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(, station=ptrace.station, location=ptrace.location,, delta=ptrace.deltat, starttime=UTCDateTime(ptrace.tmin), coordinates=dict(, longitude=station.lon, elevation=station.elevation/1000.))) return otr class FK(Snuffling): '''


based in ObsPy

This snuffling requires the ObsPy package which can be found at ObsPy's github page

On way to install this package is to do:

        git clone git://
        cd obspy
        sudo python install

- Load station information at startup
- Zoom into the data until you see only data you desire to analyse or use extended markers to selected time regions for analysis
- Press the 'Run' button

The slowness is given in s/km. The assumed location of the geometrical center is printed to the terminal.

Further information can be gathered from # noqa ObsPy's FK tutorial.

''' 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 import obspy_sequential as cmap except ImportError as _import_error:'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:'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:'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:'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): / 2 - (i + 1) * dw) * num.ones(N), height=dh * num.ones(N), width=dw, bottom=dh * num.arange(N), color=cmap(row / hist.max())) 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()]