decl_curve_rnn_predict.py

time_stage_change = 15.
num_production_history = 8 # number of productin history points used for prediction
time, production, stage = calc_two_stage_decline(p0, exp_stage_zero, exp_stage_one, time_max, 
  time_stage_change, num=num_timesteps)    

features = np.full((1, num_timesteps, num_features), NA)
features[0, :num_production_history, ifeature_production] = production[:num_production_history]
features[0, :, ifeature_stage] = stage[:]
normalizer_features.transform(features.reshape(num_timesteps, num_features))
targets = model.predict(features) 
normalizer_targets.inverse_transform(targets.reshape(num_timesteps, num_targets))

print(targets[0, :, itarget_production])