Commit 2d88c23c authored by Richard O'Shaughnessy's avatar Richard O'Shaughnessy

ILE: Port over naive vectorized call (--vectorized). Uses repacked data structures

parent 1434b759
......@@ -90,6 +90,7 @@ optp.add_option("-t", "--event-time", type=float, help="GPS time of the event --
optp.add_option("-i", "--inv-spec-trunc-time", type=float, default=8., help="Timescale of inverse spectrum truncation in seconds (Default is 8 - give 0 for no truncation)")
optp.add_option("-w", "--window-shape", type=float, default=0, help="Shape of Tukey window to apply to data (default is no windowing)")
optp.add_option("-m", "--time-marginalization", action="store_true", help="Perform marginalization over time via direct numerical integration. Default is false.")
optp.add_option("--vectorized", action="store_true", help="Perform manipulations of lm and timeseries using numpy arrays, not LAL data structures. (Combine with --gpu to enable GPU use, where available)")
optp.add_option("-o", "--output-file", help="Save result to this file.")
optp.add_option("-O", "--output-format", default='xml', help="[xml|hdf5]")
optp.add_option("-S", "--save-samples", action="store_true", help="Save sample points to output-file. Requires --output-file to be defined.")
......@@ -915,6 +916,30 @@ else: # Sum over time for every point in other extrinsic params
args = likelihood_function.func_code.co_varnames[:likelihood_function.func_code.co_argcount]
print " --------> Arguments ", args
res, var, neff, dict_return = sampler.integrate(likelihood_function, *unpinned_params, **pinned_params)
elif opts.vectorized: # integrate over intrinsic variables. Right now those variables ahave HARDCODED NAMES, alas
for det in rholms_intp.keys():
print " Packing ", det
lookupNKDict[det],lookupKNDict[det], lookupKNconjDict[det], ctUArrayDict[det], ctVArrayDict[det], rholmArrayDict[det], rholms_intpArrayDict[det], epochDict[det] = factored_likelihood.PackLikelihoodDataStructuresAsArrays( rholms[det].keys(), rholms_intp[det], rholms[det], crossTerms[det],crossTermsV[det])
def likelihood_function(right_ascension, declination,t_ref, phi_orb, inclination,
psi, distance):
global nEvals
global lnLOffsetValue
# use EXTREMELY many bits
lnL = np.zeros(right_ascension.shape,dtype=np.float128)
tvals = np.linspace(tWindowExplore[0],tWindowExplore[1],int((tWindowExplore[1]-tWindowExplore[0])/P.deltaT)) # choose an array at the target sampling rate. P is inherited globally
P.phi = right_ascension.astype(float) # cast to float
P.theta = declination.astype(float)
P.tref = float(theEpochFiducial)
P.phiref = phi_orb.astype(float)
P.incl = inclination.astype(float)
P.psi = psi.astype(float)
P.dist = (distance* 1.e6 * lalsimutils.lsu_PC).astype(float) # luminosity distance
lnL = factored_likelihood.DiscreteFactoredLogLikelihoodViaArrayVector(tvals,
P, lookupNKDict, rholmArrayDict, ctUArrayDict, ctVArrayDict,epochDict,Lmax=Lmax)
nEvals +=len(right_ascension)
return np.exp(lnLOffsetValue)*np.exp(lnL-lnLOffsetValue)
else: # integrate over intrinsic variables. Right now those variables ahave HARDCODED NAMES, alas
def likelihood_function(right_ascension, declination, phi_orb, inclination,
psi, distance,q):
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