add python script for generating both the data and the input file for DSurfTomo

scripts/GenerateDSurfTomoInputFile.py

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+#/* -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.
+#
+# File Name : GenerateDSurfTomoInputFile.py
+#
+# Purpose : Generate the Input File for DSurfTomo using the data themselves
+#
+# Creation Date : 05-07-2023
+#
+# Last Modified : Tue 18 Jul 2023 08:10:22 PM CST
+#
+# Created By : Hongjian Fang: fanghj1990@gmail.com
+#
+#_._._._._._._._._._._._._._._._._._._._._.*/
+
+import os
+import numpy as np
+import glob
+import pandas as pd
+
+#These are for generating the right format
+DataPath = 'TaipeiRawData'
+DataNames = 'CDisp*.dat'
+wavetype = 2
+phasetype = 0
+
+# These following can be changed in DSurfTomo.in
+nz = 9
+sublayers = 3
+minvel = 0.5
+maxvel = 2.8
+noiselevel = 0.02
+datathreshold = 1.5
+DataFiles = glob.glob(DataPath+'/'+DataNames)
+
+sta1latall = []
+sta1lonall = []
+sta2latall = []
+sta2lonall = []
+periodsall = []
+dispersionall = []
+stationpairID = []
+
+for ifile in DataFiles:
+    dispersion = np.loadtxt(ifile)
+    pairID = ifile.split('/')[-1]
+    sta1lon = dispersion[0,0]
+    sta1lat = dispersion[0,1]
+    sta2lon = dispersion[1,0]
+    sta2lat = dispersion[1,1]
+    nperiods,_ = dispersion.shape
+    nperiods = nperiods - 2
+    periods = np.zeros(nperiods,)
+    disper = np.zeros(nperiods,)
+    for ii in range(nperiods):
+        periods[ii] = dispersion[ii+2,0]
+        disper[ii] = dispersion[ii+2,1]
+   
+    dispersionall = np.hstack([dispersionall,disper])
+    periodsall = np.hstack([periodsall,periods])
+    sta1latall = np.hstack([sta1latall,np.ones(nperiods,)*sta1lat])
+    sta1lonall = np.hstack([sta1lonall,np.ones(nperiods,)*sta1lon])
+    sta2latall = np.hstack([sta2latall,np.ones(nperiods,)*sta2lat])
+    sta2lonall = np.hstack([sta2lonall,np.ones(nperiods,)*sta2lon])
+    stationpairID = stationpairID + [pairID] * nperiods
+
+dataall = pd.DataFrame({'sta1lat':sta1latall, 'sta1lon':sta1lonall, \
+                        'sta2lat':sta2latall, 'sta2lon':sta2lonall, \
+                        'periods':periodsall, 'dispersion': dispersionall, \
+                        'pairid':stationpairID})
+
+dataall = dataall.sort_values(by = ['periods', 'pairid'])
+dataall = dataall.set_index('periods')
+with open('surfdata.dat','w') as fout:
+    UniqPeriods = dataall.index.unique()
+    for iperiod,period in enumerate(UniqPeriods):
+        datasubperiod = dataall.loc[period]
+        if isinstance(datasubperiod,pd.Series):
+            continue
+        datasubperiod = datasubperiod.reset_index().set_index('sta1lat')
+        sta1lat = datasubperiod.index.unique()
+        for ista1 in sta1lat:
+            datasubstation = datasubperiod.loc[ista1]
+            if isinstance(datasubstation,pd.DataFrame):
+                fout.write(f'# {datasubstation.index[0]} {datasubstation["sta1lon"].iloc[0]} {iperiod+1} {wavetype} {phasetype}\n')
+                for ista2 in range(len(datasubstation)):
+                    fout.write(f'{datasubstation["sta2lat"].iloc[ista2]} {datasubstation["sta2lon"].iloc[ista2]} {datasubstation["dispersion"].iloc[ista2]}\n')
+            else:
+                fout.write(f'# {datasubstation.name} {datasubstation["sta1lon"]} {iperiod+1} {wavetype} {phasetype}\n')
+                fout.write(f'{datasubstation["sta2lat"]} {datasubstation["sta2lon"]} {datasubstation["dispersion"]}\n')
+
+
+print('Finish reformatting disperison data')
+
+# Determin the grid interval, 1/3 of the smallest station distance
+
+# Weight and Damp, you will have to run the code a couple of time to decide
+# The default values is 5.0 and 1.0, respectively
+weight = 1.0
+damp = 1.0
+
+# Sparsity fraction, this parameter represent the sparsity of the sensitivity matrix and is set to be 0.2 for safe keeping, but could be as low as 0.02.it is not important as long as your code does not throw you memory erros
+sparsityfraction = 0.1
+
+# Iteration number, the default is 10 time, most often you get stable results after 4-5 iterations.
+maxiteration = 10
+
+
+distall = np.sqrt((dataall['sta2lat']-dataall['sta1lat'])**2+(dataall['sta2lon']-dataall['sta1lon'])**2)
+mindist = distall.min()
+gridintval = int(np.ceil(mindist/3*1000))/1000
+# Determine the origin, 1 grids
+originLat = np.max([np.max(dataall.sta1lat.max()),np.max(dataall.sta2lat.max())]) + 1*gridintval
+originLon = np.min([np.min(dataall.sta1lon.min()),np.min(dataall.sta2lon.min())]) - 1*gridintval
+largestLat = np.max([dataall['sta1lat'].max()-dataall['sta1lat'].min(),dataall['sta2lat'].max()-dataall['sta2lat'].min()])
+largestLon = np.max([dataall['sta1lon'].max()-dataall['sta1lon'].min(),dataall['sta2lon'].max()-dataall['sta2lon'].min()])
+inputfile = open('DSurfTomo.in','w')
+dummy = 'cccc'
+inputfile.write(f'{dummy*10}\n')
+inputfile.write(f'{dummy*10}\n')
+inputfile.write(f'{dummy*10}\n')
+inputfile.write('surfdata.dat\n')
+nx = int(np.ceil(largestLat/gridintval))+5
+ny = int(np.ceil(largestLon/gridintval))+5
+nreceivers = len(dataall.sta1lat.unique())+1
+inputfile.write(f'{nx} {ny} {nz}\n')
+inputfile.write(f'{originLat:<7.3f} {originLon:7.3f}\n')
+inputfile.write(f'{gridintval:<7.3f} {gridintval:7.3f}\n')
+inputfile.write(f'{nreceivers}\n')
+inputfile.write(f'{weight} {damp}\n')
+inputfile.write(f'{sublayers}\n')
+inputfile.write(f'{minvel} {maxvel}\n')
+inputfile.write(f'{maxiteration}\n')
+inputfile.write(f'{sparsityfraction}\n')
+nperiods = len(UniqPeriods)
+inputfile.write(f'{nperiods}\n')
+#inputfile.write(f'{*UniqPeriods}\n')
+inputfile.write(' '.join([str(iperiod) for iperiod in UniqPeriods])+'\n')
+inputfile.write(f'0\n')
+inputfile.write(f'0\n')
+inputfile.write(f'0\n')
+inputfile.write(f'0\n')
+inputfile.write(f'{noiselevel}\n')
+inputfile.write(f'{datathreshold}\n')
+inputfile.close()
+
+print('Finishing generating input file\n')