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")
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)+\
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)+\
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")
