rf_obs, swd_obs, all_lims = input_data.LoadObservations() save_name += '_%d' % rnd_sd suffix = None def outdir_fn(): if suffix is None: return os.path.join('output', save_name) else: return os.path.join('output', '%s_%05d' % (save_name, suffix)) while os.path.exists(outdir_fn()): if suffix is None: suffix = 0 else: suffix += 1 outdir = outdir_fn() os.mkdir(outdir) shutil.copyfile('input_data.py', os.path.join(outdir, 'input_data.py')) with tf.Session(): out = pipeline.JointInversion(rf_obs, swd_obs, all_lims, max_it, rnd_sd, os.path.join(outdir, save_name), 'Ps') #pr.disable() #s=open(os.path.join(outdir, 'profiletimes.txt'), 'w') #sortby = 'cumulative' #ps = pstats.Stats(pr, stream=s).sort_stats(sortby) #ps.print_stats() #s.close()
swd_obs = pipeline.SurfaceWaveDisp(period=np.array( [9.0, 10.1, 11.6, 13.5, 16.2, 20.3, 25.0, 32.0, 40.0, 50.0, 60.0, 80.0]), c=np.array([ 3.212, 3.215, 3.233, 3.288, 3.339, 3.388, 3.514, 3.647, 3.715, 3.798, 3.847, 3.937 ])) all_lims = pipeline.Limits(vs=(0.5, 5.5), dep=(0, 200), std_rf=(0, 0.05), lam_rf=(0.05, 0.5), std_swd=(0, 0.15)) out = pipeline.JointInversion(rf_obs, swd_obs, all_lims, max_it, rnd_sd) #actual_model = pipeline.SaveModel(pipeline.MakeFullModel(model),out[1][:,0]) #%% all_models = np.load('testsave.npy') good_mods = all_models[:, np.where(all_models[0, ] > 0)[0]] nit = good_mods.shape[1] good_mods = good_mods[:, -int(nit / 5):] mean_mod = np.mean(good_mods, axis=1) std_mod = np.std(good_mods, axis=1) good_mod = pipeline.Model(vs=mean_mod, all_deps=all_models[:, 0], idep=np.arange(0, mean_mod.size), lam_rf=0,