"""Qualitative source classification for CBC events."""
import json
import pickle
from urllib import error, request
from ligo import computeDiskMass, em_bright
from celery.utils.log import get_task_logger
from ..import app
log = get_task_logger(__name__)
def _em_bright(m1, m2, c1, c2, threshold=2.83):
"""This is the place-holder function for the source classfication pipeline.
This placeholder code will only act upon the mass2 point estimate value and
classify the systems as whether they have a neutron or not."""
disk_mass = computeDiskMass.computeDiskMass(m1, m2, c1, c2)
p_ns = 1.0 if m2 <= threshold else 0.0
p_emb = 1.0 if disk_mass > 0.0 or m1 < threshold else 0.0
return p_ns, p_emb
[docs]@app.task(shared=False)
def classifier_other(args, graceid):
"""
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
-------
str
JSON formatted string storing ``HasNS`` and ``HasRemnant``
probabilities
Example
-------
>>> em_bright.classifier_other((2.0, 1.0, 0.0, 0.0, 10.), 'S123456')
'{"HasNS": 1.0, "HasRemnant": 1.0}'
"""
mass1, mass2, chi1, chi2, snr = args
p_ns, p_em = _em_bright(mass1, mass2, chi1, chi2)
data = json.dumps({
'HasNS': p_ns,
'HasRemnant': p_em
})
return data
[docs]@app.task(shared=False)
def classifier_gstlal(args, graceid):
"""
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
-------
str
JSON formatted string storing ``HasNS`` and ``HasRemnant``
probabilities
Notes
-----
This task would only work from within the CIT cluster.
"""
mass1, mass2, chi1, chi2, snr = args
try:
response = request.urlopen(app.conf['em_bright_url'])
ns_classifier, emb_classifier, scaler, filename = \
pickle.loads(response.read())
kwargs = {'ns_classifier': ns_classifier,
'emb_classifier': emb_classifier,
'scaler': scaler}
except (pickle.UnpicklingError, error.HTTPError):
kwargs = {}
log.exception("Error in unpickling classifier or 404. Using defaults.")
p_ns, p_em = em_bright.source_classification(mass1, mass2,
chi1, chi2,
snr, **kwargs)
data = json.dumps({
'HasNS': p_ns,
'HasRemnant': p_em
})
return data
