def compute_search_efficiency_in_bins(found, total, ndbins, sim_to_bins_function = lambda sim: (sim.distance,)):
"""
This program creates the search efficiency in the provided ndbins. The
first dimension of ndbins must be the distance. You also must provide a
function that maps a sim inspiral row to the correct tuple to index the ndbins.
"""
input = rate.BinnedRatios(ndbins)
# increment the numerator with the missed injections
[input.incnumerator(sim_to_bins_function(sim)) for sim in found]
# increment the denominator with the total injections
[input.incdenominator(sim_to_bins_function(sim)) for sim in total]
# regularize by setting denoms to 1 to avoid nans
input.regularize()
# pull out the efficiency array, it is the ratio
eff = rate.BinnedArray(rate.NDBins(ndbins), array = input.ratio())
# compute binomial uncertainties in each bin
err_arr = numpy.sqrt(eff.array * (1-eff.array)/input.denominator.array)
err = rate.BinnedArray(rate.NDBins(ndbins), array = err_arr)
return eff, err
def _bin_events(self, binning = None):
# called internally by finish()
if binning is None:
minx, maxx = min(self.injected_x), max(self.injected_x)
miny, maxy = min(self.injected_y), max(self.injected_y)
binning = rate.NDBins((rate.LogarithmicBins(minx, maxx, 256), rate.LogarithmicBins(miny, maxy, 256)))
self.efficiency = rate.BinnedRatios(binning)
for xy in zip(self.injected_x, self.injected_y):
self.efficiency.incdenominator(xy)
for xy in zip(self.found_x, self.found_y):
self.efficiency.incnumerator(xy)
# 1 / error^2 is the number of injections that need to be
# within the window in order for the fractional uncertainty
# in that number to be = error. multiplying by
# bins_per_inj tells us how many bins the window needs to
# cover, and taking the square root translates that into
# the window's length on a side in bins. because the
# contours tend to run parallel to the x axis, the window
# is dilated in that direction to improve resolution.
bins_per_inj = self.efficiency.used() / float(len(self.injected_x))
self.window_size_x = self.window_size_y = math.sqrt(bins_per_inj / self.error**2)
self.window_size_x *= math.sqrt(2)
self.window_size_y /= math.sqrt(2)
if self.window_size_x > 100 or self.window_size_y > 100:
# program will take too long to run
raise ValueError("smoothing filter too large (not enough injections)")
print >>sys.stderr, "The smoothing window for %s is %g x %g bins" % ("+".join(self.instruments), self.window_size_x, self.window_size_y),
print >>sys.stderr, "which is %g%% x %g%% of the binning" % (100.0 * self.window_size_x / binning[0].n, 100.0 * self.window_size_y / binning[1].n)
def add_contents(self, contents):
if self.tisi_rows is None:
# get a list of time slide dictionaries
self.tisi_rows = contents.time_slide_table.as_dict().values()
# find the largest and smallest offsets
min_offset = min(offset for vector in self.tisi_rows
for offset in vector.values())
max_offset = max(offset for vector in self.tisi_rows
for offset in vector.values())
# a guess at the time slide spacing: works if the
# time slides are distributed as a square grid over
# the plot area. (max - min)^2 gives the area of
# the time slide square in square seconds; dividing
# by the length of the time slide list gives the
# average area per time slide; taking the square
# root of that gives the average distance between
# adjacent time slides in seconds
time_slide_spacing = ((max_offset - min_offset)**2 /
len(self.tisi_rows))**0.5
# use an average of 3 bins per time slide in each
# direction, but round to an odd integer
nbins = math.ceil(
(max_offset - min_offset) / time_slide_spacing * 3)
# construct the binning
self.bins = rate.BinnedRatios(
rate.NDBins((rate.LinearBins(min_offset, max_offset, nbins),
rate.LinearBins(min_offset, max_offset, nbins))))
self.seglists |= contents.seglists
for offsets in contents.connection.cursor().execute(
"""
SELECT tx.offset, ty.offset FROM
coinc_event
JOIN time_slide AS tx ON (
tx.time_slide_id == coinc_event.time_slide_id
)
JOIN time_slide AS ty ON (
ty.time_slide_id == coinc_event.time_slide_id
)
WHERE
coinc_event.coinc_def_id == ?
AND tx.instrument == ?
AND ty.instrument == ?
""", (contents.bb_definer_id, self.x_instrument, self.y_instrument)):
try:
self.bins.incnumerator(offsets)
except IndexError:
# beyond plot boundaries
pass
def compute_search_efficiency_in_bins(found,
total,
ndbins,
sim_to_bins_function=lambda sim:
(sim.distance, )):
"""
This program creates the search efficiency in the provided ndbins. The
first dimension of ndbins must be the distance. You also must provide a
function that maps a sim inspiral row to the correct tuple to index the ndbins.
"""
input = rate.BinnedRatios(ndbins)
# increment the numerator with the missed injections
[input.incnumerator(sim_to_bins_function(sim)) for sim in found]
# increment the denominator with the total injections
[input.incdenominator(sim_to_bins_function(sim)) for sim in total]
# regularize by setting empty bins to zero efficiency
input.denominator.array[input.numerator.array < 1] = 1e35
# pull out the efficiency array, it is the ratio
eff = rate.BinnedArray(rate.NDBins(ndbins), array=input.ratio())
# compute binomial uncertainties in each bin
k = input.numerator.array
N = input.denominator.array
eff_lo_arr = (N * (2 * k + 1) - numpy.sqrt(4 * N * k *
(N - k) + N**2)) / (2 * N *
(N + 1))
eff_hi_arr = (N * (2 * k + 1) + numpy.sqrt(4 * N * k *
(N - k) + N**2)) / (2 * N *
(N + 1))
eff_lo = rate.BinnedArray(rate.NDBins(ndbins), array=eff_lo_arr)
eff_hi = rate.BinnedArray(rate.NDBins(ndbins), array=eff_hi_arr)
return eff_lo, eff, eff_hi
def twoD_SearchVolume(self, instruments, dbin=None, FAR=None, bootnum=None, derr=0.197, dsys=0.074):
"""
Compute the search volume in the mass/mass plane, bootstrap
and measure the first and second moment (assumes the underlying
distribution can be characterized by those two parameters)
This is gonna be brutally slow
derr = (0.134**2+.103**2+.102**2)**.5 = 0.197 which is the 3 detector
calibration uncertainty in quadrature. This is conservative since some injections
will be H1L1 and have a lower error of .17
the dsys is the DC offset which is the max offset of .074.
"""
if not FAR: FAR = self.far[instruments]
found, missed = self.get_injections(instruments, FAR)
twodbin = self.twoDMassBins
wnfunc = self.gw
livetime = self.livetime[instruments]
if not bootnum: bootnum = self.bootnum
if wnfunc: wnfunc /= wnfunc[(wnfunc.shape[0]-1) / 2, (wnfunc.shape[1]-1) / 2]
x = twodbin.shape[0]
y = twodbin.shape[1]
z = int(self.opts.dist_bins)
rArrays = []
volArray=rate.BinnedArray(twodbin)
volArray2=rate.BinnedArray(twodbin)
#set up ratio arrays for each distance bin
for k in range(z):
rArrays.append(rate.BinnedRatios(twodbin))
# Bootstrap to account for errors
for n in range(bootnum):
#initialize by setting these to zero
for k in range(z):
rArrays[k].numerator.array = numpy.zeros(rArrays[k].numerator.bins.shape)
rArrays[k].denominator.array = numpy.zeros(rArrays[k].numerator.bins.shape)
#Scramble the inj population and distances
if bootnum > 1:
sm, sf = self._scramble_pop(missed, found)
# I make a separate array of distances to speed up this calculation
f_dist = self._scramble_dist(sf, derr, dsys)
else:
sm, sf = missed, found
f_dist = numpy.array([l.distance for l in found])
# compute the distance bins
if not dbin:
dbin = rate.LogarithmicBins(min(f_dist),max(f_dist), z)
#else: print dbin.centres()
# get rid of all missed injections outside the distance bins
# to prevent binning errors
sm, m_dist = self.cut_distance(sm, dbin)
sf, f_dist = self.cut_distance(sf, dbin)
for i, l in enumerate(sf):#found:
tbin = rArrays[dbin[f_dist[i]]]
tbin.incnumerator( (l.mass1, l.mass2) )
for i, l in enumerate(sm):#missed:
tbin = rArrays[dbin[m_dist[i]]]
tbin.incdenominator( (l.mass1, l.mass2) )
tmpArray2=rate.BinnedArray(twodbin) #start with a zero array to compute the mean square
for k in range(z):
tbins = rArrays[k]
tbins.denominator.array += tbins.numerator.array
if wnfunc: rate.filter_array(tbins.denominator.array,wnfunc)
if wnfunc: rate.filter_array(tbins.numerator.array,wnfunc)
tbins.regularize()
# logarithmic(d)
integrand = 4.0 * pi * tbins.ratio() * dbin.centres()[k]**3 * dbin.delta
volArray.array += integrand
tmpArray2.array += integrand #4.0 * pi * tbins.ratio() * dbin.centres()[k]**3 * dbin.delta
print >>sys.stderr, "bootstrapping:\t%.1f%% and Calculating smoothed volume:\t%.1f%%\r" % ((100.0 * n / bootnum), (100.0 * k / z)),
tmpArray2.array *= tmpArray2.array
volArray2.array += tmpArray2.array
print >>sys.stderr, ""
#Mean and variance
volArray.array /= bootnum
volArray2.array /= bootnum
volArray2.array -= volArray.array**2 # Variance
volArray.array *= livetime
volArray2.array *= livetime*livetime # this gets two powers of live time
return volArray, volArray2
def twoD_SearchVolume(found, missed, twodbin, dbin, wnfunc, livetime, bootnum=1, derr=0.197, dsys=0.074):
"""
Compute the search volume in the mass/mass plane, bootstrap
and measure the first and second moment (assumes the underlying
distribution can be characterized by those two parameters)
This is gonna be brutally slow
derr = (0.134**2+.103**2+.102**2)**.5 = 0.197 which is the 3 detector
calibration uncertainty in quadrature. This is conservative since some injections
will be H1L1 and have a lower error of .17
the dsys is the DC offset which is the max offset of .074.
"""
if wnfunc: wnfunc /= wnfunc[(wnfunc.shape[0]-1) / 2, (wnfunc.shape[1]-1) / 2]
x = twodbin.shape[0]
y = twodbin.shape[1]
z = dbin.n
rArrays = []
volArray=rate.BinnedArray(twodbin)
volArray2=rate.BinnedArray(twodbin)
#set up ratio arrays for each distance bin
for k in range(z):
rArrays.append(rate.BinnedRatios(twodbin))
# Bootstrap to account for errors
for n in range(bootnum):
#initialize by setting these to zero
for k in range(z):
rArrays[k].numerator.array = numpy.zeros(rArrays[k].numerator.bins.shape)
rArrays[k].denominator.array = numpy.zeros(rArrays[k].numerator.bins.shape)
#Scramble the inj population
if bootnum > 1: sm, sf = scramble_pop(missed, found)
else: sm, sf = missed, found
for l in sf:#found:
tbin = rArrays[dbin[scramble_dist(l.distance,derr,dsys)]]
tbin.incnumerator( (l.mass1, l.mass2) )
for l in sm:#missed:
tbin = rArrays[dbin[scramble_dist(l.distance,derr,dsys)]]
tbin.incdenominator( (l.mass1, l.mass2) )
tmpArray2=rate.BinnedArray(twodbin) #start with a zero array to compute the mean square
for k in range(z):
tbins = rArrays[k]
tbins.denominator.array += tbins.numerator.array
if wnfunc: rate.filter_array(tbins.denominator.array,wnfunc)
if wnfunc: rate.filter_array(tbins.numerator.array,wnfunc)
tbins.regularize()
# logarithmic(d)
integrand = 4.0 * pi * tbins.ratio() * dbin.centres()[k]**3 * dbin.delta
volArray.array += integrand
tmpArray2.array += integrand #4.0 * pi * tbins.ratio() * dbin.centres()[k]**3 * dbin.delta
print >>sys.stderr, "bootstrapping:\t%.1f%% and Calculating smoothed volume:\t%.1f%%\r" % ((100.0 * n / bootnum), (100.0 * k / z)),
tmpArray2.array *= tmpArray2.array
volArray2.array += tmpArray2.array
print >>sys.stderr, ""
#Mean and variance
volArray.array /= bootnum
volArray2.array /= bootnum
volArray2.array -= volArray.array**2 # Variance
volArray.array *= livetime
volArray2.array *= livetime*livetime # this gets two powers of live time
return volArray, volArray2
def finish(self):
fig, axes = SnglBurstUtils.make_burst_plot(r"Injection Amplitude (\(\mathrm{s}^{-\frac{1}{3}}\))", "Detection Efficiency", width = 108.0)
axes.set_title(r"Detection Efficiency vs.\ Amplitude")
axes.semilogx()
axes.set_position([0.10, 0.150, 0.86, 0.77])
# set desired yticks
axes.set_yticks((0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))
axes.set_yticklabels((r"\(0\)", r"\(0.1\)", r"\(0.2\)", r"\(0.3\)", r"\(0.4\)", r"\(0.5\)", r"\(0.6\)", r"\(0.7\)", r"\(0.8\)", r"\(0.9\)", r"\(1.0\)"))
axes.xaxis.grid(True, which = "major,minor")
axes.yaxis.grid(True, which = "major,minor")
# put made and found injections in the denominators and
# numerators of the efficiency bins
bins = rate.NDBins((rate.LogarithmicBins(min(sim.amplitude for sim in self.all), max(sim.amplitude for sim in self.all), 400),))
efficiency = rate.BinnedRatios(bins)
for sim in self.found:
efficiency.incnumerator((sim.amplitude,))
for sim in self.all:
efficiency.incdenominator((sim.amplitude,))
# generate and plot trend curves. adjust window function
# normalization so that denominator array correctly
# represents the number of injections contributing to each
# bin: make w(0) = 1.0. note that this factor has no
# effect on the efficiency because it is common to the
# numerator and denominator arrays. we do this for the
# purpose of computing the Poisson error bars, which
# requires us to know the counts for the bins
windowfunc = rate.gaussian_window(self.filter_width)
windowfunc /= windowfunc[len(windowfunc) / 2 + 1]
rate.filter_binned_ratios(efficiency, windowfunc)
# regularize: adjust unused bins so that the efficiency is
# 0, not NaN
efficiency.regularize()
line1, A50, A50_err = render_data_from_bins(file("string_efficiency.dat", "w"), axes, efficiency, self.cal_uncertainty, self.filter_width, colour = "k", linestyle = "-", erroralpha = 0.2)
print >>sys.stderr, "Pipeline's 50%% efficiency point for all detections = %g +/- %g%%\n" % (A50, A50_err * 100)
# add a legend to the axes
axes.legend((line1,), (r"\noindent Injections recovered with $\Lambda > %s$" % SnglBurstUtils.latexnumber("%.2e" % self.detection_threshold),), loc = "lower right")
# adjust limits
axes.set_xlim([1e-21, 2e-18])
axes.set_ylim([0.0, 1.0])
#
# dump some information about the highest-amplitude missed
# and quietest-amplitude found injections
#
self.loudest_missed.sort(reverse = True)
self.quietest_found.sort(reverse = True)
f = file("string_loud_missed_injections.txt", "w")
print >>f, "Highest Amplitude Missed Injections"
print >>f, "==================================="
for amplitude, sim, offsetvector, filename, likelihood_ratio in self.loudest_missed:
print >>f
print >>f, "%s in %s:" % (str(sim.simulation_id), filename)
if likelihood_ratio is None:
print >>f, "Not recovered"
else:
print >>f, "Recovered with \\Lambda = %.16g, detection threshold was %.16g" % (likelihood_ratio, self.detection_threshold)
for instrument in self.seglists:
print >>f, "In %s:" % instrument
print >>f, "\tInjected amplitude:\t%.16g" % SimBurstUtils.string_amplitude_in_instrument(sim, instrument, offsetvector)
print >>f, "\tTime of injection:\t%s s" % sim.time_at_instrument(instrument, offsetvector)
print >>f, "Amplitude in waveframe:\t%.16g" % sim.amplitude
t = sim.get_time_geocent()
print >>f, "Time at geocentre:\t%s s" % t
print >>f, "Segments within 60 seconds:\t%s" % segmentsUtils.segmentlistdict_to_short_string(self.seglists & segments.segmentlistdict((instrument, segments.segmentlist([segments.segment(t-offsetvector[instrument]-60, t-offsetvector[instrument]+60)])) for instrument in self.seglists))
print >>f, "Vetoes within 60 seconds:\t%s" % segmentsUtils.segmentlistdict_to_short_string(self.vetoseglists & segments.segmentlistdict((instrument, segments.segmentlist([segments.segment(t-offsetvector[instrument]-60, t-offsetvector[instrument]+60)])) for instrument in self.vetoseglists))
f = file("string_quiet_found_injections.txt", "w")
print >>f, "Lowest Amplitude Found Injections"
print >>f, "================================="
for inv_amplitude, sim, offsetvector, filename, likelihood_ratio in self.quietest_found:
print >>f
print >>f, "%s in %s:" % (str(sim.simulation_id), filename)
if likelihood_ratio is None:
print >>f, "Not recovered"
else:
print >>f, "Recovered with \\Lambda = %.16g, detection threshold was %.16g" % (likelihood_ratio, self.detection_threshold)
for instrument in self.seglists:
print >>f, "In %s:" % instrument
print >>f, "\tInjected amplitude:\t%.16g" % SimBurstUtils.string_amplitude_in_instrument(sim, instrument, offsetvector)
print >>f, "\tTime of injection:\t%s s" % sim.time_at_instrument(instrument, offsetvector)
print >>f, "Amplitude in waveframe:\t%.16g" % sim.amplitude
t = sim.get_time_geocent()
print >>f, "Time at geocentre:\t%s s" % t
print >>f, "Segments within 60 seconds:\t%s" % segmentsUtils.segmentlistdict_to_short_string(self.seglists & segments.segmentlistdict((instrument, segments.segmentlist([segments.segment(t-offsetvector[instrument]-60, t-offsetvector[instrument]+60)])) for instrument in self.seglists))
print >>f, "Vetoes within 60 seconds:\t%s" % segmentsUtils.segmentlistdict_to_short_string(self.vetoseglists & segments.segmentlistdict((instrument, segments.segmentlist([segments.segment(t-offsetvector[instrument]-60, t-offsetvector[instrument]+60)])) for instrument in self.vetoseglists))
#
# done
#
return fig,
