def plot_result(params, labels, xmin, xmax):
"""
Plot result of inversion
"""
deposit_c = []
totalthick = []
symbollist = ['-','-.','--',':','-','-.']
#Calculate results from given parameter sets
for j in range(len(params)):
deposit_c.append([])
totalthick.append([])
(x, C, x_dep, deposit_c[j]) = fmodel.forward(params[j]) #best result
cnum = fmodel.cnum #number of grain size classes
totalthick[j] = np.sum(deposit_c[j],axis=0)
#Correction of offset of x coordinate
#This is necessary because the seaward end of calculation domain is
#assumed to be x=0
x_dep = x_dep + spoints[0]
#Formatting plot area
plt.figure(num=None, figsize=(7, 8.5), dpi=150, facecolor='w', edgecolor='k')
fp = FontProperties(size=9)
plt.rcParams["font.size"] = 9
plt.subplot(cnum+1,1,1)
plt.plot(spoints, np.sum(deposit_o,axis=0),marker='o', markersize=4,\
fillstyle='none', linestyle = 'None', label = "Observation")
for l in range(len(params)):
plt.plot(x_dep[xmin:xmax], totalthick[l][xmin:xmax], symbollist[l],\
linewidth = 0.75, label = labels[l])
plt.title('Total Thickness')
plt.ylabel('Thickness (m)')
plt.yscale("log")
plt.ylim(0,0.4)
plt.legend(prop = fp, loc='best', borderaxespad=1)
for i in range(cnum):
plt.subplot(cnum+1,1,i+2) #Prepare graphs
plt.plot(spoints, deposit_o[i,:], marker = 'o', markersize=4,\
fillstyle='none', color = 'k', linestyle = 'None',\
label = "Observation")
for k in range(len(params)):
plt.plot(x_dep[xmin:xmax], deposit_c[k][i,xmin:xmax],\
symbollist[k], linewidth = 0.75, label = labels[k])
if i == cnum - 1:
plt.xlabel('Distance (m)')
plt.ylabel('Sed. Vol. per \n Unit Area (m)')
d = fmodel.Ds[i]*10**6
gclassname='{0:.0f} $\mu$m'.format(d[0])
plt.yscale("log")
plt.ylim(0,0.4)
plt.title(gclassname)
plt.subplots_adjust(hspace=0.7)
plt.show()
def save_result(resultfile, spointfile, depositfile, res):
"""
Save the inversion results
"""
#Calculate the forward model using the inversion result
(x, C, x_dep, deposit_c) = fmodel.forward(res.x)
#Save the best result
fmodel.export_result(spointfile, depositfile)
np.savetxt(resultfile, res.x)
def costfunction(optim_params):
"""
Calculate objective function that quantifies the difference between
field observations and results of the forward model calculation
Fist, this function runs the forward model using a given set of parameters.
Then, the mean square error of results was calculated.
"""
(x, C, x_dep, deposit_c) = fmodel.forward(optim_params)
f = ip.interp1d(x_dep, deposit_c, kind='cubic', bounds_error=False, fill_value=0.0)
deposit_c_interp = f(spoints)
dep_norm = np.matrix(np.max(deposit_o, axis = 1)).T
residual = np.array((deposit_o - deposit_c_interp)/dep_norm)
cost = np.sum((residual) ** 2)
return cost