def do_summary_table(xmldoc, sim_tree, liv_tree):
try:
search_summary = lsctables.SearchSummaryTable.get_table(xmldoc)
except ValueError:
search_summary = lsctables.New(lsctables.SearchSummaryTable, [
"process_id", "nevents", "ifos", "comment", "in_start_time",
"in_start_time_ns", "out_start_time", "out_start_time_ns",
"in_end_time", "in_end_time_ns", "out_end_time", "out_end_time_ns"
])
xmldoc.childNodes[0].appendChild(search_summary)
process_id_type = lsctables.ProcessID
runids = set()
for i in range(0, sim_tree.GetEntries()):
sim_tree.GetEntry(i)
# Id for the run processed by WaveBurst -> process ID
if sim_tree.run in runids:
continue
row = search_summary.RowType()
row.process_id = process_id_type(sim_tree.run)
runids.add(sim_tree.run)
# Search Summary Table
# events found in the run -> nevents
setattr(row, "nevents", sim_tree.GetEntries())
# Imstruments involved in the search
row.ifos = lsctables.ifos_from_instrument_set(
get_ifos_from_index(
branch_array_to_list(sim_tree.ifo, sim_tree.ndim)))
setattr(row, "comment", "waveburst")
# Begin and end time of the segment
# TODO: This is a typical offset on either side of the job for artifacts
# It can, and probably will change in the future, and should not be hardcoded
# TODO: Make this work properly. We need a gps end from the livetime
waveoffset = 8
livetime = 600
#live_entries = live_tree.GetEntries()
# This is WAAAAAAAAAAAAAY too slow
#for l in range(0, live_entries):
#liv_tree.GetEntry(l)
#livetime = max(livetime, liv_tree.live)
#if livetime < 0:
#sys.exit("Could not find livetime, cannot fill all of summary table.")
# in -- with waveoffset
# out -- without waveoffset
row.set_in(
segments.segment(LIGOTimeGPS(sim_tree.gps - waveoffset),
LIGOTimeGPS(sim_tree.gps + livetime +
waveoffset)))
row.set_out(
segments.segment(LIGOTimeGPS(sim_tree.gps),
LIGOTimeGPS(sim_tree.gps + livetime)))
search_summary.append(row)
def get_output_cache(self):
"""
Returns a LAL cache of the output file name. Calling this
method also induces the output name to get set, so it must
be at least once.
"""
if not self.output_cache:
self.output_cache = [
CacheEntry(
self.get_ifo(), self.__usertag,
segments.segment(LIGOTimeGPS(self.get_start()),
LIGOTimeGPS(self.get_end())),
"file://localhost" + os.path.abspath(self.get_output()))
]
return self.output_cache
def get_sngl_burst_row(self, sngl_burst_table, sim_tree, d):
"""
Fill in a sngl_burst row for a cwb event
"""
row = sngl_burst_table.RowType()
row.search = u"waveburst"
# Interferometer name -> ifo
row.ifo = get_ifos_from_index(sim_tree.ifo[d])
# Timing
peak = LIGOTimeGPS(sim_tree.time[d])
seg = segments.segment(LIGOTimeGPS(sim_tree.start[d]),
LIGOTimeGPS(sim_tree.stop[d]))
# Central time in the detector -> cent_time
row.set_peak(peak)
# Start time in the detector -> end_time
row.set_start(seg[0])
# Stop time in the detector -> star_time
row.set_stop(seg[1])
# Event duration
row.duration = abs(seg)
# TODO: Make sure this is right = Time lag used to shift detector -> lag
row.time_lag = sim_tree.lag[d]
# Frequency
# Central frequency in the detector -> frequency
row.peak_frequency = sim_tree.frequency[d]
try:
# Low frequency of the event in the detector -> flow
row.flow = sim_tree.low[d]
# High frequency of the event in the detector -> fhigh
row.fhigh = sim_tree.high[d]
except TypeError:
row.flow = sim_tree.low
row.fhigh = sim_tree.high
# Bandwidth
row.bandwidth = sim_tree.bandwidth[d]
# Shape
# number of pizels on the TF plane -> tfsize
row.tfvolume = sim_tree.size[d]
# Energy
# energy / noise variance -> snr
row.snr = sim_tree.snr[d]**(1. / 2.)
# TODO: What to do with this? GW strain
#row.strain = sim_tree.strain[d])
# h _ root square sum
row.hrss = sim_tree.hrss[d]
return row
def get_multi_burst_row(self, multi_burst_table, sim_tree):
"""
Fill in a multi_burst row for a cwb event -- these should exactly correlate to the sngl_burst events within a single coherent event
"""
row = multi_burst_table.RowType()
# TODO : Script chokes on the line below: we need a better way to handle this anyway
#row.set_peak(sum(LIGOTimeGPS(t) for t in sim_tree.time[0:3]) / int(sim_tree.ndim))
row.set_peak(LIGOTimeGPS(sim_tree.time[0]))
return row
def get_output(self):
"""
Returns the file name of output from the ring code. This must be kept
synchronized with the name of the output file in ring.c.
"""
if self._AnalysisNode__output is None:
if None in (self.get_start(), self.get_end(), self.get_ifo(),
self.__usertag):
raise ValueError, "start time, end time, ifo, or user tag has not been set"
seg = segments.segment(LIGOTimeGPS(self.get_start()),
LIGOTimeGPS(self.get_end()))
self.set_output(
os.path.join(
self.output_dir, "%s-STRINGSEARCH_%s-%d-%d.xml.gz" %
(self.get_ifo(), self.__usertag, int(self.get_start()),
int(self.get_end()) - int(self.get_start()))))
return self._AnalysisNode__output
def create_xml(ts_data,psd_segment_length,window_fraction,event_list,station,setname="MagneticFields"):
__program__ = 'pyburst_excesspower'
start_time = LIGOTimeGPS(int(ts_data.start_time))
end_time = LIGOTimeGPS(int(ts_data.end_time))
inseg = segment(start_time,end_time)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
ifo = 'H1'#channel_name.split(":")[0]
straindict = psd.insert_psd_option_group.__dict__
proc_row = register_to_xmldoc(xmldoc, __program__,straindict, ifos=[ifo],version=git_version.id, cvs_repository=git_version.branch, cvs_entry_time=git_version.date)
outseg = determine_output_segment(inseg, psd_segment_length, ts_data.sample_rate, window_fraction)
ss = append_search_summary(xmldoc, proc_row, ifos=(station,), inseg=inseg, outseg=outseg)
for sb in event_list:
sb.process_id = proc_row.process_id
sb.search = proc_row.program
sb.ifo, sb.channel = station, setname
xmldoc.childNodes[0].appendChild(event_list)
fname = make_filename(station, inseg)
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
def get_sngl_burst_row(sngl_burst_table, sim_tree, d):
row = sngl_burst_table.RowType()
setattr(row, "search", "waveburst")
# Interferometer name -> ifo
setattr(row, "ifo", get_ifos_from_index(sim_tree.ifo[d]))
# Timing
peak = LIGOTimeGPS(sim_tree.time[d])
seg = segments.segment(LIGOTimeGPS(sim_tree.start[d]),
LIGOTimeGPS(sim_tree.stop[d]))
# Central time in the detector -> cent_time
row.set_peak(peak)
# Start time in the detector -> end_time
row.set_start(seg[0])
# Stop time in the detector -> star_time
row.set_stop(seg[1])
# Event duration
row.duration = abs(seg)
# TODO: Make sure this is right = Time lag used to shift detector -> lag
setattr(row, "time_lag", sim_tree.lag[d])
# Frequency
# Central frequency in the detector -> frequency
setattr(row, "peak_frequency", sim_tree.frequency[d])
# Low frequency of the event in the detector -> flow
setattr(row, "flow", sim_tree.low[d])
# High frequency of the event in the detector -> fhigh
setattr(row, "fhigh", sim_tree.high[d])
# Bandwidth
setattr(row, "bandwidth", sim_tree.bandwidth[d])
# Shape
# number of pizels on the TF plane -> tfsize
setattr(row, "tfvolume", sim_tree.size[d])
# Energy
# energy / noise variance -> snr
setattr(row, "snr", sim_tree.snr[d])
# TODO: What to do with this? GW strain
#setattr(row, "strain", sim_tree.strain[d])
# h _ root square sum
setattr(row, "hrss", sim_tree.hrss[d])
return row
def segmentlist_from_hdf5(f, name=None, gpstype=LIGOTimeGPS):
"""Read a `SegmentList` object from an HDF5 file or group.
"""
h5file = open_hdf5(f)
try:
# find dataset
if isinstance(h5file, h5py.Dataset):
dataset = h5file
else:
dataset = h5file[name]
try:
data = dataset[()]
except ValueError:
data = []
out = SegmentList()
for row in data:
row = map(int, row)
# extract as LIGOTimeGPS
start = LIGOTimeGPS(*row[:2])
end = LIGOTimeGPS(*row[2:])
# convert to user type
try:
start = gpstype(start)
except TypeError:
start = gpstype(float(start))
try:
end = gpstype(end)
except TypeError:
end = gpstype(float(end))
out.append(Segment(start, end))
finally:
if not isinstance(f, (h5py.Dataset, h5py.Group)):
h5file.close()
return out
def trigger_list_from_map(tfmap, event_list, threshold, start_time, start_freq, duration, band, df, dt, psd=None):
# FIXME: If we don't convert this the calculation takes forever ---
# but we should convert it once and handle deltaF better later
if psd is not None:
npy_psd = psd.numpy()
start_time = LIGOTimeGPS(float(start_time))
ndof = 2 * duration * band
for i, j in zip(*numpy.where(tfmap > threshold)):
event = event_list.RowType()
# The points are summed forward in time and thus a `summed point' is the
# sum of the previous N points. If this point is above threshold, it
# corresponds to a tile which spans the previous N points. However, the
# 0th point (due to the convolution specifier 'valid') is actually
# already a duration from the start time. All of this means, the +
# duration and the - duration cancels, and the tile 'start' is, by
# definition, the start of the time frequency map if j = 0
# FIXME: I think this needs a + dt/2 to center the tile properly
event.set_start(start_time + float(j * dt))
event.set_stop(start_time + float(j * dt) + duration)
event.set_peak(event.get_start() + duration / 2)
event.central_freq = start_freq + i * df + 0.5 * band
event.duration = duration
event.bandwidth = band
event.chisq_dof = ndof
event.snr = math.sqrt(tfmap[i,j] / event.chisq_dof - 1)
# FIXME: Magic number 0.62 should be determine empircally
event.confidence = -lal.LogChisqCCDF(event.snr * 0.62, event.chisq_dof * 0.62)
if psd is not None:
# NOTE: I think the pycbc PSDs always start at 0 Hz --- check
psd_idx_min = int((event.central_freq - event.bandwidth / 2) / psd.delta_f)
psd_idx_max = int((event.central_freq + event.bandwidth / 2) / psd.delta_f)
# FIXME: heuristically this works better with E - D -- it's all
# going away with the better h_rss calculation soon anyway
event.amplitude = measure_hrss_poorly(tfmap[i,j] - event.chisq_dof, npy_psd[psd_idx_min:psd_idx_max])
else:
event.amplitude = None
event.process_id = None
event.event_id = event_list.get_next_id()
event_list.append(event)
def read_hveto_triggers(f, columns=HVETO_COLUMNS, filt=None, nproc=1):
"""Read a `SnglBurstTable` of triggers from an Hveto txt file.
"""
# allow multiprocessing
if nproc != 1:
from gwpy.table.io.cache import read_cache
return read_cache(f,
lsctables.SnglBurstTable.tableName,
columns=columns,
nproc=nproc,
format='hveto')
# format list of files
if isinstance(f, CacheEntry):
files = [f.path]
elif isinstance(f, (str, unicode)) and f.endswith(('.cache', '.lcf')):
files = open_cache(f).pfnlist()
elif isinstance(f, (str, unicode)):
files = f.split(',')
elif isinstance(f, Cache):
files = f.pfnlist()
else:
files = list(f)
# generate output
out = lsctables.New(lsctables.SnglBurstTable, columns=columns)
append = out.append
# iterate over files
for f in files:
trigs = loadtxt(f, dtype=float)
for t, f, snr in trigs:
b = lsctables.SnglBurst()
b.set_peak(LIGOTimeGPS(float(t)))
b.peak_frequency = f
b.snr = snr
if filt is None or filt(b):
append(b)
return out
def excess_power2(
ts_data, # Time series from magnetic field data
psd_segment_length, # Length of each segment in seconds
psd_segment_stride, # Separation between 2 consecutive segments in seconds
psd_estimation, # Average method
window_fraction, # Withening window fraction
tile_fap, # Tile false alarm probability threshold in Gaussian noise.
station, # Station
nchans=None, # Total number of channels
band=None, # Channel bandwidth
fmin=0, # Lowest frequency of the filter bank.
fmax=None, # Highest frequency of the filter bank.
max_duration=None, # Maximum duration of the tile
wtype='tukey'): # Whitening type, can tukey or hann
"""
Perform excess-power search analysis on magnetic field data.
This method will produce a bunch of time-frequency plots for every
tile duration and bandwidth analysed as well as a XML file identifying
all the triggers found in the selected data within the user-defined
time range.
Parameters
----------
ts_data : TimeSeries
Time Series from magnetic field data
psd_segment_length : float
Length of each segment in seconds
psd_segment_stride : float
Separation between 2 consecutive segments in seconds
psd_estimation : string
Average method
window_fraction : float
Withening window fraction
tile_fap : float
Tile false alarm probability threshold in Gaussian noise.
nchans : int
Total number of channels
band : float
Channel bandwidth
fmin : float
Lowest frequency of the filter bank.
fmax : float
Highest frequency of the filter bank
"""
# Determine sampling rate based on extracted time series
sample_rate = ts_data.sample_rate
# Check if tile maximum frequency is not defined
if fmax is None or fmax > sample_rate / 2.:
# Set the tile maximum frequency equal to the Nyquist frequency
# (i.e. half the sampling rate)
fmax = sample_rate / 2.0
# Check whether or not tile bandwidth and channel are defined
if band is None and nchans is None:
# Exit program with error message
exit("Either bandwidth or number of channels must be specified...")
else:
# Check if tile maximum frequency larger than its minimum frequency
assert fmax >= fmin
# Define spectral band of data
data_band = fmax - fmin
# Check whether tile bandwidth or channel is defined
if band is not None:
# Define number of possible filter bands
nchans = int(data_band / band) - 1
elif nchans is not None:
# Define filter bandwidth
band = data_band / nchans
nchans = nchans - 1
# Check if number of channels is superior than unity
assert nchans > 1
# Print segment information
print '|- Estimating PSD from segments of time',
print '%.2f s in length, with %.2f s stride...' % (psd_segment_length,
psd_segment_stride)
# Convert time series as array of float
data = ts_data.astype(numpy.float64)
# Define segment length for PSD estimation in sample unit
seg_len = int(psd_segment_length * sample_rate)
# Define separation between consecutive segments in sample unit
seg_stride = int(psd_segment_stride * sample_rate)
# Calculate the overall PSD from individual PSD segments
fd_psd = psd.welch(data,
avg_method=psd_estimation,
seg_len=seg_len,
seg_stride=seg_stride)
# We need this for the SWIG functions...
lal_psd = fd_psd.lal()
# Plot the power spectral density
plot_spectrum(fd_psd)
# Create whitening window
print "|- Whitening window and spectral correlation..."
if wtype == 'hann':
window = lal.CreateHannREAL8Window(seg_len)
elif wtype == 'tukey':
window = lal.CreateTukeyREAL8Window(seg_len, window_fraction)
else:
raise ValueError("Can't handle window type %s" % wtype)
# Create FFT plan
fft_plan = lal.CreateForwardREAL8FFTPlan(len(window.data.data), 1)
# Perform two point spectral correlation
spec_corr = lal.REAL8WindowTwoPointSpectralCorrelation(window, fft_plan)
# Initialise filter bank
print "|- Create filter..."
filter_bank, fdb = [], []
# Loop for each channels
for i in range(nchans):
channel_flow = fmin + band / 2 + i * band
channel_width = band
# Create excess power filter
lal_filter = lalburst.CreateExcessPowerFilter(channel_flow,
channel_width, lal_psd,
spec_corr)
filter_bank.append(lal_filter)
fdb.append(Spectrum.from_lal(lal_filter))
# Calculate the minimum bandwidth
min_band = (len(filter_bank[0].data.data) - 1) * filter_bank[0].deltaF / 2
# Plot filter bank
plot_bank(fdb)
# Convert filter bank from frequency to time domain
print "|- Convert all the frequency domain to the time domain..."
tdb = []
# Loop for each filter's spectrum
for fdt in fdb:
zero_padded = numpy.zeros(int((fdt.f0 / fdt.df).value) + len(fdt))
st = int((fdt.f0 / fdt.df).value)
zero_padded[st:st + len(fdt)] = numpy.real_if_close(fdt.value)
n_freq = int(sample_rate / 2 / fdt.df.value) * 2
tdt = numpy.fft.irfft(zero_padded, n_freq) * math.sqrt(sample_rate)
tdt = numpy.roll(tdt, len(tdt) / 2)
tdt = TimeSeries(tdt,
name="",
epoch=fdt.epoch,
sample_rate=sample_rate)
tdb.append(tdt)
# Plot time series filter
plot_filters(tdb, fmin, band)
# Compute the renormalization for the base filters up to a given bandwidth.
mu_sq_dict = {}
# Loop through powers of 2 up to number of channels
for nc_sum in range(0, int(math.log(nchans, 2))):
nc_sum = 2**nc_sum - 1
print "|- Calculating renormalization for resolution level containing %d %fHz channels" % (
nc_sum + 1, min_band)
mu_sq = (nc_sum + 1) * numpy.array([
lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, None)
for f in filter_bank
])
# Uncomment to get all possible frequency renormalizations
#for n in xrange(nc_sum, nchans): # channel position index
for n in xrange(nc_sum, nchans, nc_sum + 1): # channel position index
for k in xrange(0, nc_sum): # channel sum index
# FIXME: We've precomputed this, so use it instead
mu_sq[n] += 2 * lalburst.ExcessPowerFilterInnerProduct(
filter_bank[n - k], filter_bank[n - 1 - k], spec_corr,
None)
#print mu_sq[nc_sum::nc_sum+1]
mu_sq_dict[nc_sum] = mu_sq
# Create an event list where all the triggers will be stored
event_list = lsctables.New(lsctables.SnglBurstTable, [
'start_time', 'start_time_ns', 'peak_time', 'peak_time_ns', 'duration',
'bandwidth', 'central_freq', 'chisq_dof', 'confidence', 'snr',
'amplitude', 'channel', 'ifo', 'process_id', 'event_id', 'search',
'stop_time', 'stop_time_ns'
])
# Create repositories to save TF and time series plots
os.system('mkdir -p segments/time-frequency')
os.system('mkdir -p segments/time-series')
# Define time edges
t_idx_min, t_idx_max = 0, seg_len
while t_idx_max <= len(ts_data):
# Define starting and ending time of the segment in seconds
start_time = ts_data.start_time + t_idx_min / float(
ts_data.sample_rate)
end_time = ts_data.start_time + t_idx_max / float(ts_data.sample_rate)
print "\n|-- Analyzing block %i to %i (%.2f percent)" % (
start_time, end_time, 100 * float(t_idx_max) / len(ts_data))
# Model a withen time series for the block
tmp_ts_data = types.TimeSeries(ts_data[t_idx_min:t_idx_max] *
window.data.data,
delta_t=1. / ts_data.sample_rate,
epoch=start_time)
# Save time series in relevant repository
segfolder = 'segments/%i-%i' % (start_time, end_time)
os.system('mkdir -p ' + segfolder)
plot_ts(tmp_ts_data,
fname='segments/time-series/%i-%i.png' %
(start_time, end_time))
# Convert times series to frequency series
fs_data = tmp_ts_data.to_frequencyseries()
print "|-- Frequency series data has variance: %s" % fs_data.data.std(
)**2
# Whitening (FIXME: Whiten the filters, not the data)
fs_data.data /= numpy.sqrt(fd_psd) / numpy.sqrt(2 * fd_psd.delta_f)
print "|-- Whitened frequency series data has variance: %s" % fs_data.data.std(
)**2
print "|-- Create time-frequency plane for current block"
# Return the complex snr, along with its associated normalization of the template,
# matched filtered against the data
#filter.matched_filter_core(types.FrequencySeries(tmp_filter_bank,delta_f=fd_psd.delta_f),
# fs_data,h_norm=1,psd=fd_psd,low_frequency_cutoff=filter_bank[0].f0,
# high_frequency_cutoff=filter_bank[0].f0+2*band)
print "|-- Filtering all %d channels..." % nchans
# Initialise 2D zero array
tmp_filter_bank = numpy.zeros(len(fd_psd), dtype=numpy.complex128)
# Initialise 2D zero array for time-frequency map
tf_map = numpy.zeros((nchans, seg_len), dtype=numpy.complex128)
# Loop over all the channels
for i in range(nchans):
# Reset filter bank series
tmp_filter_bank *= 0.0
# Index of starting frequency
f1 = int(filter_bank[i].f0 / fd_psd.delta_f)
# Index of ending frequency
f2 = int((filter_bank[i].f0 + 2 * band) / fd_psd.delta_f) + 1
# (FIXME: Why is there a factor of 2 here?)
tmp_filter_bank[f1:f2] = filter_bank[i].data.data * 2
# Define the template to filter the frequency series with
template = types.FrequencySeries(tmp_filter_bank,
delta_f=fd_psd.delta_f,
copy=False)
# Create filtered series
filtered_series = filter.matched_filter_core(
template,
fs_data,
h_norm=None,
psd=None,
low_frequency_cutoff=filter_bank[i].f0,
high_frequency_cutoff=filter_bank[i].f0 + 2 * band)
# Include filtered series in the map
tf_map[i, :] = filtered_series[0].numpy()
# Plot spectrogram
plot_spectrogram(numpy.abs(tf_map).T,
tmp_ts_data.delta_t,
band,
ts_data.sample_rate,
start_time,
end_time,
fname='segments/time-frequency/%i-%i.png' %
(start_time, end_time))
# Loop through all summed channels
for nc_sum in range(0, int(math.log(nchans, 2)))[::-1]:
nc_sum = 2**nc_sum - 1
mu_sq = mu_sq_dict[nc_sum]
# Clip the boundaries to remove window corruption
clip_samples = int(psd_segment_length * window_fraction *
ts_data.sample_rate / 2)
# Constructing tile and calculate their energy
print "\n|--- Constructing tile with %d summed channels..." % (
nc_sum + 1)
# Current bandwidth of the time-frequency map tiles
df = band * (nc_sum + 1)
dt = 1.0 / (2 * df)
# How much each "step" is in the time domain -- under sampling rate
us_rate = int(round(dt / ts_data.delta_t))
print "|--- Undersampling rate for this level: %f" % (
ts_data.sample_rate / us_rate)
print "|--- Calculating tiles..."
# Making independent tiles
# because [0:-0] does not give the full array
tf_map_temp = tf_map[:,clip_samples:-clip_samples:us_rate] \
if clip_samples > 0 else tf_map[:,::us_rate]
tiles = tf_map_temp.copy()
# Here's the deal: we're going to keep only the valid output and
# it's *always* going to exist in the lowest available indices
stride = nc_sum + 1
for i in xrange(tiles.shape[0] / stride):
numpy.absolute(tiles[stride * i:stride * (i + 1)].sum(axis=0),
tiles[stride * (i + 1) - 1])
tiles = tiles[nc_sum::nc_sum + 1].real**2 / mu_sq[nc_sum::nc_sum +
1].reshape(
-1, 1)
print "|--- TF-plane is %dx%s samples" % tiles.shape
print "|--- Tile energy mean %f, var %f" % (numpy.mean(tiles),
numpy.var(tiles))
# Define maximum number of degrees of freedom and check it larger or equal to 2
max_dof = 32 if max_duration == None else 2 * max_duration * df
assert max_dof >= 2
# Loop through multiple degrees of freedom
for j in [2**l for l in xrange(0, int(math.log(max_dof, 2)))]:
# Duration is fixed by the NDOF and bandwidth
duration = j * dt
print "\n|----- Explore signal duration of %f s..." % duration
print "|----- Summing DOF = %d ..." % (2 * j)
tlen = tiles.shape[1] - 2 * j + 1 + 1
dof_tiles = numpy.zeros((tiles.shape[0], tlen))
sum_filter = numpy.array([1, 0] * (j - 1) + [1])
for f in range(tiles.shape[0]):
# Sum and drop correlate tiles
dof_tiles[f] = fftconvolve(tiles[f], sum_filter, 'valid')
print "|----- Summed tile energy mean: %f, var %f" % (
numpy.mean(dof_tiles), numpy.var(dof_tiles))
plot_spectrogram(
dof_tiles.T,
dt,
df,
ts_data.sample_rate,
start_time,
end_time,
fname='segments/%i-%i/tf_%02ichans_%02idof.png' %
(start_time, end_time, nc_sum + 1, 2 * j))
threshold = scipy.stats.chi2.isf(tile_fap, j)
print "|------ Threshold for this level: %f" % threshold
spant, spanf = dof_tiles.shape[1] * dt, dof_tiles.shape[0] * df
print "|------ Processing %.2fx%.2f time-frequency map." % (
spant, spanf)
# Since we clip the data, the start time needs to be adjusted accordingly
window_offset_epoch = fs_data.epoch + psd_segment_length * window_fraction / 2
window_offset_epoch = LIGOTimeGPS(float(window_offset_epoch))
for i, j in zip(*numpy.where(dof_tiles > threshold)):
event = event_list.RowType()
# The points are summed forward in time and thus a `summed point' is the
# sum of the previous N points. If this point is above threshold, it
# corresponds to a tile which spans the previous N points. However, the
# 0th point (due to the convolution specifier 'valid') is actually
# already a duration from the start time. All of this means, the +
# duration and the - duration cancels, and the tile 'start' is, by
# definition, the start of the time frequency map if j = 0
# FIXME: I think this needs a + dt/2 to center the tile properly
event.set_start(window_offset_epoch + float(j * dt))
event.set_stop(window_offset_epoch + float(j * dt) +
duration)
event.set_peak(event.get_start() + duration / 2)
event.central_freq = filter_bank[
0].f0 + band / 2 + i * df + 0.5 * df
event.duration = duration
event.bandwidth = df
event.chisq_dof = 2 * duration * df
event.snr = math.sqrt(dof_tiles[i, j] / event.chisq_dof -
1)
# FIXME: Magic number 0.62 should be determine empircally
event.confidence = -lal.LogChisqCCDF(
event.snr * 0.62, event.chisq_dof * 0.62)
event.amplitude = None
event.process_id = None
event.event_id = event_list.get_next_id()
event_list.append(event)
for event in event_list[::-1]:
if event.amplitude != None:
continue
etime_min_idx = float(event.get_start()) - float(
fs_data.epoch)
etime_min_idx = int(etime_min_idx / tmp_ts_data.delta_t)
etime_max_idx = float(event.get_start()) - float(
fs_data.epoch) + event.duration
etime_max_idx = int(etime_max_idx / tmp_ts_data.delta_t)
# (band / 2) to account for sin^2 wings from finest filters
flow_idx = int((event.central_freq - event.bandwidth / 2 -
(df / 2) - fmin) / df)
fhigh_idx = int((event.central_freq + event.bandwidth / 2 +
(df / 2) - fmin) / df)
# TODO: Check that the undersampling rate is always commensurate
# with the indexing: that is to say that
# mod(etime_min_idx, us_rate) == 0 always
z_j_b = tf_map[flow_idx:fhigh_idx,
etime_min_idx:etime_max_idx:us_rate]
event.amplitude = 0
print "|------ Total number of events: %d" % len(event_list)
t_idx_min += int(seg_len * (1 - window_fraction))
t_idx_max += int(seg_len * (1 - window_fraction))
setname = "MagneticFields"
__program__ = 'pyburst_excesspower'
start_time = LIGOTimeGPS(int(ts_data.start_time))
end_time = LIGOTimeGPS(int(ts_data.end_time))
inseg = segment(start_time, end_time)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
ifo = 'H1' #channel_name.split(":")[0]
straindict = psd.insert_psd_option_group.__dict__
proc_row = register_to_xmldoc(xmldoc,
__program__,
straindict,
ifos=[ifo],
version=git_version.id,
cvs_repository=git_version.branch,
cvs_entry_time=git_version.date)
dt_stride = psd_segment_length
sample_rate = ts_data.sample_rate
# Amount to overlap successive blocks so as not to lose data
window_overlap_samples = window_fraction * sample_rate
outseg = inseg.contract(window_fraction * dt_stride / 2)
# With a given dt_stride, we cannot process the remainder of this data
remainder = math.fmod(abs(outseg), dt_stride * (1 - window_fraction))
# ...so make an accounting of it
outseg = segment(outseg[0], outseg[1] - remainder)
ss = append_search_summary(xmldoc,
proc_row,
ifos=(station, ),
inseg=inseg,
outseg=outseg)
for sb in event_list:
sb.process_id = proc_row.process_id
sb.search = proc_row.program
sb.ifo, sb.channel = station, setname
xmldoc.childNodes[0].appendChild(event_list)
fname = 'excesspower.xml.gz'
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
else:
seglist.extend(dqsegs.threshold_data_to_seglist(data, start, dt, min_threshold=min_threshold, max_threshold=max_threshold, invert=invert))
seglist.coalesce()
all_segs["%s: %s" % (channel, str(opthresholds))] = seglist
if opts.verbose:
print "Summary of segments for %s %s %s %s" % (inst, channel_name, op, str(threshold))
int_time = reduce(float.__add__, [float(abs(seg & s)) for s in seglist], 0.0)
print "Total time covered from cache: %f" % int_time
for i, seg in enumerate(seglist):
#
# Segments are expected in LIGOTimeGPS format
#
seglist[i] = segment(LIGOTimeGPS(seg[0]), LIGOTimeGPS(seg[1]))
if opts.verbose:
print "\t" + str(seg)
#
# Fill in some metadata about the flags
#
name = "detchar %s threshold flags" % channel
comment = " ".join(["%s %s %s" % (channel, stringify(op), str(v)) for op, v in opthresholds])
lwsegs.insert_from_segmentlistdict(segmentlistdict({channel[:2]: seglist}), name=name, comment=comment)
#
# After recording segments, one can take the intersection (all must be on) or
# union (any can be on)
#
# Possible enhancement: instead of giving all keys, give a user selection
def fake_trigger_generator(instrument='H1'):
"""
Generate fake trigger maps.
Parameters
----------
instrument : str
Instrument name
"""
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
# Process information
proc = process.append_process(xmldoc, "fake_search")
process.append_process_params(xmldoc, proc, {})
t0 = 1e9
ntrig = 1000
ifo = instrument
inseg = segment(LIGOTimeGPS(t0), LIGOTimeGPS(t0 + ntrig / 10))
outseg = segment(LIGOTimeGPS(t0), LIGOTimeGPS(t0 + ntrig / 10))
# Search summary
search_summary.append_search_summary(xmldoc,
proc,
comment="Fake triggers",
ifos=(ifo, ),
inseg=inseg,
outseg=outseg)
columns = [
'chisq_dof', 'bandwidth', 'central_freq', 'confidence', 'peak_time_ns',
'start_time', 'process_id', 'fhigh', 'stop_time_ns', 'channel', 'ifo',
'duration', 'event_id', 'hrss', 'stop_time', 'peak_time', 'snr',
'search', 'start_time_ns', 'flow', 'amplitude'
]
table = lsctables.New(lsctables.SnglBurstTable, columns)
# Generate uniformly distributed trigger times with approximate rate of 10 s
times = t0 + uniform.rvs(0, ntrig / 10., ntrig)
for t in times:
row = table.RowType()
# time frequency position and extent
row.chisq_dof = int(2 + expon.rvs(2))
row.duration = 1. / 2**int(uniform.rvs(0, 7))
row.bandwidth = row.chisq_dof / row.duration / 2
row.central_freq = uniform.rvs(16, 2048)
row.flow = max(row.central_freq - row.bandwidth, 0)
row.fhigh = min(row.central_freq + row.bandwidth, 2048)
ns, sec = math.modf(t)
ns = int("%09d" % (ns * 1e9))
row.peak_time, row.peak_time_ns = int(sec), ns
ns, sec = math.modf(t - row.duration / 2)
ns = int("%09d" % (ns * 1e9))
row.start_time, row.start_time_ns = int(sec), ns
ns, sec = math.modf(t + row.duration / 2)
ns = int("%09d" % (ns * 1e9))
row.stop_time, row.stop_time_ns = int(sec), ns
# TODO: Correlate some triggers, an upward fluctuation often triggers a few
# tiles ontop of each other
# SNR and confidence
row.snr = 5.
while row.snr < 2 * row.chisq_dof:
row.snr = chi2.rvs(row.chisq_dof)
row.confidence = chi2.sf(row.snr, row.chisq_dof)
row.snr = math.sqrt(row.snr / row.chisq_dof - 1)
row.hrss = row.amplitude = 1e-21
# metadata
row.search = "fake_search"
row.channel = "FAKE"
row.ifo = ifo
row.event_id = table.get_next_id()
row.process_id = proc.process_id
table.append(row)
xmldoc.childNodes[0].appendChild(table)
utils.write_filename(xmldoc,
"%s-FAKE_SEARCH-%d-%d.xml.gz" % (ifo, int(t0), 10000),
gz=True)
__author__ = "Chris Pankow <*****@*****.**>"
def assign_id(row, i):
row.simulation_id = ilwd.ilwdchar("sim_inspiral_table:sim_inspiral:%d" % i)
CMAP = {
"right_ascension": "longitude",
"longitude": "longitude",
"latitude": "latitude",
"declination": "latitude",
"inclination": "inclination",
"polarization": "polarization",
"t_ref": lambda r, t: r.set_time_geocent(LIGOTimeGPS(float(t))),
"coa_phase": "coa_phase",
"distance": "distance",
"mass1": "mass1",
"mass2": "mass2",
"lam_tilde": "psi0",
"dlam_tilde": "psi3",
"psi0": "psi0",
"psi3": "psi3",
# SHOEHORN ALERT
"sample_n": assign_id,
"alpha1": "alpha1",
"alpha2": "alpha2",
"alpha3": "alpha3",
"loglikelihood": "alpha1",
"joint_prior": "alpha2",
def do_summary_table(self, xmldoc, sim_tree):
"""
Create the search_summary table for the cWB job(s). This function exists as a backup in case no job list exists. It will try and reconstruct the job segments from the event data, but this list will be incomplete in the case where no events were found during a job.
"""
try:
search_summary = lsctables.SearchSummaryTable.get_table(xmldoc)
except ValueError:
search_summary = lsctables.New(lsctables.SearchSummaryTable, [
"process_id", "nevents", "ifos", "comment", "in_start_time",
"in_start_time_ns", "out_start_time", "out_start_time_ns",
"in_end_time", "in_end_time_ns", "out_end_time",
"out_end_time_ns"
])
xmldoc.childNodes[0].appendChild(search_summary)
process_id_type = type(
lsctables.ProcessTable.get_table(xmldoc).next_id)
runids = set()
entries = sim_tree.GetEntries()
for i in range(0, entries):
sim_tree.GetEntry(i)
if self.start != None:
if float(self.start) > sim_tree.start[0]: continue
if self.end != None:
if float(self.end) < sim_tree.stop[0]: continue
# Id for the run processed by WaveBurst -> process ID
run = sim_tree.run
if run in runids:
continue
row = search_summary.RowType()
row.process_id = process_id_type(run)
runids.add(run)
# Search Summary Table
# events found in the run -> nevents
# TODO: Destroy ROOT, because it hates me
#row.nevents = sim_tree.nevent
row.nevents = 0
# Imstruments involved in the search
ifos = lsctables.ifos_from_instrument_set(
get_ifos_from_index(
branch_array_to_list(sim_tree.ifo, sim_tree.ndim)))
# Imstruments involved in the search
if (ifos == None or len(ifos) == 0):
if (self.instruments):
ifos = self.instruments
else: # Not enough information to completely fill out the table
sys.exit(
"Found a job with no IFOs on, or not enough to determine IFOs. Try specifying instruments directly."
)
row.ifos = ifos
row.comment = "waveburst"
# Begin and end time of the segment
if self.waveoffset != None:
waveoffset = self.waveoffset
else:
waveoffset = 0
livetime = -1
# WARNING: This will fail miserably if there are no events in analyzed
# see do_search_summary_from_joblist for a better solution
livetime = sim_tree.left[0] + sim_tree.duration[
0] + sim_tree.right[0]
if livetime < 0:
print >> sys.stderr, "WARNING: Run %d will have zero livetime because no events are recorded in this run, and therefore livetime cannot be calculated." % run
# in -- with waveoffset
row.set_in(segments.segment(LIGOTimeGPS(0), LIGOTimeGPS(1)))
# out -- without waveoffset
row.set_out(segments.segment(LIGOTimeGPS(0), LIGOTimeGPS(1)))
else:
seg_start_with_offset = sim_tree.gps - waveoffset
seg_start_without_offset = sim_tree.gps
seg_end_with_offset = sim_tree.gps + waveoffset + livetime
seg_end_without_offset = sim_tree.gps + livetime
# in -- with waveoffset
row.set_in(
segments.segment(LIGOTimeGPS(seg_start_with_offset),
LIGOTimeGPS(seg_end_with_offset)))
# out -- without waveoffset
row.set_out(
segments.segment(LIGOTimeGPS(seg_start_without_offset),
LIGOTimeGPS(seg_end_without_offset)))
search_summary.append(row)
def do_summary_table_from_joblist(self, xmldoc, sim_tree):
"""
Create the search_summary table for the cWB job(s) and a provided cWB joblist. The function will try to determine the proper job intervals from the waveoffset, if specified.
"""
try:
search_summary = lsctables.SearchSummaryTable.get_table(xmldoc)
except ValueError:
search_summary = lsctables.New(lsctables.SearchSummaryTable, [
"process_id", "nevents", "ifos", "comment", "in_start_time",
"in_start_time_ns", "out_start_time", "out_start_time_ns",
"in_end_time", "in_end_time_ns", "out_end_time",
"out_end_time_ns"
])
xmldoc.childNodes[0].appendChild(search_summary)
process_id_type = type(
lsctables.ProcessTable.get_table(xmldoc).next_id)
runid = dict()
itr = 0
for line in file(self.job_list):
if line[0] == '#':
continue # skip comments
# Determine the file type
# WARNING: Mixing the two types could cause overwrite behavior
line = line.split()
if len(line) == 2: # start and stop
seg = segments.segment(map(LIGOTimeGPS, map(float, line[0:2])))
runid[itr] = seg
itr += 1
elif len(line) == 3: # index start and stop
seg = segments.segment(map(LIGOTimeGPS, map(float, line[1:3])))
runid[int(line[0])] = seg
elif len(line) == 4: # index start and stop and length
seg = segments.segment(map(LIGOTimeGPS, map(float, line[1:3])))
runid[int(line[0])] = seg
else: # dunno!
sys.exit("Unable to understand job list segment format.")
for run, seg in runid.iteritems():
if self.start != None:
if float(self.start) > seg[0]: continue
if self.end != None:
if float(self.end) < seg[1]: continue
row = search_summary.RowType()
row.process_id = process_id_type(run)
# Search Summary Table
# events found in the run -> nevents
row.nevents = 0
#entries = sim_tree.GetEntries()
# Imstruments involved in the search
sim_tree.GetEntry(0)
ifos = lsctables.ifos_from_instrument_set(
get_ifos_from_index(
branch_array_to_list(sim_tree.ifo, sim_tree.ndim)))
# Imstruments involved in the search
if (ifos == None or len(ifos) == 0):
if (self.instruments):
ifos = self.instruments
else: # Not enough information to completely fill out the table
sys.exit(
"Found a job with no IFOs on, or not enough to determine IFOs. Try specifying instruments directly."
)
row.ifos = ifos
row.comment = "waveburst"
# Begin and end time of the segment
# TODO: This is a typical offset on either side of the job for artifacts
# It can, and probably will change in the future, and shouldn't be hardcoded
#waveoffset, livetime = 8, -1
waveoffset, livetime = self.waveoffset, -1
if waveoffset == None: waveoffset = 0
livetime = abs(seg)
if livetime < 0:
print >> sys.stderr, "WARNING: Run %d will have zero livetime because no events are recorded in this run, and therefore livetime cannot be calculated." % run
# in -- with waveoffset
row.set_in(segments.segment(LIGOTimeGPS(0), LIGOTimeGPS(1)))
# out -- without waveoffset
row.set_out(segments.segment(LIGOTimeGPS(0), LIGOTimeGPS(1)))
else:
# in -- with waveoffset
row.set_in(seg)
# out -- without waveoffset
row.set_out(
segments.segment(seg[0] + waveoffset, seg[1] - waveoffset))
search_summary.append(row)
def excess_power(
ts_data, # Time series from magnetic field data
band=None, # Channel bandwidth
channel_name='channel-name', # Channel name
fmin=0, # Lowest frequency of the filter bank.
fmax=None, # Highest frequency of the filter bank.
impulse=False, # Impulse response
make_plot=True, # Condition to produce plots
max_duration=None, # Maximum duration of the tile
nchans=256, # Total number of channels
psd_estimation='median-mean', # Average method
psd_segment_length=60, # Length of each segment in seconds
psd_segment_stride=30, # Separation between 2 consecutive segments in seconds
station='station-name', # Station name
tile_fap=1e-7, # Tile false alarm probability threshold in Gaussian noise.
verbose=True, # Print details
window_fraction=0, # Withening window fraction
wtype='tukey'): # Whitening type, can tukey or hann
'''
Perform excess-power search analysis on magnetic field data.
This method will produce a bunch of time-frequency plots for every
tile duration and bandwidth analysed as well as a XML file identifying
all the triggers found in the selected data within the user-defined
time range.
Parameters
----------
ts_data : TimeSeries
Time Series from magnetic field data
psd_segment_length : float
Length of each segment in seconds
psd_segment_stride : float
Separation between 2 consecutive segments in seconds
psd_estimation : string
Average method
window_fraction : float
Withening window fraction
tile_fap : float
Tile false alarm probability threshold in Gaussian noise.
nchans : int
Total number of channels
band : float
Channel bandwidth
fmin : float
Lowest frequency of the filter bank.
fmax : float
Highest frequency of the filter bank
Examples
--------
The program can be ran as an executable by using the ``excesspower`` command
line as follows::
excesspower --station "mainz01" \\
--start-time "2017-04-15-17-1" \\
--end-time "2017-04-15-18" \\
--rep "/Users/vincent/ASTRO/data/GNOME/GNOMEDrive/gnome/serverdata/" \\
--resample 512 \\
--verbose
'''
# Determine sampling rate based on extracted time series
sample_rate = ts_data.sample_rate
# Check if tile maximum frequency is not defined
if fmax is None or fmax > sample_rate / 2.:
# Set the tile maximum frequency equal to the Nyquist frequency
# (i.e. half the sampling rate)
fmax = sample_rate / 2.0
# Check whether or not tile bandwidth and channel are defined
if band is None and nchans is None:
# Exit program with error message
exit("Either bandwidth or number of channels must be specified...")
else:
# Check if tile maximum frequency larger than its minimum frequency
assert fmax >= fmin
# Define spectral band of data
data_band = fmax - fmin
# Check whether tile bandwidth or channel is defined
if band is not None:
# Define number of possible filter bands
nchans = int(data_band / band)
elif nchans is not None:
# Define filter bandwidth
band = data_band / nchans
nchans -= 1
# Check if number of channels is superior than unity
assert nchans > 1
# Print segment information
if verbose: print '|- Estimating PSD from segments of',
if verbose:
print '%.2f s, with %.2f s stride...' % (psd_segment_length,
psd_segment_stride)
# Convert time series as array of float
data = ts_data.astype(numpy.float64)
# Define segment length for PSD estimation in sample unit
seg_len = int(psd_segment_length * sample_rate)
# Define separation between consecutive segments in sample unit
seg_stride = int(psd_segment_stride * sample_rate)
# Minimum frequency of detectable signal in a segment
delta_f = 1. / psd_segment_length
# Calculate PSD length counting the zero frequency element
fd_len = fmax / delta_f + 1
# Calculate the overall PSD from individual PSD segments
if impulse:
# Produce flat data
flat_data = numpy.ones(int(fd_len)) * 2. / fd_len
# Create PSD frequency series
fd_psd = types.FrequencySeries(flat_data, 1. / psd_segment_length,
ts_data.start_time)
else:
# Create overall PSD using Welch's method
fd_psd = psd.welch(data,
avg_method=psd_estimation,
seg_len=seg_len,
seg_stride=seg_stride)
if make_plot:
# Plot the power spectral density
plot_spectrum(fd_psd)
# We need this for the SWIG functions
lal_psd = fd_psd.lal()
# Create whitening window
if verbose: print "|- Whitening window and spectral correlation..."
if wtype == 'hann': window = lal.CreateHannREAL8Window(seg_len)
elif wtype == 'tukey':
window = lal.CreateTukeyREAL8Window(seg_len, window_fraction)
else:
raise ValueError("Can't handle window type %s" % wtype)
# Create FFT plan
fft_plan = lal.CreateForwardREAL8FFTPlan(len(window.data.data), 1)
# Perform two point spectral correlation
spec_corr = lal.REAL8WindowTwoPointSpectralCorrelation(window, fft_plan)
# Determine length of individual filters
filter_length = int(2 * band / fd_psd.delta_f) + 1
# Initialise filter bank
if verbose:
print "|- Create bank of %i filters of %i Hz bandwidth..." % (
nchans, filter_length)
# Initialise array to store filter's frequency series and metadata
lal_filters = []
# Initialise array to store filter's time series
fdb = []
# Loop over the channels
for i in range(nchans):
# Define central position of the filter
freq = fmin + band / 2 + i * band
# Create excess power filter
lal_filter = lalburst.CreateExcessPowerFilter(freq, band, lal_psd,
spec_corr)
# Testing spectral correlation on filter
#print lalburst.ExcessPowerFilterInnerProduct(lal_filter, lal_filter, spec_corr, None)
# Append entire filter structure
lal_filters.append(lal_filter)
# Append filter's spectrum
fdb.append(FrequencySeries.from_lal(lal_filter))
#print fdb[0].frequencies
#print fdb[0]
if make_plot:
# Plot filter bank
plot_bank(fdb)
# Convert filter bank from frequency to time domain
if verbose:
print "|- Convert all the frequency domain to the time domain..."
tdb = []
# Loop for each filter's spectrum
for fdt in fdb:
zero_padded = numpy.zeros(int((fdt.f0 / fdt.df).value) + len(fdt))
st = int((fdt.f0 / fdt.df).value)
zero_padded[st:st + len(fdt)] = numpy.real_if_close(fdt.value)
n_freq = int(sample_rate / 2 / fdt.df.value) * 2
tdt = numpy.fft.irfft(zero_padded, n_freq) * math.sqrt(sample_rate)
tdt = numpy.roll(tdt, len(tdt) / 2)
tdt = TimeSeries(tdt,
name="",
epoch=fdt.epoch,
sample_rate=sample_rate)
tdb.append(tdt)
# Plot time series filter
plot_filters(tdb, fmin, band)
# Computer whitened inner products of input filters with themselves
#white_filter_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, None) for f in lal_filters])
# Computer unwhitened inner products of input filters with themselves
#unwhite_filter_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f, f, spec_corr, lal_psd) for f in lal_filters])
# Computer whitened filter inner products between input adjacent filters
#white_ss_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f1, f2, spec_corr, None) for f1, f2 in zip(lal_filters[:-1], lal_filters[1:])])
# Computer unwhitened filter inner products between input adjacent filters
#unwhite_ss_ip = numpy.array([lalburst.ExcessPowerFilterInnerProduct(f1, f2, spec_corr, lal_psd) for f1, f2 in zip(lal_filters[:-1], lal_filters[1:])])
# Check filter's bandwidth is equal to user defined channel bandwidth
min_band = (len(lal_filters[0].data.data) - 1) * lal_filters[0].deltaF / 2
assert min_band == band
# Create an event list where all the triggers will be stored
event_list = lsctables.New(lsctables.SnglBurstTable, [
'start_time', 'start_time_ns', 'peak_time', 'peak_time_ns', 'duration',
'bandwidth', 'central_freq', 'chisq_dof', 'confidence', 'snr',
'amplitude', 'channel', 'ifo', 'process_id', 'event_id', 'search',
'stop_time', 'stop_time_ns'
])
# Create repositories to save TF and time series plots
os.system('mkdir -p segments/time-frequency')
os.system('mkdir -p segments/time-series')
# Define time edges
t_idx_min, t_idx_max = 0, seg_len
# Loop over each segment
while t_idx_max <= len(ts_data):
# Define first and last timestamps of the block
start_time = ts_data.start_time + t_idx_min / float(
ts_data.sample_rate)
end_time = ts_data.start_time + t_idx_max / float(ts_data.sample_rate)
if verbose:
print "\n|- Analyzing block %i to %i (%.2f percent)" % (
start_time, end_time, 100 * float(t_idx_max) / len(ts_data))
# Debug for impulse response
if impulse:
for i in range(t_idx_min, t_idx_max):
ts_data[i] = 1000. if i == (t_idx_max + t_idx_min) / 2 else 0.
# Model a withen time series for the block
tmp_ts_data = types.TimeSeries(ts_data[t_idx_min:t_idx_max] *
window.data.data,
delta_t=1. / ts_data.sample_rate,
epoch=start_time)
# Save time series in relevant repository
os.system('mkdir -p segments/%i-%i' % (start_time, end_time))
if make_plot:
# Plot time series
plot_ts(tmp_ts_data,
fname='segments/time-series/%i-%i.png' %
(start_time, end_time))
# Convert times series to frequency series
fs_data = tmp_ts_data.to_frequencyseries()
if verbose:
print "|- Frequency series data has variance: %s" % fs_data.data.std(
)**2
# Whitening (FIXME: Whiten the filters, not the data)
fs_data.data /= numpy.sqrt(fd_psd) / numpy.sqrt(2 * fd_psd.delta_f)
if verbose:
print "|- Whitened frequency series data has variance: %s" % fs_data.data.std(
)**2
if verbose: print "|- Create time-frequency plane for current block"
# Return the complex snr, along with its associated normalization of the template,
# matched filtered against the data
#filter.matched_filter_core(types.FrequencySeries(tmp_filter_bank,delta_f=fd_psd.delta_f),
# fs_data,h_norm=1,psd=fd_psd,low_frequency_cutoff=lal_filters[0].f0,
# high_frequency_cutoff=lal_filters[0].f0+2*band)
if verbose: print "|- Filtering all %d channels...\n" % nchans,
# Initialise 2D zero array
tmp_filter_bank = numpy.zeros(len(fd_psd), dtype=numpy.complex128)
# Initialise 2D zero array for time-frequency map
tf_map = numpy.zeros((nchans, seg_len), dtype=numpy.complex128)
# Loop over all the channels
for i in range(nchans):
# Reset filter bank series
tmp_filter_bank *= 0.0
# Index of starting frequency
f1 = int(lal_filters[i].f0 / fd_psd.delta_f)
# Index of last frequency bin
f2 = int((lal_filters[i].f0 + 2 * band) / fd_psd.delta_f) + 1
# (FIXME: Why is there a factor of 2 here?)
tmp_filter_bank[f1:f2] = lal_filters[i].data.data * 2
# Define the template to filter the frequency series with
template = types.FrequencySeries(tmp_filter_bank,
delta_f=fd_psd.delta_f,
copy=False)
# Create filtered series
filtered_series = filter.matched_filter_core(
template,
fs_data,
h_norm=None,
psd=None,
low_frequency_cutoff=lal_filters[i].f0,
high_frequency_cutoff=lal_filters[i].f0 + 2 * band)
# Include filtered series in the map
tf_map[i, :] = filtered_series[0].numpy()
if make_plot:
# Plot spectrogram
plot_spectrogram(numpy.abs(tf_map).T,
dt=tmp_ts_data.delta_t,
df=band,
ymax=ts_data.sample_rate / 2.,
t0=start_time,
t1=end_time,
fname='segments/time-frequency/%i-%i.png' %
(start_time, end_time))
plot_tiles_ts(numpy.abs(tf_map),
2,
1,
sample_rate=ts_data.sample_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/ts.png' %
(start_time, end_time))
#plot_tiles_tf(numpy.abs(tf_map),2,1,ymax=ts_data.sample_rate/2,
# sample_rate=ts_data.sample_rate,t0=start_time,t1=end_time,
# fname='segments/%i-%i/tf.png'%(start_time,end_time))
# Loop through powers of 2 up to number of channels
for nc_sum in range(0, int(math.log(nchans, 2)))[::-1]:
# Calculate total number of summed channels
nc_sum = 2**nc_sum
if verbose:
print "\n\t|- Contructing tiles containing %d narrow band channels" % nc_sum
# Compute full bandwidth of virtual channel
df = band * nc_sum
# Compute minimal signal's duration in virtual channel
dt = 1.0 / (2 * df)
# Compute under sampling rate
us_rate = int(round(dt / ts_data.delta_t))
if verbose:
print "\t|- Undersampling rate for this level: %f" % (
ts_data.sample_rate / us_rate)
if verbose: print "\t|- Calculating tiles..."
# Clip the boundaries to remove window corruption
clip_samples = int(psd_segment_length * window_fraction *
ts_data.sample_rate / 2)
# Undersample narrow band channel's time series
# Apply clipping condition because [0:-0] does not give the full array
tf_map_temp = tf_map[:,clip_samples:-clip_samples:us_rate] \
if clip_samples > 0 else tf_map[:,::us_rate]
# Initialise final tile time-frequency map
tiles = numpy.zeros(((nchans + 1) / nc_sum, tf_map_temp.shape[1]))
# Loop over tile index
for i in xrange(len(tiles)):
# Sum all inner narrow band channels
ts_tile = numpy.absolute(tf_map_temp[nc_sum * i:nc_sum *
(i + 1)].sum(axis=0))
# Define index of last narrow band channel for given tile
n = (i + 1) * nc_sum - 1
n = n - 1 if n == len(lal_filters) else n
# Computer withened inner products of each input filter with itself
mu_sq = nc_sum * lalburst.ExcessPowerFilterInnerProduct(
lal_filters[n], lal_filters[n], spec_corr, None)
#kmax = nc_sum-1 if n==len(lal_filters) else nc_sum-2
# Loop over the inner narrow band channels
for k in xrange(0, nc_sum - 1):
# Computer whitened filter inner products between input adjacent filters
mu_sq += 2 * lalburst.ExcessPowerFilterInnerProduct(
lal_filters[n - k], lal_filters[n - 1 - k], spec_corr,
None)
# Normalise tile's time series
tiles[i] = ts_tile.real**2 / mu_sq
if verbose: print "\t|- TF-plane is %dx%s samples" % tiles.shape
if verbose:
print "\t|- Tile energy mean %f, var %f" % (numpy.mean(tiles),
numpy.var(tiles))
# Define maximum number of degrees of freedom and check it larger or equal to 2
max_dof = 32 if max_duration == None else int(max_duration / dt)
assert max_dof >= 2
# Loop through multiple degrees of freedom
for j in [2**l for l in xrange(0, int(math.log(max_dof, 2)))]:
# Duration is fixed by the NDOF and bandwidth
duration = j * dt
if verbose: print "\n\t\t|- Summing DOF = %d ..." % (2 * j)
if verbose:
print "\t\t|- Explore signal duration of %f s..." % duration
# Construct filter
sum_filter = numpy.array([1, 0] * (j - 1) + [1])
# Calculate length of filtered time series
tlen = tiles.shape[1] - sum_filter.shape[0] + 1
# Initialise filtered time series array
dof_tiles = numpy.zeros((tiles.shape[0], tlen))
# Loop over tiles
for f in range(tiles.shape[0]):
# Sum and drop correlate tiles
dof_tiles[f] = fftconvolve(tiles[f], sum_filter, 'valid')
if verbose:
print "\t\t|- Summed tile energy mean: %f" % (
numpy.mean(dof_tiles))
if verbose:
print "\t\t|- Variance tile energy: %f" % (
numpy.var(dof_tiles))
if make_plot:
plot_spectrogram(
dof_tiles.T,
dt,
df,
ymax=ts_data.sample_rate / 2,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof.png' %
(start_time, end_time, nc_sum, 2 * j))
plot_tiles_ts(
dof_tiles,
2 * j,
df,
sample_rate=ts_data.sample_rate / us_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof_ts.png' %
(start_time, end_time, nc_sum, 2 * j))
plot_tiles_tf(
dof_tiles,
2 * j,
df,
ymax=ts_data.sample_rate / 2,
sample_rate=ts_data.sample_rate / us_rate,
t0=start_time,
t1=end_time,
fname='segments/%i-%i/%02ichans_%02idof_tf.png' %
(start_time, end_time, nc_sum, 2 * j))
threshold = scipy.stats.chi2.isf(tile_fap, j)
if verbose:
print "\t\t|- Threshold for this level: %f" % threshold
spant, spanf = dof_tiles.shape[1] * dt, dof_tiles.shape[0] * df
if verbose:
print "\t\t|- Processing %.2fx%.2f time-frequency map." % (
spant, spanf)
# Since we clip the data, the start time needs to be adjusted accordingly
window_offset_epoch = fs_data.epoch + psd_segment_length * window_fraction / 2
window_offset_epoch = LIGOTimeGPS(float(window_offset_epoch))
for i, j in zip(*numpy.where(dof_tiles > threshold)):
event = event_list.RowType()
# The points are summed forward in time and thus a `summed point' is the
# sum of the previous N points. If this point is above threshold, it
# corresponds to a tile which spans the previous N points. However, the
# 0th point (due to the convolution specifier 'valid') is actually
# already a duration from the start time. All of this means, the +
# duration and the - duration cancels, and the tile 'start' is, by
# definition, the start of the time frequency map if j = 0
# FIXME: I think this needs a + dt/2 to center the tile properly
event.set_start(window_offset_epoch + float(j * dt))
event.set_stop(window_offset_epoch + float(j * dt) +
duration)
event.set_peak(event.get_start() + duration / 2)
event.central_freq = lal_filters[
0].f0 + band / 2 + i * df + 0.5 * df
event.duration = duration
event.bandwidth = df
event.chisq_dof = 2 * duration * df
event.snr = math.sqrt(dof_tiles[i, j] / event.chisq_dof -
1)
# FIXME: Magic number 0.62 should be determine empircally
event.confidence = -lal.LogChisqCCDF(
event.snr * 0.62, event.chisq_dof * 0.62)
event.amplitude = None
event.process_id = None
event.event_id = event_list.get_next_id()
event_list.append(event)
for event in event_list[::-1]:
if event.amplitude != None:
continue
etime_min_idx = float(event.get_start()) - float(
fs_data.epoch)
etime_min_idx = int(etime_min_idx / tmp_ts_data.delta_t)
etime_max_idx = float(event.get_start()) - float(
fs_data.epoch) + event.duration
etime_max_idx = int(etime_max_idx / tmp_ts_data.delta_t)
# (band / 2) to account for sin^2 wings from finest filters
flow_idx = int((event.central_freq - event.bandwidth / 2 -
(df / 2) - fmin) / df)
fhigh_idx = int((event.central_freq + event.bandwidth / 2 +
(df / 2) - fmin) / df)
# TODO: Check that the undersampling rate is always commensurate
# with the indexing: that is to say that
# mod(etime_min_idx, us_rate) == 0 always
z_j_b = tf_map[flow_idx:fhigh_idx,
etime_min_idx:etime_max_idx:us_rate]
# FIXME: Deal with negative hrss^2 -- e.g. remove the event
try:
event.amplitude = measure_hrss(
z_j_b, unwhite_filter_ip[flow_idx:fhigh_idx],
unwhite_ss_ip[flow_idx:fhigh_idx - 1],
white_ss_ip[flow_idx:fhigh_idx - 1],
fd_psd.delta_f, tmp_ts_data.delta_t,
len(lal_filters[0].data.data), event.chisq_dof)
except ValueError:
event.amplitude = 0
if verbose:
print "\t\t|- Total number of events: %d" % len(event_list)
t_idx_min += int(seg_len * (1 - window_fraction))
t_idx_max += int(seg_len * (1 - window_fraction))
setname = "MagneticFields"
__program__ = 'pyburst_excesspower_gnome'
start_time = LIGOTimeGPS(int(ts_data.start_time))
end_time = LIGOTimeGPS(int(ts_data.end_time))
inseg = segment(start_time, end_time)
xmldoc = ligolw.Document()
xmldoc.appendChild(ligolw.LIGO_LW())
ifo = channel_name.split(":")[0]
straindict = psd.insert_psd_option_group.__dict__
proc_row = register_to_xmldoc(xmldoc,
__program__,
straindict,
ifos=[ifo],
version=git_version.id,
cvs_repository=git_version.branch,
cvs_entry_time=git_version.date)
dt_stride = psd_segment_length
sample_rate = ts_data.sample_rate
# Amount to overlap successive blocks so as not to lose data
window_overlap_samples = window_fraction * sample_rate
outseg = inseg.contract(window_fraction * dt_stride / 2)
# With a given dt_stride, we cannot process the remainder of this data
remainder = math.fmod(abs(outseg), dt_stride * (1 - window_fraction))
# ...so make an accounting of it
outseg = segment(outseg[0], outseg[1] - remainder)
ss = append_search_summary(xmldoc,
proc_row,
ifos=(station, ),
inseg=inseg,
outseg=outseg)
for sb in event_list:
sb.process_id = proc_row.process_id
sb.search = proc_row.program
sb.ifo, sb.channel = station, setname
xmldoc.childNodes[0].appendChild(event_list)
ifostr = ifo if isinstance(ifo, str) else "".join(ifo)
st_rnd, end_rnd = int(math.floor(inseg[0])), int(math.ceil(inseg[1]))
dur = end_rnd - st_rnd
fname = "%s-excesspower-%d-%d.xml.gz" % (ifostr, st_rnd, dur)
utils.write_filename(xmldoc, fname, gz=fname.endswith("gz"))
plot_triggers(fname)
def get_multi_burst_row(multi_burst_table, sim_tree):
row = multi_burst_table.RowType()
row.set_peak(sum(LIGOTimeGPS(t) for t in sim_tree.time) / sim_tree.ndim)
return row
def create_tables(self, xmldoc, rootfiles):
"""
Sets up table structures and calls populating methods.
"""
if os.path.splitext(rootfiles[0])[1] == ".root":
sim_tree = TChain("waveburst")
else: # If the file is (for example) text, use a proxy class
sim_tree = CWBTextConverter()
for rootfile in rootfiles:
sim_tree.Add(rootfile)
# Define tables
sngl_burst_table = lsctables.New(lsctables.SnglBurstTable, [
"peak_time_ns", "start_time_ns", "stop_time_ns", "process_id",
"ifo", "peak_time", "start_time", "stop_time", "duration",
"time_lag", "peak_frequency", "search", "flow", "fhigh",
"bandwidth", "tfvolume", "hrss", "event_id", "snr"
])
xmldoc.childNodes[0].appendChild(sngl_burst_table)
sngl_burst_table.sync_next_id()
coinc_event_table = lsctables.New(lsctables.CoincTable, [
"process_id", "coinc_event_id", "nevents", "instruments",
"time_slide_id", "coinc_def_id", "likelihood"
])
xmldoc.childNodes[0].appendChild(coinc_event_table)
coinc_event_table.sync_next_id()
# TODO: Reimplement this when the cwb_table module is included
#if self.cwbtable:
#cohwb_table = lsctables.New(cwb_table.CoherentWaveburstTable,
#["ellipticity", "correlated_energy", "eff_correlated_energy",
#"coherent_network_amp", "penalty", "network_correlation",
#"energy_disbalance", "ellip_energy_disbalance", "process_id",
#"coinc_event_id", "cwb_id"])
#xmldoc.childNodes[0].appendChild(cohwb_table)
#cohwb_table.sync_next_id()
multi_burst_table = lsctables.New(
lsctables.MultiBurstTable,
[
"process_id",
"peak_time",
"peak_time_ns",
"coinc_event_id",
"snr",
"ifos",
# NOTE: Added to the table definition
"false_alarm_rate",
"ligo_axis_ra",
"ligo_axis_dec"
])
xmldoc.childNodes[0].appendChild(multi_burst_table)
coinc_event_map_table = lsctables.New(lsctables.CoincMapTable)
xmldoc.childNodes[0].appendChild(coinc_event_map_table)
jobsegment = None
if self.start and self.end:
jobsegment = segments.segment(LIGOTimeGPS(self.start),
LIGOTimeGPS(self.end))
if self.verbose:
print "Creating Process Table...",
if self.job_list:
self.do_process_table(xmldoc, sim_tree)
else:
self.do_process_table_from_segment(xmldoc, sim_tree, jobsegment)
process_index = dict(
(int(row.process_id), row)
for row in lsctables.ProcessTable.get_table(xmldoc))
if self.verbose:
print " done."
if self.verbose:
print "Creating Summary Table...",
# If we are provided a joblist, use it to generate the list
if self.job_list:
self.do_summary_table_from_joblist(xmldoc, sim_tree)
elif self.job_list == None and self.start and self.end:
self.do_summary_table_from_segment(xmldoc, jobsegment, sim_tree)
else:
self.do_summary_table(xmldoc, sim_tree)
if self.verbose:
print " done."
# create coinc_definer row
row = self.get_coinc_def_row(sim_tree)
coinc_def_id = llwapp.get_coinc_def_id(xmldoc,
row.search,
row.search_coinc_type,
description=row.description)
entries = sim_tree.GetEntries()
for i in range(0, entries):
sim_tree.GetEntry(i)
if self.start != None:
if float(self.start) > sim_tree.start[0]: continue
if self.end != None:
if float(self.end) < sim_tree.stop[0]: continue
offset_vector = dict(
(get_ifos_from_index(instrument_index), offset)
for instrument_index, offset in zip(sim_tree.ifo,
sim_tree.lag))
coinc_event = coinc_event_table.RowType()
coinc_event.process_id = process_index[sim_tree.run].process_id
coinc_event.coinc_event_id = coinc_event_table.get_next_id()
coinc_event.coinc_def_id = coinc_def_id
coinc_event.nevents = sim_tree.ndim
coinc_event.instruments = lsctables.ifos_from_instrument_set(
get_ifos_from_index(
branch_array_to_list(sim_tree.ifo, sim_tree.ndim)))
coinc_event.time_slide_id = time_slide.get_time_slide_id(
xmldoc, offset_vector, process_index[sim_tree.run])
coinc_event.likelihood = sim_tree.likelihood
coinc_event_table.append(coinc_event)
for d in range(0, sim_tree.ndim):
sngl_burst = self.get_sngl_burst_row(sngl_burst_table,
sim_tree, d)
sngl_burst.process_id = coinc_event.process_id
sngl_burst.event_id = sngl_burst_table.get_next_id()
sngl_burst_table.append(sngl_burst)
coinc_event_map = coinc_event_map_table.RowType()
coinc_event_map.event_id = sngl_burst.event_id
coinc_event_map.table_name = sngl_burst.event_id.table_name
coinc_event_map.coinc_event_id = coinc_event.coinc_event_id
coinc_event_map_table.append(coinc_event_map)
# TODO: Reimplement when cwb_table module is included
#if self.cwbtable:
#cwb = get_cwb_row(cohwb_table, sim_tree)
#cwb.process_id = coinc_event.process_id
#cwb.coinc_event_id = coinc_event.coinc_event_id
#cwb.cwb_id = cohwb_table.get_next_id()
#cohwb_table.append(cwb)
multi_burst = self.get_multi_burst_row(multi_burst_table, sim_tree)
multi_burst.process_id = coinc_event.process_id
multi_burst.coinc_event_id = coinc_event.coinc_event_id
# NOTE: Until we have an embedded cwb table definition, this will be
# copied here so official tools have a ranking statistic to use
try:
multi_burst.snr = sim_tree.rho[1]
except TypeError: # difference in definition between ROOT and text
multi_burst.snr = sim_tree.rho
# NOTE: To be filled in later by farburst
multi_burst.false_alarm_rate = -1.0
# Reconstructed right ascension and declination
multi_burst.ligo_axis_ra = sim_tree.phi[2]
multi_burst.ligo_axis_dec = sim_tree.theta[2]
multi_burst.ifos = lsctables.ifos_from_instrument_set(
get_ifos_from_index(
branch_array_to_list(sim_tree.ifo, sim_tree.ndim)))
multi_burst_table.append(multi_burst)
def do_summary_table_from_segment(self,
xmldoc,
segment,
sim_tree,
jobid=-1):
"""
Create the search_summary table for a cWB from a segment specified from the command line. The function will try to determine the proper job intervals from the waveoffset, if specified.
"""
try:
search_summary = lsctables.SearchSummaryTable.get_table(xmldoc)
except ValueError:
search_summary = lsctables.New(lsctables.SearchSummaryTable, [
"process_id", "nevents", "ifos", "comment", "in_start_time",
"in_start_time_ns", "out_start_time", "out_start_time_ns",
"in_end_time", "in_end_time_ns", "out_end_time",
"out_end_time_ns"
])
xmldoc.childNodes[0].appendChild(search_summary)
process_id_type = type(
lsctables.ProcessTable.get_table(xmldoc).next_id)
sim_tree.GetEntry(0)
if (jobid < 0):
run = sim_tree.run
else:
run = jobid
seg = segment
# Search Summary Table
# events found in the run -> nevents
row = search_summary.RowType()
row.process_id = process_id_type(run)
row.nevents = sim_tree.GetEntries()
ifos = lsctables.ifos_from_instrument_set(
get_ifos_from_index(
branch_array_to_list(sim_tree.ifo, sim_tree.ndim)))
# Imstruments involved in the search
if (ifos == None or len(ifos) == 0):
if (self.instruments):
ifos = self.instruments
else: # Not enough information to completely fill out the table
sys.exit(
"Found a job with no IFOs on, or not enough to determine IFOs. Try specifying instruments directly."
)
row.ifos = ifos
row.comment = "waveburst"
# Begin and end time of the segment
waveoffset = self.waveoffset
if waveoffset == None: waveoffset = 0
# in -- with waveoffset
row.set_in(seg)
# out -- without waveoffset
waveoffset = LIGOTimeGPS(waveoffset)
row.set_out(segments.segment(seg[0] + waveoffset, seg[1] - waveoffset))
search_summary.append(row)
def get_segment(self): """ Return the segment described by this row. """ return segments.segment(LIGOTimeGPS(self.start_time, 0), LIGOTimeGPS(self.end_time, 0))
