def calculate_receiver_weights_asdf_one_event(cat, event_info, param):
results = {}
nperiods = len(event_info)
period_idx = 0
origin = cat[0].preferred_origin()
src_info = {
"latitude": origin.latitude,
"longitude": origin.longitude,
"depth_in_m": origin.depth
}
# determine receiver weightings for each asdf file
for period, period_info in event_info["period_info"].iteritems():
period_idx += 1
logger.info("-" * 15 + "[%d/%d]Period band: %s" %
(period_idx, nperiods, period) + "-" * 15)
_path_info = {
"station_file": event_info["stationfile"],
"window_file": period_info["window_file"],
"output_file": period_info["output_file"]
}
# the _results contains three components data
results[period] = calculate_receiver_weights_interface(
src_info, _path_info, param)
outputdir = os.path.dirname(period_info["output_file"])
receiver_weights_file = os.path.join(outputdir,
"receiver_weights.json")
print("Receiver weights log file: %s" % receiver_weights_file)
dump_json(results[period], receiver_weights_file)
return results
def calculate_receiver_weights_asdf_one_event(cat, event_info, param):
results = {}
nperiods = len(event_info)
period_idx = 0
origin = cat[0].preferred_origin()
src_info = {"latitude": origin.latitude, "longitude": origin.longitude,
"depth_in_m": origin.depth}
# determine receiver weightings for each asdf file
for period, period_info in event_info["period_info"].iteritems():
period_idx += 1
logger.info("-" * 15 + "[%d/%d]Period band: %s"
% (period_idx, nperiods, period) + "-" * 15)
_path_info = {"station_file": event_info["stationfile"],
"window_file": period_info["window_file"],
"output_file": period_info["output_file"]}
# the _results contains three components data
results[period] = calculate_receiver_weights_interface(
src_info, _path_info, param)
outputdir = os.path.dirname(period_info["output_file"])
receiver_weights_file = os.path.join(
outputdir, "receiver_weights.json")
print("Receiver weights log file: %s" % receiver_weights_file)
dump_json(results[period], receiver_weights_file)
return results
def calculate_category_weights(src_weights, path_info, param, logdir):
cat_wcounts = get_event_category_window_counts(path_info)
# category weight = 1 / (N_c * \sum_{s} w_{s} N_{sc})
sumv = {}
for ev in cat_wcounts:
srcw = src_weights[ev]
for pb in cat_wcounts[ev]:
if pb not in sumv:
sumv[pb] = {}
for comp in cat_wcounts[ev][pb]:
if comp not in sumv[pb]:
sumv[pb][comp] = 0
sumv[pb][comp] += srcw * cat_wcounts[ev][pb][comp]
print("\sum_{s} source_weight * N_{sc}: %s" % sumv)
cat_weights, ratios = normalize_category_weights(sumv)
print("Final value of category weightings:")
pprint(cat_weights)
print("Ratio:")
pprint(ratios)
log_content = {"weights": cat_weights, "ratio": ratios}
outputfn = os.path.join(logdir, "category_weights.log.json")
print("category weighting log file: %s" % outputfn)
dump_json(log_content, outputfn)
return cat_weights
def save_log(self):
def _get_max_and_min(dictv):
minv = 9999999999
maxv = -1
for e, einfo in dictv.iteritems():
for p, pinfo in einfo.iteritems():
for c, cinfo in pinfo.iteritems():
if minv > cinfo:
minv = cinfo
if maxv < cinfo:
maxv = cinfo
return minv, maxv
logfile = self.path["logfile"]
cond_min, cond_max = _get_max_and_min(self.rec_cond_nums)
ref_min, ref_max = _get_max_and_min(self.rec_ref_dists)
log_content = {
"receiver_ref_dist": self.rec_ref_dists,
"recevier_cond_numb": self.rec_cond_nums,
"summary": {
"receiver_ref_dist": {
"min": ref_min,
"max": ref_max
},
"receiver_cond_num": {
"min": cond_min,
"max": cond_max
}
}
}
print("log file: %s" % logfile)
dump_json(log_content, logfile)
def calculate_category_weights(src_weights, path_info, param, logdir):
cat_wcounts = get_event_category_window_counts(path_info)
# category weight = 1 / (N_c * \sum_{s} w_{s} N_{sc})
sumv = {}
for ev in cat_wcounts:
srcw = src_weights[ev]
for pb in cat_wcounts[ev]:
if pb not in sumv:
sumv[pb] = {}
for comp in cat_wcounts[ev][pb]:
if comp not in sumv[pb]:
sumv[pb][comp] = 0
sumv[pb][comp] += srcw * cat_wcounts[ev][pb][comp]
print("\sum_{s} source_weight * N_{sc}: %s" % sumv)
cat_weights, ratios = normalize_category_weights(sumv)
print("Final value of category weightings:")
pprint(cat_weights)
print("Ratio:")
pprint(ratios)
log_content = {"weights": cat_weights, "ratio": ratios}
outputfn = os.path.join(logdir, "category_weights.log.json")
print("category weighting log file: %s" % outputfn)
dump_json(log_content, outputfn)
return cat_weights
def analyze_overall_weights(weights, rec_wcounts, log_prefix):
nwins_array = []
weights_array = []
# validate the sum of all weights is 1
for _p, _pw in weights.iteritems():
for _chan, _chanw in _pw.iteritems():
comp = _chan.split(".")[-1]
nwins_array.append(rec_wcounts[_p][comp][_chan])
weights_array.append(_chanw["weight"])
validate_overall_weights(weights_array, nwins_array)
figname = log_prefix + ".weights.hist.png"
plot_histogram(figname, weights_array)
figname = log_prefix + ".wcounts.hist.png"
plot_histogram(figname, nwins_array)
maxw = max(weights_array)
minw = min(weights_array)
nreceivers = len(weights_array)
nwindows = np.sum(nwins_array)
logger.info("Total number of receivers: %d" % nreceivers)
logger.info("Total number of windows: %d" % nwindows)
logger.info("Weight max, min, max/min: %f, %f, %f"
% (maxw, minw, maxw/minw))
logfile = log_prefix + ".weights.summary.json"
content = {"max_weights": maxw, "min_weights": minw,
"total_nwindows": np.sum(nwins_array),
"windows": nwindows, "receivers": nreceivers}
logger.info("Overall log file: %s" % logfile)
dump_json(content, logfile)
def dump_weights(self):
""" dump weights to files """
for ev, ev_info in self.weights.iteritems():
for period, period_info in ev_info.iteritems():
_info = self.path['input'][ev]["period_info"]
outputfn = _info[period]["output_file"]
# print("Final weights dumped to: %s" % outputfn)
dump_json(period_info, outputfn)
def dump_weights(self):
""" dump weights to files """
for ev, ev_info in self.weights.iteritems():
for period, period_info in ev_info.iteritems():
_info = self.path['input'][ev]["period_info"]
outputfn = _info[period]["output_file"]
# print("Final weights dumped to: %s" % outputfn)
dump_json(period_info, outputfn)
def analyze_category_weights(cat_weights, logfile):
log = {"category_weights": cat_weights}
maxw = 0
minw = 10**9
for _p, _pw in cat_weights.iteritems():
for _comp, _compw in _pw.iteritems():
if _compw > maxw:
maxw = _compw
if _compw < minw:
minw = _compw
log["summary"] = {"maxw": maxw, "minw": minw, "cond_num": maxw / minw}
dump_json(log, logfile)
def analyze_category_weights(cat_weights, logfile):
log = {"category_weights": cat_weights}
maxw = 0
minw = 10**9
for _p, _pw in cat_weights.iteritems():
for _comp, _compw in _pw.iteritems():
if _compw > maxw:
maxw = _compw
if _compw < minw:
minw = _compw
log["summary"] = {"maxw": maxw, "minw": minw,
"cond_num": maxw/minw}
dump_json(log, logfile)
def analyze_receiver_weights(self, logfile):
log = {}
for _p, _pw in self.weights.iteritems():
log[_p] = {}
maxw = defaultdict(lambda: 0)
minw = defaultdict(lambda: 10**9)
for _chan, _chanw in _pw.iteritems():
comp = _chan.split(".")[-1]
if _chanw["weight"] > maxw[comp]:
maxw[comp] = _chanw["weight"]
if _chanw["weight"] < minw[comp]:
minw[comp] = _chanw["weight"]
for comp in maxw:
log[_p][comp] = \
{"maxw": maxw[comp], "minw": minw[comp],
"nwindows": self.cat_wcounts[_p][comp],
"ref_dist": self.rec_ref_dists[_p][comp],
"cond_num": self.rec_cond_nums[_p][comp]}
dump_json(log, logfile)
def calculate_source_weights(src_info, param, logdir):
logger_block("Source Weighting")
points = assign_source_to_points(src_info)
ref_distance = -1.0
cond_numb = -1.0
if param["flag"]:
ref_distance, cond_numb = calculate_source_weights_on_location(
points, param["search_ratio"], param["plot"], logdir)
src_weights = normalize_source_weights(points)
# generate log file
log_content = \
{"weights": src_weights, "reference_distance": ref_distance,
"cond_num": cond_numb, "weight_flag": param["flag"],
"search_ratio": param["search_ratio"]}
outputfn = os.path.join(logdir, "source_weights.log.json")
print("Source weights log file: %s" % outputfn)
dump_json(log_content, outputfn)
return src_weights
def analyze_overall_weights(weights, rec_wcounts, log_prefix):
nwins_array = []
weights_array = []
# validate the sum of all weights is 1
for _p, _pw in weights.iteritems():
for _chan, _chanw in _pw.iteritems():
comp = _chan.split(".")[-1]
nwins_array.append(rec_wcounts[_p][comp][_chan])
weights_array.append(_chanw["weight"])
validate_overall_weights(weights_array, nwins_array)
figname = log_prefix + ".weights.hist.png"
plot_histogram(figname, weights_array)
figname = log_prefix + ".wcounts.hist.png"
plot_histogram(figname, nwins_array)
nreceivers = len(weights_array)
nwindows = np.sum(nwins_array)
if len(weights_array) > 0:
maxw = max(weights_array)
minw = min(weights_array)
max_over_min = maxw / minw
else:
maxw = 0.0
minw = 0.0
max_over_min = 0.0
logger.info("Total number of receivers: %d" % nreceivers)
logger.info("Total number of windows: %d" % nwindows)
logger.info("Weight max, min, max/min: %f, %f, %f"
% (maxw, minw, max_over_min))
logfile = log_prefix + ".weights.summary.json"
content = {"max_weights": maxw, "min_weights": minw,
"total_nwindows": np.sum(nwins_array),
"windows": nwindows, "receivers": nreceivers}
logger.info("Overall log file: %s" % logfile)
dump_json(content, logfile)
def calculate_source_weights(src_info, param, logdir):
logger_block("Source Weighting")
points = assign_source_to_points(src_info)
ref_distance = -1.0
cond_numb = -1.0
if param["flag"]:
ref_distance, cond_numb = calculate_source_weights_on_location(
points, param["search_ratio"], param["plot"], logdir)
src_weights = normalize_source_weights(points)
# generate log file
log_content = \
{"weights": src_weights, "reference_distance": ref_distance,
"cond_num": cond_numb, "weight_flag": param["flag"],
"search_ratio": param["search_ratio"]}
outputfn = os.path.join(logdir, "source_weights.log.json")
print("Source weights log file: %s" % outputfn)
dump_json(log_content, outputfn)
return src_weights
def analysis_source(self, logfile):
"""
dump source weights and some statistic information
"""
log = {"source_weights": self.src_weights}
summary = {}
for _p, _pw in self.src_weights.iteritems():
summary[_p] = {}
for _comp, _compw in _pw.iteritems():
maxw = 0
minw = 10**9
for _ev, _evw in _compw.iteritems():
if _evw > maxw:
maxw = _evw
if _evw < minw:
minw = _evw
summary[_p][_comp] = \
{"maxw": maxw, "minw": minw,
"ref_distance": self.src_ref_dists[_p][_comp],
"cond_num": self.src_cond_nums[_p][_comp],
"nwindows": self.cat_wcounts[_p][_comp]}
log["summary"] = summary
dump_json(log, logfile)
def save_log(self):
def _get_max_and_min(dictv):
minv = 9999999999
maxv = -1
for e, einfo in dictv.iteritems():
for p, pinfo in einfo.iteritems():
for c, cinfo in pinfo.iteritems():
if minv > cinfo:
minv = cinfo
if maxv < cinfo:
maxv = cinfo
return minv, maxv
logfile = self.path["logfile"]
cond_min, cond_max = _get_max_and_min(self.rec_cond_nums)
ref_min, ref_max = _get_max_and_min(self.rec_ref_dists)
log_content = {
"receiver_ref_dist": self.rec_ref_dists,
"recevier_cond_numb": self.rec_cond_nums,
"summary": {
"receiver_ref_dist": {"min": ref_min, "max": ref_max},
"receiver_cond_num": {"min": cond_min, "max": cond_max}}}
print("log file: %s" % logfile)
dump_json(log_content, logfile)
def dump_weights(self):
""" dump weights to files """
for period, period_info in self.weights.iteritems():
for event, event_info in period_info.iteritems():
outputfn = self.path['input'][period][event]["output_file"]
dump_json(event_info, outputfn)
def dump_weights(self):
""" dump weights to files """
for period, period_info in self.weights.iteritems():
outputfn = self.path['input'][period]["output_file"]
dump_json(period_info, outputfn)
