def get_event_info(starttime, endtime, streams):
events = []
arrivals = {}
try:
client = FDSNClient("NERIES")
events = client.get_events(starttime=starttime - 20 * 60,
endtime=endtime)
for ev in events[::-1]:
has_arrivals = False
origin = ev.origins[0]
origin_time = origin.time
lon1 = origin.longitude
lat1 = origin.latitude
depth = abs(origin.depth / 1e3)
for st in streams:
sta = st[0].stats.station
lon2 = st[0].stats.coordinates['longitude']
lat2 = st[0].stats.coordinates['latitude']
dist = locations2degrees(lat1, lon1, lat2, lon2)
tts = getTravelTimes(dist, depth)
list_ = arrivals.setdefault(sta, [])
for tt in tts:
tt['time'] = origin_time + tt['time']
if starttime < tt['time'] < endtime:
has_arrivals = True
list_.append(tt)
if not has_arrivals:
events[:] = events[:-1]
except Exception as e:
msg = ("Problem while fetching events or determining theoretical "
"phases: %s: %s" % (e.__class__.__name__, str(e)))
return None, None, msg
return events, arrivals, None
def calculate_time_phase(event, sta):
"""
calculate arrival time of the requested phase to use in retrieving
waveforms.
:param event:
:param sta:
:return:
"""
ev_lat = event['latitude']
ev_lon = event['longitude']
ev_dp = abs(float(event['depth']))
sta_lat = float(sta[4])
sta_lon = float(sta[5])
delta = locations2degrees(ev_lat, ev_lon, sta_lat, sta_lon)
tt = taup.getTravelTimes(delta, ev_dp)
phase_list = ['P', 'Pdiff', 'PKIKP']
time_ph = 0
flag = False
for ph in phase_list:
for i in range(len(tt)):
if tt[i]['phase_name'] == ph:
flag = True
time_ph = tt[i]['time']
break
else:
continue
if flag:
print 'Phase: %s' % ph
break
t_start = event['t1'] + time_ph
t_end = event['t2'] + time_ph
return t_start, t_end
def get_neries_info(starttime, endtime, streams):
events = []
arrivals = {}
try:
client = neries.Client()
events = client.getEvents(min_datetime=starttime - 20 * 60,
max_datetime=endtime,
format="list")
for ev in events[::-1]:
has_arrivals = False
origin_time = ev['datetime']
lon1 = ev['longitude']
lat1 = ev['latitude']
depth = abs(ev['depth'])
for st in streams:
sta = st[0].stats.station
lon2 = st[0].stats.coordinates['longitude']
lat2 = st[0].stats.coordinates['latitude']
dist = locations2degrees(lat1, lon1, lat2, lon2)
tts = getTravelTimes(dist, depth)
list_ = arrivals.setdefault(sta, [])
for tt in tts:
tt['time'] = origin_time + tt['time']
if starttime < tt['time'] < endtime:
has_arrivals = True
list_.append(tt)
if not has_arrivals:
events.remove(ev)
except Exception as e:
msg = ("Problem while determining theoretical phases using "
"neries/taup: %s: %s" % (e.__class__.__name__, str(e)))
return None, None, msg
return events, arrivals, None
def ray_density(lat1, lon1, lat2, lon2,
dt=1, gr_x=360, gr_y=180,
npts=180, projection='robin',
ray_coverage=False):
'''
Create the DATA array which contains the
info for ray density
'''
global long_0
mymap = Basemap(projection=projection, lon_0=long_0, lat_0=0)
#npts=max(gr_x, gr_y)
# grd[2]: longitude
# grd[3]: latitude
grd = mymap.makegrid(gr_x, gr_y, returnxy=True)
lons, lats = mymap.gcpoints(lon1, lat1, lon2, lat2, npts)
dist = locations2degrees(lat1, lon1, lat2, lon2)
bap = int((dist - 97.0)*npts/dist)/2
midlon = len(lons)/2
midlat = len(lats)/2
lons = lons[midlon-bap:midlon+1+bap]
lats = lats[midlat-bap:midlat+1+bap]
data = np.zeros([len(grd[2]), len(grd[3])])
for i in range(len(lons)):
xi, yi = point_finder(lons[i], lats[i], grd)
# first one is latitude and second longitude
try:
#data[yi][xi] = dt/float(dist-97.0)
data[yi][xi] += dt/len(lons)
except Exception, e:
print e
def get_event_info(starttime, endtime, streams):
events = []
arrivals = {}
try:
client = FDSNClient("NERIES")
events = client.get_events(starttime=starttime - 20 * 60,
endtime=endtime)
for ev in events[::-1]:
has_arrivals = False
origin = ev.origins[0]
origin_time = origin.time
lon1 = origin.longitude
lat1 = origin.latitude
depth = abs(origin.depth / 1e3)
for st in streams:
sta = st[0].stats.station
lon2 = st[0].stats.coordinates['longitude']
lat2 = st[0].stats.coordinates['latitude']
dist = locations2degrees(lat1, lon1, lat2, lon2)
tts = getTravelTimes(dist, depth)
list_ = arrivals.setdefault(sta, [])
for tt in tts:
tt['time'] = origin_time + tt['time']
if starttime < tt['time'] < endtime:
has_arrivals = True
list_.append(tt)
if not has_arrivals:
events[:] = events[:-1]
except Exception as e:
msg = ("Problem while fetching events or determining theoretical "
"phases: %s: %s" % (e.__class__.__name__, str(e)))
return None, None, msg
return events, arrivals, None
def loc2degrees(a, b):
if type(a) is dict: a1 = Basic.dictToLocation(a)
else: a1 = a
if type(b) is dict: b1 = Basic.dictToLocation(b)
else: b1 = b
delta = locations2degrees(float(a1.lat), float(a1.lon), float(b1.lat),
float(b1.lon))
return delta
def get_time_correction(traces, source_dict):
from obspy.taup import TauPyModel
model = TauPyModel(model="iasp91")
from obspy.core.util import locations2degrees
time_correction = []
for trace in traces:
name = trace.stats.station
lat_inline = source_dict[name][4]
lon_inline = source_dict[name][3]
lat = trace.stats.sac.stla
lon = trace.stats.sac.stlo
evla = trace.stats.sac.evla
evlo = trace.stats.sac.evlo
evdp = trace.stats.sac.evdp
dist_inline = locations2degrees(lat_inline, lon_inline, evla, evlo)
dist = locations2degrees(lat, lon, evla, evlo)
time_inline = model.get_travel_times(source_depth_in_km=evdp/1000.0, distance_in_degree=dist_inline, phase_list=["P"])[0].time
time = model.get_travel_times(source_depth_in_km=evdp/1000.0, distance_in_degree=dist, phase_list=["P"])[0].time
time_correction.append(time - time_inline)
# print time_inline - time
return time_correction
def check_collision(lats, lons, radius, dist_bt, angle_step):
'''
This function will check if the moment tensors are collide on
the plot, if there are collisions, then we will put all the
MT on a circle lined back to the location of the event
input:
lats - list of latitudes of the events
lons - list of longitudes of the events
radius - radius of the circle to put the MT
dist_bt - the distance difference between MTs to be checked, if
the distance between the two MTs is smaller than dist_bt,
then we will put them on a circle
angle_step - the increase of angle on the circle to put the
MTs.
returns:
lats_m - list of latitudes of the modified location
lons_m - list of longitudes of the modified location
indicator - a list of flag showing which events we modified
'''
lats_m = np.zeros(len(lats))
lons_m = np.zeros(len(lats))
indicator = np.zeros(len(lats))
angles = range(0, 360, angle_step)
j = 0
for i in range(len(lats) - 1):
for k in range(i + 1, len(lats)):
dist = locations2degrees(lats[i], lons[i], lats[k], lons[k]) * 111
if dist < dist_bt:
indicator[i] = 1
indicator[k] = 1
else:
pass
for i in range(len(lats)):
if indicator[i] == 1:
ix = j % len(angles)
a = radius * np.cos(angles[ix] * math.pi / 180)
b = radius * np.sin(angles[ix] * math.pi / 180)
lats_m[i] = lats[i] + a
lons_m[i] = lons[i] + b
j += 1
else:
lats_m[i] = lats[i]
lons_m[i] = lons[i]
return lats_m, lons_m, indicator
def check_collision(lats, lons, radius, dist_bt, angle_step):
'''
This function will check if the moment tensors are collide on
the plot, if there are collisions, then we will put all the
MT on a circle lined back to the location of the event
input:
lats - list of latitudes of the events
lons - list of longitudes of the events
radius - radius of the circle to put the MT
dist_bt - the distance difference between MTs to be checked, if
the distance between the two MTs is smaller than dist_bt,
then we will put them on a circle
angle_step - the increase of angle on the circle to put the
MTs.
returns:
lats_m - list of latitudes of the modified location
lons_m - list of longitudes of the modified location
indicator - a list of flag showing which events we modified
'''
lats_m = np.zeros(len(lats))
lons_m = np.zeros(len(lats))
indicator = np.zeros(len(lats))
angles = range(0,360,angle_step)
j = 0
for i in range(len(lats)-1):
for k in range(i+1, len(lats)):
dist = locations2degrees(lats[i], lons[i], lats[k], lons[k]) * 111
if dist < dist_bt:
indicator[i] =1
indicator[k] =1
else:
pass
for i in range(len(lats)):
if indicator[i] == 1:
ix = j%len(angles)
a = radius*np.cos(angles[ix]*math.pi/180)
b = radius*np.sin(angles[ix]*math.pi/180)
lats_m[i] = lats[i] + a
lons_m[i] = lons[i] + b
j +=1
else:
lats_m[i] = lats[i]
lons_m[i] = lons[i]
return lats_m, lons_m, indicator
def ray_density(lat1, lon1, lat2, lon2, dt=1, gr_x=360, gr_y=180, npts=180, projection='robin', ray_coverage=False):
"""
Create the DATA array which contains the info for ray density
Procedure:
1. make a grid based on the inputs (grd)
grd: lon, lat, x, y
2. find the great circle points:
note that lon , lat are actually x and y!
3. calculate the distance and find the middle point
4. subtracting 97 degrees from the distance and find all the points on that section
5. data ---> zero array with x*y elements
"""
global long_0
mymap = Basemap(projection=projection, lon_0=long_0, lat_0=0)
#npts=max(gr_x, gr_y)
# grd[2]: longitude
# grd[3]: latitude
grd = mymap.makegrid(gr_x, gr_y, returnxy=True)
lons, lats = mymap.gcpoints(lon1, lat1, lon2, lat2, npts)
dist = locations2degrees(lat1, lon1, lat2, lon2)
# npts points on dist...how many on (dist-97)!: (dist-97)*npts/dist....but we also need to make it half!
bap = int((dist - 97.0)*npts/dist)/2
midlon = len(lons)/2
midlat = len(lats)/2
lons = lons[midlon-bap:midlon+1+bap]
lats = lats[midlat-bap:midlat+1+bap]
data = np.zeros([len(grd[2]), len(grd[3])])
if not len(lons) == len(lats):
sys.exit('ERROR: Lengths longitudes and latitudes are not the same! %s and %s' % (len(lons), len(lats)))
for i in range(len(lons)):
xi, yi = point_finder(lons[i], lats[i], grd)
# first one is latitude and second longitude
try:
#data[yi][xi] = dt/float(dist-97.0)
data[yi][xi] += dt/len(lons)
except Exception, e:
print '\nException: %s' % e
def filterStationsDistance (stationList, parameter):
'''
Filters stationslist via distance attribute
:type stationList: list
:param stationList: list of stationobjects
:type parameter: list
:param parameter: list of parameter used filter routines
'''
D = []
for stationobject in stationList:
sdelta = locations2degrees (parameter['elat'], parameter['elon'],
float (stationobject.lat), float (stationobject.lon))
if sdelta > parameter['minDist'] and sdelta < parameter['maxDist']:
D.append(stationobject)
#endfor
return D
def getSlowestStation(lat,lon,depth,calc):
client = Client("IRIS")
inventory = client.get_stations(latitude=lat, longitude=lon,maxradius=1.5)
lats = []
lons = []
codes = []
for network in inventory.networks:
for station in network.stations:
lats.append(station.latitude)
lons.append(station.longitude)
codes.append(station.code)
lats = np.array(lats)
lons = np.array(lons)
codes = np.array(codes)
distances = []
times = []
for i in range(0,len(lats)):
slat = lats[i]
slon = lons[i]
distance = locations2degrees(lat,lon,slat,slon)
distances.append(distance)
ptime,stime = calc.getTravelTimes(distance,depth)
times.append(ptime)
times = np.array(times)
distances = np.array(distances)
sortidx = np.argsort(distances)
distances = distances[sortidx]
times = times[sortidx]
lats = lats[sortidx]
lons = lons[sortidx]
codes = codes[sortidx]
distances = distances[0:4]
times = times[0:4] + TELEMETRY_DELAY + PROCESSING_DELAY
lats = lats[0:4]
lons = lons[0:4]
codes = codes[0:4]
idx = times.argmax()
sdict = {'lat':lats[idx],'lon':lons[idx],'time':times[idx],'code':codes[idx]}
return sdict
def ProcessLoopP(filepath):
'''The file processing loop associated with P wave tomography: Just deal with the BHZ files to save time'''
sacfiles = glob.glob('*.BHZ')
#Check to see if there is more than one location for a given station
stationnames = []
for sacfile in sorted(sacfiles):
sacfilenameparts = sacfile.split('.')
stationname = sacfilenameparts[2]
if stationname not in stationnames:
stationnames.append(stationname)
trace = read(sacfile)
evlat = trace[0].stats.sac.evla
evlon = trace[0].stats.sac.evlo
evdep = trace[0].stats.sac.evdp
stlat = trace[0].stats.sac.stla
stlon = trace[0].stats.sac.stlo
dist = locations2degrees(evlat,evlon,stlat,stlon) #find distance from the quake to the station
arcs = IRISclient.distaz(stalat=stlat,stalon=stlon,evtlat=evlat,evtlon=evlon)
az = arcs['backazimuth']
baz = arcs['azimuth']
#If we're running this code twice in a row, need to correct the evdep accordingly. The evdep that comes from obspy will be in km, but needs
#to be in the SAC header in meters. If the depth is already in meters in the SACfile, then it will almost certainly be >1000. This if statement check for
#this, and converts to km if necessary
if evdep > 1e3:
evdep = evdep/1000.0
traveltimes = getTravelTimes(dist,evdep, model='iasp91')
P = 0
S = 0
for element in traveltimes:
phaseinfo = element['phase_name']
if phaseinfo == 'P':
Ptime = element['time']
P = 1
if phaseinfo == 'S':
Stime = element['time']
S = 1
if (P==1 and S==1):
break
try:
P = Ptime
except:
Ptime = 0
try:
S = Stime
except:
Stime = 0
#Set the P and S times
trace[0].stats.sac.az = float(az)
trace[0].stats.sac.baz = float(baz)
trace[0].stats.sac.o = 0.0 #add origin time
if Ptime > 0:
trace[0].stats.sac.t1 = Ptime
if Stime > 0:
trace[0].stats.sac.t2 = Stime
trace[0].stats.sac.evdp = evdep*1000 #dbpick wants depth to be in meters
#other operations
trace[0].detrend('demean')
#THE CROSS CORRELATION CODE ASSUMES A SAMPLING RATE OF 0.05 (20 SAMPLES/SECOND). IT WILL NOT WORK
#OTHERWISE!!!
trace[0].resample(20)
if results.autop:
tracestreamP = trace.copy()
df = tracestreamP[0].stats.sampling_rate
filter1 = 0.02
filter2 = 0.1
tracestreamP.filter("bandpass",freqmin=filter1,freqmax=filter2,corners=2)
tracestreamP.taper(max_percentage=0.05, type='cosine')
p_pick, phase_info = pkBaer(tracestreamP[0].data,df,10,2,2,10,20,6) #output from this is in samples.
autoPtime = p_pick/df
#Append the autopicker's time to the
if abs(Ptime-autoPtime) < 20:
print 'Autopick accepted!'
trace[0].stats.sac.t3 = autoPtime
#Important - must write to the SAC file!
trace.write(sacfile,format='SAC')
print 'Appended arrivals to %s' %sacfile
else:
print 'Found multiple instruments at station %s. Removing all but 1' %(stationname)
os.system('rm %s' %sacfile)
#create antelope database for P arrivals
os.system('sac2db *.BHZ Z')
def naloc3(csspath, nalocfile, locmd='./loc3d_aug', ttgridpath='../ttgrid',filter='*'):
# if ttgridpath != "../austtg":
# os.system('ln -s %s ../austtg' % (ttgridpath) )
if ttgridpath != "../ttgrid":
os.system('ln -s %s ../ttgrid' % (ttgridpath) )
# get CSS3 arrival file
cssfilelst = glob.glob(csspath+"/"+filter+"*.arrival")
cssfilelst.sort()
logFile = open('naloc.log','w')
locfp = open(nalocfile, 'a+')
evid = 0
for cssfile in cssfilelst:
print cssfile
os.system('cp ' + cssfile + ' arrival_list')
fp = open('arrival_list','r')
lineTemp = fp.readlines()
fp.close()
numberPhase = len(lineTemp)
if numberPhase < 3:
continue
row = lineTemp[0].split()
originOld = UTCDateTime(float(row[14]))
evlaOld = float(row[15])
evloOld = float(row[16])
evdpOld = float(row[17])
p = subprocess.Popen([locmd],stdout = logFile).communicate()[0]
# os.system('./loc3d_aug')
if not os.path.exists('fort.13'):
logFile.write('Location failed.')
# print 'Loc fail'
else:
fp = open('fort.13', 'r')
line = fp.readline()
fp.close()
row = line.split()
evla = float(row[1])
evlo = float(row[2])
evdp = float(row[3])
origin = UTCDateTime(float(row[10]))+float(row[4])
eorigin = origin - originOld
edist = locations2degrees(evla, evlo, evlaOld, evloOld)
edepth = evdp - evdpOld
[date, time] = datetimefmt(origin)
[dateold, timeold] = datetimefmt(originOld)
# sstring = '%s %12.4f %12.4f %8.3f %s %12.4f %12.4f %8.3f %8.3f %8.3f %8.3f %4d \n' % (str(origin), evla, evlo, evdp, str(originOld), evlaOld, evloOld, evdpOld, eorigin, edist, edepth, numberPhase)
# locfp.write(sstring)
# evid += 1
sstring = '%12s %14s %12.4f %12.4f %8.3f %12s %14s %12.4f %12.4f %8.3f %8.3f %8.3f %8.3f %4d \n' % (date, time, evla, evlo, evdp, dateold, timeold, evlaOld, evloOld, evdpOld, eorigin, edist, edepth, numberPhase)
locfp.write(sstring)
try:
#os.remove('fort.13')
pass
except:
pass
swCleanupLoc()
locfp.close()
logFile.close()
if ttgridpath != "../austtg":
os.system('rm -rf ../austtg')
def naloc3v2(csspath, nalocfile, locmd='./loc3d_aug', ttgridpath='../austtg',filter='*'):
if ttgridpath != "../austtg":
os.system('ln -s %s ../austtg' % (ttgridpath) )
resdir = "./residuals"
if not os.path.exists(resdir):
os.makedirs(resdir)
# get CSS3 arrival file
cssfilelst = glob.glob(csspath+"/"+filter+"*.arrival")
cssfilelst.sort()
logFile = open('naloc.log','w')
locfp = open(nalocfile, 'w')
evid = 0
for cssfile in cssfilelst:
print cssfile
os.system('cp ' + cssfile + ' arrival_list')
# try:
# os.system("rm fort.13")
# except:
# pass
fp = open('arrival_list','r')
lineTemp = fp.readlines()
fp.close()
numberPhase = len(lineTemp)
if numberPhase < 3:
continue
row = lineTemp[0].split()
originOld = UTCDateTime(float(row[14]))
evlaOld = float(row[15])
evloOld = float(row[16])
evdpOld = float(row[17])
p = subprocess.Popen([locmd],stdout = logFile).communicate()[0]
# os.system('./loc3d_aug')
if not os.path.exists('fort.13'):
logFile.write('Location failed.')
# print 'Loc fail'
else:
fp = open('fort.13', 'r')
line = fp.readline()
fp.close()
row = line.split()
evla = float(row[1])
evlo = float(row[2])
evdp = float(row[3])
origin = UTCDateTime(float(row[10]))+float(row[4])
eorigin = origin - originOld
edist = locations2degrees(evla, evlo, evlaOld, evloOld)
edepth = evdp - evdpOld
[date, time] = datetimefmt(origin)
[dateold, timeold] = datetimefmt(originOld)
# open residuals.dat
with open("residuals.dat", "r") as fp:
lst = fp.readlines()
pfres = 0.0
sfres = 0.0
pfres = float(lst[-2].split()[-1])
sfres = float(lst[-1].split()[-1])
# sstring = '%s %12.4f %12.4f %8.3f %s %12.4f %12.4f %8.3f %8.3f %8.3f %8.3f %4d \n' % (str(origin), evla, evlo, evdp, str(originOld), evlaOld, evloOld, evdpOld, eorigin, edist, edepth, numberPhase)
# locfp.write(sstring)
# evid += 1
sstring = '%12s %14s %12.4f %12.4f %8.3f %12s %14s %12.4f %12.4f %8.3f %8.3f %8.3f %8.3f %4d %8.3f %8.3f\n' % (date, time, evla, evlo, evdp, dateold, timeold, evlaOld, evloOld, evdpOld, eorigin, edist, edepth, numberPhase, pfres, sfres)
locfp.write(sstring)
resfn = resdir+"/"+dateold.replace("-","")+timeold.replace(":", "")[0:6]+".res"
os.system("cp residuals.dat %s" % resfn)
swCleanupLoc()
locfp.close()
logFile.close()
if ttgridpath != "../austtg":
os.system('rm -rf ../austtg')
def main(args):
globaldict = getGlobalConfig()
shakehome = globaldict['shakehome']
popfile = globaldict['popfile']
if shakehome is None:
print 'Cannot find ShakeMap home folder on this system.'
sys.exit(1)
datadir = os.path.join(shakehome,'data',args.event)
if not os.path.isdir(datadir):
print 'Cannot find event %s on the system' % args.event
sys.exit(1)
#Make sure the timeoutput folder is there (can't put our time grids in output - that gets
#wiped out every time shakemap runs
outfolder = os.path.join(datadir,'timeoutput')
if not os.path.isdir(outfolder):
os.makedirs(outfolder)
#now look for config file in top-level folder
configfile = os.path.join(datadir,'alert.conf')
if not os.path.isfile(configfile):
print 'Cannot find alert config file for %s in the data directory' % args.event
sys.exit(1)
config = ConfigParser.ConfigParser()
config.readfp(open(configfile))
#get the bounds of the map so we can find cities
xmin = float(config.get('MAP','xmin'))
xmax = float(config.get('MAP','xmax'))
ymin = float(config.get('MAP','ymin'))
ymax = float(config.get('MAP','ymax'))
citylist = getCityList(xmin,xmax,ymin,ymax,globaldict['cityfile'])
#Get the MMI threshold below which alert times will NOT be saved
mmithresh = float(config.get('MAP','mmithresh'))
#get the array of epicenters
lats = [float(p) for p in config.get('FAULT','lats').split()]
lons = [float(p) for p in config.get('FAULT','lons').split()]
#write out a new grind.conf file
writeGrind(config,datadir)
#instantiate our p/s travel time calculator
calc = TravelTimeCalculator()
#where is the grind binary?
grindbin = os.path.join(shakehome,'bin','grind')
#specify the event.xml file, get the depth of the event
eventfile = os.path.join(datadir,'input','event.xml')
root = parse(eventfile)
eq = root.getElementsByTagName('earthquake')[0]
depth = float(eq.getAttribute('depth'))
root.unlink()
#get the dimensionality of the grid file and of the pop grid we'll interpolate to
gridfile = os.path.join(datadir,'output','grid.xml')
if not os.path.isfile(gridfile):
grindcmd = '%s -event %s' % (grindbin,args.event)
res,stdout,stderr = getCommandOutput(grindcmd)
mmigrid = ShakeGrid(gridfile,variable='MMI')
popgrid = EsriGrid(popfile)
popgrid.load(bounds=mmigrid.getRange())
m,n = popgrid.griddata.shape
#loop over all the event realizations
timefiles = []
timestack = np.zeros((m,n,len(lats)),dtype=np.float32)
for i in range(0,len(lats)):
print 'Calculating arrival times for scenario %i of %i' % (i+1,len(lats))
lat = lats[i]
lon = lons[i]
if i == 0:
lonoff = 0
latoff = 0
else:
lonoff = -1* (lons[i] - lons[i-1])
latoff = lats[i] - lats[i-1]
#modify the event.xml file to have the new lat/lon epicenter
sourcetext = getEventText(eventfile,lat,lon)
f = open(eventfile,'wt')
f.write(sourcetext)
f.close()
sdict = getSlowestStation(lat,lon,depth,calc)
ptime = sdict['time']
stationlat = sdict['lat']
stationlon = sdict['lon']
grindcmd = '%s -latoff %f -lonoff %f -event %s' % (grindbin,latoff,lonoff,args.event)
res,stdout,stderr = getCommandOutput(grindcmd)
if not res:
print 'Grind command failed: "%s", "%s"' % (stdout,stderr)
sys.exit(1)
#Get the grid.xml output, do some time calculations
mmigrid = ShakeGrid(gridfile,variable='MMI')
timegrid = np.zeros((m,n),dtype=np.float32)
for row in range(0,m):
for col in range(0,n):
mmilat,mmilon = mmigrid.getLatLon(row,col)
distance = locations2degrees(lat,lon,mmilat,mmilon)
tmp,stime = calc.getTravelTimes(distance,depth)
timegrid[row,col] = stime - ptime
#debugging
f = plt.figure()
plt.subplot(2,1,1)
plt.imshow(mmigrid.griddata)
plt.colorbar()
plt.subplot(2,1,2)
plt.imshow(timegrid)
plt.colorbar()
plt.savefig(os.path.join(outfolder,'timegrid.png'))
plt.close(f)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
exposure,timegrid = getTimeExposure(timegrid,mmigrid,popfile,mmithresh)
print 'Population Warning Times for epicenter %.4f,%.4f' % (lat,lon)
printExposure(exposure)
expofile = os.path.join(outfolder,'expo%03i.json' % (i+1))
f = open(expofile,'wt')
f.write(json.dumps(exposure))
f.close()
timefile = os.path.join(outfolder,'timegrid%03i.flt' % (i+1))
timefiles.append(timefile)
metadict = {'epilat':lat,'epilon':lon,'eventid':args.event}
saveTimeGrid(timefile,timegrid,mmigrid.geodict,metadict)
timestack[:,:,i] = timegrid
alertgrid = popgrid
alertgrid.griddata = timegrid
makeMap(alertgrid,'alertmap_%i' % i,outfolder,popfile,globaldict['popcolormap'],sdict,citylist,[lat],[lon])
methods = config.get('MAP','output').split(',')
for method in methods:
if method == 'median':
statgrid = np.median(timestack,axis=2)
if method == 'mean':
statgrid = np.nanmean(timestack,axis=2)
if method == 'min':
statgrid = np.nanmin(timestack,axis=2)
if method == 'max':
statgrid = np.nanmax(timestack,axis=2)
timegrid = popgrid
timegrid.griddata = statgrid
makeMap(timegrid,method,outfolder,popfile,globaldict['popcolormap'],sdict,citylist,lats,lons)
def cc_core(ls_first, ls_second, identity_all, max_ts, print_sta):
"""
Perform the main part of the cross correlation and creating
the cc.txt file
"""
global input
try:
cc_open = open('./cc.txt', 'a')
tr1 = read(ls_first)[0]
if input['phase'] != 'N':
evsta_dist = util.locations2degrees(lat1 = tr1.stats.sac.evla, \
long1 = tr1.stats.sac.evlo, lat2 = tr1.stats.sac.stla, \
long2 = tr1.stats.sac.stlo)
taup_tt = taup.getTravelTimes(delta=evsta_dist,
depth=tr1.stats.sac.evdp)
phase_exist = 'N'
for tt_item in taup_tt:
if tt_item['phase_name'] == input['phase']:
print 'Requested phase:'
print input['phase']
print '------'
print tt_item['phase_name']
print 'exists in the waveform!'
print '-----------------------'
t_phase = tt_item['time']
phase_exist = 'Y'
break
if input['phase'] == 'N' or (input['phase'] != 'N'
and phase_exist == 'Y'):
# identity of the current waveform
identity = tr1.stats.network + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
# Keep the current identity in a new variable
id_name = identity
try:
tr2 = read(os.path.join(input['second_path'], identity))[0]
except Exception, error:
# if it is not possible to read the identity in the second path
# then change the network part of the identity based on
# correction unit
identity = input['corr_unit'] + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
tr2 = read(os.path.join(input['second_path'], identity))[0]
if input['resample'] != 'N':
print 'WARNING: you are using resample!!!'
tr1.resample(input['resample'])
tr2.resample(input['resample'])
if input['tw'] == 'Y':
t_cut_1 = tr1.stats.starttime + t_phase - input['preset']
t_cut_2 = tr1.stats.starttime + t_phase + input['offset']
tr1.trim(starttime=t_cut_1, endtime=t_cut_2)
t_cut_1 = tr2.stats.starttime + t_phase - input['preset']
t_cut_2 = tr2.stats.starttime + t_phase + input['offset']
tr2.trim(starttime=t_cut_1, endtime=t_cut_2)
if input['hlfilter'] == 'Y':
tr1.filter('lowpass', freq=input['hfreq'], corners=2)
tr2.filter('lowpass', freq=input['hfreq'], corners=2)
tr1.filter('highpass', freq=input['lfreq'], corners=2)
tr2.filter('highpass', freq=input['lfreq'], corners=2)
# normalization of all three waveforms to the
# max(max(tr1), max(tr2), max(tr3)) to keep the scales
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max(), abs(tr3.data).max())
'''
maxi = max(abs(tr1.data).max(), abs(tr2.data).max())
tr1_data = tr1.data/abs(maxi)
tr2_data = tr2.data/abs(maxi)
tr3_data = tr3.data/abs(maxi)
'''
tr1.data = tr1.data / abs(max(tr1.data))
tr2.data = tr2.data / abs(max(tr2.data))
cc_np = tr1.stats.sampling_rate * max_ts
np_shift, coeff = cross_correlation.xcorr(tr1, tr2, int(cc_np))
t_shift = float(np_shift) / tr1.stats.sampling_rate
# scale_str shows whether the scale of the waveforms are the same or not
# if scale_str = 'Y' then the scale is correct.
scale_str = 'Y'
if abs(tr1.data).max() > 2.0 * abs(tr2.data).max():
label_tr1 = ls_first.split('/')[-2]
label_tr2 = ls_second[0].split('/')[-2]
print '#####################################################'
print "Scale is not correct! " + label_tr1 + '>' + label_tr2
print '#####################################################'
scale_str = 'N'
elif abs(tr2.data).max() >= 2.0 * abs(tr1.data).max():
label_tr1 = ls_first.split('/')[-2]
label_tr2 = ls_second[0].split('/')[-2]
print '#####################################################'
print "Scale is not correct! " + label_tr2 + '>' + label_tr1
print '#####################################################'
scale_str = 'N'
if not str(coeff) == 'nan':
cc_open.writelines(id_name + ',' + str(round(coeff, 4)) + ',' + str(t_shift) + \
',' + scale_str + ',' + '\n')
print "Cross Correlation:"
print id_name
print "Shift: " + str(t_shift)
print "Coefficient: " + str(coeff)
print print_sta
print '------------------'
cc_open.close()
cc_open.close()
except Exception, error:
print '##################'
print error
print '##################'
def single_comparison():
"""
one by one comparison of the waveforms in the first path with the second path.
"""
client = Client()
global input
# identity of the waveforms (first and second paths) to be compared with each other
identity_all = input['net'] + '.' + input['sta'] + '.' + \
input['loc'] + '.' + input['cha']
ls_first = glob.glob(os.path.join(input['first_path'], identity_all))
ls_second = glob.glob(os.path.join(input['second_path'], identity_all))
for i in range(0, len(ls_first)):
try:
tr1 = read(ls_first[i])[0]
if input['phase'] != 'N':
evsta_dist = util.locations2degrees(lat1 = tr1.stats.sac.evla, \
long1 = tr1.stats.sac.evlo, lat2 = tr1.stats.sac.stla, \
long2 = tr1.stats.sac.stlo)
taup_tt = taup.getTravelTimes(delta=evsta_dist,
depth=tr1.stats.sac.evdp)
phase_exist = 'N'
for tt_item in taup_tt:
if tt_item['phase_name'] == input['phase']:
print 'Requested phase:'
print input['phase']
print '------'
print tt_item['phase_name']
print 'exists in the waveform!'
print '-----------------------'
t_phase = tt_item['time']
phase_exist = 'Y'
break
if phase_exist != 'Y':
continue
# identity of the current waveform
identity = tr1.stats.network + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
# tr1: first path, tr2: second path, tr3: Raw data
#tr3 = read(os.path.join(input['first_path'], '..', 'BH_RAW', identity))[0]
if input['resp_paz'] == 'Y':
response_file = os.path.join(input['first_path'], '..',
'Resp/RESP.' + identity)
# Extract the PAZ info from response file
paz = readRESP(response_file, unit=input['corr_unit'])
poles = paz['poles']
zeros = paz['zeros']
scale_fac = paz['gain']
sensitivity = paz['sensitivity']
print paz
# Convert Poles and Zeros (PAZ) to frequency response.
h, f = pazToFreqResp(poles, zeros, scale_fac, \
1./tr1.stats.sampling_rate, tr1.stats.npts*2, freq=True)
# Use the evalresp library to extract
# instrument response information from a SEED RESP-file.
resp = invsim.evalresp(t_samp = 1./tr1.stats.sampling_rate, \
nfft = tr1.stats.npts*2, filename = response_file, \
date = tr1.stats.starttime, units = input['corr_unit'].upper())
# Keep the current identity in a new variable
id_name = identity
try:
tr2 = read(os.path.join(input['second_path'], identity))[0]
except Exception, error:
# if it is not possible to read the identity in the second path
# then change the network part of the identity based on
# correction unit
identity = input['corr_unit'] + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
tr2 = read(os.path.join(input['second_path'], identity))[0]
if input['resample'] != 'N':
print 'WARNING: you are using resample!!!'
tr1.resample(input['resample'])
tr2.resample(input['resample'])
if input['tw'] == 'Y':
t_cut_1 = tr1.stats.starttime + t_phase - input['preset']
t_cut_2 = tr1.stats.starttime + t_phase + input['offset']
tr1.trim(starttime=t_cut_1, endtime=t_cut_2)
t_cut_1 = tr2.stats.starttime + t_phase - input['preset']
t_cut_2 = tr2.stats.starttime + t_phase + input['offset']
tr2.trim(starttime=t_cut_1, endtime=t_cut_2)
if input['hlfilter'] == 'Y':
tr1.filter('lowpass', freq=input['hfreq'], corners=2)
tr2.filter('lowpass', freq=input['hfreq'], corners=2)
tr1.filter('highpass', freq=input['lfreq'], corners=2)
tr2.filter('highpass', freq=input['lfreq'], corners=2)
# normalization of all three waveforms to the
# max(max(tr1), max(tr2), max(tr3)) to keep the scales
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max(), abs(tr3.data).max())
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max())
#tr1_data = tr1.data/abs(maxi)
#tr2_data = tr2.data/abs(maxi)
#tr3_data = tr3.data/abs(maxi)
tr1_data = tr1.data / abs(max(tr1.data))
tr2_data = tr2.data / abs(max(tr2.data))
#tr1_data = tr1.data
#tr2_data = tr2.data*1e9
print max(tr1.data)
print max(tr2.data)
# create time arrays for tr1, tr2 and tr3
time_tr1 = np.arange(0, tr1.stats.npts/tr1.stats.sampling_rate, \
1./tr1.stats.sampling_rate)
time_tr2 = np.arange(0, tr2.stats.npts/tr2.stats.sampling_rate, \
1./tr2.stats.sampling_rate)
#time_tr3 = np.arange(0, tr3.stats.npts/tr3.stats.sampling_rate, \
# 1./tr3.stats.sampling_rate)
# label for plotting
label_tr1 = ls_first[i].split('/')[-2]
label_tr2 = ls_second[i].split('/')[-2]
label_tr3 = 'RAW'
if input['resp_paz'] == 'Y':
# start plotting
plt.figure()
plt.subplot2grid((3, 4), (0, 0), colspan=4, rowspan=2)
#plt.subplot(211)
plt.plot(time_tr1, tr1_data, color='blue', label=label_tr1, lw=3)
plt.plot(time_tr2, tr2_data, color='red', label=label_tr2, lw=3)
#plt.plot(time_tr3, tr3_data, color = 'black', ls = '--', label = label_tr3)
plt.xlabel('Time (sec)', fontsize='xx-large', weight='bold')
if input['corr_unit'] == 'dis':
ylabel_str = 'Relative Displacement'
elif input['corr_unit'] == 'vel':
ylabel_str = 'Relative Vel'
elif input['corr_unit'] == 'acc':
ylabel_str = 'Relative Acc'
plt.ylabel(ylabel_str, fontsize='xx-large', weight='bold')
plt.xticks(fontsize='xx-large', weight='bold')
plt.yticks(fontsize='xx-large', weight='bold')
plt.legend(loc=1, prop={'size': 20})
#-------------------Cross Correlation
# 5 seconds as total length of samples to shift for cross correlation.
cc_np = tr1.stats.sampling_rate * 3
np_shift, coeff = cross_correlation.xcorr(tr1, tr2, int(cc_np))
t_shift = float(np_shift) / tr1.stats.sampling_rate
print "Cross Correlation:"
print "Shift: " + str(t_shift)
print "Coefficient: " + str(coeff)
plt.title('Single Comparison' + '\n' + str(t_shift) + \
' sec , coeff: ' + str(round(coeff, 5)) + \
'\n' + id_name, \
fontsize = 'xx-large', weight = 'bold')
if input['resp_paz'] == 'Y':
# -----------------------
#plt.subplot(223)
plt.subplot2grid((3, 4), (2, 0), colspan=2)
'''
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)), \
color = 'blue', label = 'RESP', lw=3)
plt.plot(np.log10(f), np.log10(abs(h)/sensitivity), \
color = 'red', label = 'PAZ', lw=3)
'''
plt.loglog(f, abs(resp)/(sensitivity*sensitivity), \
color = 'blue', label = 'RESP', lw=3)
plt.loglog(f, abs(h)/sensitivity, \
color = 'red', label = 'PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0]:
plt.axvline(np.log10(j), linestyle='--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
#plt.ylabel('Amplitude\n (power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]',
fontsize='xx-large',
weight='bold')
plt.ylabel('Amplitude', fontsize='xx-large', weight='bold')
plt.xticks(fontsize='xx-large', weight='bold')
#plt.yticks = MaxNLocator(nbins=4)
plt.yticks(fontsize='xx-large', weight='bold')
plt.legend(loc=2, prop={'size': 20})
# -----------------------
#plt.subplot(224)
plt.subplot2grid((3, 4), (2, 2), colspan=2)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
#plt.plot(np.log10(f), phase_resp, color = 'blue', label = 'RESP', lw=3)
#plt.plot(np.log10(f), phase_paz, color = 'red', label = 'PAZ', lw=3)
plt.semilogx(f, phase_resp, color='blue', label='RESP', lw=3)
plt.semilogx(f, phase_paz, color='red', label='PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0.0]:
plt.axvline(np.log10(j), linestyle='--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]',
fontsize='xx-large',
weight='bold')
plt.ylabel('Phase [radian]',
fontsize='xx-large',
weight='bold')
plt.xticks(fontsize='xx-large', weight='bold')
plt.yticks(fontsize='xx-large', weight='bold')
plt.legend(loc=3, prop={'size': 20})
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.4, hspace=0.3)
"""
# -----------------------
plt.subplot(325)
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)) - \
np.log10(abs(h)/sensitivity), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Amplitude (power of 10)')
plt.legend()
# -----------------------
plt.subplot(326)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
plt.plot(np.log10(f), np.log10(phase_resp) - np.log10(phase_paz), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Phase [radian] (power of 10)')
plt.legend()
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.3)
"""
plt.show()
print str(i + 1) + '/' + str(len(ls_first))
print ls_first[i]
print '------------------'
wait = raw_input(id_name)
print '***************************'
except Exception, error:
print '##################'
print error
print '##################'
def downloadRemoteData(self, net, evtTime, evtLat, evtLon, evtDep, mag, fileFormat):
acceptedStations = [
"SPB",
"CPUP",
"LPAZ",
"PLCA",
"LCO",
"LVC",
"OTAV",
"PTGA",
"RCBR",
"SAML",
"SDV",
"TRQA",
"CAN",
"FDF",
"HDC",
"INU",
"KIP",
"PPTF",
"TAM",
"TRIS",
"CNG",
"CVNA",
"GRM",
"HVD",
"SWZ",
"UPI",
"WIN",
]
stations = self.iris.station(network=net, station="*", starttime=evtTime, endtime=evtTime + 3600, level="sta")
dom = parseString(stations)
nStations = len(dom.getElementsByTagName("Station"))
i = 0
while i < nStations:
for node in dom.getElementsByTagName("Station"):
staName = node.getAttribute("sta_code")
if staName not in acceptedStations:
i += 1
else:
xmlStationLat = dom.getElementsByTagName("Lat")[i].toxml()
staLat = float(xmlStationLat.replace("<Lat>", "").replace("</Lat>", ""))
xmlStationLon = dom.getElementsByTagName("Lon")[i].toxml()
staLon = float(xmlStationLon.replace("<Lon>", "").replace("</Lon>", ""))
xmlStationElev = dom.getElementsByTagName("Elevation")[i].toxml()
staElev = float(xmlStationElev.replace("<Elevation>", "").replace("</Elevation>", ""))
# delta = taup.locations2degrees(evtLat, evtLon, staLat, staLon) # delta stores distance in degrees
delta = util.locations2degrees(evtLat, evtLon, staLat, staLon) # delta stores distance in degrees
itp = taup() # calling IAG Taup class
itp.getTravelTimes(delta, evtDep)
pTime = itp.P()
sTime = itp.S()
# to use in sac headers...
seisTime = evtTime + pTime - self.beforeP
originTime = self.beforeP - pTime
fileTime = str(evtTime).replace("T", "-").replace(":", "-")[:-8]
try:
st = self.iris.getWaveform(
net, staName, "*", "BH*", evtTime + pTime - self.beforeP, evtTime + sTime + self.afterS
)
print "\nData found for station " + staName
st.merge(method=1, fill_value="interpolate")
try:
os.mkdir(staName)
except:
pass
os.mkdir(staName + "/" + fileTime)
for tr in st:
loc = str(tr.stats.location)
fileName = (
staName
+ "/"
+ fileTime
+ "/"
+ net
+ "."
+ staName
+ "."
+ loc
+ "."
+ str(tr.stats.channel)
+ "."
+ fileTime
+ "."
+ fileFormat
)
tr.write(fileName, fileFormat)
print fileName, "saved."
if fileFormat == "SAC":
self.fillSACHeaders(
fileName, seisTime, originTime, evtLat, evtLon, evtDep, mag, staLat, staLon, staElev
)
except:
pass
i += 1
def _validate_and_write_waveforms(st, callback, starttime, endtime, scale,
source, receiver, db, label, format):
if not label:
label = ""
else:
label += "_"
for tr in st:
# Half the filesize but definitely sufficiently accurate.
tr.data = np.require(tr.data, dtype=np.float32)
if scale != 1.0:
for tr in st:
tr.data *= scale
# Sanity checks. Raise internal server errors in case something fails.
# This should not happen and should have been caught before.
if endtime > st[0].stats.endtime:
msg = ("Endtime larger than the extracted endtime: endtime=%s, "
"largest db endtime=%s" % (
_format_utc_datetime(endtime),
_format_utc_datetime(st[0].stats.endtime)))
callback((tornado.web.HTTPError(500, log_message=msg, reason=msg),
None))
return
if starttime < st[0].stats.starttime - 3600.0:
msg = ("Starttime more than one hour before the starttime of the "
"seismograms.")
callback((tornado.web.HTTPError(500, log_message=msg, reason=msg),
None))
return
if isinstance(source, FiniteSource):
mu = None
else:
mu = st[0].stats.instaseis.mu
# Trim, potentially pad with zeroes.
st.trim(starttime, endtime, pad=True, fill_value=0.0, nearest_sample=False)
# Checked in another function and just a sanity check.
assert format in ("miniseed", "saczip")
if format == "miniseed":
with io.BytesIO() as fh:
st.write(fh, format="mseed")
fh.seek(0, 0)
binary_data = fh.read()
callback((binary_data, mu))
# Write a number of SAC files into an archive.
elif format == "saczip":
byte_strings = []
for tr in st:
# Write SAC headers.
tr.stats.sac = obspy.core.AttribDict()
# Write WGS84 coordinates to the SAC files.
tr.stats.sac.stla = geocentric_to_elliptic_latitude(
receiver.latitude)
tr.stats.sac.stlo = receiver.longitude
tr.stats.sac.stdp = receiver.depth_in_m
tr.stats.sac.stel = 0.0
if isinstance(source, FiniteSource):
tr.stats.sac.evla = geocentric_to_elliptic_latitude(
source.hypocenter_latitude)
tr.stats.sac.evlo = source.hypocenter_longitude
tr.stats.sac.evdp = source.hypocenter_depth_in_m
# Force source has no magnitude.
if not isinstance(source, ForceSource):
tr.stats.sac.mag = source.moment_magnitude
src_lat = source.hypocenter_latitude
src_lng = source.hypocenter_longitude
else:
tr.stats.sac.evla = geocentric_to_elliptic_latitude(
source.latitude)
tr.stats.sac.evlo = source.longitude
tr.stats.sac.evdp = source.depth_in_m
# Force source has no magnitude.
if not isinstance(source, ForceSource):
tr.stats.sac.mag = source.moment_magnitude
src_lat = source.latitude
src_lng = source.longitude
# Thats what SPECFEM uses for a moment magnitude....
tr.stats.sac.imagtyp = 55
# The event origin time relative to the reference which I'll
# just assume to be the starttime here?
tr.stats.sac.o = source.origin_time - starttime
# Sac coordinates are elliptical thus it only makes sense to
# have elliptical distances.
dist_in_m, az, baz = gps2DistAzimuth(
lat1=tr.stats.sac.evla,
lon1=tr.stats.sac.evlo,
lat2=tr.stats.sac.stla,
lon2=tr.stats.sac.stlo)
tr.stats.sac.dist = dist_in_m / 1000.0
tr.stats.sac.az = az
tr.stats.sac.baz = baz
# XXX: Is this correct? Maybe better use some function in
# geographiclib?
tr.stats.sac.gcarc = locations2degrees(
lat1=src_lat,
long1=src_lng,
lat2=receiver.latitude,
long2=receiver.longitude)
# Some provenance.
tr.stats.sac.kuser0 = "InstSeis"
tr.stats.sac.kuser1 = db.info.velocity_model[:8]
tr.stats.sac.user0 = scale
# Prefix version numbers to identify them at a glance.
tr.stats.sac.kt7 = "A" + db.info.axisem_version[:7]
tr.stats.sac.kt8 = "I" + __version__[:7]
with io.BytesIO() as temp:
tr.write(temp, format="sac")
temp.seek(0, 0)
filename = "%s%s.sac" % (label, tr.id)
byte_strings.append((filename, temp.read()))
callback((byte_strings, mu))
def single_comparison():
"""
one by one comparison of the waveforms in the first path with the second path.
"""
client = Client()
global input
# identity of the waveforms (first and second paths) to be compared with each other
identity_all = input['net'] + '.' + input['sta'] + '.' + \
input['loc'] + '.' + input['cha']
ls_first = glob.glob(os.path.join(input['first_path'], identity_all))
ls_second = glob.glob(os.path.join(input['second_path'], identity_all))
for i in range(0, len(ls_first)):
try:
tr1 = read(ls_first[i])[0]
if input['phase'] != 'N':
evsta_dist = util.locations2degrees(lat1 = tr1.stats.sac.evla, \
long1 = tr1.stats.sac.evlo, lat2 = tr1.stats.sac.stla, \
long2 = tr1.stats.sac.stlo)
taup_tt = taup.getTravelTimes(delta = evsta_dist, depth = tr1.stats.sac.evdp)
phase_exist = 'N'
for tt_item in taup_tt:
if tt_item['phase_name'] == input['phase']:
print 'Requested phase:'
print input['phase']
print '------'
print tt_item['phase_name']
print 'exists in the waveform!'
print '-----------------------'
t_phase = tt_item['time']
phase_exist = 'Y'
break
if phase_exist != 'Y':
continue
# identity of the current waveform
identity = tr1.stats.network + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
# tr1: first path, tr2: second path, tr3: Raw data
#tr3 = read(os.path.join(input['first_path'], '..', 'BH_RAW', identity))[0]
if input['resp_paz'] == 'Y':
response_file = os.path.join(input['first_path'], '..', 'Resp/RESP.' + identity)
# Extract the PAZ info from response file
paz = readRESP(response_file, unit = input['corr_unit'])
poles = paz['poles']
zeros = paz['zeros']
scale_fac = paz['gain']
sensitivity = paz['sensitivity']
print paz
# Convert Poles and Zeros (PAZ) to frequency response.
h, f = pazToFreqResp(poles, zeros, scale_fac, \
1./tr1.stats.sampling_rate, tr1.stats.npts*2, freq=True)
# Use the evalresp library to extract
# instrument response information from a SEED RESP-file.
resp = invsim.evalresp(t_samp = 1./tr1.stats.sampling_rate, \
nfft = tr1.stats.npts*2, filename = response_file, \
date = tr1.stats.starttime, units = input['corr_unit'].upper())
# Keep the current identity in a new variable
id_name = identity
try:
tr2 = read(os.path.join(input['second_path'], identity))[0]
except Exception, error:
# if it is not possible to read the identity in the second path
# then change the network part of the identity based on
# correction unit
identity = input['corr_unit'] + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
tr2 = read(os.path.join(input['second_path'], identity))[0]
if input['resample'] != 'N':
print 'WARNING: you are using resample!!!'
tr1.resample(input['resample'])
tr2.resample(input['resample'])
if input['tw'] == 'Y':
t_cut_1 = tr1.stats.starttime + t_phase - input['preset']
t_cut_2 = tr1.stats.starttime + t_phase + input['offset']
tr1.trim(starttime = t_cut_1, endtime = t_cut_2)
t_cut_1 = tr2.stats.starttime + t_phase - input['preset']
t_cut_2 = tr2.stats.starttime + t_phase + input['offset']
tr2.trim(starttime = t_cut_1, endtime = t_cut_2)
if input['hlfilter'] == 'Y':
tr1.filter('lowpass', freq=input['hfreq'], corners=2)
tr2.filter('lowpass', freq=input['hfreq'], corners=2)
tr1.filter('highpass', freq=input['lfreq'], corners=2)
tr2.filter('highpass', freq=input['lfreq'], corners=2)
# normalization of all three waveforms to the
# max(max(tr1), max(tr2), max(tr3)) to keep the scales
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max(), abs(tr3.data).max())
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max())
#tr1_data = tr1.data/abs(maxi)
#tr2_data = tr2.data/abs(maxi)
#tr3_data = tr3.data/abs(maxi)
tr1_data = tr1.data/abs(max(tr1.data))
tr2_data = tr2.data/abs(max(tr2.data))
#tr1_data = tr1.data
#tr2_data = tr2.data*1e9
print max(tr1.data)
print max(tr2.data)
# create time arrays for tr1, tr2 and tr3
time_tr1 = np.arange(0, tr1.stats.npts/tr1.stats.sampling_rate, \
1./tr1.stats.sampling_rate)
time_tr2 = np.arange(0, tr2.stats.npts/tr2.stats.sampling_rate, \
1./tr2.stats.sampling_rate)
#time_tr3 = np.arange(0, tr3.stats.npts/tr3.stats.sampling_rate, \
# 1./tr3.stats.sampling_rate)
# label for plotting
label_tr1 = ls_first[i].split('/')[-2]
label_tr2 = ls_second[i].split('/')[-2]
label_tr3 = 'RAW'
if input['resp_paz'] == 'Y':
# start plotting
plt.figure()
plt.subplot2grid((3,4), (0,0), colspan=4, rowspan=2)
#plt.subplot(211)
plt.plot(time_tr1, tr1_data, color = 'blue', label = label_tr1, lw=3)
plt.plot(time_tr2, tr2_data, color = 'red', label = label_tr2, lw=3)
#plt.plot(time_tr3, tr3_data, color = 'black', ls = '--', label = label_tr3)
plt.xlabel('Time (sec)', fontsize = 'xx-large', weight = 'bold')
if input['corr_unit'] == 'dis':
ylabel_str = 'Relative Displacement'
elif input['corr_unit'] == 'vel':
ylabel_str = 'Relative Vel'
elif input['corr_unit'] == 'acc':
ylabel_str = 'Relative Acc'
plt.ylabel(ylabel_str, fontsize = 'xx-large', weight = 'bold')
plt.xticks(fontsize = 'xx-large', weight = 'bold')
plt.yticks(fontsize = 'xx-large', weight = 'bold')
plt.legend(loc=1,prop={'size':20})
#-------------------Cross Correlation
# 5 seconds as total length of samples to shift for cross correlation.
cc_np = tr1.stats.sampling_rate * 3
np_shift, coeff = cross_correlation.xcorr(tr1, tr2, int(cc_np))
t_shift = float(np_shift)/tr1.stats.sampling_rate
print "Cross Correlation:"
print "Shift: " + str(t_shift)
print "Coefficient: " + str(coeff)
plt.title('Single Comparison' + '\n' + str(t_shift) + \
' sec , coeff: ' + str(round(coeff, 5)) + \
'\n' + id_name, \
fontsize = 'xx-large', weight = 'bold')
if input['resp_paz'] == 'Y':
# -----------------------
#plt.subplot(223)
plt.subplot2grid((3,4), (2,0), colspan=2)
'''
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)), \
color = 'blue', label = 'RESP', lw=3)
plt.plot(np.log10(f), np.log10(abs(h)/sensitivity), \
color = 'red', label = 'PAZ', lw=3)
'''
plt.loglog(f, abs(resp)/(sensitivity*sensitivity), \
color = 'blue', label = 'RESP', lw=3)
plt.loglog(f, abs(h)/sensitivity, \
color = 'red', label = 'PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0]:
plt.axvline(np.log10(j), linestyle = '--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
#plt.ylabel('Amplitude\n (power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]', fontsize = 'xx-large', weight = 'bold')
plt.ylabel('Amplitude', fontsize = 'xx-large', weight = 'bold')
plt.xticks(fontsize = 'xx-large', weight = 'bold')
#plt.yticks = MaxNLocator(nbins=4)
plt.yticks(fontsize = 'xx-large', weight = 'bold')
plt.legend(loc=2,prop={'size':20})
# -----------------------
#plt.subplot(224)
plt.subplot2grid((3,4), (2,2), colspan=2)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
#plt.plot(np.log10(f), phase_resp, color = 'blue', label = 'RESP', lw=3)
#plt.plot(np.log10(f), phase_paz, color = 'red', label = 'PAZ', lw=3)
plt.semilogx(f, phase_resp, color = 'blue', label = 'RESP', lw=3)
plt.semilogx(f, phase_paz, color = 'red', label = 'PAZ', lw=3)
#for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
for j in [0.0]:
plt.axvline(np.log10(j), linestyle = '--')
#plt.xlabel('Frequency [Hz]\n(power of 10)', fontsize = 'xx-large', weight = 'bold')
plt.xlabel('Frequency [Hz]', fontsize = 'xx-large', weight = 'bold')
plt.ylabel('Phase [radian]', fontsize = 'xx-large', weight = 'bold')
plt.xticks(fontsize = 'xx-large', weight = 'bold')
plt.yticks(fontsize = 'xx-large', weight = 'bold')
plt.legend(loc=3,prop={'size':20})
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.4, hspace=0.3)
"""
# -----------------------
plt.subplot(325)
plt.plot(np.log10(f), np.log10(abs(resp)/(sensitivity*sensitivity)) - \
np.log10(abs(h)/sensitivity), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Amplitude (power of 10)')
plt.legend()
# -----------------------
plt.subplot(326)
#take negative of imaginary part
phase_paz = np.unwrap(np.arctan2(h.imag, h.real))
phase_resp = np.unwrap(np.arctan2(resp.imag, resp.real))
plt.plot(np.log10(f), np.log10(phase_resp) - np.log10(phase_paz), \
color = 'black', label = 'RESP - PAZ')
for j in [0.008, 0.012, 0.025, 0.5, 1, 2, 3, 4]:
plt.axvline(np.log10(j), linestyle = '--')
plt.xlabel('Frequency [Hz] (power of 10)')
plt.ylabel('Phase [radian] (power of 10)')
plt.legend()
# title, centered above both subplots
# make more room in between subplots for the ylabel of right plot
plt.subplots_adjust(wspace=0.3)
"""
plt.show()
print str(i+1) + '/' + str(len(ls_first))
print ls_first[i]
print '------------------'
wait = raw_input(id_name)
print '***************************'
except Exception, error:
print '##################'
print error
print '##################'
def ProcessLoopS(filepath,stationnames):
'''File processing loop for S tomography: Take the components and convert to RTZ, and delte the E and N comps'''
p = os.getcwd()
for station in stationnames:
print 'Dealing with %s' %station
Rstream = obspy.Stream()
#Get all SAC files associated with that station
sacfiles = reversed(sorted(glob.glob('*.%s..*' %station)))
saccount = 0
for sacfile in sacfiles:
trace = read(sacfile)
#Only determine the distance and back-azimuth once: This is what the saccount variable is here for
if saccount == 0:
evlat = trace[0].stats.sac.evla
evlon = trace[0].stats.sac.evlo
evdep = trace[0].stats.sac.evdp
stlat = trace[0].stats.sac.stla
stlon = trace[0].stats.sac.stlo
dist = locations2degrees(evlat,evlon,stlat,stlon) #find distance from the quake to the station
arcs = IRISclient.distaz(stalat=stlat,stalon=stlon,evtlat=evlat,evtlon=evlon)
baz = arcs['backazimuth']
az = arcs['azimuth']
if evdep > 1e3:
evdep = evdep/1000.0;
traveltimes = getTravelTimes(dist,evdep, model='iasp91')
P = 0
S = 0
for element in traveltimes:
phaseinfo = element['phase_name']
if phaseinfo == 'P':
Ptime = element['time']
P = 1
if phaseinfo == 'S':
Stime = element['time']
S = 1
if (P==1 and S ==1):
break
try:
P = Ptime
except:
Ptime = 0
try:
S = Stime
except:
Stime = 0
#Set the P and S times, and other SAC header data
trace[0].stats.sac.az = float(az)
trace[0].stats.sac.baz = float(baz)
if Ptime > 0:
trace[0].stats.sac.t1 = Ptime
if Stime > 0:
trace[0].stats.sac.t2 = Stime
trace[0].stats.sac.evdp = evdep*1000 #dbpick wants depth to be in meters
trace[0].stats.sac.o = 0 #add origin time
#other operations - remove the mean and resample
trace[0].detrend('demean')
trace[0].resample(20)
if ('BHE' in sacfile) or ('BHN' in sacfile):
Rstream += trace
#to save disk space, delete the E and N components
os.system('rm %s' %sacfile)
else:
#Write the Z component directly (writes over the existing file)
trace.write(sacfile,format='SAC')
#print 'Appended arrivals to %s' %sacfile
saccount += 1
#Convert to radial and transverse components
try:
rotstream = Rstream.rotate(method='NE->RT',back_azimuth=baz)
for obj in rotstream:
nt = obj.stats.network
sta = obj.stats.station
channel = obj.stats.channel
name = "vel."+str(nt)+"."+str(station)+".."+str(channel)
obj.write(name,format="SAC")
#print 'Written new file %s' %name
except:
print 'Cannot rotate files in Rstream %s' %Rstream
#create antelope database - just for S waves, which are picked on the traverse.
os.system('rm *.01.*')
os.system('sac2db *.BHT T')
def downloadLocalData(self, net, evtTime, evtLat, evtLon, evtDep, mag, fileFormat):
if fileFormat == "MSEED":
fileFormat = str.lower(fileFormat)
print "Setting local DB..."
stations = self.arclink.getStations(evtTime, evtTime + 3600, net)
# return stations
for station in stations:
staName = station["code"]
staLon = station["longitude"]
staLat = station["latitude"]
staElev = station["elevation"]
# delta = taup.locations2degrees(evtLat, evtLon, staLat, staLon) # delta stores distance in degrees
delta = util.locations2degrees(evtLat, evtLon, staLat, staLon) # delta stores distance in degrees
itp = taup() # calling IAG Taup class
itp.getTravelTimes(delta, evtDep)
pTime = itp.P()
sTime = itp.S()
# to use in sac headers...
seisTime = evtTime + pTime - self.beforeP
originTime = self.beforeP - pTime
fileTime = str(evtTime).replace("T", "-").replace(":", "-")[:-8]
try:
st = self.arclink.getWaveform(
net, staName, "*", "*", evtTime + pTime - self.beforeP, evtTime + sTime + self.afterS
)
print "\nData found for station " + staName
st.merge(method=1, fill_value="interpolate")
try:
os.mkdir(staName)
except:
pass
os.mkdir(staName + "/" + fileTime)
for tr in st:
loc = str(tr.stats.location)
fileName = (
staName
+ "/"
+ fileTime
+ "/"
+ net
+ "."
+ staName
+ "."
+ loc
+ "."
+ str(tr.stats.channel)
+ "."
+ fileTime
+ "."
+ fileFormat
)
tr.write(fileName, fileFormat)
print fileName, "saved."
if fileFormat == "SAC":
self.fillSACHeaders(
fileName, seisTime, originTime, evtLat, evtLon, evtDep, mag, staLat, staLon, staElev
)
except:
pass
def geteventslist(times, stationlocation, radii, eventbox, mags, homedir):
''''Takes a set of lists containing the values of these parameters. Outputs all the quakes that satisfy the paramters by first looking
at the global quake catalog
'''
start = time.time()
quakefile = open(
str(homedir) + '/Global_Quake_Cat/' + 'globalquake_parsed.dat', 'r')
lines = quakefile.readlines()
quakefile.close()
extractquakes = []
##########################
#Introducted for speed: first determine the indices of each of the years, so that the quake seatch can be as fast as possible
##########################
import datetime
cyear = int(str(datetime.datetime.now()).split('-')[0])
years = range(1970, cyear + 1)
i = 0
yearcount = len(years) - 1
yearslist = {}
for element in lines:
vals = element.split(',')
year = float(vals[0].split('-')[0])
if year != years[yearcount]:
yearslist[year] = i
yearcount = yearcount - 1
i += 1
##########################
endtimeyearnum = times[0].year
starttimeyearnum = times[1].year
maxcount = yearslist[endtimeyearnum - 1]
if starttimeyearnum < cyear - 1:
mincount = yearslist[starttimeyearnum + 1]
else:
mincount = 0
for line in lines[mincount:maxcount]:
vals = line.split(',')
quaketime = UTCDateTime(str(vals[0]))
quakelat = float(vals[1])
quakelon = float(vals[2])
quakemag = float(vals[4])
quakedepth = float(vals[3])
#print times[0], times[1], quaketime, quakelat, quakelon, quakemag, quakedepth, stationlocation, radii, eventbox
if (times[0] < quaketime < times[1]) and (float(mags[0]) < quakemag <
float(mags[1])):
quakedist = locations2degrees(quakelat, quakelon,
stationlocation[1],
stationlocation[0])
if (float(radii[0]) < quakedist < float(radii[1])) and (float(
eventbox[0]) < quakelon < float(eventbox[1])) and (float(
eventbox[2]) < quakelat < float(eventbox[3])):
extractquakes.append(
[quaketime, quakelon, quakelat, quakemag, quakedepth])
return extractquakes
def ncCreate_core(input, ls_saved_stas, address, eventgrp):
"""
"""
global rootgrp, client_iris
eventname = address.split('/')[-1]
print "========================"
print "Create netCDF file from:"
print address
print "========================"
print "All available stations:"
print len(ls_saved_stas)
tr_tmp = read(ls_saved_stas[0])[0]
eventgrp.evla = tr_tmp.stats.sac.evla
eventgrp.evlo = tr_tmp.stats.sac.evlo
eventgrp.evdp = tr_tmp.stats.sac.evdp
eventgrp.mag = tr_tmp.stats.sac.mag
stgrp = eventgrp.createGroup('stations')
stationIDS = str(len(ls_saved_stas)) + ' --- , '
eventgrp.createDimension('latitude', len(ls_saved_stas))
eventgrp.createDimension('longitude', len(ls_saved_stas))
eventgrp.createDimension('depth', len(ls_saved_stas))
eventgrp.createDimension('elevation', len(ls_saved_stas))
eventgrp.createDimension('epicentral', len(ls_saved_stas))
stasla = eventgrp.createVariable('latitude', 'f4', ('latitude',) , zlib = True)
staslo = eventgrp.createVariable('longitude', 'f4', ('longitude',) , zlib = True)
stasdp = eventgrp.createVariable('depth', 'f4', ('depth',) , zlib = True)
stasel = eventgrp.createVariable('elevation', 'f4', ('elevation',) , zlib = True)
stasepi = eventgrp.createVariable('epicentral', 'f4', ('epicentral',) , zlib = True)
for i in range(0, len(ls_saved_stas)):
print str(i+1),
try:
tr = read(ls_saved_stas[i])[0]
except Exception, e:
print "\nProblem with reading the: " + ls_saved_stas[i]
print e
print "------------------------------------------------"
continue
stationID = tr.stats.network + '.' + tr.stats.station + '.' + \
tr.stats.location + '.' + tr.stats.channel
stationIDS += stationID + ' , '
stasla[i] = tr.stats.sac.stla
staslo[i] = tr.stats.sac.stlo
stasdp[i] = tr.stats.sac.stdp
stasel[i] = tr.stats.sac.stel
stasepi[i] = locations2degrees(lat1 = eventgrp.evla, \
long1 = eventgrp.evlo, lat2 = stasla[i], \
long2 = staslo[i])
"""
def ncCreate_core(input, ls_saved_stas, address, eventgrp):
"""
"""
global rootgrp, client_iris
eventname = address.split('/')[-1]
print "========================"
print "Create netCDF file from:"
print address
print "========================"
print "All available stations:"
print len(ls_saved_stas)
tr_tmp = read(ls_saved_stas[0])[0]
eventgrp.evla = tr_tmp.stats.sac.evla
eventgrp.evlo = tr_tmp.stats.sac.evlo
eventgrp.evdp = tr_tmp.stats.sac.evdp
eventgrp.mag = tr_tmp.stats.sac.mag
stgrp = eventgrp.createGroup('stations')
stationIDS = str(len(ls_saved_stas)) + ' --- , '
eventgrp.createDimension('latitude', len(ls_saved_stas))
eventgrp.createDimension('longitude', len(ls_saved_stas))
eventgrp.createDimension('depth', len(ls_saved_stas))
eventgrp.createDimension('elevation', len(ls_saved_stas))
eventgrp.createDimension('epicentral', len(ls_saved_stas))
stasla = eventgrp.createVariable('latitude',
'f4', ('latitude', ),
zlib=True)
staslo = eventgrp.createVariable('longitude',
'f4', ('longitude', ),
zlib=True)
stasdp = eventgrp.createVariable('depth', 'f4', ('depth', ), zlib=True)
stasel = eventgrp.createVariable('elevation',
'f4', ('elevation', ),
zlib=True)
stasepi = eventgrp.createVariable('epicentral',
'f4', ('epicentral', ),
zlib=True)
for i in range(0, len(ls_saved_stas)):
print str(i + 1),
try:
tr = read(ls_saved_stas[i])[0]
except Exception, e:
print "\nProblem with reading the: " + ls_saved_stas[i]
print e
print "------------------------------------------------"
continue
stationID = tr.stats.network + '.' + tr.stats.station + '.' + \
tr.stats.location + '.' + tr.stats.channel
stationIDS += stationID + ' , '
stasla[i] = tr.stats.sac.stla
staslo[i] = tr.stats.sac.stlo
stasdp[i] = tr.stats.sac.stdp
stasel[i] = tr.stats.sac.stel
stasepi[i] = locations2degrees(lat1 = eventgrp.evla, \
long1 = eventgrp.evlo, lat2 = stasla[i], \
long2 = staslo[i])
"""
event_time_str = str(event_time.year) + '.' + str(event_time.julday).zfill(3) + '.' +\
str(event_time.hour).zfill(2) + str(event_time.minute).zfill(2) + \
str(event_time.second).zfill(2)
event_dir = default_dir + '/' + event_time_str
mkdir(event_dir)
os.chdir(event_dir)
for net in stations.select():
for station in net:
# print station
station_latitude = station.latitude
station_longitude = station.longitude
station_elevation = station.elevation # km
station_code = station.code
distance_in_degree = locations2degrees(
event_latitude, event_longitude,
station_latitude, station_longitude)
# try:
# arrivals = model.get_travel_times(
# source_depth_in_km=event_depth / 1000.0,
# distance_in_degree=distance_in_degree) # ,
# except Exception, e:
# arrivals = model.get_travel_times(
# source_depth_in_km=event_depth / 1000.0,
# distance_in_degree=distance_in_degree,
# phase_list=["P"]) ## unexcept in taup maybe the program is wrong.
# try:
arrivals = model.get_travel_times(
source_depth_in_km=event_depth / 1000.0,
distance_in_degree=distance_in_degree,
phase_list=["P","Pms"]) ## unexcept in taup maybe the program is wrong.
def cc_core(ls_first, ls_second, identity_all, max_ts, print_sta):
"""
Perform the main part of the cross correlation and creating
the cc.txt file
"""
global input
try:
cc_open = open('./cc.txt', 'a')
tr1 = read(ls_first)[0]
if input['phase'] != 'N':
evsta_dist = util.locations2degrees(lat1 = tr1.stats.sac.evla, \
long1 = tr1.stats.sac.evlo, lat2 = tr1.stats.sac.stla, \
long2 = tr1.stats.sac.stlo)
taup_tt = taup.getTravelTimes(delta = evsta_dist, depth = tr1.stats.sac.evdp)
phase_exist = 'N'
for tt_item in taup_tt:
if tt_item['phase_name'] == input['phase']:
print 'Requested phase:'
print input['phase']
print '------'
print tt_item['phase_name']
print 'exists in the waveform!'
print '-----------------------'
t_phase = tt_item['time']
phase_exist = 'Y'
break
if input['phase'] == 'N' or (input['phase'] != 'N' and phase_exist == 'Y'):
# identity of the current waveform
identity = tr1.stats.network + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
# Keep the current identity in a new variable
id_name = identity
try:
tr2 = read(os.path.join(input['second_path'], identity))[0]
except Exception, error:
# if it is not possible to read the identity in the second path
# then change the network part of the identity based on
# correction unit
identity = input['corr_unit'] + '.' + tr1.stats.station + '.' + \
tr1.stats.location + '.' + tr1.stats.channel
tr2 = read(os.path.join(input['second_path'], identity))[0]
if input['resample'] != 'N':
print 'WARNING: you are using resample!!!'
tr1.resample(input['resample'])
tr2.resample(input['resample'])
if input['tw'] == 'Y':
t_cut_1 = tr1.stats.starttime + t_phase - input['preset']
t_cut_2 = tr1.stats.starttime + t_phase + input['offset']
tr1.trim(starttime = t_cut_1, endtime = t_cut_2)
t_cut_1 = tr2.stats.starttime + t_phase - input['preset']
t_cut_2 = tr2.stats.starttime + t_phase + input['offset']
tr2.trim(starttime = t_cut_1, endtime = t_cut_2)
if input['hlfilter'] == 'Y':
tr1.filter('lowpass', freq=input['hfreq'], corners=2)
tr2.filter('lowpass', freq=input['hfreq'], corners=2)
tr1.filter('highpass', freq=input['lfreq'], corners=2)
tr2.filter('highpass', freq=input['lfreq'], corners=2)
# normalization of all three waveforms to the
# max(max(tr1), max(tr2), max(tr3)) to keep the scales
#maxi = max(abs(tr1.data).max(), abs(tr2.data).max(), abs(tr3.data).max())
'''
maxi = max(abs(tr1.data).max(), abs(tr2.data).max())
tr1_data = tr1.data/abs(maxi)
tr2_data = tr2.data/abs(maxi)
tr3_data = tr3.data/abs(maxi)
'''
tr1.data = tr1.data/abs(max(tr1.data))
tr2.data = tr2.data/abs(max(tr2.data))
cc_np = tr1.stats.sampling_rate * max_ts
np_shift, coeff = cross_correlation.xcorr(tr1, tr2, int(cc_np))
t_shift = float(np_shift)/tr1.stats.sampling_rate
# scale_str shows whether the scale of the waveforms are the same or not
# if scale_str = 'Y' then the scale is correct.
scale_str = 'Y'
if abs(tr1.data).max() > 2.0 * abs(tr2.data).max():
label_tr1 = ls_first.split('/')[-2]
label_tr2 = ls_second[0].split('/')[-2]
print '#####################################################'
print "Scale is not correct! " + label_tr1 + '>' + label_tr2
print '#####################################################'
scale_str = 'N'
elif abs(tr2.data).max() >= 2.0 * abs(tr1.data).max():
label_tr1 = ls_first.split('/')[-2]
label_tr2 = ls_second[0].split('/')[-2]
print '#####################################################'
print "Scale is not correct! " + label_tr2 + '>' + label_tr1
print '#####################################################'
scale_str = 'N'
if not str(coeff) == 'nan':
cc_open.writelines(id_name + ',' + str(round(coeff, 4)) + ',' + str(t_shift) + \
',' + scale_str + ',' + '\n')
print "Cross Correlation:"
print id_name
print "Shift: " + str(t_shift)
print "Coefficient: " + str(coeff)
print print_sta
print '------------------'
cc_open.close()
cc_open.close()
except Exception, error:
print '##################'
print error
print '##################'
