def test_average(ind=0):
R1phi = []
R1cc = []
R2phi = []
R2cc = []
for ind in range(4):
meta = test_calc(ind)
if meta is None:
continue
R1phi.append(meta.R1phi)
R2phi.append(meta.R2phi)
R1cc.append(meta.R1cc)
R2cc.append(meta.R2cc)
R1phi = np.array(R1phi).flatten()
R1cc = np.array(R1cc).flatten()
R2phi = np.array(R2phi).flatten()
R2cc = np.array(R2cc).flatten()
stkey = ''
phi = np.concatenate((R1phi, R2phi), axis=None)
cc = np.concatenate((R1cc, R2cc), axis=None)
ind = cc > 0.5
val, err = utils.estimate(phi, ind)
plot = plotting.plot_dl_results(stkey, R1phi, R1cc, R2phi, R2cc, ind, val,
err, phi, cc, 0.5)
def test_average():
phi = []
cc = []
snr = []
TR = []
RZ = []
baz = []
mag = []
for ind in range(20):
meta = test_calc(ind)
if meta is None:
continue
phi.append(meta.phi)
snr.append(meta.snr)
cc.append(np.abs(meta.cc))
TR.append(meta.TR)
RZ.append(meta.RZ)
baz.append(meta.baz)
mag.append(meta.mag)
phi = np.array(phi)
cc = np.array(cc)
snr = np.array(snr)
TR = np.array(TR)
RZ = np.array(RZ)
baz = np.array(baz)
mag = np.array(mag)
stkey = ''
# Set conditions for good result
snrp = snr > -1.
ccp = cc > -1.
TRp = TR > -1.
RZp = RZ > -1.
# Indices where conditions are met
ind = snrp * ccp * TRp * RZp
# Get estimate and uncertainty
val, err = utils.estimate(phi, ind)
plot = plotting.plot_bng_conditions(stkey, snr, cc, TR, RZ, ind)
plot = plotting.plot_bng_results(stkey, phi, snr, cc, TR, RZ, baz, mag,
ind, val, err)
def main():
# Run the Input Parser
args = arguments.get_dl_average_arguments()
# Load Database
db, stkeys = stdb.io.load_db(fname=args.indb, keys=args.stkeys)
# Loop over station keys
for stkey in list(stkeys):
sta = db[stkey]
# Input directory
indir = Path(args.loadloc) / stkey.upper()
if not indir.exists():
raise (Exception("Directory does not exist: ", indir,
", aborting"))
# Temporary print locations
tlocs = sta.location
if len(tlocs) == 0:
tlocs = ['']
for il in range(0, len(tlocs)):
if len(tlocs[il]) == 0:
tlocs[il] = "--"
sta.location = tlocs
# Update Display
if args.verb > 1:
print("|==============================================|")
print("| {0:>8s} |".format(
sta.station))
print("|==============================================|")
print("| Station: {0:>2s}.{1:5s} |".
format(sta.network, sta.station))
print("| Channel: {0:2s}; Locations: {1:15s} |".format(
sta.channel, ",".join(tlocs)))
print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format(
sta.longitude, sta.latitude))
print("| Input Directory: ", args.loadloc)
print("| Plot Results: ", args.showplot)
print("|")
R1phi = []
R1cc = []
R2phi = []
R2cc = []
for folder in os.listdir(indir):
# Load meta data
filename = indir / folder / "Meta_Data.pkl"
if not filename.is_file():
continue
meta = pickle.load(open(filename, 'rb'))
R1phi.append(meta.R1phi)
R2phi.append(meta.R2phi)
R1cc.append(meta.R1cc)
R2cc.append(meta.R2cc)
R1phi = np.array(R1phi).flatten()
R1cc = np.array(R1cc).flatten()
R2phi = np.array(R2phi).flatten()
R2cc = np.array(R2cc).flatten()
phi = np.concatenate((R1phi, R2phi), axis=None)
cc = np.concatenate((R1cc, R2cc), axis=None)
ind = cc > args.cc
val, err = utils.estimate(phi, ind)
# output results to termianl
print("| D-L mean, error, data included: " +
"{0:.2f}, {1:.2f}, {2}".format(val, err, np.sum(ind)))
print("| D-L CC level: {0:.1f}".format(args.cc))
print()
if np.sum(np.isnan(np.array([val, err]))) > 0:
continue
if args.showplot or args.saveplot:
plot = plotting.plot_dl_results(stkey, R1phi, R1cc, R2phi, R2cc,
ind, val, err, phi, cc, args.cc)
# save figure
if args.saveplot:
figname = indir / ('results.' + args.fmt)
plot.savefig(figname, fmt=args.fmt)
if args.showplot:
plot.show()
def main():
# Run the Input Parser
args = arguments.get_bng_average_arguments()
# Load Database
db, stkeys = stdb.io.load_db(fname=args.indb, keys=args.stkeys)
# Loop over station keys
for stkey in list(stkeys):
sta = db[stkey]
# Input directory
indir = Path(args.loadloc) / stkey.upper()
if not indir.exists():
raise (Exception("Directory does not exist: ", indir,
", aborting"))
# Temporary print locations
tlocs = sta.location
if len(tlocs) == 0:
tlocs = ['']
for il in range(0, len(tlocs)):
if len(tlocs[il]) == 0:
tlocs[il] = "--"
sta.location = tlocs
# Update Display
if args.verb > 1:
print("|==============================================|")
print("| {0:>8s} |".format(
sta.station))
print("|==============================================|")
print("| Station: {0:>2s}.{1:5s} |".
format(sta.network, sta.station))
print("| Channel: {0:2s}; Locations: {1:15s} |".format(
sta.channel, ",".join(tlocs)))
print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format(
sta.longitude, sta.latitude))
print("| Input Directory: ", args.loadloc)
print("| Plot Results: ", args.showplot)
print("|")
phi = []
cc = []
snr = []
TR = []
RZ = []
baz = []
mag = []
for folder in os.listdir(indir):
# Load meta data
filename = indir / folder / "Meta_Data.pkl"
if not filename.is_file():
continue
meta = pickle.load(open(filename, 'rb'))
phi.append(meta.phi)
snr.append(meta.snr)
cc.append(np.abs(meta.cc))
TR.append(meta.TR)
RZ.append(meta.RZ)
baz.append(meta.baz)
mag.append(meta.mag)
phi = np.array(phi)
cc = np.array(cc)
snr = np.array(snr)
TR = np.array(TR)
RZ = np.array(RZ)
baz = np.array(baz)
mag = np.array(mag)
# Set conditions for good result
snrp = snr > args.snr
ccp = cc > args.cc
TRp = TR > args.TR
RZp = RZ > args.RZ
# Indices where conditions are met
ind = snrp * ccp * TRp * RZp
# Get estimate and uncertainty
val, err = utils.estimate(phi, ind)
# output results to termianl
print("| B-N-G mean, error, data included: " +
"{0:.1f}, {1:.1f}, {2}".format(val, err, np.sum(ind)))
print()
if np.sum(np.isnan(np.array([val, err]))) > 0:
continue
if args.showplot or args.saveplot:
plot = plotting.plot_bng_conditions(stkey, snr, cc, TR, RZ, ind)
# save figure
if args.saveplot:
figname = indir / ('conditions.' + args.fmt)
plot.savefig(figname, fmt=args.fmt)
if args.showplot:
plot.show()
plot = plotting.plot_bng_results(stkey, phi, snr, cc, TR, RZ, baz,
mag, ind, val, err)
# save figure
if args.saveplot:
figname = indir / ('results.' + args.fmt)
plot.savefig(figname, fmt=args.fmt)
if args.showplot:
plot.show()
