def main(argv):
parser = MdpArgs(
description=
"extract mean CO2 emissions at intervals of following MDP instance optimal policy"
)
parser.add_paramsfile_single()
parser.add_cycle_length()
parser.add_iterations(default=500)
parser.add_save()
args = parser.get_args()
if not args.paramsfile:
print("error: must pass in paramsfile.")
sys.exit(2)
params = cl.get_params_single(MDP_VERSION, args.paramsfile)
mdp_model = cl.get_mdp_model(MDP_VERSION, [params])
mdp_fh = cl.get_mdp_instance_single(mdp_model, params)
t_range = [0, mdp_fh.n_years]
_, _, y_emit, _ = mv.avg_co2_probabilistic_v(mdp_fh, t_range[0],
t_range[1], args.iterations,
True)
y_emit = np.sum(y_emit, axis=0) / args.iterations
for y in args.cycle:
targets_mean, targets_dec = (dict() for i in range(2))
emit_dec = calc_emit_dec(mdp_fh, y_emit, y)
years_sampled = [i * y for i in range(0, (mdp_fh.n_years // y))]
# Sampled from mean of optimal policy.
targets_mean['x'] = years_sampled
targets_mean['y'] = [y_emit[i] for i in years_sampled]
# Decrement evenly to align with optimal policy.
targets_dec['x'] = years_sampled
targets_dec['y'] = [
y_emit[0] - i * emit_dec for i in range(mdp_fh.n_years // y)
]
targets_dir = Path("visuals/v{}/targets".format(DIR_VERSION))
name = args.paramsfile.replace("_exp", "").replace("_lin", "")
tf_mean = targets_dir / "e_v{}_{}_{}_mean.txt".format(
DIR_VERSION, y, name)
with open(tf_mean, 'w+') as targetsfile:
targetsfile.write(str(targets_mean))
tf_dec = targets_dir / "e_v{}_{}_{}_dec.txt".format(
DIR_VERSION, y, name)
with open(tf_dec, 'w+') as targetsfile:
targetsfile.write(str(targets_dec))
targetsfile.close()
def main(argv):
parser = MdpArgs(description="debug")
parser.add_model_version()
parser.add_paramsfile_single()
args = parser.get_args()
params = cl.get_params_single(args.version, args.paramsfile)
mdp_model = cl.get_mdp_model(args.version, [params])
mdp_fh = cl.get_mdp_instance_single(mdp_model, params)
# Iterations set to 1, timerange set to 0-20 yr.
mdp_data = MdpDataGatherer(mdp_model, 1, [0, 20])
# Variable pulled out of _adjust_co2_tax function in MdpCostCalculator.
y_base = mdp_data.co2_base(mdp_fh)
# Variable pulled from
y_l = mdp_data.get_state_variable(mdp_fh, 'l')
y_price = mdp_data.co2_current_price(mdp_fh)
y_e = mdp_data.get_state_variable(mdp_fh, 'e')
y_inc = mdp_data.co2_inc(mdp_fh)
# # Use if changing iterations to > 1.
# y_base = np.mean(mdp_data.co2_base(mdp_fh), axis=0)
# y_l = np.mean(mdp_data.get_state_variable(mdp_fh, 'l'), axis=0)
# y_price = np.mean(mdp_data.co2_current_price(mdp_fh), axis=0)
# y_e = np.mean(mdp_data.get_state_variable(mdp_fh, 'e'), axis=0)
# y_inc = np.mean(mdp_data.co2_inc(mdp_fh), axis=0)
np.set_printoptions(linewidth=200)
with open("debug_co2_output.txt", "a+") as debugfile:
sys.stdout = debugfile
print("### DEBUGGING CO2 ###\n\n")
print(params, "\n")
time = 0
# for l, e, base, inc in zip(y_l, y_e, y_base, y_inc):
# print("TIME: ", time)
# print("level: ", l)
# print("act: ", e)
# print("base: ", base)
# print("inc: ", inc)
# time += 1
print("inc: ", y_inc)
print("base: ", y_base)
print("level: ", y_l)
print("price: ", y_price)
print("act: ", y_e)
print("\n\n")
sys.stdout = og
debugfile.close()