def assign_likelihood_ratios_xml(xmldoc, coinc_def_id, offset_vectors, vetoseglists, events_func, veto_func, ln_likelihood_ratio_func, likelihood_params_func, verbose = False, params_func_extra_args = ()):
"""
Assigns likelihood ratio values to coincidences (XML version).
"""
#
# Iterate over all coincs, assigning likelihood ratios.
#
coinc_event_table = lsctables.CoincTable.get_table(xmldoc)
if verbose:
progressbar = ProgressBar("computing likelihood ratios", max = len(coinc_event_table))
else:
progressbar = None
for coinc_event in coinc_event_table:
if progressbar is not None:
progressbar.increment()
if coinc_event.coinc_def_id != coinc_def_id:
continue
params = likelihood_params_func([event for event in events_func(None, coinc_event.coinc_event_id) if veto_func(event, vetoseglists)], offset_vectors[coinc_event.time_slide_id], *params_func_extra_args)
coinc_event.likelihood = ln_likelihood_ratio_func(params) if params is not None else None
del progressbar
#
# Done
#
return
def ligolw_inspinjfind(xmldoc, process, search, snglcomparefunc, nearcoinccomparefunc, end_time_bisect_window = 1.0, verbose = False):
#
# Analyze the document's contents.
#
if verbose:
print("indexing ...", file=sys.stderr)
bbdef = {"inspiral": thinca.InspiralCoincDef}[search]
sbdef = {"inspiral": InspiralSICoincDef}[search]
scedef = {"inspiral": InspiralSCExactCoincDef}[search]
scndef = {"inspiral": InspiralSCNearCoincDef}[search]
contents = DocContents(xmldoc = xmldoc, bbdef = bbdef, sbdef = sbdef, scedef = scedef, scndef = scndef, process = process, end_time_bisect_window = end_time_bisect_window)
#
# Find sim_inspiral <--> sngl_inspiral coincidences.
#
progressbar = ProgressBar(max = len(contents.siminspiraltable), textwidth = 35, text = sbdef.description) if verbose else None
for sim in contents.siminspiraltable:
if progressbar is not None:
progressbar.increment()
inspirals = find_sngl_inspiral_matches(contents, sim, snglcomparefunc)
if inspirals:
add_sim_inspiral_coinc(contents, sim, inspirals)
del progressbar
#
# Find sim_inspiral <--> coinc_event coincidences.
#
if contents.scn_coinc_def_id:
progressbar = ProgressBar(max = len(contents.siminspiraltable), textwidth = 35, text = scndef.description) if verbose else None
for sim in contents.siminspiraltable:
if progressbar is not None:
progressbar.increment()
coincs = contents.coincs_near_endtime(sim.time_geocent)
exact_coinc_event_ids = find_exact_coinc_matches(coincs, sim, snglcomparefunc)
near_coinc_event_ids = find_near_coinc_matches(coincs, sim, nearcoinccomparefunc)
assert exact_coinc_event_ids.issubset(near_coinc_event_ids)
if exact_coinc_event_ids:
add_sim_coinc_coinc(contents, sim, exact_coinc_event_ids, contents.sce_coinc_def_id)
if near_coinc_event_ids:
add_sim_coinc_coinc(contents, sim, near_coinc_event_ids, contents.scn_coinc_def_id)
del progressbar
#
# Restore the original event order.
#
if verbose:
print("finishing ...", file=sys.stderr)
contents.sort_triggers_by_id()
#
# Done.
#
return xmldoc
def ligolw_inspinjfind(xmldoc, process, search, snglcomparefunc, nearcoinccomparefunc, end_time_bisect_window = 1.0, verbose = False):
#
# Analyze the document's contents.
#
if verbose:
print >>sys.stderr, "indexing ..."
bbdef = {"inspiral": ligolw_thinca.InspiralCoincDef}[search]
sbdef = {"inspiral": InspiralSICoincDef}[search]
scedef = {"inspiral": InspiralSCExactCoincDef}[search]
scndef = {"inspiral": InspiralSCNearCoincDef}[search]
contents = DocContents(xmldoc = xmldoc, bbdef = bbdef, sbdef = sbdef, scedef = scedef, scndef = scndef, process = process, end_time_bisect_window = end_time_bisect_window)
#
# Find sim_inspiral <--> sngl_inspiral coincidences.
#
progressbar = ProgressBar(max = len(contents.siminspiraltable), textwidth = 35, text = sbdef.description) if verbose else None
for sim in contents.siminspiraltable:
if progressbar is not None:
progressbar.increment()
inspirals = find_sngl_inspiral_matches(contents, sim, snglcomparefunc)
if inspirals:
add_sim_inspiral_coinc(contents, sim, inspirals)
del progressbar
#
# Find sim_inspiral <--> coinc_event coincidences.
#
if contents.scn_coinc_def_id:
progressbar = ProgressBar(max = len(contents.siminspiraltable), textwidth = 35, text = scndef.description) if verbose else None
for sim in contents.siminspiraltable:
if progressbar is not None:
progressbar.increment()
coincs = contents.coincs_near_endtime(sim.get_end())
exact_coinc_event_ids = find_exact_coinc_matches(coincs, sim, snglcomparefunc)
near_coinc_event_ids = find_near_coinc_matches(coincs, sim, nearcoinccomparefunc)
assert exact_coinc_event_ids.issubset(near_coinc_event_ids)
if exact_coinc_event_ids:
add_sim_coinc_coinc(contents, sim, exact_coinc_event_ids, contents.sce_coinc_def_id)
if near_coinc_event_ids:
add_sim_coinc_coinc(contents, sim, near_coinc_event_ids, contents.scn_coinc_def_id)
del progressbar
#
# Restore the original event order.
#
if verbose:
print >>sys.stderr, "finishing ..."
contents.sort_triggers_by_id()
#
# Done.
#
return xmldoc
def make_db(db_filename, filenames):
# Make the sqlite database if one does not exist
connection = sqlite3.connect(db_filename)
cursor = connection.cursor()
# Create Table 1 that contains:
# - the filenames of the hdf5 subbank files
# - their respective ID integers
cursor.execute("CREATE TABLE fname (filename_id INTEGER PRIMARY KEY, filename TEXT);")
# Create Table 2 that contains:
# - k (integers which will be populated later, this column gives the order in which overlaps are placed),
# - ID integers of the filenames (instead of saving the entire filename to the database - this is to save memory)
# - m1, m2, chi1, chi2 of templates (separate column for each)
# - row and column in which this overlap is found in the file (if row=None, means that the overlaps are found in the columns only)
cursor.execute("CREATE TABLE bank (k INTEGER, filename_id INTEGER, m1 REAL, m2 REAL, chi1 REAL, chi2 REAL, row INTEGER, column INTEGER);")
# Populate Table 1:
for filename in filenames:
cursor.execute("INSERT INTO fname (filename) VALUES (?)", (filename,))
connection.commit()
cursor.execute("CREATE INDEX filename_index ON fname (filename)")
progress = ProgressBar(max=len(filenames))
# Populate Table 2:
for filename in filenames:
progress.increment(text=filename)
try:
f = h5py.File(filename,"r")
nrows = f[f.keys()[0]]['overlaps'].shape[0] # find number of rows in subbank file
for i, (m1, m2, chi1, chi2) in enumerate(zip(f[f.keys()[0]]['mass1'].value, f[f.keys()[0]]['mass2'].value, f[f.keys()[0]]['spin1z'].value, f[f.keys()[0]]['spin2z'].value)):
cursor.execute("INSERT INTO bank (filename_id, m1, m2, chi1, chi2, row, column) VALUES ((SELECT filename_id FROM fname WHERE filename = ?),?,?,?,?,?,?)", (filename, m1, m2, chi1, chi2, i if i <= nrows else None, i))
#FIXME: After building the database, confirm that each template only appears once in one row of the hdf5 files
except: # ignore corrupted/broken subbank files
print "Cannot load h5py file:", filename
cursor.execute("CREATE INDEX template_index ON bank (m1, m2, chi1, chi2)")
cursor.execute("CREATE INDEX filename_id_index ON bank (filename_id)")
cursor.execute("CREATE TEMPORARY TABLE template AS SELECT DISTINCT m1,m2,chi1,chi2 FROM bank;")
cursor.execute("CREATE INDEX tmp ON template (m1,m2,chi1,chi2);")
cursor.execute("UPDATE bank SET k = (SELECT rowid-(SELECT MIN(rowid) FROM template) FROM template WHERE m1 = bank.m1 and m2 = bank.m2 and chi1 = bank.chi1 and chi2 = bank.chi2);") # populate column k
cursor.execute("DROP TABLE template;")
cursor.execute("CREATE INDEX k_index ON bank (k);")
connection.commit()
return connection
def make_db(db_filename, filenames):
# Make the sqlite database if one does not exist
connection = sqlite3.connect(db_filename)
cursor = connection.cursor()
# Create Table 1 that contains:
# - the filenames of the hdf5 subbank files
# - their respective ID integers
cursor.execute("CREATE TABLE fname (filename_id INTEGER PRIMARY KEY, filename TEXT);")
# Create Table 2 that contains:
# - k (integers which will be populated later, this column gives the order in which overlaps are placed),
# - ID integers of the filenames (instead of saving the entire filename to the database - this is to save memory)
# - m1, m2, chi1, chi2 of templates (separate column for each)
# - row and column in which this overlap is found in the file (if row=None, means that the overlaps are found in the columns only)
cursor.execute("CREATE TABLE bank (k INTEGER, filename_id INTEGER, m1 REAL, m2 REAL, chi1 REAL, chi2 REAL, row INTEGER, column INTEGER);")
# Populate Table 1:
for filename in filenames:
cursor.execute("INSERT INTO fname (filename) VALUES (?)", (filename,))
connection.commit()
cursor.execute("CREATE INDEX filename_index ON fname (filename)")
progress = ProgressBar(max=len(filenames))
# Populate Table 2:
for filename in filenames:
progress.increment(text=filename)
try:
f = h5py.File(filename,"r")
nrows = f[f.keys()[0]]['overlaps'].shape[0] # find number of rows in subbank file
for i, (m1, m2, chi1, chi2) in enumerate(zip(f[f.keys()[0]]['mass1'].value, f[f.keys()[0]]['mass2'].value, f[f.keys()[0]]['spin1z'].value, f[f.keys()[0]]['spin2z'].value)):
cursor.execute("INSERT INTO bank (filename_id, m1, m2, chi1, chi2, row, column) VALUES ((SELECT filename_id FROM fname WHERE filename = ?),?,?,?,?,?,?)", (filename, m1, m2, chi1, chi2, i if i <= nrows else None, i))
#FIXME: After building the database, confirm that each template only appears once in one row of the hdf5 files
except: # ignore corrupted/broken subbank files
print "Cannot load h5py file:", filename
cursor.execute("CREATE INDEX template_index ON bank (m1, m2, chi1, chi2)")
cursor.execute("CREATE INDEX filename_id_index ON bank (filename_id)")
cursor.execute("CREATE TEMPORARY TABLE template AS SELECT DISTINCT m1,m2,chi1,chi2 FROM bank;")
cursor.execute("CREATE INDEX tmp ON template (m1,m2,chi1,chi2);")
cursor.execute("UPDATE bank SET k = (SELECT rowid-(SELECT MIN(rowid) FROM template) FROM template WHERE m1 = bank.m1 and m2 = bank.m2 and chi1 = bank.chi1 and chi2 = bank.chi2);") # populate column k
cursor.execute("DROP TABLE template;")
cursor.execute("CREATE INDEX k_index ON bank (k);")
connection.commit()
return connection
ln_p_j, p_j_templates, p_j_indices = find_ln_p_j_voronoi(
mass, f, popt
) #p_j_indices = indices of the templates for m1, m2 arrays (eg. p_j_indices[0] = 1, which means p_j_template[0] corresponds to the 1th template in m1, m2)
t_k = np.array(range(len(mass)))
ln_p_jk = np.log(np.zeros(
(len(rho), len(t_k)))) # P(signal t_j is recovered by template t_k)
#p_j_indices.tolist().sort(key=ln_p_j.__getitem__) # doing loop in order of p_j (smallest to largest) for numerical accuracy
order = np.argsort(ln_p_j)
ln_p_j, p_j_templates, p_j_indices = ln_p_j[order], p_j_templates[
order], p_j_indices[order]
progress = ProgressBar(max=len(p_j_indices))
for i in range(len(p_j_indices)): # loop over all signal population
progress.increment(text=str(p_j_templates[order][i]))
ovrlp = overlap[p_j_indices[i]]
for r in range(len(rho)): # loop over all rho
ln_p_jk[r, :] = np.logaddexp(
ln_p_jk[r, :], ln_p_j[order][i] + ln_p_k(ovrlp, rho[r], t_k))
print "ln(P_jk) computed for all templates. Time elapsed:", time.time(
) - start_time
# Save data to hdf5 file
if save_data:
directory = ""
filename = "logP_vs_rho_" + datetime
f = h5py.File(directory + filename + ".hdf5", "w")
f.create_dataset('rho', data=rho)
f.create_dataset('ln_P_jk', data=ln_p_jk)
k = np.arange(1,n+1)
rho = 30*np.cos((2*k-1)*np.pi/(2*n)) + 30 # min(rho)~0, max(rho)~80
rho.sort()
rho = np.insert(rho,0,0)
ln_p_j, p_j_templates, p_j_indices = find_ln_p_j_voronoi(mass, f, popt) #p_j_indices = indices of the templates for m1, m2 arrays (eg. p_j_indices[0] = 1, which means p_j_template[0] corresponds to the 1th template in m1, m2)
t_k = np.array(range(len(mass)))
ln_p_jk = np.log(np.zeros((len(rho), len(t_k)))) # P(signal t_j is recovered by template t_k)
#p_j_indices.tolist().sort(key=ln_p_j.__getitem__) # doing loop in order of p_j (smallest to largest) for numerical accuracy
order = np.argsort(ln_p_j)
ln_p_j, p_j_templates, p_j_indices = ln_p_j[order], p_j_templates[order], p_j_indices[order]
progress = ProgressBar(max=len(p_j_indices))
for i in range(len(p_j_indices)): # loop over all signal population
progress.increment(text=str(p_j_templates[order][i]))
ovrlp = overlap[p_j_indices[i]]
for r in range(len(rho)): # loop over all rho
ln_p_jk[r,:] = np.logaddexp(ln_p_jk[r,:], ln_p_j[order][i]+ln_p_k(ovrlp, rho[r], t_k))
print "ln(P_jk) computed for all templates. Time elapsed:", time.time()-start_time
# Save data to hdf5 file
if save_data:
directory = ""
filename = "logP_vs_rho_"+datetime
f = h5py.File(directory+filename+".hdf5","w")
f.create_dataset('rho', data=rho)
f.create_dataset('ln_P_jk', data=ln_p_jk)
f.create_dataset('masses', data=mass)
f.close()
img = Image.open(filename)
width, height = img.size
width, height = int(round(width * options.height /
float(height))), options.height
if options.verbose:
print("converting to %dx%d grayscale ... " % (width, height),
file=sys.stderr)
img = img.resize((width, height)).convert("L")
progress = ProgressBar("computing pixels", max=width *
height) if options.verbose else None
for i in range(width):
for j in range(height):
if progress is not None:
progress.increment()
# amplitude. hrss column is ignored by waveform
# generation code. it is included for convenience,
# to record the desired pixel brightness. because
# band- and time-limited white-noise burst
# waveforms are random, the waveform's final
# ampltiude (in the egw_over_rsquared column) is
# determined by generating the burst at a canonical
# amplitude, measuring its hrss, then rescaling to
# achieve the desired value. this process requires
# the final sample rate to be known.
hrss = options.hrss_scale * img.getpixel(
(i, height - 1 - j)) / 255.0
if hrss == 0.:
# don't generate injections for black
# pixels
