def compute_deficit(self, func=lambda (s,t): abs(1-(t/s)**2)**(1/2)):
"""
Calculate the deficit of thise NoiseBudget as the normalised quadrature
difference ratio the sum of the NoiseTerms and the target. Defaults to:
deficit = $\sqrt{1 - \left(\frac{target}{sum of terms}\right)^2}
Keyword arguments:
func : callable
any callable function that accepts noise sum and target arrays
"""
if self.target is None:
raise RuntimeError("NoiseBudget target has not been set. "+\
"Run NoiseBudget.set_target(mytarget).")
if self.noise_sum is None:
raise RuntimeError("NoiseBudget sum has not be computed. "+\
"Run NoiseBudget.compute_noise_sum().")
deficit = NoiseTerm('%d budget deficit' % self.name)
deficit.epoch = self.target.epoch
deficit.deltaF = self.target.deltaF
deficit.f0 = self.target.f0
data = func(self.noise_sum.spectrum, self.target.spectrum)
deficit.frequencyseries = seriesutils.fromarray(data,\
name=deficit.name,\
epoch=deficit.epoch,\
deltaT=deficit.deltaF,\
f0=deficit.f0,\
frequencyseries=True)
return deficit
def loadreference(self, referencefile, epoch=seriesutils.lal.LIGOTimeGPS(),\
sampleUnits=seriesutils.lal.lalStrainUnit, fcol=0,acol=1):
"""
Read the reference spectrum from a two-column file of frequency and ASD.
Arguments:
referencefile : str
path to file with space-delimited columns of frequency and ASD
Keyword arguments:
epoch : lal.LIGOTimeGPS
GPS epoch of this reference
sampleUnits : lal.LALUnit
amplitude unit for reference spectrum
fcol : int
number of column holding frequency array, default 0
acol : int
number of column holding amplitude array, default 1
"""
f, data = numpy.loadtxt(referencefile, dtype=float, unpack=True,\
usecols=[fcol, acol])
deltaF = f[1]-f[0]
f0 = f[0]
self.ref_frequencyseries = seriesutils.fromarray(data, str(self.name),
epoch=epoch,\
deltaT=deltaF, f0=f0,\
frequencyseries=True)
self.ref_spectrum = data
self.ref_f0 = f0
self.ref_deltaF = deltaF
def fetch_raw_data(channel, start, end):
from pylal import seriesutils
from lal import LIGOTimeGPS
cache = get_cache(IFO_TO_FRAMETYPE[channel.ifo])
try:
data = cache.fetch("{0.ifo}:{0.subsystem}_{0.name}".format(channel),
start, end)
dt = data.metadata.dt
except ValueError:
print "WARNING: raw data missing for {0}-{1} on {2}"\
.format(start, end, channel)
data = np.zeros(NUM_SAMPLES, dtype=np.float64)
dt = 1.0/SAMPLING_RATE
return seriesutils.fromarray(data,
epoch=LIGOTimeGPS(start),
deltaT=dt)
def create_h5_data(coinc, group):
#defining important variables
file_name = str(coinc["id"])+".cache"
start_time = coinc['times'][0]-2
end_time = coinc['times'][0]+2
freq = coinc['freqs'][0]
channels = group.channels
#temporary file, will create better file naming system soon
name=RAW_DIR+str(coinc["id"])+'.h5'
try:
os.remove(name)
except OSError:
pass
#open h5 file
with hdf5.write_h5(name) as h5:
#for each channel, generate bandpassed data
for channel in channels:
print coinc
channel_name=channel[1]+':'+channel[2]+'_'+channel[3]
f=open(RAW_DIR+file_name)
framedata = frutils.FrameCache(lal.Cache.fromfile(f),verbose=False).fetch(channel_name, start_time, end_time)
f.close()
data = np.array(framedata)
deltaT=framedata.metadata.dt
timeseries = seriesutils.fromarray(data, deltaT=framedata.metadata.dt)
seriesutils.bandpass(timeseries,channel['weighted_freqs'][0],channel['weighted_freqs'][1])
band_passed_data=timeseries.data.data
times = np.arange(start_time, end_time, deltaT)
#send bandpassed data to a dataset in the h5 file.
table = h5.create_dataset(name=str(channel[0]),
data=band_passed_data)
table.attrs.start_time = start_time
table.attrs.end_time = end_time
table.attrs.sample_rate=int(1.0/deltaT)
def compute_noise_sum(self, name="Noise sum"):
"""
Calculate the quadrature sum of terms in the NoiseBudget. All terms whose
sum attribute evaluates to True will be included.
"""
# get metadata from first sum term
sum_terms = [t for t in self if t.sum]
if len(sum_terms):
t = sum_terms[0]
epoch = t.epoch
deltaF = t.deltaF
f0 = t.f0
else:
raise RuntimeError("No NoiseTerms were set to be included in the "+\
"budget sum.")
self.noise_sum = NoiseTerm(name=name)
data = (numpy.asarray([t.spectrum for t in\
sum_terms])**2).sum(axis=0)**(1/2)
self.noise_sum.frequencyseries =\
seriesutils.fromarray(data, str(name), epoch=epoch,\
deltaT=deltaF, f0=f0, frequencyseries=True)
self.noise_sum.fromarray