from akvo.tressel.SlidesPlot import * import numpy as np import sys import matplotlib.pyplot as plt import cmocean from pylab import meshgrid from akvo.tressel.logbarrier import * import yaml,os from matplotlib.colors import LogNorm from matplotlib.colors import LightSource from matplotlib.ticker import ScalarFormatter from matplotlib.ticker import MaxNLocator from matplotlib.ticker import AutoMinorLocator from matplotlib.ticker import LogLocator from matplotlib.ticker import FormatStrFormatter import cmocean from akvo.tressel.lemma_yaml import * def buildKQT(K0,tg,T2Bins): """ Constructs a QT inversion kernel from an initial amplitude one. """ nlay, nq = np.shape(K0) nt2 = len(T2Bins) nt = len(tg) KQT = np.zeros( ( nq*nt,nt2*nlay) ) for iq in range(nq): for it in range(nt): for ilay in range(nlay): for it2 in range(nt2): #KQT[iq*nt + it,ilay*nt2+it2] = K0[ilay,iq]*np.exp(-((10+tg[it])*1e-3)/(1e-3*T2Bins[it2])) KQT[iq*nt + it,ilay*nt2+it2] = K0[ilay,iq]*np.exp(-((10+tg[it])*1e-3)/(1e-3*T2Bins[it2])) return KQT def loadAkvoData(fnamein, chan): """ Loads data from an Akvo YAML file. The 0.02 is hard coded as the pulse length. This needs to be corrected in future kernel calculations. The current was reported but not the pulse length. """ fname = (os.path.splitext(fnamein)[0]) with open(fnamein, 'r') as stream: try: AKVO = (yaml.load(stream, Loader=yaml.Loader)) except yaml.YAMLError as exc: print(exc) exit() Z = np.zeros( (AKVO.nPulseMoments, AKVO.Gated["Pulse 1"]["abscissa"].size ) ) ZS = np.zeros( (AKVO.nPulseMoments, AKVO.Gated["Pulse 1"]["abscissa"].size ) ) for q in range(AKVO.nPulseMoments): Z[q] = AKVO.Gated["Pulse 1"][chan]["Q-"+str(q) + " CA"].data if chan == "Chan. 1": ZS[q] = AKVO.Gated["Pulse 1"][chan]["STD"].data elif chan == "Chan. 2": ZS[q] = AKVO.Gated["Pulse 1"][chan]["STD"].data elif chan == "Chan. 3": ZS[q] = AKVO.Gated["Pulse 1"][chan]["STD"].data elif chan == "Chan. 4": ZS[q] = AKVO.Gated["Pulse 1"][chan]["STD"].data else: print("DOOM!!!") exit() #Z *= 1e-9 #ZS *= 1e-9 J = AKVO.Pulses["Pulse 1"]["current"].data J = np.append(J,J[-1]+(J[-1]-J[-2])) Q = AKVO.pulseLength[0]*J return Z, ZS, AKVO.Gated["Pulse 1"]["abscissa"].data #, Q def catLayers(K0): K = np.zeros( (len(K0.keys()), len(K0["layer-0"].data)) , dtype=complex ) for lay in range(len(K0.keys())): #print(K0["layer-"+str(lay)].data) # print (lay) K[lay] =K0["layer-"+str(lay)].data # print (lay) return 1e9*K # invert in nV def loadK0(fname): """ Loads in initial amplitude kernel """ print("loading K0", fname) with open(fname) as f: K0 = yaml.load(f, Loader=yaml.Loader) K = catLayers(K0.K0) ifaces = np.array(K0.Interfaces.data) return ifaces, np.abs(K) def main(): if (len (sys.argv) < 2): print ("akvoQT invertParameters.yaml") exit() with open(sys.argv[1], 'r') as stream: try: cont = (yaml.load(stream, Loader=yaml.Loader)) except yaml.YAMLError as exc: print(exc) ############################################### # Load in data ############################################### V = [] VS = [] tg = 0 for dat in cont['data']: for ch in cont['data'][dat]['channels']: print("dat", dat, "ch", ch) v,vs,tg = loadAkvoData(dat, ch) V.append(v) VS.append(vs) for iv in range(1, len(V)): V[0] = np.concatenate( (V[0], V[iv]) ) VS[0] = np.concatenate( (VS[0], VS[iv]) ) V = V[0] VS = VS[0] ############################################### # Load in kernels ############################################### K0 = [] for kern in cont["K0"]: ifaces,k0 = loadK0( kern ) K0.append(k0) for ik in range(1, len(K0)): K0[0] = np.concatenate( (K0[0].T, K0[ik].T) ).T K0 = K0[0] #plt.matshow(K0) ############################################### # Build full kernel ############################################### T2Bins = np.logspace( np.log10(cont["T2Bins"]["low"]), np.log10(cont["T2Bins"]["high"]), cont["T2Bins"]["number"], endpoint=True, base=10) KQT = buildKQT(K0,tg,T2Bins) ############################################### # Invert ############################################### print("Calling inversion", flush=True) inv, ibreak, errn, phim, phid, mkappa = logBarrier(KQT, np.ravel(V), T2Bins, "lcurve", MAXITER=150, sigma=np.ravel(VS), alpha=1e6, smooth="Smallest" ) ############################################### # Appraise ############################################### pre = np.dot(KQT,inv) PRE = np.reshape( pre, np.shape(V) ) plt.matshow(PRE, cmap='Blues') plt.gca().set_title("predicted") plt.colorbar() DIFF = (PRE-V) / VS md = np.max(np.abs(DIFF)) plt.matshow(DIFF, cmap=cmocean.cm.balance, vmin=-md, vmax=md) plt.gca().set_title("misfit / $\widehat{\sigma}$") plt.colorbar() plt.matshow(V, cmap='Blues') plt.gca().set_title("observed") plt.colorbar() T2Bins = np.append( T2Bins, T2Bins[-1] + (T2Bins[-1]-T2Bins[-2]) ) INV = np.reshape(inv, (len(ifaces)-1,cont["T2Bins"]["number"]) ) Y,X = meshgrid( ifaces, T2Bins ) fig = plt.figure( figsize=(pc2in(20.0),pc2in(22.)) ) ax1 = fig.add_axes( [.2,.15,.6,.7] ) im = ax1.pcolor(X, Y, INV.T, cmap=cmocean.cm.tempo) #cmap='viridis') im.set_edgecolor('face') ax1.set_xlim( T2Bins[0], T2Bins[-1] ) ax1.set_ylim( ifaces[-1], ifaces[0] ) cb = plt.colorbar(im, label = u"PWC (m$^3$/m$^3$)") #, format='%1.1f') cb.locator = MaxNLocator( nbins = 4) cb.ax.yaxis.set_offset_position('left') cb.update_ticks() ax1.set_xlabel(u"$T_2^*$ (ms)") ax1.set_ylabel(u"depth (m)") ax1.get_xaxis().set_major_formatter(FormatStrFormatter('%1.0f')) ax1.get_yaxis().set_major_formatter(FormatStrFormatter('%1.0f')) ax1.xaxis.set_major_locator( MaxNLocator(nbins = 4) ) #ax1.xaxis.set_label_position('top') ax2 = ax1.twiny() ax2.plot( np.sum(INV, axis=1), (ifaces[1:]+ifaces[0:-1])/2 , color='red' ) ax2.set_xlabel(u"total water (m$^3$/m$^3$)") ax2.set_ylim( ifaces[-1], ifaces[0] ) ax2.xaxis.set_major_locator( MaxNLocator(nbins = 3) ) ax2.get_xaxis().set_major_formatter(FormatStrFormatter('%0.2f')) #ax2.xaxis.set_label_position('bottom') plt.savefig("akvoInversion.pdf") plt.show() if __name__ == "__main__": main()