channel=chan,
starttime=UTCDateTime('2004-001T00:00:00.0'),
endtime=day + secperday,
filename=respfilename(ch))
resp = irisclient.evalresp(network,
station,
loc,
chan,
filename="%s%s.png" % (qcfigs, ch),
output='plot')
except:
print("No response data for channel %s" % (ch))
data = {}
for ch in ids:
print(respfilename(ch))
stch = st.select(id=ch) # Just take the data for a single channel
calc_daily_stats(stch)
try:
ppsd = PPSD(stch[0].stats, metadata=str(respfilename(ch)))
ppsd.add(stch)
figname = "%s%d/%03d/%s.png" % (qcfigs, day.year, day.julday, ch)
path_verify(figname)
ppsd.plot(figname, cmap=pqlx)
data = ppsd.get_percentile(percentile=50)
fname = "%s%d/%03d/PPSDper50_%s.npz" % (qcdata, day.year,
day.julday, ch)
path_verify(fname)
np.savez(fname, data)
except:
print("Error with PPSD for %s check for response" % (ch))
#Make the PDF
ppsd = PPSD(st[0].stats,paz=pazval,ppsd_length=parserval.len,overlap=parserval.overlap)
for tr in st:
ppsd.add(tr)
if debug:
for pdftime in ppsd.times:
print 'Here is what is in the PDF: ' + str(pdftime)
try:
pdfstring = "PDF" + st[0].stats.station + st[0].stats.channel + str(st[0].stats.starttime.year)+ \
str(st[0].stats.starttime.julday).zfill(3) + ".jpg"
medianstring = "MEDIAN" + st[0].stats.station + st[0].stats.channel + str(st[0].stats.starttime.year)+ \
str(st[0].stats.starttime.julday).zfill(3)
if debug:
print 'Saving the PDF to : ' + pdfstring
print 'Saving the median to : ' + medianstring
ppsd.plot(show_percentiles=True,percentiles=[50], filename=pdfstring,
show = True, show_histogram=True, grid= False, show_coverage=False, \
period_lim=(parserval.minper,parserval.maxper))
per,perval = ppsd.get_percentile(percentile=50,hist_cum=None)
perFile = open(medianstring, 'w')
for index, val in enumerate(per):
perFile.write(str("%.2f" % val) + ',' + str(perval[index]) + '\n')
perFile.close()
except:
'No PPSD saved'
network,
station,
location=loc,
channel=chan,
starttime=UTCDateTime('2004-001T00:00:00.0'),
endtime=day + secperday,
filename=respfilename(ch))
resp = irisclient.evalresp(network,
station,
loc,
chan,
filename="%s%s.png" % (qcfigs, ch),
output='plot')
except:
print("No response data for channel %s" % (ch))
data = {}
for ch in ids:
print(respfilename(ch))
stch = st.select(id=ch) # Just take the data for a single channel
try:
ppsd = PPSD(stch[0].stats, metadata=str(respfilename(ch)))
ppsd.add(stch)
figname = "%s%d/%03d/%s.png" % (qcfigs, day.year, day.julday, ch)
path_verify(figname)
ppsd.plot(figname, cmap=pqlx)
data[ch] = ppsd.get_percentile(percentile=50)
except:
print("Error with PPSD for %s check for response" % (ch))
print(data)
# channels = ['EHU']
# channels = ['SHU', 'MHV', 'MHW']
st = read(ondeckdatafile)
inv = read_inventory(ondeckmetadata)
# On deck SP data
print("Working on on-deck data")
chn = 'EHU'
tr = st.select(channel=chn)[1] #first one may have metadata problem
ppsd = PPSD(tr.stats, metadata=inv, ppsd_length=600.0, skip_on_gaps=True,
period_limits=(0.02, 100.0), db_bins=(-200,-50, 1.))
st_select = st.select(channel=chn)
ppsd.add(st_select)
(ondeckpd, ondeckpsd) = ppsd.get_mean()
(ondeck05pd, ondeck05psd) = ppsd.get_percentile(percentile=5)
(ondeck95pd, ondeck95psd) = ppsd.get_percentile(percentile=95)
weeklydir = 'datafiles/weeklies/'
metadata = 'datafiles/ELYSE.all.dl0226.response.xml'
# On ground SP
print("Working on on-ground data")
chn = 'SHU'
loc = '68'
weeks = np.array(['2019-01-06-2019-01-12', '2019-01-13-2019-01-19',
'2019-01-20-2019-01-26', '2019-01-27-2019-02-02'])
# Do first file
week = weeks[0]
datafile = weeklydir + week + '.ELYSE.allseispress.mseed'
refperiod = 3.
nullwrite = NullWriter()
amp_by_obs_length = []
for obslength in tqdm(obslengths):
peak_amp = []
start = starttime
end = start + obslength*3600.
while (end < endtime):
# print((end-starttime)/length)
st = read(noisefile, starttime=start, endtime=end)
oldstdout = sys.stdout
sys.stdout = nullwrite
ppsd = PPSD(st[0].stats, paz, db_bins=[-300, -75, 5],
period_limits=[1.0, 200], ppsd_length=ppsd_length)
ppsd.add(st)
(pd, psd) = ppsd.get_percentile(percentile=95)
sys.stdout = oldstdout
peak_amp.append(float(psd.max()))
start = end
end = start + obslength*3600.
peak_amp = np.array(peak_amp)
amp_by_obs_length.append(peak_amp)
with open(outputfile, 'w') as f:
f.write('Obs period, mean, std, median, 5th percentile\n')
for i, obslength in enumerate(obslengths):
f.write('%.1f, %.2f, %.1f, %.1f, %.1f\n' %
(obslength, np.mean(amp_by_obs_length[i]),
np.std(amp_by_obs_length[i]),
np.median(amp_by_obs_length[i]),
