def _calcresp(calfile, nfft, sampfreq):
"""
Calculate transfer function of known system.
:type calfile: String
:param calfile: file containing poles, zeros and scale factor for known
system
:returns: complex transfer function, array of frequencies
"""
# avoid top level dependency on gse2
try:
from obspy.gse2.paz import readPaz
except ImportError:
msg = "Error during import from obspy.gse2. Please make " + \
"sure obspy.gse2 is installed properly."
raise ImportError(msg)
# calculate transfer function
poles, zeros, scale_fac = readPaz(calfile)
h, f = pazToFreqResp(poles,
zeros,
scale_fac,
1.0 / sampfreq,
nfft,
freq=True)
return h, f
def update(self):
"""
This method should do everything to update the plot.
"""
try:
# clear axes before anything else
ax1 = self.ax1
ax1.clear()
ax2 = self.ax2
ax2.clear()
# plot theoretical responses from paz here
paz = self.paz
h, f = pazToFreqResp(paz['poles'], paz['zeros'], paz['gain'],
self.spr, self.nfft, freq=True)
ampl = abs(h)
phase = np.unwrap(np.arctan2(-h.imag, h.real)) #take negative of imaginary part
ax1.loglog(f, ampl, color="r")
ax2.semilogx(f, phase, color="r", label='paz fit')
# plot read in calibration output
ax1.loglog(self.freq, self.ampl, color="b", ls="--")
ax2.semilogx(self.freq, self.phase, color="b", ls="--", label='calibration output')
ax2.legend()
# update matplotlib canvas
self.canv.draw()
except:
msg = "Problem during plot-update. Value ranges OK?"
warnings.warn(msg)
def _getRespFromModel(self, pazModel, nfft, delta):
'''
:param dictionary pazModel: dictionary or poles and zeros
:param int nfft:
:param delta: delta T between samples
'''
# This function returns the response without normalization
resp = pazToFreqResp(pazModel['poles'], pazModel['zeros'], 1, delta,
nfft, freq=False)
return resp
def _calcresp(calfile, nfft, sampfreq):
"""
Calculate transfer function of known system.
:type calfile: String
:param calfile: file containing poles, zeros and scale factor for known
system
:returns: complex transfer function, array of frequencies
"""
# calculate transfer function
poles, zeros, scale_fac = readPaz(calfile)
h, f = pazToFreqResp(poles, zeros, scale_fac, 1.0 / sampfreq, nfft, freq=True)
return h, f
def _calcresp(calfile, nfft, sampfreq):
"""
Calculate transfer function of known system.
:type calfile: String
:param calfile: file containing poles, zeros and scale factor for known
system
:returns: complex transfer function, array of frequencies
"""
# calculate transfer function
poles, zeros, scale_fac = readPaz(calfile)
h, f = pazToFreqResp(poles, zeros, scale_fac, 1.0 / sampfreq,
nfft, freq=True)
return h, f
def _getRespFromModel(self, pazModel, nfft, delta):
'''
:param dictionary pazModel: dictionary or poles and zeros
:param int nfft:
:param delta: delta T between samples
'''
# This function returns the response without normalization
resp = pazToFreqResp(pazModel['poles'],
pazModel['zeros'],
1,
delta,
nfft,
freq=False)
return resp
def update(self):
"""
This method should do everything to update the plot.
"""
try:
# clear axes before anything else
ax1 = self.ax1
ax1.clear()
ax2 = self.ax2
ax2.clear()
# plot theoretical responses from paz here
paz = self.paz
h, f = pazToFreqResp(paz['poles'],
paz['zeros'],
paz['gain'],
self.spr,
self.nfft,
freq=True)
ampl = abs(h)
#compute the residuum
resid = 0
ampl1 = self.ampl[(f > self.box_lfreq.value())
& (f < self.box_hfreq.value())]
ampl2 = ampl[(f > self.box_lfreq.value())
& (f < self.box_hfreq.value())]
ampl1s = ampl1 - ampl1.mean()
resid = (((ampl2 - ampl1)**2).sum()) / (
(ampl1.mean())**2 * len(ampl1))
res = "Residuum: %f" % (resid)
phase = np.unwrap(np.arctan2(
-h.imag, h.real)) #take negative of imaginary part
ax1.loglog(f, ampl, color="r")
ax2.semilogx(f, phase, color="r", label=res)
# plot read in calibration output
ax1.loglog(self.freq, self.ampl, color="b", ls="--")
# ax2.semilogx(self.freq, self.phase, color="b", ls="--", label='calibration output')
ax2.semilogx(self.freq, self.phase, color="b", ls="--")
ax2.legend()
# update matplotlib canvas
self.canv.draw()
except:
msg = "Problem during plot-update. Value ranges OK?"
warnings.warn(msg)
def update(self):
"""
This method should do everything to update the plot.
"""
try:
# clear axes before anything else
ax1 = self.ax1
ax1.clear()
ax2 = self.ax2
ax2.clear()
# plot theoretical responses from paz here
paz = self.paz
h, f = pazToFreqResp(paz['poles'], paz['zeros'], paz['gain'],
self.spr, self.nfft, freq=True)
ampl = abs(h)
#compute the residuum
resid = 0
ampl1 = self.ampl[(f>self.box_lfreq.value()) & (f<self.box_hfreq.value())]
ampl2 = ampl[(f>self.box_lfreq.value()) & (f<self.box_hfreq.value())]
ampl1s = ampl1 - ampl1.mean()
resid = (((ampl2 - ampl1) ** 2).sum()) / ((ampl1.mean())**2 * len(ampl1))
res = "Residuum: %f"%(resid)
phase = np.unwrap(np.arctan2(-h.imag, h.real)) #take negative of imaginary part
ax1.loglog(f, ampl, color="r")
ax2.semilogx(f, phase, color="r", label=res)
# plot read in calibration output
ax1.loglog(self.freq, self.ampl, color="b", ls="--")
# ax2.semilogx(self.freq, self.phase, color="b", ls="--", label='calibration output')
ax2.semilogx(self.freq, self.phase, color="b",ls="--")
ax2.legend()
# update matplotlib canvas
self.canv.draw()
except:
msg = "Problem during plot-update. Value ranges OK?"
warnings.warn(msg)
def _calcresp(calfile, nfft, sampfreq):
"""
Calculate transfer function of known system.
:type calfile: String
:param calfile: file containing poles, zeros and scale factor for known
system
:returns: complex transfer function, array of frequencies
"""
# avoid top level dependency on gse2
try:
from obspy.gse2.paz import readPaz
except ImportError:
msg = "Error during import from obspy.gse2. Please make " + \
"sure obspy.gse2 is installed properly."
raise ImportError(msg)
# calculate transfer function
poles, zeros, scale_fac = readPaz(calfile)
h, f = pazToFreqResp(poles, zeros, scale_fac, 1.0 / sampfreq,
nfft, freq=True)
return h, f
def update(self):
"""
This method should do everything to update the plot.
"""
try:
# clear axes before anything else
ax1 = self.ax1
ax1.clear()
ax2 = self.ax2
ax2.clear()
# plot theoretical responses from paz here
paz = self.paz
h, f = pazToFreqResp(paz['poles'],
paz['zeros'],
paz['gain'],
self.spr,
self.nfft,
freq=True)
ampl = abs(h)
phase = np.unwrap(np.arctan2(
-h.imag, h.real)) #take negative of imaginary part
ax1.loglog(f, ampl, color="r")
ax2.semilogx(f, phase, color="r", label='paz fit')
# plot read in calibration output
ax1.loglog(self.freq, self.ampl, color="b", ls="--")
ax2.semilogx(self.freq,
self.phase,
color="b",
ls="--",
label='calibration output')
ax2.legend()
# update matplotlib canvas
self.canv.draw()
except:
msg = "Problem during plot-update. Value ranges OK?"
warnings.warn(msg)
import numpy as np
import matplotlib.pyplot as plt
from obspy.signal import pazToFreqResp
poles = [-4.440 + 4.440j, -4.440 - 4.440j, -1.083 + 0.0j]
zeros = [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j]
scale_fac = 0.4
h, f = pazToFreqResp(poles, zeros, scale_fac, 0.005, 16384, freq=True)
plt.figure()
plt.subplot(121)
plt.loglog(f, abs(h))
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude')
plt.subplot(122)
# take negative of imaginary part
phase = np.unwrap(np.arctan2(-h.imag, h.real))
plt.semilogx(f, phase)
plt.xlabel('Frequency [Hz]')
plt.ylabel('Phase [radian]')
# title, centered above both subplots
plt.suptitle('Frequency Response of LE-3D/1s Seismometer')
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.3)
plt.show()
def computeresp(resp,delta,lenfft): respval = pazToFreqResp(resp['poles'],resp['zeros'],resp['sensitivity'],t_samp = delta, nfft=lenfft,freq = False) respval = numpy.absolute(respval*numpy.conjugate(respval)) respval = respval[1:] return respval
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 '##################'
def plot_xml_response(input_dics):
"""
plot the transfer function of stationXML file(s)
:param input_dics:
:return:
"""
plt.rc('font', family='serif')
print('[INFO] plotting StationXML file/files in: %s' % \
input_dics['datapath'])
if not os.path.isdir('./stationxml_plots'):
print('[INFO] creating stationxml_plots directory...')
os.mkdir('./stationxml_plots')
# assign the input_dics parameters to the running parameters:
stxml_dir = input_dics['datapath']
plotxml_datetime = input_dics['plotxml_date']
min_freq = input_dics['plotxml_min_freq']
output = input_dics['plotxml_output']
if 'dis' in output.lower():
output = 'DISP'
elif 'vel' in output.lower():
output = 'VEL'
elif 'acc' in output.lower():
output = 'ACC'
else:
output = output.upper()
start_stage = input_dics['plotxml_start_stage']
end_stage_input = input_dics['plotxml_end_stage']
percentage = input_dics['plotxml_percentage']/100.
threshold = input_dics['plotxml_phase_threshold']
plot_response = input_dics['plotxml_response']
plotstage12 = input_dics['plotxml_plotstage12']
plotpaz = input_dics['plotxml_paz']
plotallstages = input_dics['plotxml_allstages']
plot_map_compare = input_dics['plotxml_map_compare']
if os.path.isfile(stxml_dir):
addxml_all = glob.glob(os.path.join(stxml_dir))
elif os.path.isdir(stxml_dir):
addxml_all = glob.glob(os.path.join(stxml_dir, 'STXML.*'))
else:
try:
addxml_all = glob.glob(os.path.join(stxml_dir))
except Exception as error:
print('[ERROR] %s' % error)
sys.exit('[ERROR] wrong address: %s' % stxml_dir)
addxml_all.sort()
sta_lat = []
sta_lon = []
latlon_color = []
report_fio = open(os.path.join('stationxml_plots',
'report_stationxml'), 'wt')
report_fio.writelines('channel_id\t\t\t\t%(phase_diff)\t\t'
'abs(max_diff)\tlat\t\t\tlon\t\t\tdatetime\t'
'decimation delay\tdecimation corr\n')
report_fio.close()
add_counter = 0
for addxml in addxml_all:
end_stage = end_stage_input
add_counter += 1
print(40*'-')
print('%s/%s' % (add_counter, len(addxml_all)))
try:
xml_inv = read_inventory(addxml, format='stationXML')
print("[STATIONXML] %s" % addxml)
# we only take into account the first channel...
cha_name = xml_inv.get_contents()['channels'][0]
if plotxml_datetime:
cha_date = plotxml_datetime
else:
cha_date = xml_inv.networks[0][0][-1].start_date
print('[INFO] plotxml_date has not been set, the start_date ' \
'of the last channel in stationXML file will be used ' \
'instead: %s' % cha_date)
xml_response = xml_inv.get_response(cha_name, cha_date + 0.1)
if xml_inv[0][0][0].sample_rate:
sampling_rate = xml_inv[0][0][0].sample_rate
else:
for stage in xml_response.response_stages[::-1]:
if (stage.decimation_input_sample_rate is not None) and\
(stage.decimation_factor is not None):
sampling_rate = (stage.decimation_input_sample_rate /
stage.decimation_factor)
break
t_samp = 1.0 / sampling_rate
nyquist = sampling_rate / 2.0
nfft = int(sampling_rate / min_freq)
end_stage = min(len(xml_response.response_stages), end_stage)
if plotallstages:
plot_xml_plotallstages(xml_response, t_samp, nyquist, nfft,
min_freq, output,
start_stage, end_stage, cha_name)
try:
cpx_response, freq = xml_response.get_evalresp_response(
t_samp=t_samp, nfft=nfft, output=output,
start_stage=start_stage, end_stage=end_stage)
cpx_12, freq = xml_response.get_evalresp_response(
t_samp=t_samp, nfft=nfft, output=output, start_stage=1,
end_stage=2)
except Exception as error:
print('[WARNING] %s' % error)
continue
paz, decimation_delay, decimation_correction = \
convert_xml_paz(xml_response, output, cha_name, cha_date)
if not paz:
continue
h, f = pazToFreqResp(paz['poles'], paz['zeros'], paz['gain'],
1./sampling_rate, nfft, freq=True)
phase_resp = np.angle(cpx_response)
phase_12 = np.angle(cpx_12)
if plot_response:
plt.figure(figsize=(20, 10))
plt.suptitle(cha_name, size=24, weight='bold')
if plotpaz or plotstage12:
plt.subplot(2, 2, 1)
else:
plt.subplot(2, 1, 1)
plt.loglog(freq, abs(cpx_response), color='blue',
lw=3, label='full-resp')
if plotstage12:
plt.loglog(freq, abs(cpx_12), ls='--', color='black',
lw=3, label='Stage1,2')
if plotpaz:
plt.loglog(f, abs(h)*paz['sensitivity'], color='red',
lw=3, label='PAZ')
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.ylabel('Amplitude', size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist+5)
plt.ylim(ymax=max(1.2*abs(cpx_response)))
plt.legend(loc=0, prop={'size': 18, 'weight': 'bold'})
plt.grid()
if plotpaz or plotstage12:
plt.subplot(2, 2, 3)
else:
plt.subplot(2, 1, 2)
plt.semilogx(freq, phase_resp, color='blue',
lw=3, label='full-resp')
if plotstage12:
plt.semilogx(freq, phase_12, ls='--', color='black',
lw=3, label='Stage1,2')
if plotpaz:
plt.semilogx(f, np.angle(h), color='red',
lw=3, label='PAZ')
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.xlabel('Frequency [Hz]', size=24, weight='bold')
plt.ylabel('Phase [rad]', size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist+5)
plt.legend(loc=0, prop={'size': 18, 'weight': 'bold'})
plt.grid()
if plotstage12 or plotpaz:
ax_222 = plt.subplot(2, 2, 2)
y_label = 'Amplitude ratio'
if plotstage12:
plt.loglog(freq, abs(abs(cpx_response) / abs(cpx_12)),
'--', color='black', lw=3,
label='|full-resp|/|Stage1,2|')
y_label = '|full-resp|/|Stage1,2|'
amp_ratio = 1
if plotpaz:
amp_ratio = abs(abs(cpx_response) /
(abs(h)*paz['sensitivity']))
plt.loglog(f, amp_ratio,
color='red', lw=3,
label='|full-resp|/|PAZ|')
y_label = '|full-resp|/|PAZ|'
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.axvline(percentage*nyquist, ls="--",
color='black', lw=3)
plt.ylabel(y_label, size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist+5)
plt.ylim(ymax=1.2*max(amp_ratio[np.logical_not(
np.isnan(amp_ratio))]))
plt.grid()
ax_224 = plt.subplot(2, 2, 4)
y_label = 'Phase difference [rad]'
if plotstage12:
plt.semilogx(freq, abs(phase_resp - phase_12),
color='black', ls='--', lw=3,
label='|full-resp - Stage1,2|')
y_label = '|full-resp - Stage1,2| [rad]'
if plotpaz:
plt.semilogx(freq, abs(phase_resp - np.angle(h)),
color='red', lw=3,
label='|full-resp - PAZ|')
y_label = '|full-resp - PAZ|'
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.axvline(percentage*nyquist, ls="--",
color='black', lw=3)
plt.xlabel('Frequency [Hz]', size=24, weight='bold')
plt.ylabel(y_label,
size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist+5)
plt.grid()
plt.savefig(os.path.join('stationxml_plots',
cha_name + '.png'))
plt.close()
# compare = abs(phase[:int(0.8*len(phase))] -
# np.angle(h[:int(0.8*len(phase))]))
# if len(compare[compare>0.1]) > 0:
# lat_red.append(xml_inv.get_coordinates(cha_name)['latitude'])
# lon_red.append(xml_inv.get_coordinates(cha_name)['longitude'])
# print cha_name
# print paz
# else:
# lat_blue.append(xml_inv.get_coordinates(cha_name)['latitude'])
# lon_blue.append(xml_inv.get_coordinates(cha_name)['longitude'])
phase_resp_check = phase_resp[:int(percentage*len(phase_resp))]
phase_h_check = np.angle(h)[:int(percentage*len(np.angle(h)))]
if not len(phase_resp_check) == len(phase_h_check):
sys.exit('[ERROR] lengths of phase responses do not match: '
'%s (StationXML) != %s (PAZ)'
% (len(phase_resp_check), len(phase_h_check)))
compare = abs(phase_resp_check - phase_h_check)
percent_compare = \
float(len(compare[compare >= 0.05]))/len(compare)*100
latlondep = get_coordinates(xml_inv.networks[0],
cha_name,
cha_date + 0.1)
sta_lat.append(latlondep['latitude'])
sta_lon.append(latlondep['longitude'])
d_c = np.sum(decimation_delay) - np.sum(decimation_correction)
if not d_c == 0:
latlon_color.append(0.)
else:
latlon_color.append(percent_compare)
if percent_compare >= threshold:
plot_xml_plotallstages(xml_response, t_samp, nyquist, nfft,
min_freq, output,
start_stage, end_stage, cha_name)
report_fio = open(os.path.join('stationxml_plots',
'report_stationxml'), 'at')
report_fio.writelines(
'%s\t\t\t%6.2f\t\t\t%6.2f\t\t\t%6.2f\t\t%7.2f\t\t%s\t%s\t%s\n'
% (cha_name,
round(percent_compare, 2),
round(max(abs(compare)), 2),
round(sta_lat[-1], 2),
round(sta_lon[-1], 2),
cha_date,
np.sum(decimation_delay),
np.sum(decimation_correction)))
report_fio.close()
except Exception as error:
print('[Exception] %s' % error)
if plot_map_compare:
from mpl_toolkits.basemap import Basemap
plt.figure()
m = Basemap(projection='robin', lon_0=input_dics['plot_lon0'], lat_0=0)
m.fillcontinents()
m.drawparallels(np.arange(-90., 120., 30.))
m.drawmeridians(np.arange(0., 420., 60.))
m.drawmapboundary()
x, y = m(sta_lon, sta_lat)
m.scatter(x, y, 100, c=latlon_color, marker="v",
edgecolor='none', zorder=10, cmap='rainbow')
plt.colorbar(orientation='horizontal')
plt.savefig(os.path.join('stationxml_plots', 'compare_plots.png'))
plt.show()
raw_input_built('Press Enter...')
sys.exit('[EXIT] obspyDMT finished normally...')
def getRespFromModel(pazModel,nfft,delta,norm): #This function returns the response without normalization resp = pazToFreqResp(pazModel['poles'],pazModel['zeros'],1, \ tracein.stats.delta, nfft, freq=False) resp = resp[1:] return resp
}
# Read in the data
tr = read("loc_RJOB20050831023349.z")[0]
# Do the instrument correction
data_corr = seisSim(tr.data,
tr.stats.sampling_rate,
le3d,
inst_sim=PAZ_WOOD_ANDERSON,
water_level=60.0)
# Just for visualization, calculate transferfuction in frequency domain
trans, freq = pazToFreqResp(le3d['poles'],
le3d['zeros'],
le3d['gain'],
1. / tr.stats.sampling_rate,
2**12,
freq=True)
#
# The plotting part
#
time = np.arange(0, tr.stats.npts) / tr.stats.sampling_rate
plt.figure()
plt.subplot(211)
plt.plot(time, tr.data, label="Original Data")
plt.legend()
plt.subplot(212)
plt.plot(time, data_corr, label="Wood Anderson Simulated Data")
plt.legend()
plt.xlabel("Time [s]")
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 '##################'
def computePSD(sp,net,sta,loc,chan,dateval): #Here we compute the PSD lenfft = 5000 lenol = 2500 #Here are the different period bands. These could be done as a dicitionary #if Adam B. wants to clean this up using a dicitionary that is fine with me permin1 = 4 permax1 = 8 permin2 = 18 permax2 = 22 permin3 = 90 permax3 = 110 permin4 = 200 permax4 = 500 try: #Get the response and compute amplitude response paz=getPAZ2(sp,net,sta,loc,chan,dateval) respval = pazToFreqResp(paz['poles'],paz['zeros'],paz['sensitivity']*paz['gain'], \ t_samp = 1, nfft = lenfft,freq=False)[1:] respval = numpy.absolute(respval*numpy.conjugate(respval)) #Get the data to compute the PSD if net in set(['IW','NE','US']): locStr = '/xs1' else: locStr = '/xs0' readDataString = locStr + '/seed/' + net + '_' + sta + '/' + str(dateval.year) + \ '/' + str(dateval.year) + '_' + str(dateval.julday).zfill(3) + '_' + net + \ '_' + sta + '/' + loc + '_' + chan + '*.seed' datafiles = glob.glob(readDataString) st = Stream() for datafile in datafiles: st += read(datafile) st.merge(method=-1) #Compute the PSD cpval,fre = psd(st[0].data,NFFT=lenfft,Fs=1,noverlap=lenol,scale_by_freq=True) per = 1/fre[1:] cpval = 10*numpy.log10(((2*pi*fre[1:])**2)*cpval[1:]/respval) perminind1 = numpy.abs(per-permin1).argmin() permaxind1 = numpy.abs(per-permax1).argmin() perminind2 = numpy.abs(per-permin2).argmin() permaxind2 = numpy.abs(per-permax2).argmin() perminind3 = numpy.abs(per-permin3).argmin() permaxind3 = numpy.abs(per-permax3).argmin() perminind4 = numpy.abs(per-permin4).argmin() permaxind4 = numpy.abs(per-permax4).argmin() perNLNM,NLNM = get_NLNM() perNLNMminind1 = numpy.abs(perNLNM-permin1).argmin() perNLNMmaxind1 = numpy.abs(perNLNM-permax1).argmin() perNLNMminind2 = numpy.abs(perNLNM-permin2).argmin() perNLNMmaxind2 = numpy.abs(perNLNM-permax2).argmin() perNLNMminind3 = numpy.abs(perNLNM-permin3).argmin() perNLNMmaxind3 = numpy.abs(perNLNM-permax3).argmin() perNLNMminind4 = numpy.abs(perNLNM-permin4).argmin() perNLNMmaxind4 = numpy.abs(perNLNM-permax4).argmin() cpval1 = round(numpy.average(cpval[permaxind1:perminind1]) - \ numpy.average(NLNM[perNLNMmaxind1:perNLNMminind1]),2) cpval2 = round(numpy.average(cpval[permaxind2:perminind2]) - \ numpy.average(NLNM[perNLNMmaxind2:perNLNMminind2]),2) cpval3 = round(numpy.average(cpval[permaxind3:perminind3]) - \ numpy.average(NLNM[perNLNMmaxind3:perNLNMminind3]),2) cpval4 = round(numpy.average(cpval[permaxind4:perminind4]) - \ numpy.average(NLNM[perNLNMmaxind4:perNLNMminind4]),2) except: cpval1 = 0 cpval2 = 0 cpval3 = 0 cpval4 = 0 return cpval1, cpval2, cpval3,cpval4
def retrieve_timeseries(params,channel,segment):
"""@retrieves timeseries for given channel and segment.
@param params
seismon params dictionary
@param channel
seismon channel structure
@param segment
[start,end] gps
"""
gpsStart = segment[0]
gpsEnd = segment[1]
# set the times
duration = np.ceil(gpsEnd-gpsStart)
dataFull = []
if params["ifo"] == "IRIS":
import obspy.iris
channelSplit = channel.station.split(":")
#starttime = lal.gpstime.gps_to_utc(params["gpsStart"])
#endtime = lal.gpstime.gps_to_utc(params["gpsEnd"])
#starttime = obspy.core.UTCDateTime(starttime)
#endtime = obspy.core.UTCDateTime(endtime)
starttime = astropy.time.Time(gpsStart, format='gps')
endtime = astropy.time.Time(gpsEnd, format='gps')
starttime = obspy.core.UTCDateTime(starttime.utc.iso)
endtime = obspy.core.UTCDateTime(endtime.utc.iso)
client = params["client"]
#client = obspy.fdsn.client.Client("IRIS")
try:
st = client.get_waveforms(channelSplit[0], channelSplit[1], channelSplit[2], channelSplit[3],\
starttime,endtime)
except:
print "data read from IRIS failed... continuing\n"
return dataFull
data = np.array(st[0].data)
data = data.astype(float)
dataFull = gwpy.timeseries.TimeSeries(data, times=None, epoch=gpsStart, channel=channel.station, unit=None,sample_rate=channel.samplef, name=channel.station)
elif params["ifo"] == "LUNAR":
import obspy
traces = []
for frame in params["frame"]:
st = obspy.read(frame)
for trace in st:
trace_station = "%s:%s:%s:%s"%(trace.stats["network"],trace.stats["station"],trace.stats["location"],trace.stats["channel"])
if trace_station == channel.station:
traces.append(trace)
st = obspy.core.stream.Stream(traces=traces)
starttime = UnixToUTCDateTime(gpsStart)
endtime = UnixToUTCDateTime(gpsEnd)
st = st.slice(starttime, endtime)
data = st[0].data
data = data.astype(float)
#data = RunningMedian(data,701,2)
sample_rate = st[0].stats.sampling_rate
dataFull = gwpy.timeseries.TimeSeries(data, times=None, epoch=gpsStart, channel=channel.station, unit=None,sample_rate=sample_rate, name=channel.station)
dataFull.resample(channel.samplef)
elif params["ifo"] == "CZKHC":
stationSplit = channel.station.split(":")
stationID = stationSplit[0]
stationType = stationSplit[1]
tt = []
data = []
import obspy
for frame in params["frame"]:
frameSplit = frame.split("/")
frameName = frameSplit[-1]
datalines = [line.strip() for line in open(frame)]
#datalines = datalines[13:]
datalines = datalines[1:]
dataStart = False
for dataline in datalines:
year = int(dataline[0:4])
month = int(dataline[5:7])
day = int(dataline[8:10])
hour = int(dataline[11:13])
minute = int(dataline[14:16])
second = int(dataline[17:19])
subsecond = int(dataline[20:26])
thisDate = datetime(year, month, day, hour, minute, second, subsecond)
#thistime = lal.gpstime.utc_to_gps(thisDate)
td = thisDate - datetime(1970, 1, 1)
thistime = td.microseconds * 1e-6 + td.seconds + td.days * 24 * 3600
tt.append(thistime)
counts = int(dataline[26:])
data.append(counts)
if thistime > gpsEnd:
break
tt, data = zip(*sorted(zip(tt, data)))
tt = np.array(tt)
data = np.array(data)
indexes = np.intersect1d(np.where(tt >= gpsStart)[0],np.where(tt <= gpsEnd)[0])
tt = tt[indexes]
data = data[indexes]
if len(data) == 0:
return []
sample_rate = channel.samplef
dataFull = gwpy.timeseries.TimeSeries(data, times=None, epoch=gpsStart, channel=channel.station, unit=None,sample_rate=sample_rate, name=channel.station)
dataFull.resample(channel.samplef)
elif params["ifo"] == "SR":
stationPeriod = channel.station.replace(":",".")
stationSplit = channel.station.split(":")
stationPeriod = "%s.%s.%s.%s"%(stationSplit[1],stationSplit[0],stationSplit[2],stationSplit[3])
tt = []
data = []
import obspy
for frame in params["frame"]:
index = frame.find(stationPeriod)
if index < 0:
continue
frameSplit = frame.split("/")
frameName = frameSplit[-1]
frameSplit = frameName.split("_")
frameDate = frameSplit[0]
#thisDate = datetime.strptime(frameDate, '%Y.%j.%H.%M.%S.%f')
#thistime = lal.gpstime.utc_to_gps(thisDate)
#td = thisDate - datetime(1970, 1, 1)
#thistime = td.microseconds * 1e-6 + td.seconds + td.days * 24 * 3600
#if thistime > gpsEnd:
# continue
#if thistime + 86400 < gpsStart:
# continue
datalines = [line.strip() for line in open(frame)]
#datalines = datalines[13:]
#datalines = datalines[1:]
dataStart = False
thistime = []
for dataline in datalines:
if dataline[0:4] == "TIME":
thistime = []
continue
if not thistime:
year = int(dataline[0:4])
month = int(dataline[5:7])
day = int(dataline[8:10])
hour = int(dataline[11:13])
minute = int(dataline[14:16])
second = int(dataline[17:19])
subsecond = int(dataline[20:26])
thisDate = datetime(year, month, day, hour, minute, second, subsecond)
#thistime = lal.gpstime.utc_to_gps(thisDate)
td = thisDate - datetime(1970, 1, 1)
thistime = td.microseconds * 1e-6 + td.seconds + td.days * 24 * 3600
else:
thistime = thistime + 1.0/channel.samplef
if thistime > gpsEnd:
break
if thistime < gpsStart:
continue
tt.append(thistime)
counts = float(dataline[26:])
data.append(counts)
if len(data) == 0:
return []
tt, data = zip(*sorted(zip(tt, data)))
tt = np.array(tt)
data = np.array(data)
indexes = np.intersect1d(np.where(tt >= gpsStart)[0],np.where(tt <= gpsEnd)[0])
tt = tt[indexes]
data = data[indexes]
if len(data) == 0:
return []
sample_rate = channel.samplef
from obspy.signal import seisSim, pazToFreqResp
paz_remove = {'gain': 6.76557e9,
'poles': [(-4.65+3.46j),
(-4.65-3.46j),
(-.118),(-40.9),
(-100.0),(-.15),
(-264.0),
(-16.7+3.4j),
(-16.7-3.4j),
(-63.3),(-63.3)],
'zeros': [0j,0j,0j,0j,-.126,-50.1],
'sensitivity': 1}
#'sensitivity': 2.00000e12}
#'sensitivity': 1}
# Translated from PITSA: spr_resg.c
delta = 1.0 / sample_rate
#
ndat = len(data)
nfft = ndat
freq_response, freqs = pazToFreqResp(paz_remove['poles'],
paz_remove['zeros'],
paz_remove['gain'], delta, nfft,
freq=True)
from obspy.signal import seisSim
#data = seisSim(data, sample_rate, paz_remove=paz_remove)
dataFull = gwpy.timeseries.TimeSeries(data, times=None, epoch=gpsStart, channel=channel.station, unit=None,sample_rate=sample_rate, name=channel.station)
dataFull.resample(channel.samplef)
elif params["ifo"] == "Gravimeter":
stationSplit = channel.station.split(":")
stationID = stationSplit[0]
stationType = stationSplit[1]
tt = []
data = []
import obspy
for frame in params["frame"]:
frameSplit = frame.split("/")
frameName = frameSplit[-1]
frameNameSplit = frameName.split("+")
frameNameSplit = frameNameSplit[1]
frameID = frameNameSplit[:2]
frameDate = frameNameSplit[2:8]
if not stationID.lower() == frameID.lower():
continue
year = int(frameDate[0:2])
if year < 20:
year = year + 2000
else:
year = year + 1900
month = int(frameDate[2:4])
day = int(frameDate[4:6])
if day == 0:
day = 1
hour = 0
minute = 0
second = 0
thisDate = datetime(year, month, day, hour, minute, second)
thistime = lal.gpstime.utc_to_gps(thisDate)
if thistime.gpsSeconds > gpsEnd:
continue
if thistime.gpsSeconds + 31*86400 < gpsStart:
continue
datalines = [line.strip() for line in open(frame)]
#datalines = datalines[13:]
#datalines = datalines[1:]
dataStart = False
for dataline in datalines:
if len(dataline) == 0:
continue
if dataline[0:5] == "77777":
dataStart = True
continue
if dataline[0:5] == "88888":
dataStart = True
continue
if dataline[0] == "#":
continue
if dataline[0:5] == "99999":
dataStart = False
break
if dataStart == False:
continue
year = int(dataline[0:4])
month = int(dataline[4:6])
day = int(dataline[6:8])
try:
hour = int(dataline[9:11])
except:
hour = 0
try:
minute = int(dataline[11:13])
except:
minute = 0
try:
second = int(dataline[13:15])
except:
second = 0
thisDate = datetime(year, month, day, hour, minute, second)
thistime = lal.gpstime.utc_to_gps(thisDate)
if thistime.gpsSeconds > gpsEnd:
break
if thistime.gpsSeconds + 31*86400 < gpsStart:
break
datalineSplit = dataline[15:].split(" ")
datalineSplit = filter(None, datalineSplit)
if datalineSplit[0][0] == "*":
gravity = 0
pressure = 0
elif len(datalineSplit) == 2:
gravity = float(datalineSplit[0])
pressure = float(datalineSplit[1])
elif len(datalineSplit) == 1:
datalineSplit2 = datalineSplit[0].split("-")
datalineSplit2 = filter(None, datalineSplit2)
if len(datalineSplit2) == 2:
gravity = float(datalineSplit2[0])
pressure = -float(datalineSplit2[1])
else:
datalineSplit = datalineSplit[0]
gravity = float(datalineSplit[0:10])
try:
pressure = float(datalineSplit[10:])
except:
pressure = 0
else:
continue
if gravity == 99.0 or gravity > 9999.9:
gravity = 0
pressure = 0
tt.append(thistime.gpsSeconds)
if stationType == "PRESSURE":
data.append(pressure)
elif stationType == "GRAVITY":
data.append(gravity)
if len(data) == 0:
return []
tt, data = zip(*sorted(zip(tt, data)))
tt = np.array(tt)
data = np.array(data)
indexes = np.intersect1d(np.where(tt >= gpsStart)[0],np.where(tt <= gpsEnd)[0])
tt = tt[indexes]
data = data[indexes]
if len(data) == 0:
return []
sample_rate = channel.samplef
dataFull = gwpy.timeseries.TimeSeries(data, times=None, epoch=gpsStart, channel=channel.station, unit=None,sample_rate=sample_rate, name=channel.station)
dataFull.resample(channel.samplef)
else:
#for frame in params["frame"]:
# print frame.path
# frame_data,data_start,_,dt,_,_ = Fr.frgetvect1d(frame.path,channel.station)
#print params["frame"]
#dataFull = gwpy.timeseries.TimeSeries.read(params["frame"], channel.station, epoch=gpsStart, duration=duration)
#print "done"
# make timeseries
#dataFull = gwpy.timeseries.TimeSeries.read(params["frame"], channel.station, start=gpsStart, end=gpsEnd, gap='pad', pad = 0.0)
try:
dataFull = gwpy.timeseries.TimeSeries.read(params["frame"], channel.station, start=gpsStart, end=gpsEnd, gap='pad', pad = 0.0)
except:
print "data read from frames failed... continuing\n"
return dataFull
return dataFull
def plot_xml_response(input_dics):
"""
plot the transfer function of stationXML file(s)
:param input_dics:
:return:
"""
plt.rc('font', family='serif')
print('[INFO] plotting StationXML file/files in: %s' % \
input_dics['datapath'])
if not os.path.isdir('./stationxml_plots'):
print('[INFO] creating stationxml_plots directory...')
os.mkdir('./stationxml_plots')
# assign the input_dics parameters to the running parameters:
stxml_dir = input_dics['datapath']
plotxml_datetime = input_dics['plotxml_date']
min_freq = input_dics['plotxml_min_freq']
output = input_dics['plotxml_output']
if 'dis' in output.lower():
output = 'DISP'
elif 'vel' in output.lower():
output = 'VEL'
elif 'acc' in output.lower():
output = 'ACC'
else:
output = output.upper()
start_stage = input_dics['plotxml_start_stage']
end_stage_input = input_dics['plotxml_end_stage']
percentage = input_dics['plotxml_percentage'] / 100.
threshold = input_dics['plotxml_phase_threshold']
plot_response = input_dics['plotxml_response']
plotstage12 = input_dics['plotxml_plotstage12']
plotpaz = input_dics['plotxml_paz']
plotallstages = input_dics['plotxml_allstages']
plot_map_compare = input_dics['plotxml_map_compare']
if os.path.isfile(stxml_dir):
addxml_all = glob.glob(os.path.join(stxml_dir))
elif os.path.isdir(stxml_dir):
addxml_all = glob.glob(os.path.join(stxml_dir, 'STXML.*'))
else:
try:
addxml_all = glob.glob(os.path.join(stxml_dir))
except Exception as error:
print('[ERROR] %s' % error)
sys.exit('[ERROR] wrong address: %s' % stxml_dir)
addxml_all.sort()
sta_lat = []
sta_lon = []
latlon_color = []
report_fio = open(os.path.join('stationxml_plots', 'report_stationxml'),
'wt')
report_fio.writelines('channel_id\t\t\t\t%(phase_diff)\t\t'
'abs(max_diff)\tlat\t\t\tlon\t\t\tdatetime\t'
'decimation delay\tdecimation corr\n')
report_fio.close()
add_counter = 0
for addxml in addxml_all:
end_stage = end_stage_input
add_counter += 1
print(40 * '-')
print('%s/%s' % (add_counter, len(addxml_all)))
try:
xml_inv = read_inventory(addxml, format='stationXML')
print("[STATIONXML] %s" % addxml)
# we only take into account the first channel...
cha_name = xml_inv.get_contents()['channels'][0]
if plotxml_datetime:
cha_date = plotxml_datetime
else:
cha_date = xml_inv.networks[0][0][-1].start_date
print('[INFO] plotxml_date has not been set, the start_date ' \
'of the last channel in stationXML file will be used ' \
'instead: %s' % cha_date)
xml_response = xml_inv.get_response(cha_name, cha_date + 0.1)
if xml_inv[0][0][0].sample_rate:
sampling_rate = xml_inv[0][0][0].sample_rate
else:
for stage in xml_response.response_stages[::-1]:
if (stage.decimation_input_sample_rate is not None) and\
(stage.decimation_factor is not None):
sampling_rate = (stage.decimation_input_sample_rate /
stage.decimation_factor)
break
t_samp = 1.0 / sampling_rate
nyquist = sampling_rate / 2.0
nfft = int(sampling_rate / min_freq)
end_stage = min(len(xml_response.response_stages), end_stage)
if plotallstages:
plot_xml_plotallstages(xml_response, t_samp, nyquist, nfft,
min_freq, output, start_stage,
end_stage, cha_name)
try:
cpx_response, freq = xml_response.get_evalresp_response(
t_samp=t_samp,
nfft=nfft,
output=output,
start_stage=start_stage,
end_stage=end_stage)
cpx_12, freq = xml_response.get_evalresp_response(
t_samp=t_samp,
nfft=nfft,
output=output,
start_stage=1,
end_stage=2)
except Exception as error:
print('[WARNING] %s' % error)
continue
paz, decimation_delay, decimation_correction = \
convert_xml_paz(xml_response, output, cha_name, cha_date)
if not paz:
continue
h, f = pazToFreqResp(paz['poles'],
paz['zeros'],
paz['gain'],
1. / sampling_rate,
nfft,
freq=True)
phase_resp = np.angle(cpx_response)
phase_12 = np.angle(cpx_12)
if plot_response:
plt.figure(figsize=(20, 10))
plt.suptitle(cha_name, size=24, weight='bold')
if plotpaz or plotstage12:
plt.subplot(2, 2, 1)
else:
plt.subplot(2, 1, 1)
plt.loglog(freq,
abs(cpx_response),
color='blue',
lw=3,
label='full-resp')
if plotstage12:
plt.loglog(freq,
abs(cpx_12),
ls='--',
color='black',
lw=3,
label='Stage1,2')
if plotpaz:
plt.loglog(f,
abs(h) * paz['sensitivity'],
color='red',
lw=3,
label='PAZ')
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.ylabel('Amplitude', size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist + 5)
plt.ylim(ymax=max(1.2 * abs(cpx_response)))
plt.legend(loc=0, prop={'size': 18, 'weight': 'bold'})
plt.grid()
if plotpaz or plotstage12:
plt.subplot(2, 2, 3)
else:
plt.subplot(2, 1, 2)
plt.semilogx(freq,
phase_resp,
color='blue',
lw=3,
label='full-resp')
if plotstage12:
plt.semilogx(freq,
phase_12,
ls='--',
color='black',
lw=3,
label='Stage1,2')
if plotpaz:
plt.semilogx(f,
np.angle(h),
color='red',
lw=3,
label='PAZ')
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.xlabel('Frequency [Hz]', size=24, weight='bold')
plt.ylabel('Phase [rad]', size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist + 5)
plt.legend(loc=0, prop={'size': 18, 'weight': 'bold'})
plt.grid()
if plotstage12 or plotpaz:
ax_222 = plt.subplot(2, 2, 2)
y_label = 'Amplitude ratio'
if plotstage12:
plt.loglog(freq,
abs(abs(cpx_response) / abs(cpx_12)),
'--',
color='black',
lw=3,
label='|full-resp|/|Stage1,2|')
y_label = '|full-resp|/|Stage1,2|'
amp_ratio = 1
if plotpaz:
amp_ratio = abs(
abs(cpx_response) / (abs(h) * paz['sensitivity']))
plt.loglog(f,
amp_ratio,
color='red',
lw=3,
label='|full-resp|/|PAZ|')
y_label = '|full-resp|/|PAZ|'
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.axvline(percentage * nyquist,
ls="--",
color='black',
lw=3)
plt.ylabel(y_label, size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist + 5)
plt.ylim(
ymax=1.2 *
max(amp_ratio[np.logical_not(np.isnan(amp_ratio))]))
plt.grid()
ax_224 = plt.subplot(2, 2, 4)
y_label = 'Phase difference [rad]'
if plotstage12:
plt.semilogx(freq,
abs(phase_resp - phase_12),
color='black',
ls='--',
lw=3,
label='|full-resp - Stage1,2|')
y_label = '|full-resp - Stage1,2| [rad]'
if plotpaz:
plt.semilogx(freq,
abs(phase_resp - np.angle(h)),
color='red',
lw=3,
label='|full-resp - PAZ|')
y_label = '|full-resp - PAZ|'
plt.axvline(nyquist, ls="--", color='black', lw=3)
plt.axvline(percentage * nyquist,
ls="--",
color='black',
lw=3)
plt.xlabel('Frequency [Hz]', size=24, weight='bold')
plt.ylabel(y_label, size=24, weight='bold')
plt.xticks(size=18, weight='bold')
plt.yticks(size=18, weight='bold')
plt.xlim(xmin=min_freq, xmax=nyquist + 5)
plt.grid()
plt.savefig(os.path.join('stationxml_plots',
cha_name + '.png'))
plt.close()
# compare = abs(phase[:int(0.8*len(phase))] -
# np.angle(h[:int(0.8*len(phase))]))
# if len(compare[compare>0.1]) > 0:
# lat_red.append(xml_inv.get_coordinates(cha_name)['latitude'])
# lon_red.append(xml_inv.get_coordinates(cha_name)['longitude'])
# print cha_name
# print paz
# else:
# lat_blue.append(xml_inv.get_coordinates(cha_name)['latitude'])
# lon_blue.append(xml_inv.get_coordinates(cha_name)['longitude'])
phase_resp_check = phase_resp[:int(percentage * len(phase_resp))]
phase_h_check = np.angle(h)[:int(percentage * len(np.angle(h)))]
if not len(phase_resp_check) == len(phase_h_check):
sys.exit('[ERROR] lengths of phase responses do not match: '
'%s (StationXML) != %s (PAZ)' %
(len(phase_resp_check), len(phase_h_check)))
compare = abs(phase_resp_check - phase_h_check)
percent_compare = \
float(len(compare[compare >= 0.05]))/len(compare)*100
latlondep = get_coordinates(xml_inv.networks[0], cha_name,
cha_date + 0.1)
sta_lat.append(latlondep['latitude'])
sta_lon.append(latlondep['longitude'])
d_c = np.sum(decimation_delay) - np.sum(decimation_correction)
if not d_c == 0:
latlon_color.append(0.)
else:
latlon_color.append(percent_compare)
if percent_compare >= threshold:
plot_xml_plotallstages(xml_response, t_samp, nyquist, nfft,
min_freq, output, start_stage,
end_stage, cha_name)
report_fio = open(
os.path.join('stationxml_plots', 'report_stationxml'), 'at')
report_fio.writelines(
'%s\t\t\t%6.2f\t\t\t%6.2f\t\t\t%6.2f\t\t%7.2f\t\t%s\t%s\t%s\n'
% (cha_name, round(percent_compare, 2),
round(max(abs(compare)), 2), round(
sta_lat[-1], 2), round(sta_lon[-1], 2), cha_date,
np.sum(decimation_delay), np.sum(decimation_correction)))
report_fio.close()
except Exception as error:
print('[Exception] %s' % error)
if plot_map_compare:
plt.figure()
m = Basemap(projection='robin', lon_0=input_dics['plot_lon0'], lat_0=0)
m.fillcontinents()
m.drawparallels(np.arange(-90., 120., 30.))
m.drawmeridians(np.arange(0., 420., 60.))
m.drawmapboundary()
x, y = m(sta_lon, sta_lat)
m.scatter(x,
y,
100,
c=latlon_color,
marker="v",
edgecolor='none',
zorder=10,
cmap='rainbow')
plt.colorbar(orientation='horizontal')
plt.savefig(os.path.join('stationxml_plots', 'compare_plots.png'))
plt.show()
raw_input_built('Press Enter...')
sys.exit('[EXIT] obspyDMT finished normally...')
'poles': [-4.21000 + 4.66000j,
- 4.21000 - 4.66000j,
- 2.105000 + 0.00000j],
'zeros': [0.0 + 0.0j] * 3, # add or remove zeros here
'gain' : 0.4
}
# Read in the data
tr = read("loc_RJOB20050831023349.z")[0]
# Do the instrument correction
data_corr = seisSim(tr.data, tr.stats.sampling_rate, le3d,
inst_sim=PAZ_WOOD_ANDERSON, water_level=60.0)
# Just for visualization, calculate transferfuction in frequency domain
trans, freq = pazToFreqResp(le3d['poles'], le3d['zeros'], le3d['gain'],
1./tr.stats.sampling_rate, 2**12, freq=True)
#
# The plotting part
#
time = np.arange(0,tr.stats.npts)/tr.stats.sampling_rate
plt.figure()
plt.subplot(211)
plt.plot(time, tr.data, label="Original Data")
plt.legend()
plt.subplot(212)
plt.plot(time, data_corr, label="Wood Anderson Simulated Data")
plt.legend()
plt.xlabel("Time [s]")
plt.suptitle("Original and Corrected Data")
