det_list = lklhds.json_to_detection_list('data/detection_set1.json')
# define joint-likelihood calculation parameters
width = 10.0
rng_max = 3000.0
# define clustering parameters
dist_max = 10.0
clustering_threshold = 5.0
trimming_thresh = 3.0
pl = Pool(cpu_count() - 1)
######################
#### Run analysis ####
######################
labels, dists = hjl.run(det_list, clustering_threshold, dist_max=dist_max, bm_width=width, rng_max=rng_max, trimming_thresh=trimming_thresh, pool=pl,show_result=True)
# Summarize clusters
clusters, qualities = hjl.summarize_clusters(labels, dists)
for n in range(len(clusters)):
print("Cluster:", clusters[n], '\t', "Cluster Quality:", 10.0**(-qualities[n]))
pl.close()
pl.terminate()
def data_processingASSOC(self):
'''
'''
print('data processing', short_time(UTCDateTime(self.time_initial)),
short_time(UTCDateTime(self.time_end)))
det_list = lklhds.db2dets(self.det_tot)
EVIDs = []
embed()
if len(det_list) > 1:
try:
#EVIDs,DAQ,CAQ=assoc(det_list, self.lims, float(self.assocthresh), show_result=False,parallel=True,num_cores=self.numcores)
#labels, dists = hjl.run(det_list, self.clusterthresh, dist_max=self.distmax, bm_width=self.beamwidth, rng_max=self.rangemax, trimming_thresh=self.trimthresh, pool=self.pl)
labels, dists = hjl.run(det_list,
self.clusterthresh,
dist_max=self.distmax,
bm_width=self.beamwidth,
rng_max=self.rangemax,
pool=self.pl)
clusters, qualities = hjl.summarize_clusters(labels, dists)
for n in range(len(clusters)):
print("Cluster:", clusters[n], '\t', "Cluster Quality:",
10.0**(-qualities[n]))
lastEVENTIDQ = self.session.query(
func.max(self.ASSOC_results.eventid)).all()
lastEVENTID = lastEVENTIDQ[0][0]
if lastEVENTID is None:
lastEVENTID = int(0)
lastEVENTID = lastEVENTID + 1
#embed()
for nn in range(len(clusters[n])):
det_id = clusters[n][nn]
id_res=self.session.query(self.ASSOC_results).filter(self.ASSOC_results.net==self.net)\
.filter(self.ASSOC_results.fdid ==self.det_tot[det_id][6])\
.filter(self.ASSOC_results.passocid ==self.passocid)\
.filter(self.ASSOC_results.timeini==self.time_initial)\
.filter(self.ASSOC_results.timeend==self.time_end)\
.filter(self.ASSOC_results.qdetcluster==10.0**(-qualities[n]))\
.filter(self.ASSOC_results.fdtable==self.det_tot[det_id][8])\
.filter(self.ASSOC_results.sta==self.det_tot[det_id][7]).all()
id_resC = self.session.query(
self.ASSOC_results).count() + 1
if bool(id_res) == False:
res=self.ASSOC_results(associd=id_resC,\
fdid=self.det_tot[det_id][6],\
eventid=int(lastEVENTID),\
passocid=self.passocid,\
net=self.net,\
timeini=self.time_initial,\
timeend=self.time_end,\
qdetcluster=10.0**(-qualities[n]),\
fdtable=self.det_tot[det_id][8],\
sta=self.det_tot[det_id][7])
self.session.add(res)
self.session.commit()
print('associations written', len(clusters))
except Exception as ex1:
print('error running assoc:', ex1)
embed()
exit()
'''
def data_processingASSOC(self,t_start,t_end,src_win,max_prop_tm):
'''
'''
print('data processing',short_time(UTCDateTime(self.time_initial)),short_time(UTCDateTime(self.time_end)))
det_list = lklhds.db2dets(self.det_tot)
min_array_pop=self.minarraypop
EVIDs=[]
if len(det_list)>1:
try:
events = []
event_qls = []
window_start=[]
window_end=[]
duration_dd = int((t_end - t_start).astype('m8[s]').astype(float) / 60.0)
#duration_dd = int((t_end - t_start) / 60.0)
for dt in range(0, duration_dd, int(src_win)):
win_start = t_start + np.timedelta64(dt, 'm')
win_end = t_start + np.timedelta64(dt + int(src_win + max_prop_tm), 'm')
print('\n' + "Computing associations for:", win_start, " - ", win_end)
temp = [(n, det) for n, det in enumerate(det_list) if np.logical_and(win_start <= det.peakF_UTCtime, det.peakF_UTCtime <= win_end)]
key = [pair[0] for pair in temp]
new_list = [pair[1] for pair in temp]
# run analysis
if len(new_list)>1:
if self.trimthresh=='None':
self.trimthresh=None
labels, dists = hjl.run(new_list, self.clusterthresh, dist_max=self.distmax, bm_width=self.beamwidth, rng_max=self.rangemax, pool=self.pl,trimming_thresh=self.trimthresh)
clusters, qualities = hjl.summarize_clusters(labels, dists,population_min=int(self.mindetpop))
for n in range(len(clusters)):
events += [[key[n] for n in clusters[n]]]
event_qls += [10.0**(-qualities[n])]
window_start.append(UTCDateTime(win_start.astype(datetime)).timestamp)
window_end.append(UTCDateTime(win_end.astype(datetime)).timestamp)
event_cnt = len(events)
for n1 in range(event_cnt):
for n2 in range(n1 + 1, event_cnt):
if len(events[n1]) > 0 and len(events[n2]) > 0:
set1, set2 = set(events[n1]), set(events[n2])
rel_overlap = len(set1.intersection(set2)) / min(len(set1), len(set2))
if rel_overlap > 0.5:
events[n1], events[n2] = list(set1.union(set2)), []
event_qls[n1], event_qls[n2] = max(event_qls[n1], event_qls[n2]), -1.0
for n, ev_ids in enumerate(events):
if len(ev_ids) > 0:
locs = np.array([[det_list[j].latitude, det_list[j].longitude] for j in ev_ids])
#embed()
unique_cnt = max(len(np.unique(locs[:, 0])), len(np.unique(locs[:, 1])))
if unique_cnt < int(min_array_pop):
events[n] = []
event_qls[n] = -1.0
events = [ei for ei in events if len(ei) > 0]
event_qls = [eqi for eqi in event_qls if eqi > 0]
print("Identified events and qualities:")
for n in range(len(events)):
print('\t', events[n], '\t', event_qls[n])
lastEVENTIDQ=self.session.query(func.max(self.ASSOC_results.eventid)).all()
lastEVENTID=lastEVENTIDQ[0][0]
if lastEVENTID is None:
lastEVENTID=int(0)
for n in range(len(events)):
for nn in range(len(events[n])):
det_id = events[n][nn]
id_res=self.session.query(self.ASSOC_results).filter(self.ASSOC_results.net==self.net)\
.filter(self.ASSOC_results.fdid ==self.det_tot[det_id][6])\
.filter(self.ASSOC_results.passocid ==self.passocid)\
.filter(self.ASSOC_results.timeini==self.time_initial)\
.filter(self.ASSOC_results.timeend==self.time_end)\
.filter(self.ASSOC_results.qdetcluster==event_qls[n])\
.filter(self.ASSOC_results.fdtable==self.det_tot[det_id][8])\
.filter(self.ASSOC_results.sta==self.det_tot[det_id][7]).all()
id_resC=self.session.query(self.ASSOC_results).count()+1
if bool(id_res)==False:
res=self.ASSOC_results(associd=id_resC,\
fdid=self.det_tot[det_id][6],\
eventid=int(lastEVENTID+1+n),\
passocid=self.passocid,\
net=self.net,\
timeini=window_start[n],\
timeend=window_end[n],\
qdetcluster=event_qls[n],\
fdtable=self.det_tot[det_id][8],\
sta=self.det_tot[det_id][7])
self.session.add(res)
self.session.commit()
print('associations written', len(events))
except Exception as ex1:
print('error running assoc:',ex1)
embed()
exit()
'''