def test_evalresp_seed_identifiers_work(self):
"""
Asserts that the network, station, location and channel identifiers can
be used to select difference responses.
"""
kwargs = {"filename": os.path.join(self.path, "RESP.OB.AAA._.BH_"),
"t_samp": 0.1, "nfft": 1024, "units": "VEL",
"date": UTCDateTime(2013, 1, 1), "network": "OP",
"station": "AAA", "locid": "", "freq": False, "debug": False}
# Get the response for the first channel
kwargs["channel"] = "BHE"
response_1 = evalresp(**kwargs)
# Get the second one. Should be different.
kwargs["channel"] = "BHN"
response_2 = evalresp(**kwargs)
# The only thing that changed was the channel code. This should change
# the response.
rel_diff = np.abs(response_2 - response_1).ptp() / \
max(np.abs(response_1).ptp(), np.abs(response_2).ptp())
self.assertGreater(rel_diff, 1E-3)
# The RESP file only contains two channels.
kwargs["channel"] = "BHZ"
# suppress a C-level "no response found" warning
with SuppressOutput():
self.assertRaises(ValueError, evalresp, **kwargs)
def test_evalresp_segfault(t_samp, filename, date, stat_id, chan_id, net_id,
loc_id, units):
out = None
try:
with CatchOutput() as out:
evalresp(
t_samp, 5, filename, date=date, station=stat_id,
channel=chan_id, network=net_id, locid=loc_id,
units=units, freq=True)
except:
if out and out.stderr and "are not supported" in out.stderr:
sys.exit(99)
else:
sys.exit(1)
def test_evalresp_with_output_from_seed(self):
"""
The StationXML file has been converted to SEED with the help of a tool
provided by IRIS:
https://seiscode.iris.washington.edu/projects/stationxml-converter
"""
t_samp = 0.05
nfft = 16384
# Test for different output units.
units = ["DISP", "VEL", "ACC"]
filenames = ["IRIS_single_channel_with_response", "XM.05", "AU.MEEK"]
for filename in filenames:
xml_filename = os.path.join(self.data_dir,
filename + os.path.extsep + "xml")
seed_filename = os.path.join(self.data_dir,
filename + os.path.extsep + "seed")
p = Parser(seed_filename)
# older systems don't like an end date in the year 2599
t_ = UTCDateTime(2030, 1, 1)
if p.blockettes[50][0].end_effective_date > t_:
p.blockettes[50][0].end_effective_date = None
if p.blockettes[52][0].end_date > t_:
p.blockettes[52][0].end_date = None
resp_filename = p.getRESP()[0][-1]
inv = read_inventory(xml_filename)
network = inv[0].code
station = inv[0][0].code
location = inv[0][0][0].location_code
channel = inv[0][0][0].code
date = inv[0][0][0].start_date
for unit in units:
resp_filename.seek(0, 0)
seed_response, seed_freq = evalresp(t_samp,
nfft,
resp_filename,
date=date,
station=station,
channel=channel,
network=network,
locid=location,
units=unit,
freq=True)
xml_response, xml_freq = \
inv[0][0][0].response.get_evalresp_response(t_samp, nfft,
output=unit)
self.assertTrue(np.allclose(seed_freq, xml_freq, rtol=1E-5))
self.assertTrue(
np.allclose(seed_response, xml_response, rtol=1E-5))
def _get_response_from_RESP(self, tr):
resp = evalresp(t_samp=self.delta, nfft=self.nfft,
filename=self.metadata, date=tr.stats.starttime,
station=self.station, channel=self.channel,
network=self.network, locid=self.location,
units="VEL", freq=False, debug=False)
return resp
def _get_response_from_RESP(self, tr):
resp = evalresp(t_samp=self.delta, nfft=self.nfft,
filename=self.metadata, date=tr.stats.starttime,
station=self.station, channel=self.channel,
network=self.network, locid=self.location,
units="VEL", freq=False, debug=False)
return resp
def test_evalresp_segfault(t_samp, filename, date, stat_id, chan_id, net_id,
loc_id, units):
out = None
try:
with CatchOutput() as out:
evalresp(t_samp,
5,
filename,
date=date,
station=stat_id,
channel=chan_id,
network=net_id,
locid=loc_id,
units=units,
freq=True)
except:
if out and out.stderr and "are not supported" in out.stderr:
sys.exit(99)
else:
sys.exit(1)
def test_evalresp_with_output_from_seed(self):
"""
The StationXML file has been converted to SEED with the help of a tool
provided by IRIS:
https://seiscode.iris.washington.edu/projects/stationxml-converter
"""
t_samp = 0.05
nfft = 16384
# Test for different output units.
units = ["DISP", "VEL", "ACC"]
filenames = ["IRIS_single_channel_with_response", "XM.05", "AU.MEEK"]
for filename in filenames:
xml_filename = os.path.join(self.data_dir,
filename + os.path.extsep + "xml")
seed_filename = os.path.join(self.data_dir,
filename + os.path.extsep + "seed")
p = Parser(seed_filename)
# older systems don't like an end date in the year 2599
t_ = UTCDateTime(2030, 1, 1)
if p.blockettes[50][0].end_effective_date > t_:
p.blockettes[50][0].end_effective_date = None
if p.blockettes[52][0].end_date > t_:
p.blockettes[52][0].end_date = None
resp_filename = p.getRESP()[0][-1]
inv = read_inventory(xml_filename)
network = inv[0].code
station = inv[0][0].code
location = inv[0][0][0].location_code
channel = inv[0][0][0].code
date = inv[0][0][0].start_date
for unit in units:
resp_filename.seek(0, 0)
seed_response, seed_freq = evalresp(
t_samp, nfft, resp_filename, date=date, station=station,
channel=channel, network=network, locid=location,
units=unit, freq=True)
xml_response, xml_freq = \
inv[0][0][0].response.get_evalresp_response(t_samp, nfft,
output=unit)
self.assertTrue(np.allclose(seed_freq, xml_freq, rtol=1E-5))
self.assertTrue(np.allclose(seed_response, xml_response,
rtol=1E-5))
def getspectra(string):
try:
#if True:
debug = True
lenfft = 20000
resppath = '/APPS/metadata/RESPS/'
stringTEMP = string.split('_')
net = stringTEMP[0]
sta = stringTEMP[1]
loc = stringTEMP[2]
chan = stringTEMP[3]
year = stringTEMP[4]
jday = stringTEMP[5]
if debug:
print 'Working on: ' + net + ' ' + sta + ' ' + loc + ' ' + chan
stime = UTCDateTime(year + '-' + jday + "T00:00:00")
st = client.timeseries(net, sta, loc, chan, stime, stime + 24*60*60)
if debug:
print(st)
if len(st) == 1:
power,freq = csd(st[0].data,st[0].data, NFFT = lenfft,
noverlap = int(lenfft*.5), Fs = 1/st[0].stats.delta,
scale_by_freq = True)
power = power[1:].real
freq = freq[1:]
resp = evalresp(t_samp = st[0].stats.delta, nfft = lenfft, filename= resppath + 'RESP.' + net + '.' + \
sta + '.' + loc + '.' + chan, date = st[0].stats.starttime, station = sta,
channel = chan, network = net, locid = loc, units = 'ACC')
resp = resp[1:]
power = 10.*np.log10(power/np.absolute(resp*np.conjugate(resp)))
else:
power =[]
freq=[]
if len(power) > 1:
if debug:
print 'Writing data for: ' + string
if not os.path.exists('/TEST_ARCHIVE/PSDS/' + sta):
os.makedirs('/TEST_ARCHIVE/PSDS/' + sta)
if not os.path.exists('/TEST_ARCHIVE/PSDS/' + sta + '/' + year):
os.makedirs('/TEST_ARCHIVE/PSDS/' + sta + '/' + year)
fpsd = open('/TEST_ARCHIVE/PSDS/' + sta + '/' + year + '/PSD_' + string,'w')
for p,f in zip(power,freq):
fpsd.write(str(p) + ', ' + str(f) + '\n')
fpsd.close()
except:
print 'Unable to process: ' + string
return
def test_evalresp_seed_identifiers_work(self):
"""
Asserts that the network, station, location and channel identifiers can
be used to select difference responses.
"""
kwargs = {
"filename": os.path.join(self.path, "RESP.OB.AAA._.BH_"),
"t_samp": 0.1,
"nfft": 1024,
"units": "VEL",
"date": UTCDateTime(2013, 1, 1),
"network": "OP",
"station": "AAA",
"locid": "",
"freq": False,
"debug": False
}
# Get the response for the first channel
kwargs["channel"] = "BHE"
response_1 = evalresp(**kwargs)
# Get the second one. Should be different.
kwargs["channel"] = "BHN"
response_2 = evalresp(**kwargs)
# The only thing that changed was the channel code. This should change
# the response.
rel_diff = np.abs(response_2 - response_1).ptp() / \
max(np.abs(response_1).ptp(), np.abs(response_2).ptp())
self.assertTrue(rel_diff > 1E-3)
# The RESP file only contains two channels.
kwargs["channel"] = "BHZ"
with CatchOutput() as out:
self.assertRaises(ValueError, evalresp, **kwargs)
self.assertTrue("no response found for" in out.stderr.lower())
def test_evalrespBug395(self):
"""
Was a bug due to inconstistent numerical range
"""
resp = os.path.join(self.path, 'RESP.CH._.HHZ.gz')
with NamedTemporaryFile() as fh:
tmpfile = fh.name
fh.write(gzip.open(resp).read())
samprate = 120.0
nfft = 56328
args = [1.0 / samprate, nfft, tmpfile,
UTCDateTime(2012, 9, 4, 5, 12, 15, 863300)]
kwargs = {'units': 'VEL', 'freq': True}
_h, f = evalresp(*args, **kwargs)
self.assertEquals(len(f), nfft // 2 + 1)
def test_evalresp_bug_395(self):
"""
Was a bug due to inconstistent numerical range
"""
resp = os.path.join(self.path, "RESP.CH._.HHZ.gz")
with NamedTemporaryFile() as fh:
tmpfile = fh.name
with gzip.open(resp) as f:
fh.write(f.read())
samprate = 120.0
nfft = 56328
args = [1.0 / samprate, nfft, tmpfile, UTCDateTime(2012, 9, 4, 5, 12, 15, 863300)]
kwargs = {"units": "VEL", "freq": True}
_h, f = evalresp(*args, **kwargs)
self.assertEqual(len(f), nfft // 2 + 1)
def test_evalrespBug395(self):
"""
Was a bug due to inconstistent numerical range
"""
resp = os.path.join(self.path, 'RESP.CH._.HHZ.gz')
with NamedTemporaryFile() as fh:
tmpfile = fh.name
fh.write(gzip.open(resp).read())
samprate = 120.0
nfft = 56328
args = [1.0 / samprate, nfft, tmpfile,
UTCDateTime(2012, 9, 4, 5, 12, 15, 863300)]
kwargs = {'units': 'VEL', 'freq': True}
_h, f = evalresp(*args, **kwargs)
self.assertEquals(len(f), nfft // 2 + 1)
def _get_response_from_parser(self, tr):
parser = self.metadata
resp_key = "RESP." + self.id
for key, resp_file in parser.getRESP():
if key == resp_key:
break
else:
msg = "Response for %s not found in Parser" % self.id
raise ValueError(msg)
resp_file.seek(0, 0)
resp = evalresp(t_samp=self.delta, nfft=self.nfft,
filename=resp_file, date=tr.stats.starttime,
station=self.station, channel=self.channel,
network=self.network, locid=self.location,
units="VEL", freq=False, debug=False)
return resp
def _get_response_from_parser(self, tr):
parser = self.metadata
resp_key = "RESP." + self.id
for key, resp_file in parser.get_RESP():
if key == resp_key:
break
else:
msg = "Response for %s not found in Parser" % self.id
raise ValueError(msg)
resp_file.seek(0, 0)
resp = evalresp(t_samp=self.delta, nfft=self.nfft,
filename=resp_file, date=tr.stats.starttime,
station=self.station, channel=self.channel,
network=self.network, locid=self.location,
units="VEL", freq=False, debug=False)
return resp
def test_evalresp_using_different_line_separator(self):
"""
The evalresp needs a file with correct line separator, so '\n' for
POSIX, '\r' for Mac OS, or '\r\n' for Windows. Here we check that
evalresp reads all three formats.
This test only checks the parsing capabilities of evalresp,
the number of fft points used (nfft) can therefore be chosen
small.
"""
dt = UTCDateTime(2003, 11, 1, 0, 0, 0)
nfft = 8
# Linux
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
evalresp(0.01, nfft, respf, dt)
# Mac
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ.mac')
evalresp(0.01, nfft, respf, dt)
# Windows
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ.windows')
evalresp(0.01, nfft, respf, dt)
def test_evalrespUsingDifferentLineSeparator(self):
"""
The evalresp needs a file with correct line separator, so '\n' for
POSIX, '\r' for Mac OS, or '\r\n' for Windows. Here we check that
evalresp reads all three formats.
This test only checks the parsing capabilities of evalresp,
the number of fft points used (nfft) can therefore be chosen
small.
"""
dt = UTCDateTime(2003, 11, 1, 0, 0, 0)
nfft = 8
# linux
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
evalresp(0.01, nfft, respf, dt)
# mac
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ.mac')
evalresp(0.01, nfft, respf, dt)
# windows
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ.windows')
evalresp(0.01, nfft, respf, dt)
power = power[1:]
power = np.abs(power)
## Import metadata ##
for tr in st.select(channel='LH*'):
if net == 'XX':
resppath = '/home/aalejandro/Pressure/RESP/RESP.' + tr.id
else:
resppath = '/APPS/metadata/RESPS/RESP.' + tr.id
if debug:
print(resppath)
resp = evalresp(t_samp=tr.stats.delta,
nfft=NFFT,
filename=resppath,
date=tr.stats.starttime,
station=tr.stats.station,
channel=tr.stats.channel,
locid=tr.stats.location,
network=tr.stats.network,
units="ACC")
st.filter('bandpass', freqmin=1. / Pmax, freqmax=1. / Pmin)
st.taper(0.05)
st.sort()
sp = Parser()
for tr in st.select(channel='LH*'):
if net == 'XX':
sp.read('/home/aalejandro/Pressure/RESP/RESP.' + net + '.' +
sta + '.' + loc + '.' + chan)
else:
plt.subplot(3, 1, 3)
per, NLNM = get_nlnm()
per, NHNM = get_nhnm()
for tr in st:
power, freq = csd(tr.data,
tr.data,
NFFT=lenfft,
noverlap=int(lenfft * .5),
Fs=1. / tr.stats.delta,
scale_by_freq=True)
power = np.abs(power[1:])
freq = freq[1:]
resp = evalresp(t_samp=tr.stats.delta,
nfft=lenfft,
filename='/APPS/metadata/RESPS/RESP.' + tr.stats.network +
'.' + tr.stats.station + '.' + tr.stats.location + '.' +
tr.stats.channel,
date=tr.stats.starttime,
units='ACC')
resp = resp[1:]
power = 10. * np.log10(power / np.absolute(resp * np.conjugate(resp)))
if tr.stats.location == '00':
lab = 'STS-6A'
else:
lab = 'T-120'
plt.semilogx(1. / freq, power, label=lab)
plt.semilogx(per, NLNM, 'k', label="NHNM/NLNM")
plt.semilogx(per, NHNM, 'k')
plt.ylabel('Power (dB rel. 1 $m/(s^2)^2/Hz$)')
n, p, fre1 = selfnoise(stgood, length, overlap)
fig = plt.figure(1, figsize=(18, 18))
plt.subplot(2, 1, 1)
nm = (n['1'] + n['2'] + n['3']) / 3.
for idx in range(1, 4):
tr = stgood[idx - 1]
resp = evalresp(t_samp=tr.stats.delta,
nfft=length,
filename='/APPS/metadata/RESPS/RESP.' + tr.id,
date=tr.stats.starttime,
station=tr.stats.station,
channel=tr.stats.channel,
network=tr.stats.network,
locid=tr.stats.location,
units='ACC')
n[str(idx)] /= np.abs(resp[1:])**2
p[str(idx)] = 10. * np.log10(p[str(idx)] / np.abs(resp[1:])**2)
p[str(idx)] = octavesmooth(p[str(idx)], 1. / 6.)
n[str(idx)] = 10. * np.log10(n[str(idx)])
n[str(idx)] = octavesmooth(n[str(idx)], 1. / 6.)
plt.semilogx(1. / fre1,
p[str(idx)],
label='PSD ' + (tr.id).replace('.', ' '),
alpha=.7)
plt.semilogx(1. / fre1,
print_info("Raw evalresp segfaults. XML response not "
"attempted.")
counter["evalresp_segfaults"] += 1
continue
elif not output:
filename.seek(0, 0)
if debug:
print "SEED"
try:
seed_response, seed_freq = evalresp(
t_samp,
nfft,
filename,
date=date,
station=stat_id,
channel=chan_id,
network=net_id,
locid=loc_id,
units=unit,
freq=True)
except ValueError as e:
seedresp_error = e
else:
with CatchOutput() as out:
try:
seed_response, seed_freq = evalresp(
t_samp,
nfft,
filename,
date=date,
def _find_resp(station, channel, network, time, delta, directory):
"""
Helper function to find the response information for a given station and \
channel at a given time and return a dictionary of poles and zeros, gain \
and sensitivity.
:type station: str
:param station: Station name (as in the response files)
:type channel: str
:param channel: Channel name (as in the response files)
:type network: str
:param network: Network to scan for, can be a wildcard
:type time: datetime.datetime
:param time: Date-time to look for repsonse information
:type delta: float
:param delta: Sample interval in seconds
:type directory: str
:param directory: Directory to scan for response information
:returns: dict, response information
"""
import glob
from obspy.signal.invsim import evalresp
from obspy import UTCDateTime
possible_respfiles = glob.glob(directory + '/RESP.' + network + '.' +
station + '.*.' +
channel) # GeoNet RESP naming
possible_respfiles += glob.glob(directory + '/RESP.' + network + '.' +
channel + '.' +
station) # RDseed RESP naming
possible_respfiles += glob.glob(directory + '/RESP.' + station + '.' +
network)
# WIZARD resp naming
# GSE format, station needs to be 5 charectars padded with _, channel is 4
# characters padded with _
possible_respfiles += glob.glob(directory + '/' + station.ljust(5, '_') +
channel[0:len(channel) - 1].ljust(3, '_') +
channel[-1] + '.*_GSE')
PAZ = []
seedresp = []
for respfile in possible_respfiles:
print 'Reading response from: ' + respfile
if respfile.split('/')[-1][0:4] == 'RESP':
# Read from a resp file
seedresp = {
'filename': respfile,
'date': UTCDateTime(time),
'units': 'DIS',
'network': network,
'station': station,
'channel': channel,
'location': '*'
}
try:
# Attempt to evaluate the response for this information, if not
# then this is not the correct response info!
freq_resp, freqs = evalresp(delta,
100,
seedresp['filename'],
seedresp['date'],
units=seedresp['units'],
freq=True,
network=seedresp['network'],
station=seedresp['station'],
channel=seedresp['channel'])
except:
print 'Issues with RESP file'
seedresp = []
continue
elif respfile[-3:] == 'GSE':
PAZ, pazdate, pazstation, pazchannel, pazsensor =\
_GSE2_PAZ_read(respfile)
# check that the date is good!
if pazdate >= time and pazchannel != channel and\
pazstation != station:
print 'Issue with GSE file'
print 'date: '+str(pazdate)+' channel: '+pazchannel +\
' station: '+pazstation
PAZ = []
else:
continue
# Check that PAZ are for the correct station, channel and date
if PAZ or seedresp:
break
if PAZ:
return PAZ
elif seedresp:
return seedresp
print('work on station ' + station)
tfiles = glob.glob(os.path.join(resp_dir, 'RESP.' + station + '*'))
if len(tfiles) == 0:
print('cannot find resp files for station ' + station)
tfile = tfiles[0]
comp = tfile.split('.')[2]
#---extract the resp------
respz, freq = evalresp(dt,
Nfft,
tfile,
tdate,
station=station,
channel=comp,
network=network,
locid='*',
units='VEL',
freq=True,
debug=False)
plt.subplot(211)
plt.loglog(freq, np.absolute(respz))
invert_spectrum(respz, water_level)
plt.subplot(212)
plt.loglog(freq, np.absolute(respz))
#cos_win = cosine_sac_taper(freq, flimit=prefilt)
#respz *=cos_win
plt.show()
output = os.path.join(resp_dir, 'resp.' + station + '.npy')
def single_comparison():
"""
one by one comparison of the waveforms in the first path with the second path.
"""
client = Client()
global input
# identity of the waveforms (first and second paths) to be compared with each other
identity_all = input['net'] + '.' + input['sta'] + '.' + \
input['loc'] + '.' + input['cha']
ls_first = glob.glob(os.path.join(input['first_path'], identity_all))
ls_second = glob.glob(os.path.join(input['second_path'], identity_all))
for i in range(0, len(ls_first)):
try:
tr1 = read(ls_first[i])[0]
if input['phase'] != 'N':
evsta_dist = util.locations2degrees(lat1 = tr1.stats.sac.evla, \
long1 = tr1.stats.sac.evlo, lat2 = tr1.stats.sac.stla, \
long2 = tr1.stats.sac.stlo)
taup_tt = taup.getTravelTimes(delta = evsta_dist, depth = tr1.stats.sac.evdp)
phase_exist = 'N'
for tt_item in taup_tt:
if tt_item['phase_name'] == input['phase']:
print 'Requested phase:'
print input['phase']
print '------'
print tt_item['phase_name']
print 'exists in the waveform!'
print '-----------------------'
t_phase = tt_item['time']
phase_exist = 'Y'
break
if phase_exist != 'Y':
continue
# identity of the current waveform
identity = tr1.stats.network + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
# tr1: first path, tr2: second path, tr3: Raw data
#tr3 = read(os.path.join(input['first_path'], '..', 'BH_RAW', identity))[0]
if input['resp_paz'] == 'Y':
response_file = os.path.join(input['first_path'], '..', 'Resp/RESP.' + identity)
# Extract the PAZ info from response file
paz = readRESP(response_file, unit = input['corr_unit'])
poles = paz['poles']
zeros = paz['zeros']
scale_fac = paz['gain']
sensitivity = paz['sensitivity']
print paz
# Convert Poles and Zeros (PAZ) to frequency response.
h, f = pazToFreqResp(poles, zeros, scale_fac, \
1./tr1.stats.sampling_rate, tr1.stats.npts*2, freq=True)
# Use the evalresp library to extract
# instrument response information from a SEED RESP-file.
resp = invsim.evalresp(t_samp = 1./tr1.stats.sampling_rate, \
nfft = tr1.stats.npts*2, filename = response_file, \
date = tr1.stats.starttime, units = input['corr_unit'].upper())
# Keep the current identity in a new variable
id_name = identity
try:
tr2 = read(os.path.join(input['second_path'], identity))[0]
except Exception, error:
# if it is not possible to read the identity in the second path
# then change the network part of the identity based on
# correction unit
identity = input['corr_unit'] + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
tr2 = read(os.path.join(input['second_path'], identity))[0]
if input['resample'] != 'N':
print 'WARNING: you are using resample!!!'
tr1.resample(input['resample'])
tr2.resample(input['resample'])
if input['tw'] == 'Y':
t_cut_1 = tr1.stats.starttime + t_phase - input['preset']
t_cut_2 = tr1.stats.starttime + t_phase + input['offset']
tr1.trim(starttime = t_cut_1, endtime = t_cut_2)
t_cut_1 = tr2.stats.starttime + t_phase - input['preset']
t_cut_2 = tr2.stats.starttime + t_phase + input['offset']
tr2.trim(starttime = t_cut_1, endtime = t_cut_2)
if input['hlfilter'] == 'Y':
tr1.filter('lowpass', freq=input['hfreq'], corners=2)
tr2.filter('lowpass', freq=input['hfreq'], corners=2)
tr1.filter('highpass', freq=input['lfreq'], corners=2)
tr2.filter('highpass', freq=input['lfreq'], corners=2)
# normalization of all three waveforms to the
# max(max(tr1), max(tr2), max(tr3)) to keep the scales
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max(), abs(tr3.data).max())
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max())
#tr1_data = tr1.data/abs(maxi)
#tr2_data = tr2.data/abs(maxi)
#tr3_data = tr3.data/abs(maxi)
tr1_data = tr1.data/abs(max(tr1.data))
tr2_data = tr2.data/abs(max(tr2.data))
#tr1_data = tr1.data
#tr2_data = tr2.data*1e9
print max(tr1.data)
print max(tr2.data)
# create time arrays for tr1, tr2 and tr3
time_tr1 = np.arange(0, tr1.stats.npts/tr1.stats.sampling_rate, \
1./tr1.stats.sampling_rate)
time_tr2 = np.arange(0, tr2.stats.npts/tr2.stats.sampling_rate, \
1./tr2.stats.sampling_rate)
#time_tr3 = np.arange(0, tr3.stats.npts/tr3.stats.sampling_rate, \
# 1./tr3.stats.sampling_rate)
# label for plotting
label_tr1 = ls_first[i].split('/')[-2]
label_tr2 = ls_second[i].split('/')[-2]
label_tr3 = 'RAW'
if input['resp_paz'] == 'Y':
# start plotting
plt.figure()
plt.subplot2grid((3,4), (0,0), colspan=4, rowspan=2)
#plt.subplot(211)
plt.plot(time_tr1, tr1_data, color = 'blue', label = label_tr1, lw=3)
plt.plot(time_tr2, tr2_data, color = 'red', label = label_tr2, lw=3)
#plt.plot(time_tr3, tr3_data, color = 'black', ls = '--', label = label_tr3)
plt.xlabel('Time (sec)', fontsize = 'xx-large', weight = 'bold')
if input['corr_unit'] == 'dis':
ylabel_str = 'Relative Displacement'
elif input['corr_unit'] == 'vel':
ylabel_str = 'Relative Vel'
elif input['corr_unit'] == 'acc':
ylabel_str = 'Relative Acc'
plt.ylabel(ylabel_str, fontsize = 'xx-large', weight = 'bold')
plt.xticks(fontsize = 'xx-large', weight = 'bold')
plt.yticks(fontsize = 'xx-large', weight = 'bold')
plt.legend(loc=1,prop={'size':20})
#-------------------Cross Correlation
# 5 seconds as total length of samples to shift for cross correlation.
cc_np = tr1.stats.sampling_rate * 3
np_shift, coeff = cross_correlation.xcorr(tr1, tr2, int(cc_np))
t_shift = float(np_shift)/tr1.stats.sampling_rate
print "Cross Correlation:"
print "Shift: " + str(t_shift)
print "Coefficient: " + str(coeff)
plt.title('Single Comparison' + '\n' + str(t_shift) + \
' sec , coeff: ' + str(round(coeff, 5)) + \
'\n' + id_name, \
fontsize = 'xx-large', weight = 'bold')
if input['resp_paz'] == 'Y':
# -----------------------
#plt.subplot(223)
plt.subplot2grid((3,4), (2,0), colspan=2)
'''
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)), \
color = 'blue', label = 'RESP', lw=3)
plt.plot(np.log10(f), np.log10(abs(h)/sensitivity), \
color = 'red', label = 'PAZ', lw=3)
'''
plt.loglog(f, abs(resp)/(sensitivity*sensitivity), \
color = 'blue', label = 'RESP', lw=3)
plt.loglog(f, abs(h)/sensitivity, \
color = 'red', label = 'PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0]:
plt.axvline(np.log10(j), linestyle = '--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
#plt.ylabel('Amplitude\n (power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]', fontsize = 'xx-large', weight = 'bold')
plt.ylabel('Amplitude', fontsize = 'xx-large', weight = 'bold')
plt.xticks(fontsize = 'xx-large', weight = 'bold')
#plt.yticks = MaxNLocator(nbins=4)
plt.yticks(fontsize = 'xx-large', weight = 'bold')
plt.legend(loc=2,prop={'size':20})
# -----------------------
#plt.subplot(224)
plt.subplot2grid((3,4), (2,2), colspan=2)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
#plt.plot(np.log10(f), phase_resp, color = 'blue', label = 'RESP', lw=3)
#plt.plot(np.log10(f), phase_paz, color = 'red', label = 'PAZ', lw=3)
plt.semilogx(f, phase_resp, color = 'blue', label = 'RESP', lw=3)
plt.semilogx(f, phase_paz, color = 'red', label = 'PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0.0]:
plt.axvline(np.log10(j), linestyle = '--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]', fontsize = 'xx-large', weight = 'bold')
plt.ylabel('Phase [radian]', fontsize = 'xx-large', weight = 'bold')
plt.xticks(fontsize = 'xx-large', weight = 'bold')
plt.yticks(fontsize = 'xx-large', weight = 'bold')
plt.legend(loc=3,prop={'size':20})
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.4, hspace=0.3)
"""
# -----------------------
plt.subplot(325)
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)) - \
np.log10(abs(h)/sensitivity), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Amplitude (power of 10)')
plt.legend()
# -----------------------
plt.subplot(326)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
plt.plot(np.log10(f), np.log10(phase_resp) - np.log10(phase_paz), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Phase [radian] (power of 10)')
plt.legend()
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.3)
"""
plt.show()
print str(i+1) + '/' + str(len(ls_first))
print ls_first[i]
print '------------------'
wait = raw_input(id_name)
print '***************************'
except Exception, error:
print '##################'
print error
print '##################'
Fs=tr.stats.sampling_rate,
NFFT=nfft,
pad_to=nfft * 2,
noverlap=olp,
scale_by_freq=True,
mode='psd')
#still need to remove response
specgram = specgram[1:, :]
freq = freq[1:]
# Grab the response
resppath = '/APPS/metadata/RESPS/'
# Return the response
resp = evalresp(t_samp = tr.stats.delta, nfft = 2 * nfft, filename = resppath + 'RESP.' + net + '.' + \
sta + '.' + loc + '.' + chan, date = tr.stats.starttime, station = sta,
channel = chan, network = net, locid = loc, units = 'ACC')
# Remove the 0 frequency
resp = resp[1:]
# Correct for the response
specgram = (specgram.T / (np.abs(resp * np.conjugate(resp)))).T
specgram = 10. * np.log10(specgram)
#for idx in range(len(specgram[1,:])):
# specgram[:,idx] = smooth(specgram[:,idx],10)
specs[str(tr.stats.channel)] = specgram
print(specs)
# We have calculated all the PSDs for the time period now we want to do things to them
def single_comparison():
"""
one by one comparison of the waveforms in the first path with the second path.
"""
client = Client()
global input
# identity of the waveforms (first and second paths) to be compared with each other
identity_all = input['net'] + '.' + input['sta'] + '.' + \
input['loc'] + '.' + input['cha']
ls_first = glob.glob(os.path.join(input['first_path'], identity_all))
ls_second = glob.glob(os.path.join(input['second_path'], identity_all))
for i in range(0, len(ls_first)):
try:
tr1 = read(ls_first[i])[0]
if input['phase'] != 'N':
evsta_dist = util.locations2degrees(lat1 = tr1.stats.sac.evla, \
long1 = tr1.stats.sac.evlo, lat2 = tr1.stats.sac.stla, \
long2 = tr1.stats.sac.stlo)
taup_tt = taup.getTravelTimes(delta=evsta_dist,
depth=tr1.stats.sac.evdp)
phase_exist = 'N'
for tt_item in taup_tt:
if tt_item['phase_name'] == input['phase']:
print 'Requested phase:'
print input['phase']
print '------'
print tt_item['phase_name']
print 'exists in the waveform!'
print '-----------------------'
t_phase = tt_item['time']
phase_exist = 'Y'
break
if phase_exist != 'Y':
continue
# identity of the current waveform
identity = tr1.stats.network + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
# tr1: first path, tr2: second path, tr3: Raw data
#tr3 = read(os.path.join(input['first_path'], '..', 'BH_RAW', identity))[0]
if input['resp_paz'] == 'Y':
response_file = os.path.join(input['first_path'], '..',
'Resp/RESP.' + identity)
# Extract the PAZ info from response file
paz = readRESP(response_file, unit=input['corr_unit'])
poles = paz['poles']
zeros = paz['zeros']
scale_fac = paz['gain']
sensitivity = paz['sensitivity']
print paz
# Convert Poles and Zeros (PAZ) to frequency response.
h, f = pazToFreqResp(poles, zeros, scale_fac, \
1./tr1.stats.sampling_rate, tr1.stats.npts*2, freq=True)
# Use the evalresp library to extract
# instrument response information from a SEED RESP-file.
resp = invsim.evalresp(t_samp = 1./tr1.stats.sampling_rate, \
nfft = tr1.stats.npts*2, filename = response_file, \
date = tr1.stats.starttime, units = input['corr_unit'].upper())
# Keep the current identity in a new variable
id_name = identity
try:
tr2 = read(os.path.join(input['second_path'], identity))[0]
except Exception, error:
# if it is not possible to read the identity in the second path
# then change the network part of the identity based on
# correction unit
identity = input['corr_unit'] + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
tr2 = read(os.path.join(input['second_path'], identity))[0]
if input['resample'] != 'N':
print 'WARNING: you are using resample!!!'
tr1.resample(input['resample'])
tr2.resample(input['resample'])
if input['tw'] == 'Y':
t_cut_1 = tr1.stats.starttime + t_phase - input['preset']
t_cut_2 = tr1.stats.starttime + t_phase + input['offset']
tr1.trim(starttime=t_cut_1, endtime=t_cut_2)
t_cut_1 = tr2.stats.starttime + t_phase - input['preset']
t_cut_2 = tr2.stats.starttime + t_phase + input['offset']
tr2.trim(starttime=t_cut_1, endtime=t_cut_2)
if input['hlfilter'] == 'Y':
tr1.filter('lowpass', freq=input['hfreq'], corners=2)
tr2.filter('lowpass', freq=input['hfreq'], corners=2)
tr1.filter('highpass', freq=input['lfreq'], corners=2)
tr2.filter('highpass', freq=input['lfreq'], corners=2)
# normalization of all three waveforms to the
# max(max(tr1), max(tr2), max(tr3)) to keep the scales
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max(), abs(tr3.data).max())
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max())
#tr1_data = tr1.data/abs(maxi)
#tr2_data = tr2.data/abs(maxi)
#tr3_data = tr3.data/abs(maxi)
tr1_data = tr1.data / abs(max(tr1.data))
tr2_data = tr2.data / abs(max(tr2.data))
#tr1_data = tr1.data
#tr2_data = tr2.data*1e9
print max(tr1.data)
print max(tr2.data)
# create time arrays for tr1, tr2 and tr3
time_tr1 = np.arange(0, tr1.stats.npts/tr1.stats.sampling_rate, \
1./tr1.stats.sampling_rate)
time_tr2 = np.arange(0, tr2.stats.npts/tr2.stats.sampling_rate, \
1./tr2.stats.sampling_rate)
#time_tr3 = np.arange(0, tr3.stats.npts/tr3.stats.sampling_rate, \
# 1./tr3.stats.sampling_rate)
# label for plotting
label_tr1 = ls_first[i].split('/')[-2]
label_tr2 = ls_second[i].split('/')[-2]
label_tr3 = 'RAW'
if input['resp_paz'] == 'Y':
# start plotting
plt.figure()
plt.subplot2grid((3, 4), (0, 0), colspan=4, rowspan=2)
#plt.subplot(211)
plt.plot(time_tr1, tr1_data, color='blue', label=label_tr1, lw=3)
plt.plot(time_tr2, tr2_data, color='red', label=label_tr2, lw=3)
#plt.plot(time_tr3, tr3_data, color = 'black', ls = '--', label = label_tr3)
plt.xlabel('Time (sec)', fontsize='xx-large', weight='bold')
if input['corr_unit'] == 'dis':
ylabel_str = 'Relative Displacement'
elif input['corr_unit'] == 'vel':
ylabel_str = 'Relative Vel'
elif input['corr_unit'] == 'acc':
ylabel_str = 'Relative Acc'
plt.ylabel(ylabel_str, fontsize='xx-large', weight='bold')
plt.xticks(fontsize='xx-large', weight='bold')
plt.yticks(fontsize='xx-large', weight='bold')
plt.legend(loc=1, prop={'size': 20})
#-------------------Cross Correlation
# 5 seconds as total length of samples to shift for cross correlation.
cc_np = tr1.stats.sampling_rate * 3
np_shift, coeff = cross_correlation.xcorr(tr1, tr2, int(cc_np))
t_shift = float(np_shift) / tr1.stats.sampling_rate
print "Cross Correlation:"
print "Shift: " + str(t_shift)
print "Coefficient: " + str(coeff)
plt.title('Single Comparison' + '\n' + str(t_shift) + \
' sec , coeff: ' + str(round(coeff, 5)) + \
'\n' + id_name, \
fontsize = 'xx-large', weight = 'bold')
if input['resp_paz'] == 'Y':
# -----------------------
#plt.subplot(223)
plt.subplot2grid((3, 4), (2, 0), colspan=2)
'''
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)), \
color = 'blue', label = 'RESP', lw=3)
plt.plot(np.log10(f), np.log10(abs(h)/sensitivity), \
color = 'red', label = 'PAZ', lw=3)
'''
plt.loglog(f, abs(resp)/(sensitivity*sensitivity), \
color = 'blue', label = 'RESP', lw=3)
plt.loglog(f, abs(h)/sensitivity, \
color = 'red', label = 'PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0]:
plt.axvline(np.log10(j), linestyle='--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
#plt.ylabel('Amplitude\n (power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]',
fontsize='xx-large',
weight='bold')
plt.ylabel('Amplitude', fontsize='xx-large', weight='bold')
plt.xticks(fontsize='xx-large', weight='bold')
#plt.yticks = MaxNLocator(nbins=4)
plt.yticks(fontsize='xx-large', weight='bold')
plt.legend(loc=2, prop={'size': 20})
# -----------------------
#plt.subplot(224)
plt.subplot2grid((3, 4), (2, 2), colspan=2)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
#plt.plot(np.log10(f), phase_resp, color = 'blue', label = 'RESP', lw=3)
#plt.plot(np.log10(f), phase_paz, color = 'red', label = 'PAZ', lw=3)
plt.semilogx(f, phase_resp, color='blue', label='RESP', lw=3)
plt.semilogx(f, phase_paz, color='red', label='PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0.0]:
plt.axvline(np.log10(j), linestyle='--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]',
fontsize='xx-large',
weight='bold')
plt.ylabel('Phase [radian]',
fontsize='xx-large',
weight='bold')
plt.xticks(fontsize='xx-large', weight='bold')
plt.yticks(fontsize='xx-large', weight='bold')
plt.legend(loc=3, prop={'size': 20})
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.4, hspace=0.3)
"""
# -----------------------
plt.subplot(325)
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)) - \
np.log10(abs(h)/sensitivity), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Amplitude (power of 10)')
plt.legend()
# -----------------------
plt.subplot(326)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
plt.plot(np.log10(f), np.log10(phase_resp) - np.log10(phase_paz), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Phase [radian] (power of 10)')
plt.legend()
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.3)
"""
plt.show()
print str(i + 1) + '/' + str(len(ls_first))
print ls_first[i]
print '------------------'
wait = raw_input(id_name)
print '***************************'
except Exception, error:
print '##################'
print error
print '##################'
output = test_for_segfault(t_samp, filename, date, stat_id,
chan_id, net_id, loc_id, unit)
if output == "segfault":
print_info("Raw evalresp segfaults. XML response not "
"attempted.")
counter["evalresp_segfaults"] += 1
continue
elif not output:
filename.seek(0, 0)
if debug:
print "SEED"
try:
seed_response, seed_freq = evalresp(
t_samp, nfft, filename, date=date,
station=stat_id, channel=chan_id,
network=net_id, locid=loc_id, units=unit,
freq=True)
except ValueError as e:
seedresp_error = e
else:
with CatchOutput() as out:
try:
seed_response, seed_freq = evalresp(
t_samp, nfft, filename, date=date,
station=stat_id, channel=chan_id,
network=net_id, locid=loc_id, units=unit,
freq=True)
except ValueError as e:
seedresp_error = e
elif output == "uniterror":
noverlap=int(0.25 * nfft),
scale_by_freq=True,
mode='psd')
specgram = specgram[1:, :]
freq = freq[1:]
# Reading in responses - responses for the XX network isn't yet with IRIS
if net2 == 'XX':
resppath = ''
else:
resppath = '/APPS/metadata/RESPS/'
# Removing response from seismic data traces
resp = evalresp(t_samp = st[0].stats.delta, nfft = 2 * nfft, filename = resppath + 'RESP.' + net2 + '.' + \
sta2 + '.' + loc2 + '.' + chan2, date = st[0].stats.starttime, station = sta2,
channel = chan2, network = net2, locid = loc2, units = 'ACC')
resp = resp[1:]
specgram = (specgram.T / (np.abs(resp * np.conjugate(resp)))).T
specgram = 10. * np.log10(specgram)
# Decimating wind speed to have a data point every minute
tr.decimate(5)
tr.decimate(2)
tr.decimate(6)
# Establishing the time (independent) variable
time *= 1. / (60. * 60.)
timews = np.asarray(range(tr.stats.npts)) / 60.
# Creating the figure and labeling it, etc.
def _find_resp(station, channel, network, time, delta, directory):
"""
Helper function to find the response information for a given station and
channel at a given time and return a dictionary of poles and zeros, gain
and sensitivity.
:type station: String
:param station: Station name (as in the response files)
:type channel: String
:param channel: Channel name (as in the response files)
:type network: String
:param network: Network to scan for, can be a wildcard
:type time: datetime.datetime
:param time: Date-time to look for repsonse information
:type delta: float
:param delta: Sample interval in seconds
:type directory: String
:param directory: Directory to scan for response information
:returns: Dictionary
"""
import glob
from obspy.signal.invsim import evalresp
from obspy import UTCDateTime
possible_respfiles=glob.glob(directory+'/RESP.'+network+'.'+station+'.*.'+\
channel) # GeoNet RESP naming
possible_respfiles+=glob.glob(directory+'/RESP.'+network+'.'+channel+'.'+\
station) # RDseed RESP naming
possible_respfiles+=glob.glob(directory+'/RESP.'+station+'.'+network)
# WIZARD resp naming
# GSE format, station needs to be 5 charectars padded with _, channel is 4
# characters padded with _
possible_respfiles+=glob.glob(directory+'/'+station.ljust(5,'_')+\
channel[0:len(channel)-1].ljust(3,'_')+\
channel[-1]+'.*_GSE')
PAZ=[]
seedresp=[]
for respfile in possible_respfiles:
print 'Reading response from: '+respfile
if respfile.split('/')[-1][0:4]=='RESP':
# Read from a resp file
seedresp={'filename': respfile, 'date': UTCDateTime(time),
'units': 'DIS', 'network': network, 'station': station,
'channel': channel, 'location': '*'}
try:
# Attempt to evaluate the response for this information, if not
# then this is not the correct response info!
freq_resp, freqs = evalresp(delta, 100, seedresp['filename'],
seedresp['date'],
units=seedresp['units'], freq=True,
network=seedresp['network'],
station=seedresp['station'],
channel=seedresp['channel'])
except:
print 'Issues with RESP file'
seedresp=[]
continue
elif respfile[-3:]=='GSE':
PAZ, pazdate, pazstation, pazchannel, pazsensor =\
_GSE2_PAZ_read(respfile)
# check that the date is good!
if pazdate >= time and pazchannel != channel and\
pazstation != station:
print 'Issue with GSE file'
print 'date: '+str(pazdate)+' channel: '+pazchannel+\
' station: '+pazstation
PAZ=[]
else:
continue
# Check that PAZ are for the correct station, channel and date
if PAZ or seedresp:
break
if PAZ:
return PAZ
elif seedresp:
return seedresp
