def analyze():
#stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC TAIQ'
stations = 'PB01 PB02 PATCX'
stations = 'PATCX'
t1 = UTC('2009-01-01')
t2 = UTC('2010-01-01')
data = IPOC()
for station in stations.split():
hours = [[] for i in range(24)]
times = []
levels = []
for t_day in daygen(t1, t2):
try:
stream = data.getRawStreamFromClient(t_day, t_day + 24 * 3600, station, component='Z')
except ValueError:
continue
for tr in stream:
tr.stats.filter = ''
stream.demean()
stream.detrend()
stream.filter2(4, 6)
stream.downsample2(5)
stream.merge()
tr = stream[0]
startt = tr.stats.starttime
endt = tr.stats.endtime
if endt - startt < 12 * 3600:
continue
tr.data = obspy.signal.cpxtrace.envelope(tr.data)[1][:len(tr.data)]
for hour in range(24):
tr2 = tr.slice(t_day + hour * 3600, t_day + (hour + 1) * 3600)
if tr2.stats.endtime - tr2.stats.starttime < 1800:
continue
num_stds = 60 # =^ every minute
len_parts = len(tr2.data) // 60 # =^ 1min
len_stds = len_parts // 6 # =^ 10s
stds = np.array([np.std(tr2.data[i:i + len_stds]) for i in np.arange(num_stds) * len_parts])
stds = stds[stds != 0.]
num_stds = len(stds)
if num_stds < 50:
continue
stds = np.sort(stds)[num_stds // 5:-num_stds // 5]
stds = stds[stds < np.min(stds) * 2.]
val = np.mean(stds)
levels.append(val)
times.append(date2num(t_day + (0.5 + hour) * 3600))
hours[hour].append(val)
errors = np.array([np.std(hours[i], ddof=1) / len(hours[i]) ** 0.5 for i in range(24)])
hours = np.array([np.mean(hours[i]) for i in range(24)])
times = np.array(times)
levels = np.array(levels)
np.savez('/home/richter/Results/IPOC/xcorr/noise_apmlitudes_%s_4-6Hz.npz' % station,
hours=hours, errors=errors, times=times, levels=levels)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05'
component = 'Z'
t1 = UTC('2009-06-01')
t2 = UTC('2009-07-01')
data = IPOC('test', use_local_LVC=False)
data.setXLogger('_test')
period = 24 * 3600
ax = None
plt.ion()
for station in stations.split():
pxxs = []
freqs_old = None
i = 0
for t in timegen(t1, t2, period):
st = data.getRawStreamFromClient(t, t + period, station, component)
st.merge(method=1,
interpolation_samples=10,
fill_value='interpolate')
print st
pxx, freqs = st.plotPSD(just_calculate=True)
assert np.all(freqs == freqs_old) or not freqs_old
freqs_old = freqs
if max(pxx[4:]) > 1e7:
print 'discard'
i += 1
continue
pxxs.append(pxx)
pxx = sum(pxxs) / len(pxxs)
del pxxs
tr = Trace(data=pxx,
header=dict(is_fft=True,
sampling_rate=2 * max(freqs),
freq_min=min(freqs),
freq_max=max(freqs)))
ax = tr.plotPSD(ax=ax,
label='%s-%d' % (st[0].stats.station, i),
figtitle=None)
plt.draw()
# embed()
ax.legend()
fig = ax.get_figure()
fig.suptitle('%s %s %s to %s' %
(stations, component, t1.strftime('%Y-%m-%d'),
t2.strftime('%Y-%m-%d')))
plt.ioff()
plt.show()
def main():
stations = 'PB01 PB02 PB03 PB04 PB05'
component = 'Z'
t1 = UTC('2009-06-01')
t2 = UTC('2009-07-01')
data = IPOC('test', use_local_LVC=False)
data.setXLogger('_test')
period = 24 * 3600
ax = None
plt.ion()
for station in stations.split():
pxxs = []
freqs_old = None
i = 0
for t in timegen(t1, t2, period):
st = data.getRawStreamFromClient(t, t + period, station, component)
st.merge(method=1, interpolation_samples=10, fill_value='interpolate')
print st
pxx, freqs = st.plotPSD(just_calculate=True)
assert np.all(freqs == freqs_old) or not freqs_old
freqs_old = freqs
if max(pxx[4:]) > 1e7:
print 'discard'
i += 1
continue
pxxs.append(pxx)
pxx = sum(pxxs) / len(pxxs)
del pxxs
tr = Trace(data=pxx,
header=dict(is_fft=True, sampling_rate=2 * max(freqs),
freq_min=min(freqs), freq_max=max(freqs)))
ax = tr.plotPSD(ax=ax, label='%s-%d' % (st[0].stats.station, i), figtitle=None)
plt.draw()
# embed()
ax.legend()
fig = ax.get_figure()
fig.suptitle('%s %s %s to %s' % (stations, component, t1.strftime('%Y-%m-%d'),
t2.strftime('%Y-%m-%d')))
plt.ioff()
plt.show()
#!/usr/bin/env python
# by TR
from sito.data import IPOC
from obspy.core import UTCDateTime as UTC
import matplotlib.pyplot as plt
from sito.util.main import streamtimegen
from sito.stream import Stream
from progressbar import ProgressBar
data = IPOC()
t_day = UTC('2008-01-01')
station = 'PB01'
stream = data.getRawStreamFromClient(
t_day, t_day + 24 * 3600, station, component='Z')
stream.setHI('filter', '')
stream.demean()
stream.filter2(0.5, 5)
stream.trim2(0, 5 * 3600)
auto = Stream()
for st in streamtimegen(stream, dt=60, start=None, shift=30, use_slice=True):
tr = st[0].copy()
tr.addZeros(60)
tr.acorr(60)
auto.append(tr)
print auto
auto.plotXcorr()
stream.plot(type='dayplot')
def analyze():
#stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC TAIQ'
stations = 'PB01 PB02 PATCX'
stations = 'PATCX'
t1 = UTC('2009-01-01')
t2 = UTC('2010-01-01')
data = IPOC()
for station in stations.split():
hours = [[] for i in range(24)]
times = []
levels = []
for t_day in daygen(t1, t2):
try:
stream = data.getRawStreamFromClient(t_day,
t_day + 24 * 3600,
station,
component='Z')
except ValueError:
continue
for tr in stream:
tr.stats.filter = ''
stream.demean()
stream.detrend()
stream.filter2(4, 6)
stream.downsample2(5)
stream.merge()
tr = stream[0]
startt = tr.stats.starttime
endt = tr.stats.endtime
if endt - startt < 12 * 3600:
continue
tr.data = obspy.signal.cpxtrace.envelope(tr.data)[1][:len(tr.data)]
for hour in range(24):
tr2 = tr.slice(t_day + hour * 3600, t_day + (hour + 1) * 3600)
if tr2.stats.endtime - tr2.stats.starttime < 1800:
continue
num_stds = 60 # =^ every minute
len_parts = len(tr2.data) // 60 # =^ 1min
len_stds = len_parts // 6 # =^ 10s
stds = np.array([
np.std(tr2.data[i:i + len_stds])
for i in np.arange(num_stds) * len_parts
])
stds = stds[stds != 0.]
num_stds = len(stds)
if num_stds < 50:
continue
stds = np.sort(stds)[num_stds // 5:-num_stds // 5]
stds = stds[stds < np.min(stds) * 2.]
val = np.mean(stds)
levels.append(val)
times.append(date2num(t_day + (0.5 + hour) * 3600))
hours[hour].append(val)
errors = np.array(
[np.std(hours[i], ddof=1) / len(hours[i])**0.5 for i in range(24)])
hours = np.array([np.mean(hours[i]) for i in range(24)])
times = np.array(times)
levels = np.array(levels)
np.savez(
'/home/richter/Results/IPOC/xcorr/noise_apmlitudes_%s_4-6Hz.npz' %
station,
hours=hours,
errors=errors,
times=times,
levels=levels)
fig.text(0.02,
y0 + (0.98 / 2) * dy,
station,
va='center',
ha='center',
rotation=90)
t_day = UTC(day)
fname = '%s/%s-%s.png' % (pic_dir, station, day)
try:
if redraw:
raise IOError
img = mpimg.imread(fname)
except IOError:
try:
stream = data.getRawStreamFromClient(t_day,
t_day + 24 * 3600,
station,
component='Z')
except ValueError:
continue
fig2 = stream.plot(type='dayplot',
vertical_scaling_range=scale_range
if station != 'LVC' else scale_LVC,
interval=30,
handle=True)
fig2.axes[0].set_position((0, 0, 1, 1))
fig2.axes[1].set_visible(False)
fig2.texts[0].set_visible(False)
fig2.canvas.draw()
fig2.savefig(fname)
img = mpimg.imread(fname)
ax = fig.add_axes([x0, y0, dx * 0.98, dy * 0.98])
if args.absolute_scale is None and args.relative_scale is None:
kwargs['scale'] = 1.
else:
station = args.file_station
if station.startswith('PB') or station == 'LVC' or station.endswith('CX'):
from sito.data import IPOC
data = IPOC(xcorr_append=args.xcorr_append)
elif station == 'PKD':
from sito.data import Parkfield
data = Parkfield(xcorr_append=args.xcorr_append)
else:
raise argparse.ArgumentError('Not a valid station name')
day = UTC(args.date)
if args.xcorr_append is None:
#stream = data.getRawStream(day, station, component=args.component)
stream = data.getRawStreamFromClient(day, day + 24 * 3600, station, component=args.component, channel=args.channel)
else:
stream = data.getStream(day, station, component=args.component)
if args.absolute_scale is None and args.relative_scale is None:
kwargs['absolutescale'] = 0.0005
tr = stream[0]
tr.stats.filter = ''
if args.frequency is False:
if tr.stats.is_fft:
tr.ifft()
print tr
#tr.plotTrace(component=args.component, **kwargs)
tr.plotTrace(**kwargs)
else:
# if not tr.stats.is_fft:
