step=100
#distances = num.arange(40, 180, step)*km
#distances = num.array([50.,100.,150,190])*km
gain = 1.1
ref_event = model.Event(load=pjoin(derec_home,
'mseeds',
'castor',
'castor_event_2013-10-01.dat'))
ref_source = DCSource.from_pyrocko_event(ref_event)
ref_source.strike = 45
ref_source.dip = 90
ref_source.rake = 0
sources = du.test_event_generator(ref_source, depths)
channels = 'Z'
targets = [Target(lat=ref_source.lat,
lon=ref_source.lon,
north_shift=north_shift,
store_id=store_id,
codes=('','','',channel)) for channel in channels for north_shift in
distances]
resp = e.process(sources=sources, targets=targets)
tracs = du.response_to_dict(resp)
stf = [[0.,0.15],[0.,1.]]
du.apply_stf(tracs, stf)
tmin_arrivals = defaultdict()
def doit(sdr=None):
azimutti = num.linspace(0,360, 31)
tmax = 2.
f = plt.figure(figsize=(2.,2.1))
ax = f.add_subplot(111, polar=True)
AZIMS = []
DS=[]
VALS=[]
STATIONAZIMS= []
STATIONDISTS = []
ref_source = DCSource.from_pyrocko_event(ref_event)
channels = 'Z'
targets = [Target(lat=ref_source.lat,
lon=ref_source.lon,
north_shift=north_shift,
store_id=store_id,
codes=('','','',channel)) for channel in channels for north_shift in
distances]
stations = model.load_stations(pjoin(derec_home, 'mseeds', 'doctar',
'doctar_2011-11-01', 'stations.txt'))
doctar_targets = du.stations2targets(stations, store_id)
for t in doctar_targets:
az = t.azibazi_to(ref_source)
d = t.distance_to(ref_source)
STATIONAZIMS.append(az[1]*(num.pi/180))
STATIONDISTS.append(d/1000.)
if sdr is not None:
ref_source.strike = sdr[0]
ref_source.dip = sdr[1]
ref_source.rake = sdr[2]
st = ref_source.strike
di = ref_source.dip
ra = ref_source.rake
for azim in azimutti:
print azim
z_intmin = defaultdict()
ymin_int = defaultdict()
ref_source.strike = azim
sources = du.test_event_generator(ref_source, depths)
resp = e.process(sources=sources, targets=targets)
tracs = du.response_to_dict(resp)
stf = [[0.,0.15],[0.,1.]]
du.apply_stf(tracs, stf)
tmin_arrivals = defaultdict()
dt = e.get_store(store_id).config.sample_rate
tmax_samples = tmax*dt
ymaxs = defaultdict(list)
phase_ids = ['p', 'pP', 'P']
all_ars = defaultdict()
for d in distances:
for z in depths:
ts = []
key = (d, z)
phases = map(cake.PhaseDef, phase_ids)
for a in m.arrivals(phases=phases, distances=[d*cake.m2d], zstart=z):
ts.append(a)
all_ars[(key)] = ts
#tmin_arrivals[key] = min([tr.t for tr in ts])
tmin_arrivals[key] = pc.cake_first_arrival(d, z, m, phase_ids)
sources_dict = defaultdict()
axes_dict = defaultdict(dict)
#for t in targets:
# fig, axs = plt.subplots(len(sources), 1, sharey=True, figsize=(4,3), dpi=160)
# fig.myadjustment = False
# ad = dict(zip(sources, axs))
# axes_dict[t] = ad
for s, tt in tracs.items():
# das muss vor die loop fuer stacking
for t, tr in tt.items():
#ax = axes_dict[t][s]
d = s.distance_to(t)
z = s.depth
tr.lowpass(2, 4)
tr.highpass(4, 0.5)
x = tr.get_xdata()-ref_source.time-tmin_arrivals[(d,z)]
y = tr.get_ydata()
x0_i = num.where(abs(x)==min(abs(x)))
#/prinprint 'has to be single value: ', x0_i
if len(x0_i)!=1:
print "WAAAARNING"
x0_i = x0_i[0][0]
y_plot = y[x0_i:x0_i+tmax_samples]
y_int = num.sum((y_plot**2))/tmax_samples
try:
if y_int<ymin_int[d]:
ymin_int[d] = y_int
z_intmin[d] = s.depth
except KeyError:
ymin_int[d] = y_int
z_intmin[d] = s.depth
#for d, minz in z_intmin.items():
azim = azim/180*num.pi
azims = [azim]*len(z_intmin.keys())
d = z_intmin.keys()
vals = z_intmin.values()
DS.extend(d)
AZIMS.extend(azims)
VALS.extend(vals)
#VALS = 5.-num.array(VALS)/1000.
#VALS = correct_depth-num.array(VALS)/1000.
VALS = num.array(VALS)/1000.
#DS = [d-ref_source.depth for d in DS]
AZIMS = num.array(AZIMS)
DS = num.array(DS)/1000.
fn = 'polar_%s_%s_%s_%s.txt'%(test_type, st, di, ra)
num.savetxt(fn, num.array((AZIMS, DS, VALS)))
