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