misc.py

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#
# Copyright (C) 2010,2011  Kipp Cannon <kipp.cannon@ligo.org>
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 2 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
#
 

 

 
#
# =============================================================================
#
#                                   Preamble
#
# =============================================================================
#
 
 
from scipy import optimize
import numpy, scipy
import sys
 
 
#
# import all symbols from _misc
#
 
 
from ._misc import *
 
 
#
# =============================================================================
#
#                                    Extras
#
# =============================================================================
#
 
 
#
# inverse of cdf_weighted_chisq_P()
#
 
 
def cdf_weighted_chisq_Pinv(A, noncent, dof, var, P, lim, accuracy):
    func = lambda x: cdf_weighted_chisq_P(A, noncent, dof, var, x, lim, accuracy) - P
    lo = 0.0
    hi = 1.0
    while func(hi) < 0:
        lo = hi
        hi *= 2
        print(lo, hi, file=sys.stderr)
    return optimize.brentq(func, lo, hi, xtol = accuracy * 4)
 
#
# Function to compute the threshold at a fixed FAR for weighted \chi^2
#
 
 
def max_stat_thresh(coeffs, fap, samp_tol=100.0):
    num = int(samp_tol/ fap)
    out = numpy.zeros(num)
    for c in coeffs: out += c*scipy.randn(num)**2
    out.sort()
    return float(out[-int(samp_tol)])
 
 
#
# Function to compute the optimal quadratic statistic coefficients given
# singular values S and a desired signal size amp
#
 
 
def ss_coeffs(S, amp=5.5):
    return S**2. / (S**2. + len(S) / amp**2. )