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,