def get_receiver_weights(component, center, points, max_ratio, plot=False,
figname_prefix=None):
"""
Calculate the receiver weights given receiver(points) distribution.
:param component:
:param center:
:param points:
:param max_ratio:
:param plot:
:param figname_prefix:
:return:
"""
# calculate weight; otherwise, leave it as default value(1)
weightobj = SphereDistRel(points, center=center)
if plot:
scan_figname = figname_prefix + ".%s.smart_scan.png" % component
else:
scan_figname = None
ref_distance, cond_number = weightobj.smart_scan(
max_ratio=max_ratio, start=0.5, gap=0.5,
drop_ratio=0.95, plot=plot,
figname=scan_figname)
if plot:
map_figname = figname_prefix + ".%s.weight.pdf" % component
weightobj.plot_global_map(figname=map_figname, lon0=180.0)
return ref_distance, cond_number
def calculate_source_weights_on_location(
points, search_ratio, plot_flag, outputdir):
"""
:param outputdir: output directory for figures
"""
# set a fake center point
center = SpherePoint(0, 180.0, tag="Center")
weightobj = SphereDistRel(points, center=center)
if plot_flag:
scan_figname = os.path.join(
outputdir, "source_weights.smart_scan.png")
else:
scan_figname = None
ref_distance, cond_number = weightobj.smart_scan(
max_ratio=search_ratio, start=0.1, gap=0.2,
drop_ratio=0.95, plot=plot_flag,
figname=scan_figname)
print("Reference distance and condition number: %f, %f"
% (ref_distance, cond_number))
if plot_flag:
map_figname = os.path.join(
outputdir, "source_weights.global_map.pdf")
weightobj.plot_global_map(figname=map_figname, lon0=180.0)
return ref_distance, cond_number
def calculate_source_weights_on_location(points, search_ratio, plot_flag,
outputdir):
"""
:param outputdir: output directory for figures
"""
# set a fake center point
center = SpherePoint(0, 180.0, tag="Center")
weightobj = SphereDistRel(points, center=center)
if plot_flag:
scan_figname = os.path.join(outputdir, "source_weights.smart_scan.png")
else:
scan_figname = None
ref_distance, cond_number = weightobj.smart_scan(max_ratio=search_ratio,
start=0.1,
gap=0.2,
drop_ratio=0.95,
plot=plot_flag,
figname=scan_figname)
print("Reference distance and condition number: %f, %f" %
(ref_distance, cond_number))
if plot_flag:
map_figname = os.path.join(outputdir, "source_weights.global_map.pdf")
weightobj.plot_global_map(figname=map_figname, lon0=180.0)
return ref_distance, cond_number
def test_spheredistrel(points, center):
outputdir = "test23/"
weight = SphereDistRel(points)
#weight.calculate_weight(23.0)
#weight.scan(start=0.1, end=50.0, gap=1.0, plot=True,
# figname="scan.png")
weight.smart_scan(plot=True)
weight.plot_exp_matrix(outputdir + "exp_matrx.png")
weight.plot_global_map(outputdir + "map.png")
weight.plot_weight_histogram(outputdir + "weight_hist.png")
def test_spheredistrel(points, center):
outputdir = "test23/"
weight = SphereDistRel(points)
#weight.calculate_weight(23.0)
#weight.scan(start=0.1, end=50.0, gap=1.0, plot=True,
# figname="scan.png")
weight.smart_scan(plot=True)
weight.plot_exp_matrix(outputdir + "exp_matrx.png")
weight.plot_global_map(outputdir + "map.png")
weight.plot_weight_histogram(outputdir + "weight_hist.png")
def get_receiver_weights(component,
center,
points,
max_ratio,
plot=False,
figname_prefix=None):
"""
Calculate the receiver weights given receiver(points) distribution.
:param component:
:param center:
:param points:
:param max_ratio:
:param plot:
:param figname_prefix:
:return:
"""
# calculate weight; otherwise, leave it as default value(1)
weightobj = SphereDistRel(points, center=center)
if plot:
scan_figname = figname_prefix + ".%s.smart_scan.png" % component
else:
scan_figname = None
ref_distance, cond_number = weightobj.smart_scan(max_ratio=max_ratio,
start=0.5,
gap=0.5,
drop_ratio=0.95,
plot=plot,
figname=scan_figname)
if plot:
map_figname = figname_prefix + ".%s.weight.pdf" % component
weightobj.plot_global_map(figname=map_figname, lon0=180.0)
return ref_distance, cond_number
def determine_source_weighting(src_info, src_wcounts, max_ratio=0.35,
flag=True, plot=False,
figname_prefix=None):
"""
Determine the source weighting based on source distribution and
window counts.
Attention here, there is still 3 components and each category
should be weighting independently with the window count information
in this components.
"""
logger.info("Number of sources: %s" % len(src_info))
logger.info("Window counts information: %s" % src_wcounts)
# determine the weightins based on location
points = []
for eventname, event_info in src_info.iteritems():
point = SpherePoint(event_info["latitude"],
event_info["longitude"],
tag=eventname,
weight=1.0)
points.append(point)
if flag:
weightobj = SphereDistRel(points)
scan_figname = figname_prefix + ".smart_scan.png"
_ref_dist, _cond_num = weightobj.smart_scan(
max_ratio=max_ratio, start=0.5, gap=0.5,
drop_ratio=0.80, plot=plot,
figname=scan_figname)
if plot:
figname = figname_prefix + ".weight.png"
weightobj.plot_global_map(figname=figname, lon0=180.0)
# stats window counts in category level
cat_wcounts = {}
for event, event_info in src_wcounts.iteritems():
for comp, comp_info in event_info.iteritems():
cat_wcounts.setdefault(comp, 0)
cat_wcounts[comp] += comp_info
weights = {}
ref_dists = {}
cond_nums = {}
# nomalization
for comp in cat_wcounts:
# ref dist and condition number are the same for different components
# because the source distribution is exactly the same
ref_dists[comp] = _ref_dist
cond_nums[comp] = _cond_num
wsum = 0
for point in points:
nwin = src_wcounts[point.tag][comp]
wsum += point.weight * nwin
norm_factor = cat_wcounts[comp] / wsum
weights[comp] = {}
for point in points:
eventname = point.tag
weights[comp][eventname] = point.weight * norm_factor
_source_validator(weights, src_wcounts, cat_wcounts)
return {"src_weights": weights, "cat_wcounts": cat_wcounts,
"src_ref_dists": ref_dists, "src_cond_nums": cond_nums}
def determine_receiver_weighting(src, stations, windows, max_ratio=0.35,
flag=True, plot=False,
figname_prefix=None):
"""
Given one station and window information, determine the receiver
weighting
In one asdf file, there are still 3 components, for example,
["BHR", "BHT", "BHZ"]. These three components should be treated
indepandently and weights will be calculated independantly.
:return: dict of weights which contains 3 components. Each components
contains weights values
"""
center = SpherePoint(src["latitude"], src["longitude"],
tag="source")
# extract window information
weights = {}
rec_wcounts = {}
src_wcounts = defaultdict(lambda: 0)
for sta, sta_window in windows.iteritems():
for chan, chan_win in sta_window.iteritems():
comp = chan.split(".")[-1]
_nwin = len(chan_win)
if _nwin == 0:
continue
weights.setdefault(comp, {}).update({chan: 0.0})
rec_wcounts[chan] = _nwin
src_wcounts[comp] += _nwin
# in each components, calculate weight
ref_dists = {}
cond_nums = {}
for comp, comp_info in weights.iteritems():
points = []
logger.info("Components:%s" % comp)
for chan in comp_info:
_comp = chan[-1]
if _comp == "Z":
point = SpherePoint(stations[chan]["latitude"],
stations[chan]["longitude"],
tag=chan, weight=1.0)
else:
# for R and T component. In station file, there
# are only E and N component. So we need transfer
echan = chan[:-1] + "E"
chan1 = chan[:-1] + "1"
zchan = chan[:-1] + "Z"
if echan in stations:
point = SpherePoint(stations[echan]["latitude"],
stations[echan]["longitude"],
tag=chan, weight=1.0)
elif chan1 in stations:
point = SpherePoint(stations[chan1]["latitude"],
stations[chan1]["longitude"],
tag=chan, weight=1.0)
elif zchan in stations:
point = SpherePoint(stations[zchan]["latitude"],
stations[zchan]["longitude"],
tag=chan, weight=1.0)
points.append(point)
if flag:
# calculate weight; otherwise, leave it as default value(1)
weightobj = SphereDistRel(points, center=center)
scan_figname = figname_prefix + "%s.smart_scan.png" % comp
ref_dists[comp], cond_nums[comp] = weightobj.smart_scan(
max_ratio=max_ratio, start=0.5, gap=0.5,
drop_ratio=0.95, plot=plot,
figname=scan_figname)
if plot:
figname = figname_prefix + "%s.weight.png" % comp
weightobj.plot_global_map(figname=figname, lon0=180.0)
else:
ref_dists[comp] = None
cond_nums[comp] = None
wsum = 0
for point in points:
nwin = rec_wcounts[point.tag]
wsum += point.weight * nwin
norm_factor = src_wcounts[comp] / wsum
for point in points:
weights[comp][point.tag] = point.weight * norm_factor
_receiver_validator(weights, rec_wcounts, src_wcounts)
return {"rec_weights": weights, "rec_wcounts": rec_wcounts,
"src_wcounts": src_wcounts, "rec_ref_dists": ref_dists,
"rec_cond_nums": cond_nums}
def plot_global_map(points, figname=None):
weightobj = SphereDistRel(points, SpherePoint(0, 180.0, tag="Center"))
weightobj.plot_global_map(figname=figname, lon0=180.0, figsize=(20, 8))