meas_fwd_data = pyres_utils.load_fwd_output(work_dir=work_dir)
inv_protocol_dict = {'meas_data':meas_fwd_data}
run_inv_r2_dict = {'protocol_dict':inv_protocol_dict, 'r2_in_dict':inv_r2_in_dict,
'run_bool':True}
iR2_inv.run_all(**run_inv_r2_dict)
#%%
# Plot inverse model results
plot_dict = {'work_dir':inv_work_dir,
'topog_xy':np.column_stack([xx_mesh,topog]),
'plt_opts':{'vmin':10**1.3,'vmax':10.**2},
'invert_y':False,
'cmap':'rainbow','keep_log':True}
fig,ax,[X,Y,ER] = plot_utils.plot_res(**plot_dict)
ax.plot(electrode_x,topo_func(4.8/electrode_x[-1],electrode_x,0.),'k.-')
# Plot target
txy = np.array(target_xypos)
txy = np.vstack([txy,txy[0,:]])
topo_y = pyres_utils.extrap(txy[:,0],xx_mesh,topog)
ax.plot(txy[:,0],topo_y-txy[:,1],'k-')
#%% Compare performance of inversion
# Define normalized misfit equation
errfunc = lambda obs,mod: (obs-mod)/obs
obs_array = np.log10(main_res)*np.ones_like(ER)
target_bool = (X>=txy[0,0]) & (X<=txy[1,0]) &\
#%%
# Plot % difference of inverse model results
plot_dict = {
'fname': os.path.join(inv_work_dir, 'f001_diffres.dat'),
'inv_col': 2,
'plt_opts': {
'vmin': -1e2,
'vmax': 1e2,
'aspect': 2
},
'invert_y': False,
'nxny': [5e2, 1e2],
'cmap': 'rainbow',
'keep_log': False
}
fig, ax, _ = plot_utils.plot_res(**plot_dict)
# Plot results of difference inversion for time 2
plot_dict = {
'work_dir': inv_work_dir,
'plt_opts': {
'vmin': 10**1.3,
'vmax': 10.**2,
'aspect': 2
},
'invert_y': False,
'nxny': [5e2, 1e2],
'cmap': 'rainbow',
'keep_log': True
}
fig, ax, _ = plot_utils.plot_res(**plot_dict)
#%% ################## Plot inverse model results ##################
plot_dict = {
'work_dir': work_dir,
'method': 'linear',
'topog_xy': mr2.topo_xyz,
'plt_opts': {
'vmin': 1.5,
'vmax': 200.,
'ticks': [1.5, 5, 10, 50, 100, 200.]
},
'invert_y': False,
'cmap': 'rainbow',
'keep_log': False
}
fig, ax, [X, Y, ER] = plot_utils.plot_res(**plot_dict)
c1 = ax.contour(X, Y, ER, [5, 10, 50, 100], colors='k')
plot_utils.plt.clabel(c1, inline=0, fontsize=10, fmt='%3.1f')
#ax.set_ylim([-6,2])
#ax.set_xlim([0,68.75])
#%% ################## Plot model sensitivity results ##################
plot_dict = {
'fname': os.path.join(work_dir, 'f001_sen.dat'),
'method': 'linear',
'topog_xy': mr2.topo_xyz,
'plt_opts': {
'vmin': 1e-6,
'vmax': 1e1
},
'invert_y': False,
