gwcelery.tasks.gcn.validate task was producing false alarms because the GCN receiver was receiving VOEvents from the production instance, which would certainly differ in content from VOEvents in the playground instance. Fix this by having gwcelery.tasks.gcn.validate discard all VOEvents if the VOEvent broadcaster is disabled.
Update ligo-followup-advocate to 0.0.27.
Wait for 1 minute before parameter estimation in case the preferred event is updated with high latency.
Ensure that P_astro accounts for very loud MBTA and PyCBC events, whose FAR saturate at certain low values depending on instrument combination, but whose SNRs can increase indefinitely.
When a user triggers a Preliminary or Update alert through the Flask interface, create a GraceDB log message to record the username.
The Flask interface will now show a confirmation dialog before sending any alerts.
Add a terrifying warning to the Flask interface to make it clear that the interface is live.

0.4.3 (2019-04-05)¶

Now that LIGO/Virgo alerts are public, switch the GCN listener that we use to confirm receipt of our own GCN Notices from a managed, private connection to an anonymous, public connection.
Migrate the Flask and Flower dashboards from ldas-jobs.ligo.caltech.edu to emfollow.ligo.caltech.edu. The new URLs are:
Remove the htaccess file from our public_html directory, since the reverse proxy configuration is now the responsibility of system administrators.
Display the GWCelery version number in the Flask application.
Add visualizations for p_astro.json source classification files.

0.4.2 (2019-04-05)¶

Calculation of number of instruments is now unified across superevent manager and orchestrator using gracedb method get_number_of_instruments.
Enable automated preliminary alerts for all pipelines because disabling them in the orchestrator introduced some issues due to the criteria for releasing a public alert drifting away from the definition of a the preferred event of a superevent. We will instead trust pipelines that are still under review will upload events to the playground rather than the production environment.

0.4.1 (2019-04-02)¶

Fixed normalization issues with p_astro_gstlal.py; normalization was being applied in the wrong places during Bayes factor computation.
Require celery < 4.3.0 because that version breaks the nagios unit tests.
Update false alarm rate trials factors for preliminary alerts.
Enable sending GCN notices for fully automated preliminary alerts.
Add threshold_snr option in online_pe.jinja2, which is used to determine the upper limit of distance prior.
Use the same criteria to decide whether to expose an event publicly in GraceDB as we use to decide whether to issue a public alert.
Do not issue public alerts for single-instrument GW events.
Disable automated preliminary alerts for all pipelines but gstlal and cWB due to outstanding review items for the other pipelines.

0.4.0 (2019-03-29)¶

This is the penultimate release before LIGO/Virgo observing run 3 (O3).
Make detchar results easier to read by formatting as HTML table.
Allow iDQ to label DQV onto events based on p(glitch). Adjustable by pipeline.
Move functions in tasks/lalinference.py to lalinference_pipe.py in lalsuite.
Take into account calibration errors in automatic Parameter Estimation.
Do not use margphi option for automatic Parameter Estimation with ROQ waveform since that option is not compatible with ROQ likelihood.
Adjust WSGI middleware configuration to adapt to a change in Werkzeug 0.15.0 that broke redirects on form submission in the Flask app. See https://github.com/pallets/werkzeug/pull/1303.
Use the new ligo.lw module for reading gstlal’s ranking_data.psd.xml.gz files, because these files are now written using the new LIGO-LW format that uses integer row IDs.
Use clean data for parameter estimation.
Use production accounting group for PE runs on gracedb events.
Change threshold from log-likelihood equals 6 to a dynamic threshold that ensures that all gstlal events uploaded to gracedb get assigned a P_astro value.

0.3.1 (2019-03-18)¶

Fix a bug in translating keys from source_classification.json to keyword arguments for GraceDb.createVOEvent that caused VOEvents to be missing the HasNS and HasRemnant fields.
FAR threshold for sending preliminary notices for CBC is changed to 1 per 2 months.
Upload log files when LALInference parameter estimation jobs fail or are aborted.
Changed the filename source_classification.json to em_bright.json.
Change condor log directory from /var/tmp to ~/.cache/condor since gwcelery workers have separate /var/tmp when they are running as condor jobs and that causes problems when gwcelery tries to read log files.
Limit the maximum version of gwpy to 0.14.0 in order to work around a unit test failure that started with gwpy 0.14.1. See https://git.ligo.org/emfollow/gwcelery/issues/95.
Upload a diff whenever a LIGO/Virgo VOEvent that we receive from GCN does not match the original that we sent.
Wait for low-latency or high-latency frame files being transferred to the cluster before parameter estimation starts.

0.3.0 (2019-03-01)¶

Fixed exponent in the expression of foreground count in p_astro_other task.
Run the sky map postprocessing and add the PE_READY tag when LALInference finishes.
Include EM_COINC triggered circulars to upload to the superevent page.
p-astro reads mean values from a file on CIT, new mass-gap category added. Removed redundant functions from p_astro_gstlal module.
Continuous deployment on the Caltech cluster now uses a robot keytab and gsissh instead of SSH keys and vanilla ssh because the new my.ligo.org SSH key management does not support scripted access.
Improve the isolation between the production and playground instances of GWCelery by deploying them under two separate user accounts on the Caltech cluster.
Add functionality for em_bright task to query emfollow/data for trained machine learning classifier and report probabilities based on it.

0.2.6 (2019-02-12)¶

Report an environment tag to Sentry corresponding to the GWCelery configuration module (production, test, playground, or development) in order to differentiate log messages from different deployments.
The gwcelery condor command now identifies jobs that it owns by matching both the job batch name and the working directory. This makes it possible to run multiple isolated instances of GWCelery under HTCondor on the same cluster in different working directories.
Change the conditions for starting parameter estimation. For every CBC superevent, create an online_pe.ini file suitable for starting LALInference. However, only start LALInference if the false alarm rate is less than once per 2 weeks.
Determine PSD segment length for LALInference automatically based on data availability and data quality.
Add a Flask-based web interface for manually triggering certain tasks such as sending updated GCN notices.

0.2.5 (2019-02-01)¶

Pass along the GWCelery version number to Sentry.
Upload stdout and stderr when dag creation fails and notifications when submitted job fails in Parameter Estimation
Allow detchar module’s create_cache to use gwdatafind when frames are no longer in llhoft.
The Nagios monitoring plugin will now report on the status of LVAlert subscriptions.
Change trials factor to 5 for both CBC and Burst categories. CBC includes the 4 CBC pipelines. Burst includes the 4 searches performed in total by the 2 Burst pipelines. An additional external coincidence search.
Automatically set up PE ini file depending on source parameters reported by detection pipelines.

0.2.4 (2018-12-17)¶

Fix broken links in log messages due to changes in GraceDb URL routes.
Whenever we send a public VOEvent using GCN, also make the corresponding VOEvent file in GraceDb public.
Don’t include Mollweide projection PNG file in VOEvents. The sky map visualizations take longer to generate than the FITS files themselves, so they were unnecessarily slowing down the preliminary alerts.
Preliminary GCN FAR threshold is modified to be group (CBC, Burst, Test) specific.

0.2.3 (2018-12-16)¶

Update frame type used in LALInference Parameter Estimation.
Handle cases where p_astro_gstlal.compute_p_astro returns NaNs by falling back to p_astro_other.compute_p_astro.
Fix a bug that prevented annotations that are specific to 3D sky maps from being performed for multi-resolution FITS files.
Fetch the graceid for the new event added from the gracedb logs since superevent packet does not provide information as to which event is added in case of type event_added.

0.2.2 (2018-12-14)¶

Add error handling for nonexistent iDQ frames in detchar module.

0.2.1 (2018-12-14)¶

Update detchar module configuration for ER13.

0.2.0 (2018-12-14)¶

This is the release of GWCelery for ER13.
Run two separate instances of Comet, one to act as a broker and one to act as a client. This breaks a cycle that would cause retransmission of GRB notices back to GCN.
Fix a race condition that could cause preliminary alerts to be sent out for events for which data quality checks had failed.
Unpin the redis package version because recent updates to Kombu and Billiard seem to have fixed the Nagios unit tests.
Start the Comet VOEvent broker as a subprocess intead of using multiprocessing and go back to using PyGCN instead of Comet as the VOEvent client. This is a workaround for suspected instability due to a bad interaction between redis-py and multiprocessing.
Reset Matplotlib’s style before running ligo-skymap-plot and ligo-skymap-plot-volume. There is some other module (probably in LALSuite) that is messing with the rcparams at module scope, which was causing Mollweide plots to come out with unusual aspect ratios.
Run check_vectors upon addition of an event to a superevent if the superevent already has an DQV label.
Do not check the DMT-DQ_VECTOR for pipelines which use gated h(t).
Remove static example VOEvents from the Open Alert Users Guide. We never used them because activating sample alerts got help until ER13.
Disable running the Orchestrator for test events for ER13. After ER13 is over, we need to carefully audit the code and make sure that test events are handled appropriately.
Enable public GraceDb entries and public GCNs for mock (MDC) events. For real events in ER13, disable public preliminary GCNs. Instead, advocate signoffs will trigger making events and GCN notices public: ADVOK for initial notices and ADVNO for retraction notices.
Include source classification output (BNS/NSBH/BBH/Terrestrial) in GCN Notices.

0.1.7 (2018-11-27)¶

Pin the redis package version at <3 because the latest version of redis breaks the Nagios unit tests.
Ditch our own homebrew VOEvent broker and use Comet instead.
In addition to traditional flat, fixed-nside sky maps, BAYESTAR will now also upload an experimental multiresolution format described in LIGO-G1800186-v4.

0.1.6 (2018-11-14)¶

Update URL for static example event.

0.1.5 (2018-11-13)¶

Add tasks for submitting HTCondor DAGs.
Add a new module, gwcelery.tasks.lalinference, which provides tasks to start parameter estimation with LALInference and upload the results to GraceDB.
Depend on lalsuite nightly build from 2018-11-04 to pick up changes to LALInference for Python 3 support.
Send static example VOEvents from the Open Alert Users Guide. This will provide a stream of example alerts for astronomers until GraceDb is ready for public access.
Add trials factor correction to the event FAR when comparing against FAR threshold to send out preliminary GCN.
Require that LIGO/Virgo VOEvents that we receive from GCN match the original VOEvents from GraceDb byte-for-byte, since GCN will now pass through our VOEvents without modification.

0.1.4 (2018-10-29)¶

Work around a bug in astropy.visualization.wcsaxes that affected all-sky plots when Matplotlib’s text.usetex rcparam is set to True (https://github.com/astropy/astropy/issues/8004). This bug has evidently been present since at least astropy 1.3, but was not being triggered until recently: it is likely that some other package that we import (e.g. lalsuite) is now globally setting text.usetex to True.
A try except is added around updateSuperevent to handle a bad request error from server side when updating superevent parameters which have nearby values.
Send automatic preliminary alerts only for events with a false alarm rate below a maximum value specified by a new configuration variable, preliminary_alert_far_threshold.
State vector vetoes will not suppress processing of preliminary sky maps and source classification. They will still suppress sending preliminary alerts.
Set open_alert to True for all automated VOEvents.

0.1.3 (2018-10-26)¶

Preliminary GCN is not sent for superevents created from offline gw events.
Add dqr_json function to gwcelery.tasks.detchar, which uploads a DQR-compatible json to GraceDb with the results of the detchar checks.
Depend on ligo.skymap >= 0.0.17.
Fix a bug in sending initial, update, and retraction GCN notices: we were sending the VOEvent filenames instead of the file contents.

0.1.2 (2018-10-11)¶

Setted vetted flag to true for all initial, update, and retraction alerts that are triggered by GraceDb signoffs.
Write GraceDb signoffs, instead of just labels, to simulate initial and retraction alerts for mock events, because merely creating the ADVNO or ADVOK label does not cause GraceDb to erase the ADVREQ label. This change makes mock alerts more realistic.
Change filename of cWB sky maps from skyprobcc_cWB.fits to cWB.fits.gz for consistency with other pipelines.
Any time that we send a VOEvent, first change the GraceDb permissions on the corresponding superevent so that it is visible to the public. Note that this has no effect during the ongoing software engineering runs because LVEM and unauthenticated access are currently disabled in GraceDb.

0.1.1 (2018-10-04)¶

Use the public tag instead of the lvem tag to mark preliminary sky maps for public access rather than LV-EM partner access. Note that GraceDb has not yet actually implemented unauthenticated access, so this should have no effect during our ongoing software engineering runs.
Add check_idq function to detchar module, which reads probabilities generated by iDQ.
Automated DQV labels should not trigger retraction notices because they prevent preliminary notices from being sent in the first place.
The criterion for selecting a superevent’s preferred event now prefers multiple-detector events to single-detector events, with precedence over source type (CBC versus burst). Any remaining tie is broken by using SNR for CBC and FAR for Burst triggers.
By default, initial and update alerts will find and send the most recently added public sky map.
The initial and update sky maps no longer perform sky map annotations, because they would only be duplicating the annotations performed as part of the preliminary alert.
Mock events now include example initial and retraction notices. Two minutes after each mock event is uploaded, there will be either an ADVOK or an ADVNO label applied at random, triggering either an initial or a retraction notice respectively.
Depend on ligo-gracedb >= 2.0.1 in order to pull in a bug fix for VOEvents with ProbHasNS or ProbHasRemnant set to 0.0.
Use the sentry-sdk package instead of the deprecated raven package for Sentry integration.

0.1.0 (2018-09-26)¶

Separated the external GCN listening handlers into two: one that listens to GCNs about SNEWS triggers and another that listens to Fermi and Swift.
Fixed calls to the raven temporal coincidence search so that search results separate SNEWS triggers from Fermi and Swift.
Add space-time FAR calculation for GRB and GW superevent coincidences. This only runs when skymaps from both triggers are available to download.
Add human vetting for initial GCN notices. For each new superevent that passes state vector checks, the ADVREQ label is applied. Rapid response team users should set their GraceDb notification preferences to alert them on ADVREQ labels. If a user sets the ADVOK label, then an initial notice is issued. If a user sets the ADVNO label, then a retraction notice is issued.
Update the LVAlert host for gracedb-playground.ligo.org.
Add experimental integration with Sentry for log aggregation and error reporting.
Track API and LVAlert schema changes in ligo-gracedb 2.0.0.

0.0.31 (2018-09-04)¶

Refactor external trigger handling to separate it from the orchestrator.
Fixed a bug in the VOEvent broker to only issue “iamalive” messages after sending the first VOEvent.
Pass group argument to set time windows appropriately when performing raven coincidence searches. Search in the [-600, 60]s range and [-5, 1]s range around external triggers for Burst events and CBC events respectively. Similarly, search in the [-60, 600]s and [-1, 5]s range around Burst and CBC events for external triggers.
Compute and upload FAR for GRB external trigger/superevent coincidence upon receipt of the EM_COINC label application to a superevent.
Add continuous integration testing for Python 3.7, and run test suite against all supported Python versions (3.6, 3.7).
Update ligo.skymap to 0.0.15.

0.0.30 (2018-08-02)¶

Manage superevents for production, test, and MDC events separately.
Add some more validation of LIGO/Virgo VOEvents from GCN.
Remove now-unused task gwcelery.tasks.orchestartor.continue_if.
Add check_vectors run for external triggers.
Change the preferred event selection criteria for burst events to be FAR instead of SNR.
Add gwcelery nagios subcommand for Nagios monitoring.
Incorporate Virgo DQ veto streams into check_vectors
Update ligo-raven to 1.3 and ligo-followup-advocate to 0.0.11.

0.0.29 (2018-07-31)¶

Add a workflow graph to superevents module documentation.
Add gwcelery condor resubmit as a shortcut for gwcelery condor rm; gwcelery condor submit.
Fix deprecation warning due to renaming of ligo.gracedb.rest.Gracedb.createTag to ligo.gracedb.rest.Gracedb.addTag.
Update ligo-gracedb to 2.0.0.dev1.

0.0.28 (2018-07-25)¶

Add injection checks to check_vector.
Bitmasks are now defined symbolically in detchar.
Refactor configuration so that it is possible to customize settings through an environment variable.

0.0.27 (2018-07-22)¶

The preferred event for superevents is now decided based on higher SNR value instead of lower FAR in the case of a tie between groups.
A check for the existence of the gstlal trigger database is performed so that compute_p_astro does not return None.

0.0.26 (2018-07-20)¶

Fix spelling of the label that is applied to events after p_astro finishes, changed from P_ASTRO_READY to PASTRO_READY.
Run p_astro calculation for mock events.
Overhaul preliminary alert pipeline so that it is mostly feature complete for both CBC and Burst events, and uses a common code path for both types. Sky map annotations now occur for both CBC and Burst localizations.
Switch to using the pre-registered port 8096 for receiving proprietary LIGO/Virgo alerts on emfollow.ligo.caltech.edu. This means that the capability to receive GCNs requires setting up a site configuration in advance with Scott Barthelmey.

Once we switch to sending public alerts exclusively, then we can switch back to using port 8099 for anonymous access, requiring no prior site configuration.

0.0.25 (2018-07-19)¶

Reintroduce pipeline-dependent pre/post peeks for check_vector after fixing issue where pipeline information was being looked for in the wrong dictionary.
check_vector checks all detectors regardless of instruments used, but only appends labels based on active instruments.
Fix a few issues in the GCN broker:
- Decrease the frequency of keepalive (“iamalive” in VOEvent Transport Protocol parlance) packets from once a second to once a minute at the request of Scott Barthelmey.
- Fix a possible race condition that might have caused queued VOEvents to be thrown away unsent shortly after a scheduled keepalive packet.
- Consume and ignore all keepalive and ack packets from the client so that the receive buffer does not overrun.
Add p_astro computation for gstlal pipeline. The copmutation is launched for all cbc_gstlal triggers.

0.0.24 (2018-07-18)¶

Revert pipeline-dependent pre/post peeks for check_vector because they introduced a regression: it caused the orchestrator failed without running any annotations.

0.0.23 (2018-07-18)¶

Add timeout and keepalive messages to GCN broker.
Update ligo-gracedb to 2.0.0.dev0 and ligo.skymap to 0.0.12.
Add superevent duration for gstlal-spiir pipeline.
Fix fallback for determining superevent duration for unknown pipelines.
Make check_vector pre/post peeks pipeline dependent.

0.0.22 (2018-07-11)¶

Process gstlal-spiir events.
Create combined LVC-Fermi skymap in case of coincident triggers and upload to GraceDb superevent page. Also upload the original external trigger sky map to the external trigger GraceDb page.
Generalize conditional processing of complex canvases by replacing the continue_if_group_is() task with a more general task that can be used like continue_if(group='CBC').
Add a check_vector_prepost configuration variable to control how much padding is added around an event for querying the state vector time series.

This should have the beneficial side effect of fixing some crashes for burst events, for which the bare duration of the superevent segment was less than one sample.

0.0.21 (2018-07-10)¶

MBTA events in GraceDb leave the search field blank. Work around this in gwcelery.tasks.detchar.check_vectors where we expected the field to be present.
Track change in GraceDb JSON response for VOEvent creation.

0.0.20 (2018-07-09)¶

After fixing some minor bugs in code that had not yet been tested live, sending VOEvents to GCN now works.

0.0.19 (2018-07-09)¶

Rewrite the GCN broker so that it does not require a dedicated worker.
Send VOEvents for preliminary alerts to GCN.
Only perform state vector checks for detectors that were online, according to the preferred event.
Exclude mock data challenge events from state vector checks.

0.0.18 (2018-07-06)¶

Add detector state vector checks to the preliminary alert workflow.

0.0.17 (2018-07-05)¶

Undo accidental configuration change in last version.

0.0.16 (2018-07-05)¶

Stop listening for three unnecessary GCN notice types: SWIFT_BAT_ALARM_LONG, SWIFT_BAT_ALARM_SHORT, and SWIFT_BAT_KNOWN_SRC.
Switch to SleekXMPP for the LVAlert client, instead of PyXMPP2. Because SleekXMPP has first-class support for publish-subscribe, the LVAlert listener can now automatically subscribe to all LVAlert nodes for which our code has handlers. Most of the client code now lives in a new external package, sleek-lvalert.

0.0.15 (2018-06-29)¶

Change superevent threshold and mock event rate to once per hour.
Add gracedb.create_label task.
Always upload external triggers to the ‘External’ group.
Add rudimentary burst event workflow to orchestrator: it just generates VOEvents and circulars.
Create a label in GraceDb whenever em_bright or bayestar completes.

0.0.14 (2018-06-28)¶

Fix typo that was causing a task to fail.
Decrease orchestrator timeout to 15 seconds.

0.0.13 (2018-06-28)¶

Change FAR threshold for creation of superevents to 1 per day.
Update ligo-followup-advocate to >= 0.0.10. Re-enable automatic generation of GCN circulars.
Add “EM bright” classification. This is rudimentary and based only on the point mass estimates from the search pipeline because some of the EM bright classifier’s dependencies are not yet ready for Python 3.
Added logic to select CBC events as preferred event over Burst. FAR acts as tie breaker when groups for preferred event and new event match.
BAYESTAR now adds GraceDb URLs of events to FITS headers.

0.0.12 (2018-06-28)¶

Prevent receiving duplicate copies of LVAlert messages by unregistering redundant LVAlert message types.
Update to ligo-followup-advocate >= 0.0.9 to update GCN Circular text for superevents. Unfortunately, circulars are still disabled due to a regression in ligo-gracedb (see https://git.ligo.org/lscsoft/gracedb-client/issues/7).
Upload BAYESTAR sky maps and annotations to superevents.
Create (but do not send) preliminary VOEvents for all superevents. No vetting is performed yet.

0.0.11 (2018-06-27)¶

Submit handler tasks to Celery as a single group.
Retry GraceDb tasks that raise a TimeoutError exception.
The superevent handler now skips LVAlert messages that do not affect the false alarm rate of an event (e.g. simple log messages).

(Note that the false alarm rate in GraceDb is set by the initial event upload and can be updated by replacing the event; however replacing the event does not produce an LVAlert message at all, so there is no way to intercept it.)
Added a query kwarg to superevents method to reduce latency in fetching the superevents from gracedb.
Refactored getting event information for update type events so that gracedb is polled only once to get the information needed for superevent manager.
Renamed the set_preferred_event task in gracedb.py to update_superevent to be a full wrapper around the updateSuperevent client function. Now it can be used to set preferred event and also update superevent time windows.
Many cwb (extra) attributes, which should be floating point numbers, are present in lvalert packet as strings. Casting them to avoid embarassing TypeErrors.
Reverted back the typecasting of far, gpstime into float. This is fixed in https://git.ligo.org/lscsoft/gracedb/issues/10
CBC t_start and t_end values are changed to 1 sec interval.
Added ligo-raven to run on external trigger and superevent creation lvalerts to search for coincidences. In case of coincidence, EM_COINC label is applied to the superevent and external trigger page and the external trigger is added to the list of em_events in superevent object dictionary.
cwb and lib nodes added to superevent handler.
Events are treated as finite segment window, initial superevent creation with preferred event window. Addition of events to superevents may change the superevent window and also the preferred event.
Change default GraceDb server to https://gracedb-playground.ligo.org/ for open public alert challenge.
Update to ligo-gracedb >= 1.29dev1.
Rename the get_superevent task to get_superevents and add a new get_superevent task that is a trivial wrapper around ligo.gracedb.rest.GraceDb.superevent().

0.0.10 (2018-06-13)¶

Model the time extent of events and superevents using the glue.segments module.
Replace GraceDb.get with GraceDb.superevents from the recent dev release of gracedb-client.
Fix possible false positive matches between GCNs for unrelated GRBs by matching on both TrigID (which is generally the mission elapsed time) and mission name.
Add the configuration variable superevent_far_threshold to limit the maximum false alarm rate of events that are included in superevents.
LVAlert handlers are now passed the actual alert data structure rather than the JSON text, so handlers are no longer responsible for calling json.loads. It is a little bit more convenient and possibly also faster for Celery to deserialize the alert messages.
Introduce Production, Development, Test, and Playground application configuration objects in order to facilitate quickly switching between GraceDb servers.
Pipeline specific start and end times for superevent segments. These values are controlled via configuration variables.

0.0.9 (2018-06-06)¶

Add missing LVAlert message types to superevent handler.

0.0.8 (2018-06-06)¶

Add some logging to the GCN and LVAlert dispatch code in order to diagnose missed messages.

0.0.7 (2018-05-31)¶

Ingest Swift, Fermi, and SNEWS GCN notices and save them in GraceDb.
Depend on the pre-release version of the GraceDb client, ligo-gracedb 1.29.dev0, because this is the only version that supports superevents at the moment.

0.0.6 (2018-05-26)¶

Generate GCN Circular drafts using ligo-followup-advocate.
In the continuous integration pipeline, validate PEP8 naming conventions using pep8-naming.
Add instructions for measuring test coverage and running the linter locally to the contributing guide.
Rename gwcelery.tasks.voevent to gwcelery.tasks.gcn to make it clear that this submodule contains functionality related to GCN notices, rather than VOEvents in general.
Rename gwcelery.tasks.dispatch to gwcelery.tasks.orchestrator to make it clear that this module encapsulates the behavior associated with the “orchestrator” in the O3 low-latency design document.
Mock up calls to BAYESTAR in test suite to speed it up.

Unify dispatch of LVAlert and GCN messages using decorators. GCN notice handlers are declared like this:

import lxml.etree
from gwcelery.tasks import gcn

@gcn.handler(gcn.NoticeType.FERMI_GBM_GND_POS,
             gcn.NoticeType.FERMI_GBM_FIN_POS)
def handle_fermi(payload):
    root = lxml.etree.fromstring(payload)
    # do work here...

LVAlert message handlers are declared like this:

import json
from gwcelery.tasks import lvalert

@lvalert.handler('cbc_gstlal',
                 'cbc_pycbc',
                 'cbc_mbta')
def handle_cbc(alert_content):
    alert = json.loads(alert_content)
    # do work here...

Instead of carrying around the GraceDb service URL in tasks, store the GraceDb host name in the Celery application config.
Create superevents by simple clustering in time. Currently this is only supported by the gracedb-dev1 host.

0.0.5 (2018-05-08)¶

Disable socket access during most unit tests. This adds some extra assurance that we don’t accidentally interact with production servers during the unit tests.
Ignore BAYESTAR jobs that raise a DetectorDisabled error. These exceptions are used for control flow and do not constitute a real error. Ignoring these jobs avoids polluting logs and the Flower monitor.

0.0.4 (2018-04-28)¶

FITS history and comment entries are now displayed in a monospaced font.
Adjust error reporting for some tasks.
Depend on newer version of ligo.skymap.
Add unit tests for the gwcelery condor submit subcommand.

0.0.3 (2018-04-27)¶

Fix some compatibility issues between the gwcelery condor submit subcommand and the format of condor_q -totals -xml with older versions of HTCondor.

0.0.2 (2018-04-27)¶

Add gwcelery condor submit and related subcommands as shortcuts for managing GWCelery running under HTCondor.

0.0.1 (2018-04-27)¶

This is the initial release. It provides rapid sky localization with BAYESTAR, sky map annotation, and sending mock alerts.
By default, GWCelery is configured to listen to the test LVAlert server.
Sending VOEvents to GCN/TAN is disabled for now.

License¶

The GWCelery Logo is a composite of Celery2 by Tiia Monto and Lorentzian Wormhole by Kes47 from Wikimedia Commons (CC BY-SA 3.0).

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Indices and tables¶

GWCelery is open source and is licensed under the GNU General Public License v2 or later.