示例#1
0
    template = template_dummy
    for tr in template:
        for i in xrange(len(stations)):
            if tr.stats.station == stations[i]:
                if not "alllags" in locals():
                    alllags = [lags[i]]
                else:
                    # print stations[i]
                    alllags = np.concatenate((alllags, [lags[i]]), axis=0)
                    # print np.shape(alllags)
    lags = alllags
    print "Lags is shaped: " + str(np.shape(lags))
    print "I have " + str(len(template)) + " channels of data"
    # Indexing will be an issue, currently don't check that stations match between data and lags
    possible_locations = moveout_check(
        template,
        nodes,
        lags,
        defaults.threshold,
        defaults.threshtype,
        tempdef.lowcut,
        tempdef.highcut,
        tempdef.filter_order,
    )
    from utils import EQcorrscan_plotting as plotting

    if not len(possible_locations) == 0:
        plotting.threeD_gridplot(possible_locations)
    else:
        raise ValueError("No possible location found")
示例#2
0
                                for tr in stream)
            stream = Stream(stream)
            print stream
    if not Prep:
        #stream_copy=stream.copy() # Keep the stream safe
        print "Running the detection routine"
        # Check that the data are okay
        detect_templates, detect_nodes=bright_lights.brightness(stations, \
                        nodes, lags, stream,
                        brightdef.threshold, brightdef.thresh_type,\
                        brightdef.coherance, instance, matchdef, templatedef)
        del detect_templates  #, stream # Delete templates from memory to conserve RAM!
        #stream=stream_copy
        nodesout += detect_nodes
        if Split:
            plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
                                 savefile='Detected_nodes_'+str(instance)+'.png')
        else:
            plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
                                 savefile='Detected_nodes.png')

    else:
        for tr in stream:
            print "Writing data as: test_data/"+tr.stats.station+'-'+tr.stats.channel+\
                    '-'+str(tr.stats.starttime.year)+\
                    '-'+str(tr.stats.starttime.month).zfill(2)+\
                    '-'+str(tr.stats.starttime.day).zfill(2)+\
                    '-processed.ms'
            tr.write('test_data/'+tr.stats.station+'-'+tr.stats.channel+\
                    '-'+str(tr.stats.starttime.year)+\
                    '-'+str(tr.stats.starttime.month).zfill(2)+\
                    '-'+str(tr.stats.starttime.day).zfill(2)+\
示例#3
0
                sys.path.insert(0,path[0:len(path)-5])
                from par import template_gen_par as templatedef
                from par import bright_lights_par as brightdef
                from core import bright_lights
                from utils import EQcorrscan_plotting as plotting
                # Use the brightness function to search for possible templates
                # First read in the travel times
                print 'Reading in the original grids'
                stations, allnodes, alllags = \
                        bright_lights._read_tt(brightdef.nllpath,brightdef.stations,\
                                    brightdef.phase)
                # Resample the grid to allow us to run it quickly!
                print 'Cutting the grid'
                stations, nodes, lags = bright_lights._resample_grid(stations, allnodes,
                                                     alllags,
                                                     brightdef.volume,
                                                     brightdef.resolution)
                # Remove lags that have a similar network moveout, e.g. the sum of the
                # differences in moveouts is small.
                print "Removing simlar lags"
                stations, nodes, lags = bright_lights._rm_similarlags(stations, nodes, lags,
                                                      brightdef.nodesimthresh)
                print "Plotting new grid"
                plotting.threeD_gridplot(nodes)
                dailycoherence = coherence_test(stream, stations, nodes, lags, \
                                templatedef.length)
            else:
                raise IOError("No traces read in for this day, are they processed?")
        else:
            raise IOError("I only know --coherence at the moment")
                                for tr in stream)
            stream=Stream(stream)
            print stream
    if not Prep:
        #stream_copy=stream.copy() # Keep the stream safe
        print "Running the detection routine"
        # Check that the data are okay
        detect_templates, detect_nodes=bright_lights.brightness(stations, \
                        nodes, lags, stream,
                        brightdef.threshold, brightdef.thresh_type,\
                        brightdef.coherance, instance, matchdef, templatedef)
        del detect_templates#, stream # Delete templates from memory to conserve RAM!
        #stream=stream_copy
        nodesout+=detect_nodes
        if Split:
            plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
                                 savefile='Detected_nodes_'+str(instance)+'.png')
        else:
            plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
                                 savefile='Detected_nodes.png')

    else:
        for tr in stream:
            print "Writing data as: test_data/"+tr.stats.station+'-'+tr.stats.channel+\
                    '-'+str(tr.stats.starttime.year)+\
                    '-'+str(tr.stats.starttime.month).zfill(2)+\
                    '-'+str(tr.stats.starttime.day).zfill(2)+\
                    '-processed.ms'
            tr.write('test_data/'+tr.stats.station+'-'+tr.stats.channel+\
                    '-'+str(tr.stats.starttime.year)+\
                    '-'+str(tr.stats.starttime.month).zfill(2)+\
                    '-'+str(tr.stats.starttime.day).zfill(2)+\
示例#5
0
    stations, nodes, lags = _resample_grid(stations, nodes, lags, brightdef.volume,\
                                           brightdef.resolution)
    # print np.shape(lags)
    for station in stations:
        if not 'template_dummy' in locals():
            template_dummy = template.select(station=station)
        else:
            template_dummy += template.select(station=station)
    template = template_dummy
    for tr in template:
        for i in xrange(len(stations)):
            if tr.stats.station == stations[i]:
                if not 'alllags' in locals():
                    alllags = [lags[i]]
                else:
                    # print stations[i]
                    alllags = np.concatenate((alllags, [lags[i]]), axis=0)
                    # print np.shape(alllags)
    lags = alllags
    print 'Lags is shaped: ' + str(np.shape(lags))
    print 'I have ' + str(len(template)) + ' channels of data'
    # Indexing will be an issue, currently don't check that stations match between data and lags
    possible_locations = moveout_check(template, nodes, lags, defaults.threshold,\
                                       defaults.threshtype, tempdef.lowcut,\
                                       tempdef.highcut, tempdef.filter_order)
    from utils import EQcorrscan_plotting as plotting
    if not len(possible_locations) == 0:
        plotting.threeD_gridplot(possible_locations)
    else:
        raise ValueError("No possible location found")