def loop1(lons, lats, testarea, slab, depgrid, strgrid, dipgrid,
slab1query, eventlist, seismo_thick,
alen, blen, mdist, sdr, ddr, mindip, maxID2, AA_data,
TR_data, maxdist, maxthickness, minstk,
tomo_sets, meanBA, slab1guide, spacing, slab1data, dipthresh, datainfo, nodeinfo, j):
''' Arguments: lons - list of longitudes (list of floats)[deg]
lats - list of latitudes (list of floats)[deg]
nodeIDs - list of nodeIDs (list of ints)
testarea - list defined as [lonmin,lonmax,latmin,latmax]
nodes within this box will print troubleshooting
statements and PDFs and data used to create them
are saved (list of floats)[deg]
slab - three letter code to indicate slab of interest (str)
depgrid - clipped depth GMT grid file from Slab 1.0 (grd)
strgrid - grid of strike values calculated from
Slab 1.0 (grd)
dipgrid - grid of dip values calculated from Slab 1.0 (grd)
slab1data - dataframe with columns [lon,lat,depth,strike,
dip] representing Slab1.0 (dataframe)
eventlist - dataframe with columns [lat,lon,depth,unc,etype,
ID,mag,S1,D1,R1,S2,D2,R2,src] representing input
dataset (dataframe)
seismo_thick - depth defining where earthquakes at and
below are intra-slab as opposed to on the
slab surface. (float)[km]
alen - long radius of ellipsoid (float)[km]
blen - short radius of search ellipsoid (float)[km]
mdist - alen*ecentricity of ellipse (float)[km]
sdr - shallow search range of ellipsoid (float)[km]
ddr - deep search range of ellipsoid (float)[km]
mindip - upper limit of dip to search vertically. Nodes with
dips greater than mindip have a perpendicular search
ellipsoid (float)[deg]
maxID2 - maxID of original input dataset (int)
AA_data - dataframe with columns [dist,depth].
if slab == 'alu' or slab == 'him', columns =
[dist,depth,avlat,avlon] (dataframe)
TR_data - dataframe with columns [lon,lat,az,
bound,slab] (dataframe)
maxdist - useless variable, need to remove
maxthickness - maximum thickness of this slab as calculated
by a function relating thickness to age (float)[km]
minstk - maximum strike difference between node and farthest
data point constraining that node (float)[deg]
tomo_sets - list of tomography datasets for this slab.
(list of strings)
OGmulti - dataframe with columns [lon,lat,depth,nID] used to
record which nodes have a multi-modal distribution
and the depths to each peak (dataframe)
i - index representing this node. (int)
Returns: lon - lons[i], search longitude of this node (float)[deg]
lat - lats[i], search latitude of this node (float)[deg]
locdep - search depth of this node (float)[km]
locstr - search strike of this node (float)[deg]
locdip - search dip of this node (float)[deg]
nID - integer value representing this node (int)
farpoint - length of long axis of ellipse after
refiltering based on strike difference from
node center (float)[km]
used_TO - list of tomography data points used to constrain
nodes on this slab. 2D numpy array with columns
[mean(|tomo_depths-EQdepths|),index representing
TO source] (array of floats)
used_tmp - list of data points used to constrain the depth
at this node. 2D numpy array with columns
[nodeID,dataID] (array of ints)
trimmedAA - dataframe with columns [lat,lon,depth,unc,etype,
ID,mag,S1,D1,R1,S2,D2,R2,src] representing average
active source and receiver functions to
add to input dataset (dataframe) '''
# Define lon,lat coordinate, integer ID of this node, and ID for added data
lat = lats[j]
lon = lons[j]
if lat>0:
nID = int('%i%i'%(lon*10,lat*10))
else:
nID = int('%i0%i'%(lon*10,lat*-10))
maxID = maxID2+nID-1
OGmulti = []
if slab == 'sol' and lon > 149 and lon < 155:
alen = 20.0
if slab == 'puy' and lat < -48:
alen = 100
if slab == 'ryu' and lon > 138:
alen = 20.0
if slab == 'alu' and lat > 63:
alen = 50.0
if slab == 'man' and lat > 17.0:
alen = 50.0
if slab == 'izu' and lat < 27 and lat > 23:
eventlist = eventlist[(eventlist.lon<lon+4.5)&(eventlist.lat<lat+4.5)&(eventlist.lon>lon-4.5)&(eventlist.lat>lat-4.5)]
else:
eventlist = eventlist[(eventlist.lon<lon+2.5)&(eventlist.lat<lat+2.5)&(eventlist.lon>lon-2.5)&(eventlist.lat>lat-2.5)]
if len(eventlist)<2:
#print ('no data here',lon,lat,nID)
return lon, lat, np.nan, np.nan, np.nan, nID, np.nan, np.nan, np.nan, [], [], [], [], False, 0, 0
# If in troubleshooting range, print ID and set testprint = True
if lon>testarea[0] and lon<testarea[1]:
if lat>testarea[2] and lat<testarea[3]:
testprint = True
print('___________________ %i ______________________'% nID)
else:
testprint = False
else:
testprint = False
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- pre-anyfilters, len(eventlist) %i\n'%(nID, len(eventlist)))
f.close()
# Get depth, strike, and dip of Slab1.0 or calculate from trend at edge
slab1, strtmp, diptmp, inside, extended, out, extlon, extlat = s2f.getslab12(slab1guide,slab1query,lon,lat,spacing,depgrid,strgrid,dipgrid,testprint,TR_data,meanBA,slab)
if slab1 > 650 and slab == 'sam' and lat<-30:
sdr = 60
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- slab1, strtmp, diptmp, inside, extended, out %0.2f,%0.2f,%0.2f,%s,%s,%s \n'%(nID, slab1, strtmp, diptmp, inside, extended, out))
f.close()
if slab == 'izu' and lat < 27 and lat > 23:
dipthresh = 45
if diptmp > dipthresh:
newlats = [lat,lat,lat+spacing/2.0,lat-spacing/2.0,lat+spacing/2.0,lat-spacing/2.0,lat+spacing/2.0,lat-spacing/2.0]
newlons = [lon-spacing/2.0,lon+spacing/2.0,lon,lon,lon+spacing/2.0,lon-spacing/2.0,lon-spacing/2.0,lon+spacing/2.0]
if slab == 'ker':
newlats1 = [lat,lat,lat+spacing/4.0,lat-spacing/4.0,lat+spacing/4.0,lat-spacing/4.0,lat+spacing/4.0,lat-spacing/4.0]
newlons1 = [lon-spacing/4.0,lon+spacing/4.0,lon,lon,lon+spacing/4.0,lon-spacing/4.0,lon-spacing/4.0,lon+spacing/4.0]
newlats2 = [lat,lat,lat+3*spacing/4.0,lat-3*spacing/4.0,lat+3*spacing/4.0,lat-3*spacing/4.0,lat+3*spacing/4.0,lat-3*spacing/4.0]
newlons2 = [lon-3*spacing/4.0,lon+3*spacing/4.0,lon,lon,lon+3*spacing/4.0,lon-3*spacing/4.0,lon-3*spacing/4.0,lon+3*spacing/4.0]
newlats.extend(newlats1)
newlats.extend(newlats2)
newlons.extend(newlons1)
newlons.extend(newlons2)
newnodes = np.zeros((len(newlons),2))
newnodes[:,0] = np.round(newlons,1)
newnodes[:,1] = np.round(newlats,1)
#diptmp = 90
if slab == 'izu' and lat>23 and lat<27 and slab1 > 400:
clen = 400
elif slab != 'sol' and slab != 'hal' and slab != 'phi':
clen = 200
elif slab == 'sol' and lon>148:
clen = 300
else:
clen = alen
else:
eventlist = eventlist[(eventlist.lon<lon+1.5)&(eventlist.lat<lat+1.5)&(eventlist.lon>lon-1.5)&(eventlist.lat>lat-1.5)]
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- after reducing OGsearch range (dip<dipthresh), len(eventlist) %i\n'%(nID, len(eventlist)))
f.close()
clen = alen
if len(eventlist)<2 and len(eventlist[(eventlist.etype == 'AS')|(eventlist.etype == 'RF')|(eventlist.etype == 'CP')])<1 and slab != 'ryu':
#print ('no data here',lon,lat,nID)
return lon, lat, slab1, strtmp, diptmp, nID, alen, blen, clen, [], [], [], [], False, 0, 0
elif slab == 'ryu' and len(eventlist)<2 and len(eventlist[(eventlist.etype == 'AS')|(eventlist.etype == 'RF')|(eventlist.etype == 'TO')|(eventlist.etype == 'CP')])<1:
return lon, lat, slab1, strtmp, diptmp, nID, alen, blen, clen, [], [], [], [], False, 0, 0
else:
newnodes = []
if slab == 'phi' and lat > 11:
minstk /= 2
if extended:
alen = s2f.refilter4(strtmp,minstk,extlon,extlat,eventlist,alen,blen,slab1query,slab,testprint)
else:
alen = s2f.refilter4(strtmp,minstk,lon,lat,eventlist,alen,blen,slab1query,slab,testprint)
if lon > 170.5 and lon < 172 and lat > -22 and lat < -21:
alen = 20
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- alen, blen, clen, minstk, seismo_thick %0.2f,%0.2f,%0.2f,%0.2f,%0.2f \n'%(nID, alen, blen, clen, minstk, seismo_thick))
f.close()
if alen == 1:
return lon, lat,slab1, strtmp, diptmp, nID, alen, blen, clen, [], [], [], [], False, 0, 0
else:
farpoint = alen
if slab == 'cam' and lon < 260 and lat > 20.5 and lon > 257.5:
alen = 20
clen = 20
if slab == 'man' and lat < 14:
alen = 20
if slab1 < 200:
clen = 20
if slab == 'mue':
alen = 150
clen = 150
strtmp = 270
# Filter data for present node
trimmed, test, sdepth, ddepth, locstr, locdip, maxID, locdep = s2f.allFilters(
eventlist, lat, lon, inside, slab1, strtmp, diptmp, seismo_thick, alen, blen,
clen, mdist, sdr, ddr, mindip, maxID, out,
AA_data, TR_data, slab, maxdist, testprint, extended, datainfo, nodeinfo,nID)
trimmed, test = s2f.doublecheckEREQ(trimmed, lon, lat)
if not test:
if testprint:
print ('not test',lon,lat,nID)
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, [], [], [], newnodes, False, sdepth, ddepth
trimmedAA = trimmed[trimmed.etype == 'AA']
trimmedBA = trimmed[trimmed.etype == 'BA']
trimmedAS = trimmed[trimmed.etype == 'AS']
trimmedRF = trimmed[trimmed.etype == 'RF']
trimmedTO = trimmed[trimmed.etype == 'TO']
trimmedCP = trimmed[trimmed.etype == 'CP']
# Save data used
# Add this trimmed list of events to the list of used input data
if len(trimmed) > 1 or (len(trimmed) < 2 and (len(trimmedAA)>0 or len(trimmedBA)>0 or len(trimmedAS)>0 or len(trimmedRF)>0 or len(trimmedCP)>0)):
trimmed_index = np.array(trimmed.ID)
node_number = np.ones(len(trimmed_index)) * nID
used_tmp = np.array(list(zip(node_number, trimmed_index)))
elif len(trimmed) > 1 or (len(trimmed) < 2 and len(trimmedTO)>0 and slab == 'ryu'):
trimmed_index = np.array(trimmed.ID)
node_number = np.ones(len(trimmed_index)) * nID
used_tmp = np.array(list(zip(node_number, trimmed_index)))
else:
if testprint:
print ('trimmed too small',lon,lat,nID)
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, [], [], [], newnodes, False, sdepth, ddepth
# We only need to do the rest of this loop if there is tomography data
if tomo_sets == 0 or slab != 'samzzz' or slab =='ryu':
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, [], used_tmp, trimmedAA, newnodes, True, sdepth, ddepth
'''nothing beyond this point in this loop gets used '''
# Trim dataset for PDF calculation and separate tomography data
# We want to run PDF on non-TO, non-RF data
TOdata = trimmed[trimmed.etype == 'TO']
trimmed = trimmed[trimmed.etype != 'RF']
trimmed = trimmed[trimmed.etype != 'TO']
trimmed = trimmed[trimmed.etype != 'CP']
# Calculate PDF
if locdip <= mindip: #or len(trimmedAS) > 0 or len(trimmedAA) >0 or len(trimmedRF) >0:
#print 'full',lon,lat
peak_depth, stdv, test2, npeaks, OGmulti, centsurf = s2f.fullPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, locdep, 'pre', slab, locstr, locdip)
peak_lon = lon
peak_lat = lat
else:
peak_lon, peak_lat, peak_depth, stdv, test2, npeaks, OGmulti, centsurf, rlist = s2f.perpPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, locdep, 'pre', slab, locstr, locdip, maxthickness)
if len(rlist) > 0:
removeIDs = np.array(rlist.ID)
used_tmp = used_tmp[~np.in1d(used_tmp[:, 1], removeIDs)]
# For each node with a peak depth and tomography data, calculate differences
# Do separately for each tomography dataset
if test2:
if len(TOdata) > 0:
for idx, src in enumerate(tomo_sets):
data = TOdata[TOdata.src == src]
if len(data) > 0:
TOmean = np.mean(data.depth)
diff = peak_depth - TOmean
temp = [diff, idx]
temp2 = np.array(temp)
if used_TO is not None:
used_TO = np.vstack((used_TO, temp2))
else:
used_TO = temp2.copy()
else:
return lon, lat, locdep, locstr, locdip, nID, farpoint, used_TO, used_tmp, trimmedAA, newnodes
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, used_TO, used_tmp, trimmedAA, newnodes
def loop3(shift_out, testarea, used_all, eventlist, sdr, ddr, seismo_thick, these_parameters, slab, maxthickness, mindip, taper, nID):
postmulti = []
bilats, bilons, binods, bistds = np.nan, np.nan, np.nan, np.nan
biindx, bistrs, bidips, bideps = np.nan, np.nan, np.nan, np.nan
if len(shift_out)>0: # used to be if slab == 'sam' bc of variable taper
taper = 0.0
loc_shift_out = shift_out[shift_out.nID == nID]
lat = loc_shift_out['lat'].values[0]
lon = loc_shift_out['lon'].values[0]
cstr = loc_shift_out['ogstr'].values[0]
cdip = loc_shift_out['ogdip'].values[0]
bflat = loc_shift_out['bzlat'].values[0]
bflon = loc_shift_out['bzlon'].values[0]
dep = loc_shift_out['psdepth'].values[0]
if len(loc_shift_out) == 0:
#print 'misnamed ID',nID
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, nID, postmulti, np.nan, np.nan
else:
testprint = False
if lon>testarea[0] and lon<testarea[1] and lat>testarea[2] and lat<testarea[3]:
testprint = True
else:
testprint = False
if testprint:
print('____________________________________ %i ___________________________________________'% nID)
# Collect used data associated with this node
loc_used_data = used_all[:, 1][used_all[:, 0] == nID]
if len(loc_used_data) > 1:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
elif len(loc_used_data) > 0:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
trimmedAA = trimmed[trimmed.etype == 'AA']
trimmedBA = trimmed[trimmed.etype == 'BA']
trimmedAS = trimmed[trimmed.etype == 'AS']
trimmedRF = trimmed[trimmed.etype == 'RF']
trimmedCP = trimmed[trimmed.etype == 'CP']
if len(trimmedAA)<1 and len(trimmedBA)<1 and len(trimmedAS)<1 and len(trimmedRF)<1 and len(trimmedCP)<1:
#if testprint:
print('skipping but returning something wonky', lon, lat, dep)
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, nID, postmulti, np.nan, np.nan
else:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, nID, postmulti, np.nan, np.nan
# Calculate average strike, rake, dip of CMTs for final output
avg_strike, avg_dip, avg_rake = s2f.avStrDipRak(trimmed)
if dep < seismo_thick or (len(trimmed[trimmed.etype == 'RF']) < 1 and \
len(trimmed[trimmed.etype == 'AA']) < 1 and \
len(trimmed[trimmed.etype == 'CP']) < 1 and \
len(trimmed[trimmed.etype == 'AS']) < 1):
#print 'shallow',lon,lat,nID
rstd = loc_shift_out['stdv'].values[0]
depth = loc_shift_out['depth'].values[0]
if dep<seismo_thick:
return depth, rstd, avg_strike, avg_dip, avg_rake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, bflon, bflat
else:
return depth, rstd, avg_strike, avg_dip, avg_rake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, lon, lat
if len(trimmed[(trimmed.etype != 'RF') & (trimmed.etype != 'CP')])<1 and len(trimmed[(trimmed.etype == 'RF') | (trimmed.etype == 'CP')])>0:
rstd = loc_shift_out['stdv'].values[0]
depth = loc_shift_out['psdepth'].values[0]
return depth, rstd, avg_strike, avg_dip, avg_rake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, bflon, bflat
if len(trimmed[(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')]) > 0:
# Calculate local depth difference between shifted and non-shifted results
dep_dif = dep - loc_shift_out['depth'].values[0]
lon_dif = bflon - lon
lat_dif = bflat - lat
# Apply shift to non-RF data
trimmed['depth'][(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')] -= dep_dif
trimmed['lon'][(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')] -= lon_dif
trimmed['lat'][(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')] -= lat_dif
# Calculate new PDF
loc_depth = trimmed['depth'].mean()
place_holder, sdepth, ddepth, depthwritten = s2f.depthRange(loc_depth, sdr, ddr, seismo_thick, trimmed, slab, these_parameters, True)
if cdip <= mindip:
if cdip > mindip:
cdip = mindip
try:
new_peak_depth, new_std, test3, npeaks, postmulti, centsurf = s2f.fullPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'post', slab, cstr, cdip)
peak_lon = lon
peak_lat = lat
except:
print('post full pdf didnt work!!', lon, lat, nID)
new_peak_depth, new_std, test3, npeaks, postmulti, centsurf = loc_shift_out['depth'].values[0], loc_shift_out['stdv'].values[0], False, 1, pd.DataFrame(), 2000
peak_lon = lon
peak_lat = lat
else:
try:
peak_lon, peak_lat, new_peak_depth, new_std, test3, npeaks, postmulti, centsurf, rlist = s2f.perpPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'post', slab, cstr, cdip, maxthickness)
except:
print('post perp pdf didnt work!!', lon, lat, nID)
peak_lon, peak_lat, new_peak_depth, new_std, test3, npeaks, postmulti, centsurf, rlist = lon, lat, loc_shift_out['depth'].values[0], loc_shift_out['stdv'].values[0], False, 1, pd.DataFrame(), 2000, pd.DataFrame()
# Populate results array
if test3:
rdepth, rstd, rstrike, rdip, rrake = new_peak_depth, new_std, avg_strike, avg_dip, avg_rake
if npeaks > 1:
bilats, bilons, binods, bistds= lat, lon, nID, new_std
biindx, bistrs, bidips, bideps = 1, cstr, cdip, loc_depth
else:
rdepth, rstd, rstrike, rdip, rrake = np.nan, np.nan, np.nan, np.nan, np.nan
return rdepth, rstd, rstrike, rdip, rrake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, peak_lon, peak_lat
def loop2(testarea, lons, lats, nIDs1, locdep, locstr, locdip, used_all, eventlist, sdr, ddr, seismo_thick, slab, maxthickness, rlist, mindip, aalen, ablen, aclen, j):
these_parameters = []
premulti = []
bilats, bilons, binods, bistds = np.nan, np.nan, np.nan, np.nan
biindx, bistrs, bidips, bideps = np.nan, np.nan, np.nan, np.nan
lat = lats[j]
lon = lons[j]
nID = nIDs1[j]
cstr = locstr[j]
cdip = locdip[j]
alen = aalen[j]
blen = ablen[j]
clen = aclen[j]
if lon>testarea[0] and lon<testarea[1] and lat>testarea[2] and lat<testarea[3]:
testprint = True
print('____________________________________ %i ___________________________________________'% nID)
else:
testprint = False
# Collect used data associated with this node
loc_used_data = used_all[:, 1][used_all[:, 0] == nID]
if len(loc_used_data) > 1:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
elif len(loc_used_data) > 0:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
trimmedAA = trimmed[trimmed.etype == 'AA']
trimmedBA = trimmed[trimmed.etype == 'BA']
trimmedAS = trimmed[trimmed.etype == 'AS']
trimmedRF = trimmed[trimmed.etype == 'RF']
trimmedCP = trimmed[trimmed.etype == 'CP']
if len(trimmedAA)<1 and len(trimmedBA)<1 and len(trimmedAS)<1 and len(trimmedRF)<1 and len(trimmedCP)<1:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
else:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
# Remove RF data for PDF analysis
if locdep[j] > seismo_thick:
rft = trimmed[(trimmed.etype == 'RF') | (trimmed.etype == 'AA') | (trimmed.etype == 'AS') | (trimmed.etype == 'CP')]
trimmed = trimmed[(trimmed.etype != 'RF') & (trimmed.etype != 'AA') & (trimmed.etype != 'AS') & (trimmed.etype != 'CP')]
else:
rft = []
if len(trimmed)<2 and len(rft)>0:
trimmed = pd.concat([rft, trimmed],sort=True)
if abs(rft['depth'].mean()-trimmed['depth'].mean())>maxthickness:
trimmed = trimmed[(trimmed.etype == 'RF') | (trimmed.etype == 'AA') | (trimmed.etype == 'AS') | (trimmed.etype == 'CP')]
if len(rft)>0:
loc_depth = np.mean(trimmed['depth'].values)
if np.isnan(loc_depth):
if testprint:
print ('1 ** nan depth found for this node, returning nan output in this directory',nID)
trimmed.to_csv('%i_faileddata.csv'%nID,header=True,index=False,na_rep=np.nan)
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
else:
loc_depth = locdep[j]
if np.isnan(loc_depth):
if testprint:
print ('2 ** nan depth found for this node, returning nan output in this directory',nID)
trimmed.to_csv('%i_faileddata.csv'%nID,header=True,index=False,na_rep=np.nan)
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
place_holder, sdepth, ddepth, depthwritten = s2f.depthRange(loc_depth, sdr, ddr, seismo_thick, trimmed, slab, these_parameters, True)
if cdip <= mindip:
if cdip > mindip:
cdip = mindip
#try:
peak_depth, stdv, test2, npeaks, premulti, centsurf = s2f.fullPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'pre2', slab, cstr, cdip)
peak_lon = lon
peak_lat = lat
else:
peak_lon, peak_lat, peak_depth, stdv, test2, npeaks, premulti, centsurf, rlist = s2f.perpPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'pre2', slab, cstr, cdip, maxthickness)
if test2:
#tmp_res[j,0],tmp_res[j,1],tmp_res[j,2],tmp_res[j,3],tmp_res[j,4],tmp_res[j,5],tmp_res[j,6],tmp_res[j,7],tmp_res[j,8],tmp_res[j,9] = peak_lat,peak_lon,peak_depth,std,nID,lat,lon,cstr,cdip,centsurf
jpeak_lat, jpeak_lon, jpeak_depth, jstd, jnID = peak_lat, peak_lon, peak_depth, stdv, nID
jlat, jlon, jcstr, jcdip, jcentsurf = lat, lon, cstr, cdip, centsurf
# If there is more than one peak
if npeaks > 1:
bilats, bilons, binods, bistds= lat, lon, nID, stdv
biindx, bistrs, bidips, bideps = j, cstr, cdip, loc_depth
else:
jpeak_lon, jpeak_lat, jpeak_depth, jstd, jnID = np.nan, np.nan, np.nan, np.nan, np.nan
jlat, jlon, jcstr, jcdip, jcentsurf = np.nan, np.nan, np.nan, np.nan, np.nan
eventtypes = list(set(trimmed.etype))
if len(eventtypes) == 1 and eventtypes[0] == 'TO':
onlyTO = 1
else:
onlyTO = 0
return jpeak_lon, jpeak_lat, jpeak_depth, jstd, jnID, jlat, jlon, jcstr, jcdip, jcentsurf, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, rlist, premulti, alen, blen, clen, onlyTO
def loop1(lons, lats, testarea, slab, depgrid, strgrid, dipgrid,
slab1query, eventlist, seismo_thick,
alen, blen, mdist, sdr, ddr, mindip, maxID2, AA_data,
TR_data, maxdist, maxthickness, minstk,
tomo_sets, meanBA, slab1guide, spacing, slab1data, dipthresh, datainfo, nodeinfo, j):
''' Arguments: lons - list of longitudes (list of floats)[deg]
lats - list of latitudes (list of floats)[deg]
nodeIDs - list of nodeIDs (list of ints)
testarea - list defined as [lonmin,lonmax,latmin,latmax]
nodes within this box will print troubleshooting
statements and PDFs and data used to create them
are saved (list of floats)[deg]
slab - three letter code to indicate slab of interest (str)
depgrid - clipped depth GMT grid file from Slab 1.0 (grd)
strgrid - grid of strike values calculated from
Slab 1.0 (grd)
dipgrid - grid of dip values calculated from Slab 1.0 (grd)
slab1data - dataframe with columns [lon,lat,depth,strike,
dip] representing Slab1.0 (dataframe)
eventlist - dataframe with columns [lat,lon,depth,unc,etype,
ID,mag,S1,D1,R1,S2,D2,R2,src] representing input
dataset (dataframe)
seismo_thick - depth defining where earthquakes at and
below are intra-slab as opposed to on the
slab surface. (float)[km]
alen - long radius of ellipsoid (float)[km]
blen - short radius of search ellipsoid (float)[km]
mdist - alen*ecentricity of ellipse (float)[km]
sdr - shallow search range of ellipsoid (float)[km]
ddr - deep search range of ellipsoid (float)[km]
mindip - upper limit of dip to search vertically. Nodes with
dips greater than mindip have a perpendicular search
ellipsoid (float)[deg]
maxID2 - maxID of original input dataset (int)
AA_data - dataframe with columns [dist,depth].
if slab == 'alu' or slab == 'him', columns =
[dist,depth,avlat,avlon] (dataframe)
TR_data - dataframe with columns [lon,lat,az,
bound,slab] (dataframe)
maxdist - useless variable, need to remove
maxthickness - maximum thickness of this slab as calculated
by a function relating thickness to age (float)[km]
minstk - maximum strike difference between node and farthest
data point constraining that node (float)[deg]
tomo_sets - list of tomography datasets for this slab.
(list of strings)
OGmulti - dataframe with columns [lon,lat,depth,nID] used to
record which nodes have a multi-modal distribution
and the depths to each peak (dataframe)
i - index representing this node. (int)
Returns: lon - lons[i], search longitude of this node (float)[deg]
lat - lats[i], search latitude of this node (float)[deg]
locdep - search depth of this node (float)[km]
locstr - search strike of this node (float)[deg]
locdip - search dip of this node (float)[deg]
nID - integer value representing this node (int)
farpoint - length of long axis of ellipse after
refiltering based on strike difference from
node center (float)[km]
used_TO - list of tomography data points used to constrain
nodes on this slab. 2D numpy array with columns
[mean(|tomo_depths-EQdepths|),index representing
TO source] (array of floats)
used_tmp - list of data points used to constrain the depth
at this node. 2D numpy array with columns
[nodeID,dataID] (array of ints)
trimmedAA - dataframe with columns [lat,lon,depth,unc,etype,
ID,mag,S1,D1,R1,S2,D2,R2,src] representing average
active source and receiver functions to
add to input dataset (dataframe) '''
# Define lon,lat coordinate, integer ID of this node, and ID for added data
lat = lats[j]
lon = lons[j]
if lat>0:
nID = int('%i%i'%(lon*10,lat*10))
else:
nID = int('%i0%i'%(lon*10,lat*-10))
maxID = maxID2+nID-1
OGmulti = []
if slab == 'sol' and lon > 149 and lon < 155:
alen = 20.0
if slab == 'puy' and lat < -48:
alen = 100
if slab == 'ryu' and lon > 138:
alen = 20.0
if slab == 'alu' and lat > 63:
alen = 50.0
if slab == 'man' and lat > 17.0:
alen = 50.0
if slab == 'izu' and lat < 27 and lat > 23:
eventlist = eventlist[(eventlist.lon<lon+4.5)&(eventlist.lat<lat+4.5)&(eventlist.lon>lon-4.5)&(eventlist.lat>lat-4.5)]
else:
eventlist = eventlist[(eventlist.lon<lon+2.5)&(eventlist.lat<lat+2.5)&(eventlist.lon>lon-2.5)&(eventlist.lat>lat-2.5)]
if len(eventlist)<2:
#print ('no data here',lon,lat,nID)
return lon, lat, np.nan, np.nan, np.nan, nID, np.nan, np.nan, np.nan, [], [], [], [], False, 0, 0
# If in troubleshooting range, print ID and set testprint = True
if lon>testarea[0] and lon<testarea[1]:
if lat>testarea[2] and lat<testarea[3]:
testprint = True
print('___________________ %i ______________________'% nID)
else:
testprint = False
else:
testprint = False
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- pre-anyfilters, len(eventlist) %i\n'%(nID, len(eventlist)))
f.close()
# Get depth, strike, and dip of Slab1.0 or calculate from trend at edge
slab1, strtmp, diptmp, inside, extended, out, extlon, extlat = s2f.getslab12(slab1guide,slab1query,lon,lat,spacing,depgrid,strgrid,dipgrid,testprint,TR_data,meanBA,slab)
if slab1 > 650 and slab == 'sam' and lat<-30:
sdr = 60
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- slab1, strtmp, diptmp, inside, extended, out %0.2f,%0.2f,%0.2f,%s,%s,%s \n'%(nID, slab1, strtmp, diptmp, inside, extended, out))
f.close()
if slab == 'izu' and lat < 27 and lat > 23:
dipthresh = 45
if diptmp > dipthresh:
newlats = [lat,lat,lat+spacing/2.0,lat-spacing/2.0,lat+spacing/2.0,lat-spacing/2.0,lat+spacing/2.0,lat-spacing/2.0]
newlons = [lon-spacing/2.0,lon+spacing/2.0,lon,lon,lon+spacing/2.0,lon-spacing/2.0,lon-spacing/2.0,lon+spacing/2.0]
if slab == 'ker':
newlats1 = [lat,lat,lat+spacing/4.0,lat-spacing/4.0,lat+spacing/4.0,lat-spacing/4.0,lat+spacing/4.0,lat-spacing/4.0]
newlons1 = [lon-spacing/4.0,lon+spacing/4.0,lon,lon,lon+spacing/4.0,lon-spacing/4.0,lon-spacing/4.0,lon+spacing/4.0]
newlats2 = [lat,lat,lat+3*spacing/4.0,lat-3*spacing/4.0,lat+3*spacing/4.0,lat-3*spacing/4.0,lat+3*spacing/4.0,lat-3*spacing/4.0]
newlons2 = [lon-3*spacing/4.0,lon+3*spacing/4.0,lon,lon,lon+3*spacing/4.0,lon-3*spacing/4.0,lon-3*spacing/4.0,lon+3*spacing/4.0]
newlats.extend(newlats1)
newlats.extend(newlats2)
newlons.extend(newlons1)
newlons.extend(newlons2)
newnodes = np.zeros((len(newlons),2))
newnodes[:,0] = np.round(newlons,1)
newnodes[:,1] = np.round(newlats,1)
#diptmp = 90
if slab == 'izu' and lat>23 and lat<27 and slab1 > 400:
clen = 400
elif slab != 'sol' and slab != 'hal' and slab != 'phi':
clen = 200
elif slab == 'sol' and lon>148:
clen = 300
else:
clen = alen
else:
eventlist = eventlist[(eventlist.lon<lon+1.5)&(eventlist.lat<lat+1.5)&(eventlist.lon>lon-1.5)&(eventlist.lat>lat-1.5)]
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- after reducing OGsearch range (dip<dipthresh), len(eventlist) %i\n'%(nID, len(eventlist)))
f.close()
clen = alen
if len(eventlist)<2 and len(eventlist[(eventlist.etype == 'AS')|(eventlist.etype == 'RF')|(eventlist.etype == 'CP')])<1 and slab != 'ryu':
#print ('no data here',lon,lat,nID)
return lon, lat, slab1, strtmp, diptmp, nID, alen, blen, clen, [], [], [], [], False, 0, 0
elif slab == 'ryu' and len(eventlist)<2 and len(eventlist[(eventlist.etype == 'AS')|(eventlist.etype == 'RF')|(eventlist.etype == 'TO')|(eventlist.etype == 'CP')])<1:
return lon, lat, slab1, strtmp, diptmp, nID, alen, blen, clen, [], [], [], [], False, 0, 0
else:
newnodes = []
if slab == 'phi' and lat > 11:
minstk /= 2
if extended:
alen = s2f.refilter4(strtmp,minstk,extlon,extlat,eventlist,alen,blen,slab1query,slab,testprint)
else:
alen = s2f.refilter4(strtmp,minstk,lon,lat,eventlist,alen,blen,slab1query,slab,testprint)
if lon > 170.5 and lon < 172 and lat > -22 and lat < -21:
alen = 20
if testprint:
f = open(nodeinfo, 'a')
f.write('-%i- alen, blen, clen, minstk, seismo_thick %0.2f,%0.2f,%0.2f,%0.2f,%0.2f \n'%(nID, alen, blen, clen, minstk, seismo_thick))
f.close()
if alen == 1:
return lon, lat,slab1, strtmp, diptmp, nID, alen, blen, clen, [], [], [], [], False, 0, 0
else:
farpoint = alen
if slab == 'cam' and lon < 260 and lat > 20.5 and lon > 257.5:
alen = 20
clen = 20
if slab == 'man' and lat < 14:
alen = 20
if slab1 < 200:
clen = 20
if slab == 'mue':
alen = 150
clen = 150
strtmp = 270
# Filter data for present node
trimmed, test, sdepth, ddepth, locstr, locdip, maxID, locdep = s2f.allFilters(
eventlist, lat, lon, inside, slab1, strtmp, diptmp, seismo_thick, alen, blen,
clen, mdist, sdr, ddr, mindip, maxID, out,
AA_data, TR_data, slab, maxdist, testprint, extended, datainfo, nodeinfo,nID)
trimmed, test = s2f.doublecheckEREQ(trimmed, lon, lat)
if not test:
if testprint:
print ('not test',lon,lat,nID)
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, [], [], [], newnodes, False, sdepth, ddepth
trimmedAA = trimmed[trimmed.etype == 'AA']
trimmedBA = trimmed[trimmed.etype == 'BA']
trimmedAS = trimmed[trimmed.etype == 'AS']
trimmedRF = trimmed[trimmed.etype == 'RF']
trimmedTO = trimmed[trimmed.etype == 'TO']
trimmedCP = trimmed[trimmed.etype == 'CP']
# Save data used
# Add this trimmed list of events to the list of used input data
if len(trimmed) > 1 or (len(trimmed) < 2 and (len(trimmedAA)>0 or len(trimmedBA)>0 or len(trimmedAS)>0 or len(trimmedRF)>0 or len(trimmedCP)>0)):
trimmed_index = np.array(trimmed.ID)
node_number = np.ones(len(trimmed_index)) * nID
used_tmp = np.array(list(zip(node_number, trimmed_index)))
elif len(trimmed) > 1 or (len(trimmed) < 2 and len(trimmedTO)>0 and slab == 'ryu'):
trimmed_index = np.array(trimmed.ID)
node_number = np.ones(len(trimmed_index)) * nID
used_tmp = np.array(list(zip(node_number, trimmed_index)))
else:
if testprint:
print ('trimmed too small',lon,lat,nID)
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, [], [], [], newnodes, False, sdepth, ddepth
# We only need to do the rest of this loop if there is tomography data
if tomo_sets == 0 or slab != 'samzzz' or slab =='ryu':
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, [], used_tmp, trimmedAA, newnodes, True, sdepth, ddepth
'''nothing beyond this point in this loop gets used '''
# Trim dataset for PDF calculation and separate tomography data
# We want to run PDF on non-TO, non-RF data
TOdata = trimmed[trimmed.etype == 'TO']
trimmed = trimmed[trimmed.etype != 'RF']
trimmed = trimmed[trimmed.etype != 'TO']
trimmed = trimmed[trimmed.etype != 'CP']
# Calculate PDF
if locdip <= mindip: #or len(trimmedAS) > 0 or len(trimmedAA) >0 or len(trimmedRF) >0:
#print 'full',lon,lat
peak_depth, stdv, test2, npeaks, OGmulti, centsurf = s2f.fullPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, locdep, 'pre', slab, locstr, locdip)
peak_lon = lon
peak_lat = lat
else:
peak_lon, peak_lat, peak_depth, stdv, test2, npeaks, OGmulti, centsurf, rlist = s2f.perpPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, locdep, 'pre', slab, locstr, locdip, maxthickness)
if len(rlist) > 0:
removeIDs = np.array(rlist.ID)
used_tmp = used_tmp[~np.in1d(used_tmp[:, 1], removeIDs)]
# For each node with a peak depth and tomography data, calculate differences
# Do separately for each tomography dataset
if test2:
if len(TOdata) > 0:
for idx, src in enumerate(tomo_sets):
data = TOdata[TOdata.src == src]
if len(data) > 0:
TOmean = np.mean(data.depth)
diff = peak_depth - TOmean
temp = [diff, idx]
temp2 = np.array(temp)
if used_TO is not None:
used_TO = np.vstack((used_TO, temp2))
else:
used_TO = temp2.copy()
else:
return lon, lat, locdep, locstr, locdip, nID, farpoint, used_TO, used_tmp, trimmedAA, newnodes
return lon, lat, locdep, locstr, locdip, nID, alen, blen, clen, used_TO, used_tmp, trimmedAA, newnodes
def loop3(shift_out, testarea, used_all, eventlist, sdr, ddr, seismo_thick, these_parameters, slab, maxthickness, mindip, taper, nID):
postmulti = []
bilats, bilons, binods, bistds = np.nan, np.nan, np.nan, np.nan
biindx, bistrs, bidips, bideps = np.nan, np.nan, np.nan, np.nan
if len(shift_out)>0: # used to be if slab == 'sam' bc of variable taper
taper = 0.0
loc_shift_out = shift_out[shift_out.nID == nID]
lat = loc_shift_out['lat'].values[0]
lon = loc_shift_out['lon'].values[0]
cstr = loc_shift_out['ogstr'].values[0]
cdip = loc_shift_out['ogdip'].values[0]
bflat = loc_shift_out['bzlat'].values[0]
bflon = loc_shift_out['bzlon'].values[0]
dep = loc_shift_out['psdepth'].values[0]
if len(loc_shift_out) == 0:
#print 'misnamed ID',nID
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, nID, postmulti, np.nan, np.nan
else:
testprint = False
if lon>testarea[0] and lon<testarea[1] and lat>testarea[2] and lat<testarea[3]:
testprint = True
else:
testprint = False
if testprint:
print('____________________________________ %i ___________________________________________'% nID)
# Collect used data associated with this node
loc_used_data = used_all[:, 1][used_all[:, 0] == nID]
if len(loc_used_data) > 1:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
elif len(loc_used_data) > 0:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
trimmedAA = trimmed[trimmed.etype == 'AA']
trimmedBA = trimmed[trimmed.etype == 'BA']
trimmedAS = trimmed[trimmed.etype == 'AS']
trimmedRF = trimmed[trimmed.etype == 'RF']
trimmedCP = trimmed[trimmed.etype == 'CP']
if len(trimmedAA)<1 and len(trimmedBA)<1 and len(trimmedAS)<1 and len(trimmedRF)<1 and len(trimmedCP)<1:
#if testprint:
print('skipping but returning something wonky', lon, lat, dep)
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, nID, postmulti, np.nan, np.nan
else:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, nID, postmulti, np.nan, np.nan
# Calculate average strike, rake, dip of CMTs for final output
avg_strike, avg_dip, avg_rake = s2f.avStrDipRak(trimmed)
if dep < seismo_thick or (len(trimmed[trimmed.etype == 'RF']) < 1 and \
len(trimmed[trimmed.etype == 'AA']) < 1 and \
len(trimmed[trimmed.etype == 'CP']) < 1 and \
len(trimmed[trimmed.etype == 'AS']) < 1):
#print 'shallow',lon,lat,nID
rstd = loc_shift_out['stdv'].values[0]
depth = loc_shift_out['depth'].values[0]
if dep<seismo_thick:
return depth, rstd, avg_strike, avg_dip, avg_rake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, bflon, bflat
else:
return depth, rstd, avg_strike, avg_dip, avg_rake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, lon, lat
if len(trimmed[(trimmed.etype != 'RF') & (trimmed.etype != 'CP')])<1 and len(trimmed[(trimmed.etype == 'RF') | (trimmed.etype == 'CP')])>0:
rstd = loc_shift_out['stdv'].values[0]
depth = loc_shift_out['psdepth'].values[0]
return depth, rstd, avg_strike, avg_dip, avg_rake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, bflon, bflat
if len(trimmed[(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')]) > 0:
# Calculate local depth difference between shifted and non-shifted results
dep_dif = dep - loc_shift_out['depth'].values[0]
lon_dif = bflon - lon
lat_dif = bflat - lat
# Apply shift to non-RF data
trimmed['depth'][(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')] -= dep_dif
trimmed['lon'][(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')] -= lon_dif
trimmed['lat'][(trimmed.etype != 'RF') & (trimmed.etype != 'AS') & (trimmed.etype != 'AA') & (trimmed.etype != 'CP')] -= lat_dif
# Calculate new PDF
loc_depth = trimmed['depth'].mean()
place_holder, sdepth, ddepth, depthwritten = s2f.depthRange(loc_depth, sdr, ddr, seismo_thick, trimmed, slab, these_parameters, True)
if cdip <= mindip:
if cdip > mindip:
cdip = mindip
try:
new_peak_depth, new_std, test3, npeaks, postmulti, centsurf = s2f.fullPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'post', slab, cstr, cdip)
peak_lon = lon
peak_lat = lat
except:
print('post full pdf didnt work!!', lon, lat, nID)
new_peak_depth, new_std, test3, npeaks, postmulti, centsurf = loc_shift_out['depth'].values[0], loc_shift_out['stdv'].values[0], False, 1, pd.DataFrame(), 2000
peak_lon = lon
peak_lat = lat
else:
try:
peak_lon, peak_lat, new_peak_depth, new_std, test3, npeaks, postmulti, centsurf, rlist = s2f.perpPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'post', slab, cstr, cdip, maxthickness)
except:
print('post perp pdf didnt work!!', lon, lat, nID)
peak_lon, peak_lat, new_peak_depth, new_std, test3, npeaks, postmulti, centsurf, rlist = lon, lat, loc_shift_out['depth'].values[0], loc_shift_out['stdv'].values[0], False, 1, pd.DataFrame(), 2000, pd.DataFrame()
# Populate results array
if test3:
rdepth, rstd, rstrike, rdip, rrake = new_peak_depth, new_std, avg_strike, avg_dip, avg_rake
if npeaks > 1:
bilats, bilons, binods, bistds= lat, lon, nID, new_std
biindx, bistrs, bidips, bideps = 1, cstr, cdip, loc_depth
else:
rdepth, rstd, rstrike, rdip, rrake = np.nan, np.nan, np.nan, np.nan, np.nan
return rdepth, rstd, rstrike, rdip, rrake, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, nID, postmulti, peak_lon, peak_lat
def loop2(testarea, lons, lats, nIDs1, locdep, locstr, locdip, used_all, eventlist, sdr, ddr, seismo_thick, slab, maxthickness, rlist, mindip, aalen, ablen, aclen, j):
these_parameters = []
premulti = []
bilats, bilons, binods, bistds = np.nan, np.nan, np.nan, np.nan
biindx, bistrs, bidips, bideps = np.nan, np.nan, np.nan, np.nan
lat = lats[j]
lon = lons[j]
nID = nIDs1[j]
cstr = locstr[j]
cdip = locdip[j]
alen = aalen[j]
blen = ablen[j]
clen = aclen[j]
if lon>testarea[0] and lon<testarea[1] and lat>testarea[2] and lat<testarea[3]:
testprint = True
print('____________________________________ %i ___________________________________________'% nID)
else:
testprint = False
# Collect used data associated with this node
loc_used_data = used_all[:, 1][used_all[:, 0] == nID]
if len(loc_used_data) > 1:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
elif len(loc_used_data) > 0:
trimmed = eventlist[eventlist['ID'].isin(loc_used_data)]
trimmedAA = trimmed[trimmed.etype == 'AA']
trimmedBA = trimmed[trimmed.etype == 'BA']
trimmedAS = trimmed[trimmed.etype == 'AS']
trimmedRF = trimmed[trimmed.etype == 'RF']
trimmedCP = trimmed[trimmed.etype == 'CP']
if len(trimmedAA)<1 and len(trimmedBA)<1 and len(trimmedAS)<1 and len(trimmedRF)<1 and len(trimmedCP)<1:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
else:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
# Remove RF data for PDF analysis
if locdep[j] > seismo_thick:
rft = trimmed[(trimmed.etype == 'RF') | (trimmed.etype == 'AA') | (trimmed.etype == 'AS') | (trimmed.etype == 'CP')]
trimmed = trimmed[(trimmed.etype != 'RF') & (trimmed.etype != 'AA') & (trimmed.etype != 'AS') & (trimmed.etype != 'CP')]
else:
rft = []
if len(trimmed)<2 and len(rft)>0:
trimmed = pd.concat([rft, trimmed])
if abs(rft['depth'].mean()-trimmed['depth'].mean())>maxthickness:
trimmed = trimmed[(trimmed.etype == 'RF') | (trimmed.etype == 'AA') | (trimmed.etype == 'AS') | (trimmed.etype == 'CP')]
if len(rft)>0:
loc_depth = np.mean(trimmed['depth'].values)
if np.isnan(loc_depth):
if testprint:
print ('1 ** nan depth found for this node, returning nan output in this directory',nID)
trimmed.to_csv('%i_faileddata.csv'%nID,header=True,index=False,na_rep=np.nan)
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
else:
loc_depth = locdep[j]
if np.isnan(loc_depth):
if testprint:
print ('2 ** nan depth found for this node, returning nan output in this directory',nID)
trimmed.to_csv('%i_faileddata.csv'%nID,header=True,index=False,na_rep=np.nan)
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti, alen, blen, clen, 0
place_holder, sdepth, ddepth, depthwritten = s2f.depthRange(loc_depth, sdr, ddr, seismo_thick, trimmed, slab, these_parameters, True)
if cdip <= mindip:# or len(trimmed[trimmed.etype == 'AS' ]) > 0 or len(trimmed[trimmed.etype == 'AA' ]) > 0 or len(rft)>0:
if cdip > mindip:
cdip = mindip
#try:
peak_depth, stdv, test2, npeaks, premulti, centsurf = s2f.fullPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'pre2', slab, cstr, cdip)
#except:
# print('full PDF didnt work! lon,lat,nID,cdip,cstr,loc_depth,trimmed', lon, lat, nID, cdip, cstr, loc_depth, trimmed)
# return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti
peak_lon = lon
peak_lat = lat
else:
#try:
peak_lon, peak_lat, peak_depth, stdv, test2, npeaks, premulti, centsurf, rlist = s2f.perpPDFcalc(trimmed, sdepth, ddepth, testprint, nID, lat, lon, loc_depth, 'pre2', slab, cstr, cdip, maxthickness)
#except:
# print('perp PDF didnt work!lon,lat,nID,cdip,cstr,loc_depth,trimmed', lon, lat, nID, cdip, cstr, loc_depth, trimmed)
# return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, rlist, premulti
if test2:
#tmp_res[j,0],tmp_res[j,1],tmp_res[j,2],tmp_res[j,3],tmp_res[j,4],tmp_res[j,5],tmp_res[j,6],tmp_res[j,7],tmp_res[j,8],tmp_res[j,9] = peak_lat,peak_lon,peak_depth,std,nID,lat,lon,cstr,cdip,centsurf
jpeak_lat, jpeak_lon, jpeak_depth, jstd, jnID = peak_lat, peak_lon, peak_depth, stdv, nID
jlat, jlon, jcstr, jcdip, jcentsurf = lat, lon, cstr, cdip, centsurf
# If there is more than one peak
if npeaks > 1:
bilats, bilons, binods, bistds= lat, lon, nID, stdv
biindx, bistrs, bidips, bideps = j, cstr, cdip, loc_depth
else:
jpeak_lon, jpeak_lat, jpeak_depth, jstd, jnID = np.nan, np.nan, np.nan, np.nan, np.nan
jlat, jlon, jcstr, jcdip, jcentsurf = np.nan, np.nan, np.nan, np.nan, np.nan
eventtypes = list(set(trimmed.etype))
if len(eventtypes) == 1 and eventtypes[0] == 'TO':
onlyTO = 1
else:
onlyTO = 0
return jpeak_lon, jpeak_lat, jpeak_depth, jstd, jnID, jlat, jlon, jcstr, jcdip, jcentsurf, bilats, bilons, binods, bistds, biindx, bistrs, bidips, bideps, rlist, premulti, alen, blen, clen, onlyTO