decl_curve_rnn_proxy.py

def calc_exponential_decline(p0, exp, time):
    return p0 * np.exp(-exp * time)

def calc_two_stage_decline(p0, exp_stage_zero, exp_stage_one, time_max, time_next_stage, time_min=0., 
  production_jumpfactor=4., num=50, noise=0.1, noise_mean=1.):
    time = np.linspace(time_min, time_max, num)
    stage_one = np.where(time > time_next_stage)    
    production = calc_exponential_decline(p0, exp_stage_zero, time) * np.random.normal(noise_mean, noise, time.shape) 
    time_stage_one_relative = np.linspace(time_min, time_max-time_next_stage, len(time[stage_one]))
    production[stage_one] = calc_exponential_decline(production[stage_one][0] + p0/production_jumpfactor, exp_stage_one, 
      time_stage_one_relative) * np.random.normal(noise_mean, noise*2., time_stage_one_relative.shape)
    stage = np.zeros_like(time)
    stage[stage_one] = 1.
    production[production < 0.] = 0.
    return time, production, stage