def iris_get_responses(stations, time, fn_template):
for sta in stations:
fn = fn_template % dict(
network=sta.network, station=sta.station, location=sta.location)
if not os.path.isfile(fn):
try:
fi = iris_ws.ws_resp(sta.network,
sta.station,
sta.location,
'*',
time=time)
util.ensuredirs(fn)
fo = open(fn, 'w')
while True:
data = fi.read(1024)
if not data:
break
fo.write(data)
fo.close()
fi.close()
except iris_ws.NotFound:
pass
for cha in sta.get_channels():
class DummyTrace:
pass
tr = DummyTrace()
tr.tmin = time
tr.tmax = time
tr.nslc_id = (sta.network, sta.station, sta.location, cha.name)
if os.path.exists(fn):
cha.inv_response = trace.InverseEvalresp(fn, tr)
else:
cha.inv_response = None
def OFF_test_response(self):
tmin = stt('2014-01-01 00:00:00')
tmax = stt('2014-01-02 00:00:00')
sx = fdsn_ws.station(
site='iris',
network='II',
channel='?HZ',
startbefore=tmin,
endafter=tmax,
level='channel', format='text', matchtimeseries=True)
for nslc in sx.nslc_code_list:
print nslc
net, sta, loc, cha = nslc
sxr = fdsn_ws.station(
site='iris',
network=net,
station=sta,
location=loc,
channel=cha,
startbefore=tmin,
endafter=tmax,
level='response', matchtimeseries=True)
fi = iris_ws.ws_resp(
network=net,
station=sta,
location=loc,
channel=cha,
tmin=tmin,
tmax=tmax)
_, fn = tempfile.mkstemp()
fo = open(fn, 'w')
while True:
d = fi.read(1024)
if not d:
break
fo.write(d)
fo.close()
resp_sx = sxr.get_pyrocko_response(
nslc, timespan=(tmin, tmax),
fake_input_units='M/S')
resp_er = trace.Evalresp(fn, target='vel', nslc_id=nslc, time=tmin)
fmin = 0.001
fmax = 100.
for _, _, channel in sxr.iter_network_station_channels(
net, sta, loc, cha, timespan=(tmin, tmax)):
if channel.response:
fmax = channel.sample_rate.value * 0.5
f = num.exp(num.linspace(num.log(fmin), num.log(fmax), 500))
try:
t_sx = resp_sx.evaluate(f)
t_er = resp_er.evaluate(f)
import pylab as lab
abs_dif = num.abs(num.abs(t_sx) - num.abs(t_er)) / num.max(
num.abs(t_er))
mda = num.mean(abs_dif[f < 0.5*fmax])
pha_dif = num.abs(num.angle(t_sx) - num.angle(t_er))
mdp = num.mean(pha_dif[f < 0.5*fmax])
print mda, mdp
if mda > 0.03 or mdp > 0.04:
lab.gcf().add_subplot(2, 1, 1)
lab.plot(f, num.abs(t_sx), color='black')
lab.plot(f, num.abs(t_er), color='red')
lab.xscale('log')
lab.yscale('log')
lab.gcf().add_subplot(2, 1, 2)
lab.plot(f, num.angle(t_sx), color='black')
lab.plot(f, num.angle(t_er), color='red')
lab.xscale('log')
lab.show()
else:
print 'ok'
except:
print 'failed: ', nslc
def OFF_test_response(self):
tmin = stt('2014-01-01 00:00:00')
tmax = stt('2014-01-02 00:00:00')
sx = fdsn.ws.station(site='iris',
network='IU',
channel='?HZ',
startbefore=tmin,
endafter=tmax,
level='channel',
format='text',
matchtimeseries=True)
for nslc in sx.nslc_code_list:
net, sta, loc, cha = nslc
sxr = fdsn.ws.station(site='iris',
network=net,
station=sta,
location=loc,
channel=cha,
startbefore=tmin,
endafter=tmax,
level='response',
matchtimeseries=True)
fi = iris_ws.ws_resp(network=net,
station=sta,
location=loc,
channel=cha,
tmin=tmin,
tmax=tmax)
_, fn = tempfile.mkstemp()
fo = open(fn, 'w')
while True:
d = fi.read(1024)
if not d:
break
fo.write(d)
fo.close()
resp_sx = sxr.get_pyrocko_response(nslc, timespan=(tmin, tmax))
resp_er = trace.Evalresp(fn, target='vel', nslc_id=nslc, time=tmin)
fmin = 0.001
fmax = 100.
for _, _, channel in sxr.iter_network_station_channels(
net, sta, loc, cha, timespan=(tmin, tmax)):
if channel.response:
fmax = channel.sample_rate.value * 0.5
f = num.exp(num.linspace(num.log(fmin), num.log(fmax), 500))
try:
t_sx = resp_sx.evaluate(f)
t_er = resp_er.evaluate(f)
import pylab as lab
abs_dif = num.abs(num.abs(t_sx) - num.abs(t_er)) / num.max(
num.abs(t_er))
mda = num.mean(abs_dif[f < 0.5 * fmax])
pha_dif = num.abs(num.angle(t_sx) - num.angle(t_er))
mdp = num.mean(pha_dif[f < 0.5 * fmax])
print mda, mdp
if mda > 0.03 or mdp > 0.04:
lab.gcf().add_subplot(2, 1, 1)
lab.plot(f, num.abs(t_sx), color='black')
lab.plot(f, num.abs(t_er), color='red')
lab.xscale('log')
lab.yscale('log')
lab.gcf().add_subplot(2, 1, 2)
lab.plot(f, num.angle(t_sx), color='black')
lab.plot(f, num.angle(t_er), color='red')
lab.xscale('log')
lab.show()
else:
print 'ok'
except:
print 'failed: ', nslc
def OFF_test_response(self):
tmin = stt("2014-01-01 00:00:00")
tmax = stt("2014-01-02 00:00:00")
sx = fdsn_ws.station(
site="iris",
network="IU",
channel="?HZ",
startbefore=tmin,
endafter=tmax,
level="channel",
format="text",
matchtimeseries=True,
)
for nslc in sx.nslc_code_list:
net, sta, loc, cha = nslc
sxr = fdsn_ws.station(
site="iris",
network=net,
station=sta,
location=loc,
channel=cha,
startbefore=tmin,
endafter=tmax,
level="response",
matchtimeseries=True,
)
fi = iris_ws.ws_resp(network=net, station=sta, location=loc, channel=cha, tmin=tmin, tmax=tmax)
_, fn = tempfile.mkstemp()
fo = open(fn, "w")
while True:
d = fi.read(1024)
if not d:
break
fo.write(d)
fo.close()
resp_sx = sxr.get_pyrocko_response(nslc, timespan=(tmin, tmax))
resp_er = trace.Evalresp(fn, target="vel", nslc_id=nslc, time=tmin)
fmin = 0.001
fmax = 100.0
for _, _, channel in sxr.iter_network_station_channels(net, sta, loc, cha, timespan=(tmin, tmax)):
if channel.response:
fmax = channel.sample_rate.value * 0.5
f = num.exp(num.linspace(num.log(fmin), num.log(fmax), 500))
try:
t_sx = resp_sx.evaluate(f)
t_er = resp_er.evaluate(f)
import pylab as lab
abs_dif = num.abs(num.abs(t_sx) - num.abs(t_er)) / num.max(num.abs(t_er))
mda = num.mean(abs_dif[f < 0.5 * fmax])
pha_dif = num.abs(num.angle(t_sx) - num.angle(t_er))
mdp = num.mean(pha_dif[f < 0.5 * fmax])
print mda, mdp
if mda > 0.03 or mdp > 0.04:
lab.gcf().add_subplot(2, 1, 1)
lab.plot(f, num.abs(t_sx), color="black")
lab.plot(f, num.abs(t_er), color="red")
lab.xscale("log")
lab.yscale("log")
lab.gcf().add_subplot(2, 1, 2)
lab.plot(f, num.angle(t_sx), color="black")
lab.plot(f, num.angle(t_er), color="red")
lab.xscale("log")
lab.show()
else:
print "ok"
except:
print "failed: ", nslc