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p0 = 50. # production rate at time zero | |
na = -p0 # numeric encoding of not available value | |
exp_stage_zero = 0.12 # exponent of production decline for stage zero | |
exp_stage_one = 0.1 | |
time_max = 55. | |
bounds_stage_change_time = (20., 40.) # we'll generate training data with stage changes between these | |
num_timesteps = 50 # so many production values per sequence | |
num_discrete_stage_changes = 5 # we'll generate profiles with this many different stage change times | |
num_realizations_per_stage_change = 10 # and for each of these times, this many realizations (random noise differs) | |
num_production_profiles = num_realizations_per_stage_change * num_discrete_stage_changes | |
# for each generated profile we provide provide this many sequences, | |
# from first datapoint to the next to last one | |
num_sequences_per_profile = num_timesteps - 1 | |
num_sequences = num_production_profiles * num_sequences_per_profile | |
# fill with not available value, overwrite subsequently where appropriate | |
features = np.full((num_sequences, num_timesteps, num_features), na) | |
targets = np.full((num_sequences, num_timesteps, num_targets), na) |
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