User contributed plug-ins for Pyrocko's seismic waveform browser Snuffler.
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from __future__ import print_function
from pyrocko.gui.snuffling import Param, Snuffling, Choice
import numpy as num
def p2o_trace(ptrace, station):
'''Convert Pyrocko trace to ObsPy trace.'''
from obspy.core import UTCDateTime
from obspy.core import Trace as oTrace
otr = oTrace(
data=ptrace.get_ydata(),
header=dict(
network=ptrace.network,
station=ptrace.station,
location=ptrace.location,
channel=ptrace.channel,
delta=ptrace.deltat,
starttime=UTCDateTime(ptrace.tmin),
coordinates=dict(
latitude=station.lat,
longitude=station.lon,
elevation=station.elevation/1000.)))
return otr
class FK(Snuffling):
'''
<html>
<head>
<style type="text/css">
body { margin-left:10px };
</style>
</head>
<body>
<h1 align='center'>FK ANALYSIS</h1>
<h2 align='center'>based in ObsPy</h2>
<p>
<u>Prerequisites</u><br>
This snuffling requires the ObsPy package which can be found at
<a href='https://github.com/obspy/obspy/wiki'> ObsPy's github page </a>
</p>
<p>
On way to install this package is to do:
<PRE>
git clone git://github.com/obspy/obspy.git
cd obspy
sudo python setup.py install
</PRE>
<p>
<u>Usage</u><br>
- Load station information at startup <br>
- Zoom into the data until you see only data you desire to analyse or
use extended markers to selected time regions for analysis<br>
- Press the 'Run' button <br>
</p>
<p>
The slowness is given in s/km.
The assumed location of the geometrical center is printed to the terminal.
</p>
<p>
Further information can be gathered from
<a href="http://docs.obspy.org/master/tutorial/code_snippets/beamforming_fk_analysis.html"> # noqa
ObsPy's FK tutorial</a>.
</p>
</body>
</html>
'''
def setup(self):
self.set_name('FK Analysis')
self.add_parameter(Param('Slowness range[+-]', 'smax', 0.2, 0., 1.))
self.add_parameter(Param(
'Number of slowness divisions', 'divisor', 20, 10, 50))
self.add_parameter(Param(
'Number of radial sections', 'numberOfFraction', 32, 4, 50))
self.add_parameter(Param(
'Length of Sliding Window [s]', 'window_lenth', 1., 0.5, 10.))
self.add_parameter(Param(
'Step fraction of Sliding Window [s]', 'win_frac', 0.05, 0., 10.))
self.add_parameter(Choice(
'If sampling rates differ', 'downresample', 'resample',
['resample', 'downsample', 'downsample to "target dt"']))
self.add_parameter(Param('target dt', 'target_dt', 0.2, 0., 10))
# self.add_parameter(Choice('Units: ','unit','[s/km]',('[s/km]','[s/deg]'))) # noqa
self.set_live_update(False)
def call(self):
try:
from obspy.core import UTCDateTime, stream
from obspy.signal import array_analysis
from obspy.imaging.cm import obspy_sequential as cmap
except ImportError as _import_error:
self.fail('ImportError:\n%s' % _import_error)
from matplotlib.colorbar import ColorbarBase
from matplotlib.colors import Normalize
import matplotlib.dates as mdates
self.cleanup()
viewer = self.get_viewer()
if viewer.lowpass is None or viewer.highpass is None:
self.fail('highpass and lowpass in viewer must be set!')
traces = []
for trs in self.chopper_selected_traces(fallback=True):
for tr in trs:
tr.lowpass(2, viewer.lowpass)
tr.highpass(2, viewer.highpass)
traces.extend(trs)
if not traces:
self.fail('no traces selected')
if self.downresample == 'resample':
dt_want = min([t.deltat for t in traces])
for t in traces:
t.resample(dt_want)
elif self.downresample == 'downsample':
dt_want = max([t.deltat for t in traces])
for t in traces:
t.downsample_to(dt_want)
elif self.downresample == 'downsample to "target dt"':
for t in traces:
t.downsample_to(float(self.target_dt))
tmin = max([t.tmin for t in traces])
tmax = min([t.tmax for t in traces])
try:
obspy_traces = [p2o_trace(
tr, viewer.get_station(viewer.station_key(tr)))
for tr in traces]
except KeyError:
self.fail('station information missing')
st = stream.Stream(traces=obspy_traces)
center = array_analysis.get_geometry(st, return_center=True)
center_lon, center_lat, center_ele = center[len(center)-1]
# Execute sonic
kwargs = dict(
sll_x=-self.smax, slm_x=self.smax, sll_y=-self.smax,
slm_y=self.smax, sl_s=self.smax/self.divisor,
win_len=self.window_lenth, win_frac=self.win_frac,
frqlow=viewer.highpass, frqhigh=viewer.lowpass, prewhiten=0,
semb_thres=-1.0e9, vel_thres=-1.0e9, verbose=True,
timestamp='mlabday', stime=UTCDateTime(tmin),
etime=UTCDateTime(tmax)
)
try:
out = array_analysis.array_processing(st, **kwargs)
except AttributeError:
from obspy.signal.array_analysis import sonic
out = sonic(st, **kwargs)
pi = num.pi
# make output human readable, adjust backazimuth to values between 0
# and 360
t, rel_power, abs_power, baz, slow = out.T
baz[baz < 0.0] += 360.
# choose number of fractions in plot (desirably 360 degree/N is an
# integer!)
N = int(self.numberOfFraction)
abins = num.arange(N + 1) * 360. / N
sbins = num.linspace(0., self.smax, N + 1)
# sum rel power in bins given by abins and sbins
hist, baz_edges, sl_edges = num.histogram2d(
baz, slow, bins=[abins, sbins], weights=rel_power)
# transform to gradient
baz_edges = baz_edges / 180. * pi
fig = self.pylab(get='figure')
cax = fig.add_axes([0.85, 0.2, 0.05, 0.5])
ax = fig.add_axes([0.10, 0.1, 0.70, 0.7], polar=True)
ax.grid(False)
dh = abs(sl_edges[1] - sl_edges[0])
dw = abs(baz_edges[1] - baz_edges[0])
# circle through backazimuth
for i, row in enumerate(hist):
ax.bar((pi / 2 - (i + 1) * dw) * num.ones(N),
height=dh * num.ones(N), width=dw,
bottom=dh * num.arange(N), color=cmap(row / hist.max()),
align="edge")
ax.set_xticks([pi / 2, 0, 3. / 2 * pi, pi])
ax.set_xticklabels(['N', 'E', 'S', 'W'])
ax.set_ylim(0., self.smax)
ColorbarBase(cax, cmap=cmap,
norm=Normalize(vmin=hist.min(), vmax=hist.max()))
fig2 = self.pylab(get='figure')
labels = ['rel.power', 'abs.power', 'baz', 'slow']
xlocator = mdates.AutoDateLocator()
ax = None
for i, lab in enumerate(labels):
ax = fig2.add_subplot(4, 1, i + 1, sharex=ax)
ax.scatter(out[:, 0], out[:, i + 1], c=out[:, 1], alpha=0.6,
edgecolors='none', cmap=cmap)
ax.set_ylabel(lab)
ax.set_xlim(out[0, 0], out[-1, 0])
ax.set_ylim(out[:, i + 1].min(), out[:, i + 1].max())
ax.xaxis.set_tick_params(which='both', direction='in')
ax.xaxis.set_major_locator(xlocator)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(xlocator))
if i != 3:
ax.set_xticklabels([])
fig2.subplots_adjust(hspace=0.)
fig2.canvas.draw()
fig.canvas.draw()
print('Center of Array at latitude %s and longitude %s' %
(center_lat, center_lon))
def __snufflings__():
return [FK()]