""" This module contains functions for calculating single-ifo ranking
statistic values
"""
import numpy
[docs]def effsnr(snr, reduced_x2, fac=250.):
"""Calculate the effective SNR statistic. See (S5y1 paper) for definition.
"""
snr = numpy.array(snr, ndmin=1, dtype=numpy.float64)
rchisq = numpy.array(reduced_x2, ndmin=1, dtype=numpy.float64)
esnr = snr / (1 + snr ** 2 / fac) ** 0.25 / rchisq ** 0.25
# If snr input is float, return a float. Otherwise return numpy array.
if hasattr(snr, '__len__'):
return esnr
else:
return esnr[0]
[docs]def newsnr(snr, reduced_x2, q=6., n=2.):
"""Calculate the re-weighted SNR statistic ('newSNR') from given SNR and
reduced chi-squared values. See http://arxiv.org/abs/1208.3491 for
definition. Previous implementation in glue/ligolw/lsctables.py
"""
nsnr = numpy.array(snr, ndmin=1, dtype=numpy.float64)
reduced_x2 = numpy.array(reduced_x2, ndmin=1, dtype=numpy.float64)
# newsnr is only different from snr if reduced chisq > 1
ind = numpy.where(reduced_x2 > 1.)[0]
nsnr[ind] *= (0.5 * (1. + reduced_x2[ind] ** (q/n))) ** (-1./q)
# If snr input is float, return a float. Otherwise return numpy array.
if hasattr(snr, '__len__'):
return nsnr
else:
return nsnr[0]
[docs]def newsnr_sgveto(snr, bchisq, sgchisq):
""" Combined SNR derived from NewSNR and Sine-Gaussian Chisq"""
nsnr = numpy.array(newsnr(snr, bchisq), ndmin=1)
sgchisq = numpy.array(sgchisq, ndmin=1)
t = numpy.array(sgchisq > 4, ndmin=1)
if len(t):
nsnr[t] = nsnr[t] / (sgchisq[t] / 4.0) ** 0.5
# If snr input is float, return a float. Otherwise return numpy array.
if hasattr(snr, '__len__'):
return nsnr
else:
return nsnr[0]
[docs]def newsnr_sgveto_psdvar(snr, bchisq, sgchisq, psd_var_val):
""" Combined SNR derived from NewSNR, Sine-Gaussian Chisq and PSD
variation statistic """
nsnr = numpy.array(newsnr_sgveto(snr, bchisq, sgchisq), ndmin=1)
psd_var_val = numpy.array(psd_var_val, ndmin=1)
# 1.2 is the expected maximum psd_var_val over gaussian noise.
lgc = psd_var_val >= 1.2
nsnr[lgc] = nsnr[lgc] / numpy.sqrt(psd_var_val[lgc])
# If snr input is float, return a float. Otherwise return numpy array.
if hasattr(snr, '__len__'):
return nsnr
else:
return nsnr[0]
[docs]def get_newsnr(trigs):
"""
Calculate newsnr ('reweighted SNR') for a trigs object
Parameters
----------
trigs: dict of numpy.ndarrays, h5py group (or similar dict-like object)
Dictionary-like object holding single detector trigger information.
'chisq_dof', 'snr', and 'chisq' are required keys
Returns
-------
numpy.ndarray
Array of newsnr values
"""
dof = 2. * trigs['chisq_dof'][:] - 2.
nsnr = newsnr(trigs['snr'][:], trigs['chisq'][:] / dof)
return numpy.array(nsnr, ndmin=1, dtype=numpy.float32)
[docs]def get_newsnr_sgveto(trigs):
"""
Calculate newsnr re-weigthed by the sine-gaussian veto
Parameters
----------
trigs: dict of numpy.ndarrays, h5py group (or similar dict-like object)
Dictionary-like object holding single detector trigger information.
'chisq_dof', 'snr', 'sg_chisq' and 'chisq' are required keys
Returns
-------
numpy.ndarray
Array of newsnr values
"""
dof = 2. * trigs['chisq_dof'][:] - 2.
nsnr_sg = newsnr_sgveto(trigs['snr'][:],
trigs['chisq'][:] / dof,
trigs['sg_chisq'][:])
return numpy.array(nsnr_sg, ndmin=1, dtype=numpy.float32)
[docs]def get_newsnr_sgveto_psdvar(trigs):
"""
Calculate newsnr re-weighted by the sine-gaussian veto and psd variation
statistic
Parameters
----------
trigs: dict of numpy.ndarrays
Dictionary holding single detector trigger information.
'chisq_dof', 'snr', 'chisq' and 'psd_var_val' are required keys
Returns
-------
numpy.ndarray
Array of newsnr values
"""
dof = 2. * trigs['chisq_dof'][:] - 2.
nsnr_sg_psd = \
newsnr_sgveto_psdvar(trigs['snr'][:], trigs['chisq'][:] / dof,
trigs['sg_chisq'][:],
trigs['psd_var_val'][:])
return numpy.array(nsnr_sg_psd, ndmin=1, dtype=numpy.float32)