Coverage for breaker/transients/annotator.py : 29%
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#!/usr/local/bin/python # encoding: utf-8 *Annotate transients with supplimentary information resulting from the gravity events*
:Author: David Young
:Date Created: March 14, 2017 """ ################# GLOBAL IMPORTS ####################
""" *Annotate transients with supplimentary information resulting from the gravity events*
**Key Arguments:** - ``log`` -- logger - ``settings`` -- the settings dictionary - ``gwid`` -- the ID of the gravity event to annotate the transients against (matched in settings file)
**Usage:**
To setup your logger, settings and database connections, please use the ``fundamentals`` package (`see tutorial here <http://fundamentals.readthedocs.io/en/latest/#tutorial>`_).
To initiate a annotator object, use the following:
.. todo::
- add a tutorial about ``annotator`` to documentation - create a blog post about what ``annotator`` does
.. code-block:: python
from breaker.transients import annotator an = annotator( log=log, settings=settings, gwid="G211117" ) transientNames, probs = an.annotate_transients(transients) """
self, transients ): """ *generate a list of the likihood contours the transients lie within*
**Key Arguments:** - ``transients`` -- a dictionary of transients with the unique transient IDs as keys and (ra, dec) as tuple value.
**Return:** - ``transientNames`` -- a list of the transient names as they appear in the original input transient dictionary - ``prosb`` -- a list of the nearest 10% likihood conntour the transient falls within (syncs with `transientNames` list)
**Usage:**
See class docstring """ self.log.info('starting the ``annotate_transients`` method')
transientNames = [] ra = [] dec = []
transientNames = [] transientNames[:] = [t for t in transients.keys()] ra = [] dec = [] ra[:] = [r[0] for r in transients.values()] dec[:] = [d[1] for d in transients.values()]
probs = self._generate_probability_distribution(ra, dec)
self.log.info('completed the ``annotate_transients`` method') return transientNames, probs
self, ra, dec): """* generate probability distribution*
**Key Arguments:** - ``ra`` -- a list of RA values for transients. - ``dec`` -- a list of DEC values for transients.
**Return:** - ``prob`` -- a list of the probabilty contours the transients lie within (indices synced with RA and DEC lists) """ self.log.info( 'starting the ``_generate_probability_distribution`` method')
pathToProbMap = self.settings[ "gravitational waves"][self.gwid]["mapPath"]
# READ HEALPIX MAPS FROM FITS FILE # THIS FILE IS A ONE COLUMN FITS BINARY, WITH EACH CELL CONTAINING AN # ARRAY OF PROBABILITIES # READ IN THE HEALPIX FITS FILE aMap, mapHeader = hp.read_map(pathToProbMap, 0, h=True, verbose=False) # DETERMINE THE SIZE OF THE HEALPIXELS nside = hp.npix2nside(len(aMap)) totalProb = sum(aMap)
# CONTOURS - NEED TO ADD THE CUMMULATIVE PROBABILITY # GET THE INDEXES ORDERED BY PROB OF PIXELS; HIGHEST TO LOWEST i = np.flipud(np.argsort(aMap)) cumsum = np.cumsum(aMap[i]) cls = np.empty_like(aMap) cls[i] = cumsum * 100
healpixId = hp.pixelfunc.ang2pix( nside, ra, dec, nest=False, lonlat=True)
vals = cls[healpixId]
probs = [] probs[:] = [roundup(p, 10) if p < 100. else 100 for p in vals]
self.log.info( 'completed the ``_generate_probability_distribution`` method') return probs
# use the tab-trigger below for new method # xt-class-method
return int(math.ceil(val / float(resolution))) * int(resolution) |