snews-pt 0.0.1.dev360

An alert application for observing supernovas.

SNEWS Publishing Tool

See the docs at

Docs:	https://snews-publishing-tools.readthedocs.io/en/latest/

This packages provides users with a Python API and CLI to publish observation messages to SNEWS

How to Install

Note: Make sure your hop credentials are set up !!
Follow the instructions here if needed

First you need to clone this repo. In your terminal run the following:

git clone https://github.com/SNEWS2/SNEWS_Publishing_Tools.git

Once cloned, install the package using pip (make sure you're in the cloned dir)

pip install .

Message Schemas

Note: Not the final schemas !!

Coincidence Tier

_id                 
detector_name     (user input)    
sent_time           
machine_time      (user input)    
neutrino_time     (user input)     
p_val             (user input)    

Significance Tier

_id                 
detector_name    (user input)      
sent_time           
machine_time     (user input)        
neutrino_time    (user input)       
p_value(s)       (user input)    

Time Series Tier

_id                
detector_name   (user input)      
sent_time           
machine_time    (user input)      
neutrino_time   (user input)     
timing_series   (user input)

False Obs

_id
detector_name           (user input)          
false_id                (user input, optional)    
which_tier              (user input)    
N_retract_latest        (user input)    
retraction_reason       (user input, optional)  
sent_time           

How to Publish

Before we get started, right now the publishing method will send your message to the test kafka server.

First you need to import your Publisher:

# Import the constructor for SNEWS Tiers and Publisher class
from SNEWS_PT.snews_pub import SNEWSTiersPublisher

Passing Message Parameters as Arguments.

To send a message you need initialize the Publisher, construct your message by initializing SNEWSTiers and passing your parameters of choice. The backend will parse your arguments, check their data types and determine which tiers you can send a message to (see Publishing Protocols). If you pass multiple parameters (see code bellow) the sender will send a message all the appropriate tiers.

SNEWSTiersPublisher(detector_name='KamLAND', neutrino_time='22/02/28 4:31:08:565',
                    timing_series=['22/02/28 4:31:08:565', '22/02/28 4:31:08:765', '22/02/28 4:31:09:001'],
                    p_val=0.000007, machine_time='22/02/28 4:31:08:565', 
                    ).send_to_snews()

This instance has parameters for CoincidenceTier and TimingTier, thus it will send a message to both. The output should look like this: !

Passing Message Parameters from JSON File.

You can also pass your input from a json file, and make modifications on the spot. Let's first create an observation object this time before sending it to snews;

observation = SNEWSTiersPublisher.from_json('my_input_asjson.json', 
                                            detector_name='XENONnT', 
                                            comment="This is submitted from a json file")

Here, we read the content from the 'my_input_asjson.json' file, and overwrite detector_name and also add a comment field (which will be accepted as a meta data). Notice we still haven't sent it to snews yet. You can display, and modify the parsed messages after you create the object instance. Depending on the fields you provided SNEWSTierPublisher will decide where to submit your data (see above). You can see these tier(s) and the individual message contents. See, observation.tiernames to get names of the tiers that your input message belongs, and observation.messages to display their content, and modify if desired.

Once you are done, you can just send that observation object to snews.

observation.send_to_snews()

See this examples notebook for more tutorial scripts

Publishing Protocols

Coincidence Tier

• p_value and neutrino_time need to be passed.
  • p_val must be a float.
  • neutrino_time must be a string, format: '%y/%m/%d %H:%M:%S'

Significance Tier

• p_values needs to be passed.
  • p_values must be a list (float).

Timing Tier

• p_value and timing_series need to be passed.
  • p_val must be a float.
  • timing_series must be a list (string), format: '%y/%m/%d %H:%M:%S'

Retraction

• n_retract_latest and which need to be passed.
  • n_retract_latest must be a int (and >0 ). You can also pass it as a 'ALL'.
  • which_tier must be a which_tier, format: '%y/%m/%d %H:%M:%S'

Pre-SN Timing Tier

• is_pre_sn and timing_series need to be passed.
  • is_pre_sn must be a bool.
  • timing_series must be a list (string), format: '%y/%m/%d %H:%M:%S'

Notice that your message can contain fields that corresponds to several tiers e.g. if you have p_value, neutrino_time, and p_values we submit two separate messages to Coincidence and Significance tiers by selecting the relevant fields from your input.

How to Subscribe

In two lines, one can subscribe to the alert topic specified in the default configuration.
This starts a stream, and waits for alert messages to be received.

from SNEWS_PT.snews_sub import Subscriber

Subscriber().subscribe()

Should there be an alert message, this will be both displayed on the screen and saved into your local machine as SNEWS_MSGs/<today fmt="%y_%m_%d">/subscribed_messages.json and if there are multiple messages in the same day e.g. hype-mode is on and for the same supernova you kept receiving alerts with every coincidence message, these will be appended in this file with the sent time as the first key. An example (partly missing) can be found here

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Command Line Interface (CLI)

There also exists tools for command line interactions. These are explained in detail here