Update to v1.0.2

README.md

@@ -1,14 +1,15 @@
-<img src="https://github.com/s-watanabe-jhod/garpos/assets/68180987/ed955d3c-4c3b-4ca3-91d5-f57b876cfa7b" width=400 alt="GARPOS">
+# GARPOS
 
 "GARPOS" (GNSS-Acoustic Ranging combined POsitioning Solver) is an analysis tool for GNSS-Acoustic seafloor positioning.
 
 ### Version
-Latest version is GARPOS v1.0.1 (Apr. 5. 2022)
+Latest version is GARPOS v1.0.2 (Jul. 2. 2024)
 
 #### Major change(s)
+* v1.0.2: to apply a mode for "array take-over" (solve each transponder's position and parallel disp. simultaneously)
+* v1.0.2: parameter "invtyp" is deleted. Users can set zero in config files to solve limited parameter(s), instead.
 * v1.0.1: to set B-spline's knots by time interval (also need to change "Setup.ini" file)
 * v1.0.1: to use Cholesky decomposition (module "sksparse" is needed)
-  * NOTE: some reported that "sksparse" cannot be used on Apple M1 Chip. 
 
 # Citation
 
@@ -17,6 +18,7 @@ Latest version is GARPOS v1.0.1 (Apr. 5. 2022)
 Watanabe, S., Ishikawa, T., Yokota, Y., & Nakamura, Y. (2020). GARPOS: analysis software for the GNSS-A seafloor positioning with simultaneous estimation of sound speed structure, Front. Earth Sci. (https://doi.org/10.3389/feart.2020.597532).
 
 ### for code
+
 Shun-ichi Watanabe, Tadashi Ishikawa, Yuto Nakamura & Yusuke Yokota. (2022). GARPOS: Analysis tool for GNSS-Acoustic seafloor positioning (Version 1.0.1). Zenodo. (https://doi.org/10.5281/zenodo.6414642)
 
 ## Corresponding author
@@ -31,11 +33,6 @@ Shun-ichi Watanabe, Tadashi Ishikawa, Yuto Nakamura & Yusuke Yokota. (2022). GAR
 "GARPOS" is distributed under the [GPL 3.0] (https://www.gnu.org/licenses/gpl-3.0.html) license.
 
 
-### Algorithm and documentation
-
-Please see Watanabe, S., Ishikawa, T., Yokota, Y., and Nakamura, Y., (2020) https://doi.org/10.3389/feart.2020.597532
-
-
 # Requirements
 
 * Python 3.7.3

bin/garpos_v101/forward.py → bin/garpos_v102/forward.py

@@ -116,8 +116,8 @@ def calc_gamma(mp, shotdat, imp0, spdeg, knots):
 	a1 = []
 	for k, kn in enumerate(knots):
 		if len(kn) == 0:
-			a0.append( 0. )
-			a1.append( 0. )
+			a0.append( np.zeros(len(shotdat.ST)) )
+			a1.append( np.zeros(len(shotdat.RT)) )
 			continue
 		ct = mp[imp0[k]:imp0[k+1]]
 		bs = BSpline(kn, ct, spdeg, extrapolate=False)

bin/garpos_v101/garpos_main.py → bin/garpos_v102/garpos_main.py

@@ -25,60 +25,57 @@
 def parallelrun(inplist, maxcore):
 	"""
 	Run the model parameter estimation in parallel.
-	
+
 	Parameters
 	----------
 	inplist : DataFrame
 		List of arguments for the function.
 	maxcore : int
 		maximum number of parallelization.
-	
+
 	Returns
 	-------
 	inplist : DataFrame
 		List of arguments for the function in which brief results are added.
 	"""
-	
+
 	npara = len(inplist.index)
 	mc = min(maxcore, npara)
-	
+
 	# Input files
 	i0 = inplist.cfgfile
 	i1 = inplist.invcfg
-	
+
 	# Output parameters
 	o1 = inplist.outdir
 	o2 = inplist.suffix
-	
+
 	# Hyperparameters
 	h0 = inplist.lamb0.values
 	h1 = inplist.lgrad.values
 	h2 = inplist.mu_t.values
 	h3 = inplist.mu_m.values
-
-	# Inversion design parameters
-	p0 = inplist.loc[:, ["de","dn","du"]].values
-
-	inp = list(zip(i0,i1,o1,o2,h0,h1,h2,h3,p0))
-
+
+	inp = list(zip(i0,i1,o1,o2,h0,h1,h2,h3))
+
 	with Pool(processes=mc) as p:
 		reslist = p.starmap(MPestimate, inp)
 		p.close()
-	
+
 	inplist["resfile"] = [ r[0] for r in reslist ]
 	inplist["RMS_dTT"] = [ r[1]/1000. for r in reslist ]
 	inplist["ABIC"] = [ r[2] for r in reslist ]
 	inplist["dE"] = [ r[3][0] for r in reslist ]
 	inplist["dN"] = [ r[3][1] for r in reslist ]
 	inplist["dU"] = [ r[3][2] for r in reslist ]
-	
+
 	return inplist
 
 
 def drive_garpos(cfgf, icfgf, outdir, suf, maxcore):
 	"""
 	Main driver to run GARPOS.
-	
+
 	Parameters
 	----------
 	cfgf : string
@@ -91,54 +88,54 @@ def drive_garpos(cfgf, icfgf, outdir, suf, maxcore):
 		Suffix to be added for result files.
 	maxcore : int
 		maximum number of parallelization.
-	
+
 	Returns
 	-------
 	resf : string
 		Result site-paramter file name (min-ABIC model).
 	"""
-	
+
 	# Set Hyperparamters for search
 	icfg = configparser.ConfigParser()
 	icfg.read(icfgf, 'UTF-8')
-	
+
 	lamb0s = icfg.get("HyperParameters", "Log_Lambda0").split()
 	glambs = icfg.get("HyperParameters", "Log_gradLambda").split()
 	mu_ts = icfg.get("HyperParameters", "mu_t").split()
 	mu_ms = icfg.get("HyperParameters", "mu_mt").split()
-	
+
 	lamb0s = np.array(list(map(float, lamb0s)))
 	glambs = np.array(list(map(float, glambs)))
 	mu_ts = np.array(list(map(float, mu_ts)))
 	mu_ms = np.array(list(map(float, mu_ms)))
-	
+
 	nl = len(lamb0s)
 	ng = len(glambs)
 	nt = len(mu_ts)
 	nm = len(mu_ms)
 	nmodels = nl * ng * nt * nm
-	
+
 	if nmodels == 1:
 		wkdir = outdir
 	elif nmodels > 1:
 		wkdir  = outdir+ "/lambda/"
 	else:
 		print("error in hyper paramter setting")
 		sys.exit(1)
-	
+
 	if not os.path.exists(wkdir+"/"):
 		os.makedirs(wkdir)
-	
+
 	# Set File Name
 	cfg = configparser.ConfigParser()
 	cfg.read(cfgf, 'UTF-8')
 	site = cfg.get("Obs-parameter", "Site_name")
 	camp = cfg.get("Obs-parameter", "Campaign")
 	filebase = site + "." + camp + suf
-	
+
 	# Set Input parameter list for ParallelRun
 	hps = np.array(list(itertools.product(lamb0s, glambs, mu_ts, mu_ms)))
-	
+
 	sufs = [ suf ] * nmodels
 	for i, hp in enumerate(hps):
 		if nl > 1:
@@ -149,31 +146,28 @@ def drive_garpos(cfgf, icfgf, outdir, suf, maxcore):
 			sufs[i] += "_T%03.1f" % hp[2]
 		if nm > 1:
 			sufs[i] += "_mt%03.1f" % hp[3]
-	
+
 	inputs = pd.DataFrame(sufs, columns = ['suffix'])
-	
+
 	inputs['lamb0'] = 10.**hps[:,0]
 	inputs['lgrad'] = 10.**hps[:,1]
 	inputs['mu_t'] = hps[:,2]*60.
 	inputs['mu_m'] = hps[:,3]
-	
+
 	print(inputs)
-
-	inputs["de"] = 0.0
-	inputs["dn"] = 0.0
-	inputs["du"] = 0.0
+
 	inputs["cfgfile"] = cfgf
 	inputs["invcfg"] = icfgf
 	inputs["outdir"] = wkdir
-	
+
 	outputs = parallelrun(inputs, maxcore)
-	
+
 	resf = outputs.resfile[0]
 	score='ABIC'
-	
+
 	df = outputs.sort_values(score, ascending=True).reset_index(drop=True)
 	resf = df.resfile[0]
-	
+
 	if nmodels > 1:
 		print(resf)
 		bestfile = os.path.basename(resf)
@@ -182,24 +176,23 @@ def drive_garpos(cfgf, icfgf, outdir, suf, maxcore):
 		fls = sorted(glob.glob(dfl+"/"+bestfile.replace("-res.dat","-*")))
 		for ff in fls:
 			shutil.copy(ff, outdir+"/"+os.path.basename(ff))
-		
+
 		# to summarize the results
 		df["log(Lambda)"] = [math.log10(l) for l in df.lamb0]
 		df["log(L_grad)"] = [math.log10(l) for l in df.lgrad]
 		df["mu(t)"] = df.mu_t/60.
-		
+
 		df = df.loc[:,[score,"log(Lambda)","log(L_grad)","mu(t)","dE","dN","dU"]]
-		
+
 		if nl  <= 1:
 			df=df.drop("log(Lambda)", axis=1)
 		if ng <= 1:
 			df=df.drop("log(L_grad)", axis=1)
 		if nt  <= 1:
 			df=df.drop("mu(t)", axis=1)
-		
+
 		print(df)
 		of = wkdir + "searchres-%s.dat" % filebase
 		df.to_csv(of)
-	
+
 	return resf
-