def __init__(self, x, y, magnitude, max_magnitude):
self.fig, self.axes = SnglBurstUtils.make_burst_plot("X", "Y")
self.fig.set_size_inches(6, 6)
self.x = x
self.y = y
self.magnitude = magnitude
self.n_foreground = 0
self.n_background = 0
self.n_injections = 0
max_magnitude = math.log10(max_magnitude)
self.foreground_bins = rate.BinnedArray(
rate.NDBins((rate.LinearBins(-max_magnitude, max_magnitude, 1024),
rate.LinearBins(-max_magnitude, max_magnitude,
1024))))
self.background_bins = rate.BinnedArray(
rate.NDBins((rate.LinearBins(-max_magnitude, max_magnitude, 1024),
rate.LinearBins(-max_magnitude, max_magnitude,
1024))))
self.coinc_injection_bins = rate.BinnedArray(
rate.NDBins((rate.LinearBins(-max_magnitude, max_magnitude, 1024),
rate.LinearBins(-max_magnitude, max_magnitude,
1024))))
self.incomplete_coinc_injection_bins = rate.BinnedArray(
rate.NDBins((rate.LinearBins(-max_magnitude, max_magnitude, 1024),
rate.LinearBins(-max_magnitude, max_magnitude,
1024))))
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 __init__(self, x_instrument, y_instrument, magnitude, desc,
min_magnitude, max_magnitude):
self.fig, self.axes = SnglBurstUtils.make_burst_plot(
"%s %s" % (x_instrument, desc), "%s %s" % (y_instrument, desc))
self.fig.set_size_inches(6, 6)
self.axes.loglog()
self.x_instrument = x_instrument
self.y_instrument = y_instrument
self.magnitude = magnitude
self.foreground_x = []
self.foreground_y = []
self.n_foreground = 0
self.n_background = 0
self.n_injections = 0
self.foreground_bins = rate.BinnedArray(
rate.NDBins(
(rate.LogarithmicBins(min_magnitude, max_magnitude, 1024),
rate.LogarithmicBins(min_magnitude, max_magnitude, 1024))))
self.background_bins = rate.BinnedArray(
rate.NDBins(
(rate.LogarithmicBins(min_magnitude, max_magnitude, 1024),
rate.LogarithmicBins(min_magnitude, max_magnitude, 1024))))
self.coinc_injection_bins = rate.BinnedArray(
rate.NDBins(
(rate.LogarithmicBins(min_magnitude, max_magnitude, 1024),
rate.LogarithmicBins(min_magnitude, max_magnitude, 1024))))
self.incomplete_coinc_injection_bins = rate.BinnedArray(
rate.NDBins(
(rate.LogarithmicBins(min_magnitude, max_magnitude, 1024),
rate.LogarithmicBins(min_magnitude, max_magnitude, 1024))))
def __init__(self, instrument, interval, width):
self.fig, self.axes = SnglBurstUtils.make_burst_plot(r"$f_{\mathrm{recovered}} / f_{\mathrm{injected}}$", "Event Number Density")
self.axes.loglog()
self.instrument = instrument
self.found = 0
# 21 bins per filter width
bins = int(float(abs(interval)) / width) * 21
binning = rate.NDBins((rate.LinearBins(interval[0], interval[1], bins),))
self.offsets = rate.BinnedArray(binning)
self.coinc_offsets = rate.BinnedArray(binning)
def __init__(self, instrument, interval, width):
self.fig, self.axes = SnglBurstUtils.make_burst_plot(r"$t_{\mathrm{recovered}} - t_{\mathrm{injected}}$ (s)", "Triggers per Unit Offset")
self.axes.semilogy()
self.instrument = instrument
self.found = 0
# 21 bins per filter width
bins = int(float(abs(interval)) / width) * 21
binning = rate.NDBins((rate.LinearBins(interval[0], interval[1], bins),))
self.offsets = rate.BinnedArray(binning)
self.coinc_offsets = rate.BinnedArray(binning)
def _derivitave_fit(self, farts, FARt, vAs, twodbin):
'''
Relies on scipy spline fits for each mass bin
to find the derivitave of the volume at a given
FAR. See how this works for a simple case where
I am clearly giving it a parabola. To high precision it calculates
the proper derivitave.
A = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
B = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
C = interpolate.splrep(B,A,s=0, k=4)
interpolate.splev(5,C,der=1)
10.000
'''
dA = rate.BinnedArray(twodbin)
for m1 in range(dA.array.shape[0]):
for m2 in range(dA.array.shape[1]):
da = []
for f in farts.centres():
da.append(vAs[farts[f]].array[m1][m2])
fit = interpolate.splrep(farts.centres(),da,k=4)
val = interpolate.splev(FARt,fit,der=1)
#print val
# FIXME this prevents negative derivitives arising from bad fits
if val < 0: val = 0
dA.array[m1][m2] = val # minus the derivitave
return dA
def get_volume_derivative(injfnames, twoDMassBins, dBin, FAR,
zero_lag_segments, gw):
if (FAR == 0):
print "\n\nFAR = 0\n \n"
# FIXME lambda = ~inf if loudest event is above loudest timeslide?
output = rate.BinnedArray(twoDMassBins)
output.array = 10**6 * numpy.ones(output.array.shape)
return output
livetime = float(abs(zero_lag_segments))
FARh = FAR * 100000
FARl = FAR * 0.001
nbins = 5
FARS = rate.LogarithmicBins(FARl, FARh, nbins)
vA = []
vA2 = []
for far in FARS.centres():
m, f = get_injections(injfnames, far, zero_lag_segments)
print >> sys.stderr, "computing volume at FAR " + str(far)
vAt, vA2t = twoD_SearchVolume(f, m, twoDMassBins, dBin, gw, livetime,
1)
# we need to compute derivitive of log according to ul paper
vAt.array = scipy.log10(vAt.array + 0.001)
vA.append(vAt)
# the derivitive is calcuated with respect to FAR * t
FARS = rate.LogarithmicBins(FARl * livetime, FARh * livetime, nbins)
return derivitave_fit(FARS, FAR * livetime, vA, twoDMassBins)
def live_time_array(self, instruments): """ return an array of live times, note every bin will be the same :) it is just a convenience. """ live_time = rate.BinnedArray(self.twoDMassBins) live_time.array += 1.0 live_time.array *= self.livetime[instruments] return live_time
def __init__(self, ifo, interval, width):
self.fig, self.axes = SnglBurstUtils.make_burst_plot(
"Peak Frequency (Hz)",
"Trigger Rate Spectral Density (triggers / s / Hz)")
self.ifo = ifo
self.nevents = 0
# 21 bins per filter width
bins = int(float(abs(interval)) / width) * 21
binning = rate.NDBins((rate.LinearBins(interval[0], interval[1],
bins), ))
self.rate = rate.BinnedArray(binning)
def __init__(self, ifo, width, max):
self.fig, self.axes = SnglBurstUtils.make_burst_plot(
"Delay (s)", "Count / Delay")
self.ifo = ifo
self.nevents = 0
# 21 bins per filter width
interval = segments.segment(0, max + 2)
self.bins = rate.BinnedArray(
rate.NDBins(
(rate.LinearBins(interval[0], interval[1],
int(float(abs(interval)) / width) * 21), )))
self.axes.semilogy()
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 compute_search_volume_in_bins(found, total, ndbins, sim_to_bins_function):
"""
This program creates the search volume in the provided ndbins. The
first dimension of ndbins must be the distance over which to integrate. You
also must provide a function that maps a sim inspiral row to the correct tuple
to index the ndbins.
"""
eff, err = compute_search_efficiency_in_bins(found, total, ndbins, sim_to_bins_function)
dx = ndbins[0].upper() - ndbins[0].lower()
r = ndbins[0].centres()
# we have one less dimension on the output
vol = rate.BinnedArray(rate.NDBins(ndbins[1:]))
errors = rate.BinnedArray(rate.NDBins(ndbins[1:]))
# integrate efficiency to obtain volume
vol.array = numpy.trapz(eff.array.T * 4. * numpy.pi * r**2, r, dx)
# propagate errors in eff to errors in V
errors.array = numpy.sqrt(( (4*numpy.pi *r**2 *err.array.T *dx)**2 ).sum(-1))
return vol, errors
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. """ num = rate.BinnedArray(ndbins) den = rate.BinnedArray(ndbins) # increment the numerator with the found injections for sim in found: num[sim_to_bins_function(sim)] += 1 # increment the denominator with the total injections for sim in total: den[sim_to_bins_function(sim)] += 1 # sanity check assert (num.array <= den.array).all(), "some bins have more found injections than were made" # regularize by setting empty bins to zero efficiency den.array[numpy.logical_and(num.array == 0, den.array == 0)] = 1 # pull out the efficiency array, it is the ratio eff = rate.BinnedArray(rate.NDBins(ndbins), array = num.array / den.array) # compute binomial uncertainties in each bin k = num.array N = den.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 compute_search_volume(eff): """ Integrate efficiency to get search volume. """ # get distance bins ndbins = eff.bins dx = ndbins[0].upper() - ndbins[0].lower() r = ndbins[0].centres() # we have one less dimension on the output vol = rate.BinnedArray(rate.NDBins(ndbins[1:])) # integrate efficiency to obtain volume vol.array = numpy.trapz(eff.array.T * 4. * numpy.pi * r**2, r, dx) return vol
def finish(self, threshold):
# bin the injections
self._bin_events()
# use the same binning for the found injection density as
# was constructed for the efficiency
self.found_density = rate.BinnedArray(self.efficiency.denominator.bins)
# construct the amplitude weighting function
amplitude_weight = rate.BinnedArray(rate.NDBins((rate.LinearBins(threshold - 100, threshold + 100, 10001),)))
# gaussian window's width is the number of bins
# corresponding to 10 units of amplitude, which is computed
# by dividing 10 by the "volume" of the bin corresponding
# to threshold. index is the index of the element in
# amplitude_weight corresponding to the threshold.
index, = amplitude_weight.bins[threshold,]
window = rate.gaussian_window(10.0 / amplitude_weight.bins.volumes()[index])
window /= 10 * window[(len(window) - 1) / 2]
# set the window data into the BinnedArray object. the
# Gaussian peaks on the middle element of the window, which
# we want to place at index in the amplitude_weight array.
lo = index - (len(window) - 1) / 2
hi = lo + len(window)
if lo < 0 or hi > len(amplitude_weight.array):
raise ValueError("amplitude weighting window too large")
amplitude_weight.array[lo:hi] = window
# store the recovered injections in the found density bins
# weighted by amplitude
for x, y, z in self.recovered_xyz:
try:
weight = amplitude_weight[z,]
except IndexError:
# beyond the edge of the window
weight = 0.0
self.found_density[x, y] += weight
# the efficiency is only valid up to the highest energy
# that has been injected. this creates problems later on
# so, instead, for each frequency, identify the highest
# energy that has been measured and copy the values for
# that bin's numerator and denominator into the bins for
# all higher energies. do the same for the counts in the
# found injection density bins.
#
# numpy.indices() returns two arrays array, the first of
# which has each element set equal to its x index, the
# second has each element set equal to its y index, we keep
# the latter. meanwhile numpy.roll() cyclically permutes
# the efficiency bins down one along the y (energy) axis.
# from this, the conditional finds bins where the
# efficiency is greater than 0.9 but <= 0.9 in the bin
# immediately above it in energy. we select the elements
# from the y index array where the conditional is true, and
# then use numpy.max() along the y direction to return the
# highest such y index for each x index, which is a 1-D
# array. finally, enumerate() is used to iterate over x
# index and corresponding y index, and if the y index is
# not negative (was found) the value from that x-y bin is
# copied to all higher bins.
n = self.efficiency.numerator.array
d = self.efficiency.denominator.array
f = self.found_density.array
bady = -1
for x, y in enumerate(numpy.max(numpy.where((d > 0) & (numpy.roll(d, -1, axis = 1) <= 0), numpy.indices(d.shape)[1], bady), axis = 1)):
if y != bady:
n[x, y + 1:] = n[x, y]
d[x, y + 1:] = d[x, y]
f[x, y + 1:] = f[x, y]
# now do the same for the bins at energies below those that
# have been measured.
bady = d.shape[1]
for x, y in enumerate(numpy.min(numpy.where((d > 0) & (numpy.roll(d, 1, axis = 1) <= 0), numpy.indices(d.shape)[1], bady), axis = 1)):
if y != bady:
n[x, 0:y] = n[x, y]
d[x, 0:y] = d[x, y]
f[x, 0:y] = f[x, y]
diagnostic_plot(self.efficiency.numerator.array, self.efficiency.denominator.bins, r"Efficiency Numerator (Before Averaging)", self.amplitude_lbl, "lalapps_excesspowerfinal_efficiency_numerator_before.png")
diagnostic_plot(self.efficiency.denominator.array, self.efficiency.denominator.bins, r"Efficiency Denominator (Before Averaging)", self.amplitude_lbl, "lalapps_excesspowerfinal_efficiency_denominator_before.png")
diagnostic_plot(self.found_density.array, self.efficiency.denominator.bins, r"Injections Lost / Unit of Threshold (Before Averaging)", self.amplitude_lbl, "lalapps_excesspowerfinal_found_density_before.png")
# smooth the efficiency bins and the found injection
# density bins using the same 2D window.
window = rate.gaussian_window(self.window_size_x, self.window_size_y)
window /= window[tuple((numpy.array(window.shape, dtype = "double") - 1) / 2)]
rate.filter_binned_ratios(self.efficiency, window)
rate.filter_array(self.found_density.array, window)
diagnostic_plot(self.efficiency.numerator.array, self.efficiency.denominator.bins, r"Efficiency Numerator (After Averaging)", self.amplitude_lbl, "lalapps_excesspowerfinal_efficiency_numerator_after.png")
diagnostic_plot(self.efficiency.denominator.array, self.efficiency.denominator.bins, r"Efficiency Denominator (After Averaging)", self.amplitude_lbl, "lalapps_excesspowerfinal_efficiency_denominator_after.png")
diagnostic_plot(self.found_density.array, self.efficiency.denominator.bins, r"Injections Lost / Unit of Threshold (After Averaging)", self.amplitude_lbl, "lalapps_excesspowerfinal_found_density_after.png")
# compute the uncertainties in the efficiency and its
# derivative by assuming these to be the binomial counting
# fluctuations in the numerators.
p = self.efficiency.ratio()
self.defficiency = numpy.sqrt(p * (1 - p) / self.efficiency.denominator.array)
p = self.found_density.array / self.efficiency.denominator.array
self.dfound_density = numpy.sqrt(p * (1 - p) / self.efficiency.denominator.array)
def __init__(self, efficiency_data, confidence): self.rate_array = rate.BinnedArray(efficiency_data.efficiency.denominator.bins) self.amplitude_lbl = efficiency_data.amplitude_lbl self.confidence = confidence
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 compute_volume_vs_mass(found, missed, mass_bins, bin_type, dbins=None,
distribution_param=None, distribution=None, limits_param=None,
max_param=None, min_param=None):
"""
Compute the average luminosity an experiment was sensitive to given the sets
of found and missed injections and assuming luminosity is uniformly distributed
in space.
If distance_param and distance_distribution are not None, use an unbinned
Monte Carlo integral (which optionally takes max_distance and min_distance
parameters) for the volume and error
Otherwise use a simple efficiency * distance**2 binned integral
In either case use distance bins to return the efficiency in each bin
"""
# initialize MC volume integral method: binned or unbinned
if distribution_param is not None and distribution is not None and limits_param is not None:
mc_unbinned = True
else:
mc_unbinned = False
# mean and std estimate for sensitive volume averaged over search time
volArray = rate.BinnedArray(mass_bins)
vol2Array = rate.BinnedArray(mass_bins)
# found/missed stats
foundArray = rate.BinnedArray(mass_bins)
missedArray = rate.BinnedArray(mass_bins)
# efficiency over distance and its standard (binomial) error
effvmass = []
errvmass = []
if bin_type == "Mass1_Mass2": # two-d case first
for j,mc1 in enumerate(mass_bins.centres()[0]):
for k,mc2 in enumerate(mass_bins.centres()[1]):
# filter out injections not in this mass bin
newfound = filter_injections_by_mass(found, mass_bins, j, bin_type, k)
newmissed = filter_injections_by_mass(missed, mass_bins, j, bin_type, k)
foundArray[(mc1,mc2)] = len(newfound)
missedArray[(mc1,mc2)] = len(newmissed)
# compute the volume using this injection set
meaneff, efferr, meanvol, volerr = mean_efficiency_volume(newfound, newmissed, dbins)
effvmass.append(meaneff)
errvmass.append(efferr)
if mc_unbinned:
meanvol, volerr = volume_montecarlo(newfound, newmissed, distribution_param,
distribution, limits_param, max_param, min_param)
volArray[(mc1,mc2)] = meanvol
vol2Array[(mc1,mc2)] = volerr
return volArray, vol2Array, foundArray, missedArray, effvmass, errvmass
for j,mc in enumerate(mass_bins.centres()[0]):
# filter out injections not in this mass bin
newfound = filter_injections_by_mass(found, mass_bins, j, bin_type)
newmissed = filter_injections_by_mass(missed, mass_bins, j, bin_type)
foundArray[(mc,)] = len(newfound)
missedArray[(mc,)] = len(newmissed)
# compute the volume using this injection set
meaneff, efferr, meanvol, volerr = mean_efficiency_volume(newfound, newmissed, dbins)
effvmass.append(meaneff)
errvmass.append(efferr)
# if the unbinned MC calculation is available, do it
if mc_unbinned:
meanvol, volerr = volume_montecarlo(newfound, newmissed, distribution_param,
distribution, limits_param, max_param, min_param)
volArray[(mc,)] = meanvol
vol2Array[(mc,)] = volerr
return volArray, vol2Array, foundArray, missedArray, effvmass, errvmass
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_num = rate.BinnedArray(bins)
efficiency_den = rate.BinnedArray(bins)
for sim in self.found:
efficiency_num[sim.amplitude,] += 1
for sim in self.all:
efficiency_den[sim.amplitude,] += 1
# 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_array(efficiency_num, windowfunc)
rate.filter_binned_array(efficiency_den, windowfunc)
# regularize: adjust unused bins so that the efficiency is
# 0, not NaN
assert (efficiency_num <= efficiency_den).all()
efficiency_den[efficiency_num == 0 & efficiency_den == 0] = 1
line1, A50, A50_err = render_data_from_bins(file("string_efficiency.dat", "w"), axes, efficiency_num, efficiency_den, 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,
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
options, filenames = parse_command_line() # # ============================================================================= # # Custom SnglBurstTable append() method to put triggers directly into bins # # ============================================================================= # nbins = int(float(abs(options.read_segment)) / options.window) * bins_per_filterwidth binning = rate.NDBins((rate.LinearBins(options.read_segment[0], options.read_segment[1], nbins),)) trigger_rate = rate.BinnedArray(binning) num_triggers = 0 def snglburst_append(self, row, verbose = options.verbose): global num_triggers, rate t = row.peak if t in options.read_segment: trigger_rate[t,] += 1.0 num_triggers += 1 if verbose and not (num_triggers % 125): print >>sys.stderr, "sngl_burst rows read: %d\r" % num_triggers, lsctables.SnglBurstTable.append = snglburst_append
def __init__(self, binning):
self.found = 0
self.offsets = rate.BinnedArray(binning)
def compute_volume_vs_mass(found,
missed,
mass_bins,
bin_type,
dbins=None,
ploteff=False):
"""
Compute the average luminosity an experiment was sensitive to given the sets
of found and missed injections and assuming luminosity is unformly distributed
in space.
"""
# mean and std estimate for luminosity (in L10s)
volArray = rate.BinnedArray(mass_bins)
vol2Array = rate.BinnedArray(mass_bins)
# found/missed stats
foundArray = rate.BinnedArray(mass_bins)
missedArray = rate.BinnedArray(mass_bins)
#
# compute the mean luminosity in each mass bin
#
effvmass = []
errvmass = []
if bin_type == "Mass1_Mass2": # two-d case first
for j, mc1 in enumerate(mass_bins.centres()[0]):
for k, mc2 in enumerate(mass_bins.centres()[1]):
newfound = filter_injections_by_mass(found, mass_bins, j,
bin_type, k)
newmissed = filter_injections_by_mass(missed, mass_bins, j,
bin_type, k)
foundArray[(mc1, mc2)] = len(newfound)
missedArray[(mc1, mc2)] = len(newmissed)
# compute the volume using this injection set
meaneff, efferr, meanvol, volerr = mean_efficiency_volume(
newfound, newmissed, dbins)
effvmass.append(meaneff)
errvmass.append(efferr)
volArray[(mc1, mc2)] = meanvol
vol2Array[(mc1, mc2)] = volerr
return volArray, vol2Array, foundArray, missedArray, effvmass, errvmass
for j, mc in enumerate(mass_bins.centres()[0]):
# filter out injections not in this mass bin
newfound = filter_injections_by_mass(found, mass_bins, j, bin_type)
newmissed = filter_injections_by_mass(missed, mass_bins, j, bin_type)
foundArray[(mc, )] = len(newfound)
missedArray[(mc, )] = len(newmissed)
# compute the volume using this injection set
meaneff, efferr, meanvol, volerr = mean_efficiency_volume(
newfound, newmissed, dbins)
effvmass.append(meaneff)
errvmass.append(efferr)
volArray[(mc, )] = meanvol
vol2Array[(mc, )] = volerr
return volArray, vol2Array, foundArray, missedArray, effvmass, errvmass
