lasair 0.0.3

A client for the Lasair database

lasair_api

Client package for Lasair

NAME lasair - Lasair API

DESCRIPTION This class enables programmatic access to the Lasair database and Sherlock service, as described at http://lasair-iris.roe.ac.uk/api/.

Args:
    token (string): The Calls are throttled by the lasair server, by use of an 
    'authorization token', as described in the api documentation above. 
    There is a free token listed there, but it is throttled at 10 calls per hour. 
    Once a user has an account at the Lasair webserver, they can get their own token
    allowing 100 calls per hour, or request to be a power user, with infinite usage.

    cache (string): Results can be cached on a local filesystem, by providing 
    the name of a writable directory. If the same calls are made repeatedly, 
    this will be much more efficient.

CLASSES class lasair_client(builtins.object) Methods defined here:

   __init__(self, token, cache=None, endpoint='http://192.41.108.37:8080/api')
       Initialize self.  See help(type(self)) for accurate signature.
       Args:
           cache: name of a directory where results can be cached 
           endpoint: endpoint of the Lasair API service
   
   cone(self, ra, dec, radius=5, requestType='all')
       Run a cone search on the Lasair database.
       Args:
           ra (float): Right Ascension in decimal degrees
           dec (float): Declination in decimal degrees
           radius (float): cone radius in arcseconds (default is 5)
           requestType: Can be 'all' to return all objects in the cone
               Can be 'nearest', only the nearest object within the cone
               Can be 'count', the number of objects within the cone
       
       Returns a dictionary with:
           objectId: The ID of the nearest object
           separation: the separation in arcseconds
   
   lightcurves(self, objectIds)
       Get simple lightcurves in machine-readable form
       args:
           objectIds: list of objectIds, maximum 10
       return:
           list of dictionaries, one for each objectId. Each of these
           is a list of dictionaries, each having attributes
           candid, fid, magpsf, sigmapsf, isdiffpos, mjd
   
   objects(self, objectIds)
       Get object pages in machine-readable form
       args:
           objectIds: list of objectIds, maximum 10
       return:
           list of dictionaries, each being all the information presented
           on the Lasair object page.
   
   query(self, selected, tables, conditions, limit=1000, offset=0)
       Run a database query on the Lasair server.
       args: 
           selected (string): The attributes to be returned by the query
           tables (string): Comma-separated list of tables to be joined
           conditions (string): the "WHERE" criteria to restrict what is returned
           limit: (int) (default 1000) the maximum number of records to return
           offset: (int) (default 0) offset of record number
       return:
           a list of dictionaries, each representing a row
   
   sherlock_objects(self, objectIds, lite=True)
       Query the Sherlock database for context information about objects
           in the database.
       args:
           objectsIds: list of objectIds, maximum 10
           lite (boolean): If true, get extended information including a 
               list of possible crossmatches.
       return:
           list of dictionaries, one for each objectId.
   
   sherlock_position(self, ra, dec, lite=True)
       Query the Sherlock database for context information about a position
           in the sky.
       args:
           ra (float): Right Ascension in decimal degrees
           dec (float): Declination in decimal degrees
           lite (boolean): If true, get extended information including a 
               list of possible crossmatches.
       return:
           dictionary of contect information
   
   streams(self, topic, limit=1000)
       Get records from a given stream
       args:
           topic (string): Name of stream to be returned.
       return:
           list of dictionaries, each representing a row
   
   streams_topics(self, regex='.*', limit=1000)
       Get a list of available streams that match a given expression.
       args:
           regex (string, default .*): Search for stream names that match a pattern
           limit: (int, default 1000): Maximum number of stream names to return.
       return:
           List of stream names

    annotate(self, topic, objectId, classification, \
        version='0.1', explanation='', classdict={}, url='')
        Send an annotation to Lasair
        Note: Only the registered owner of this topic can send annotations to it
        args:
            topic         : the topic for which this user is authenticated
            objectId      : the object that this annotation should be attached to
            classification: short string for the classification
            version       : the version of the annotation engine
            explanation   : natural language explanation
            classdict     : dictionary with further information
            url           : url with further information about this classification
        return:
            Status message

class lasair_consumer(builtins.object)
   Consume a Kafka stream from Lasair
   
   __init__(self, host, group_id, topic_in):
        Consume a Kafka stream from Lasair
        args:
            host:     Host name:port for consuming Kafka
            group_id: a string. If used before, the server will start from last message
            topic_in: The topic to be consumed. Example 'lasair_2SN-likecandidates'
        Imports confluent_kafka.
        Connects to Lasair public kafka to get the chosen topic.
        Once you have the returned consumer object, run it with poll() like this:
        loop:
            msg = consumer.poll(timeout=20)
            if msg is None: break  # no messages to fetch
            if msg.error(): 
                print(str(msg.error()))
                break
            jmsg = json.loads(msg.value())  # msg will be in json format

    poll(self, timeout = 10):
        Polls for a message on the consumer with timeout is seconds

class lasair_producer():
    Creates a Kafka producer for Lasair annotations

    def __init__(self, host, username, password, topic_out):
        Tell the Lasair client that you will be producing annotations
        args:
            host:     Host name:port for producing Kafka
            username: as given to you by Lasair staff
            password: as given to you by Lasair staff
            topic_out: as given to you by Lasair staff
        Imports confluent_kafka.

    def produce(self, objectId, classification, \
        version=None, explanation=None, classdict=None, url=None):
        Send an annotation to Lasair
        args:
            objectId      : the object that this annotation should be attached to
            classification: short string for the classification
            version       : the version of the annotation engine
            explanation   : natural language explanation
            classdict     : dictionary with further information

    def flush(self):
        Finish an annotation session and close the producer
        If not called, your annotations will not go through!