'weight':
1.0
},
{
'name': 'carbon',
'strategy':
'cumulative_maximize', # target the max cumulative value
'weight': 1.0
},
{
'name': 'cost proxy',
'strategy':
'cumulative_minimize', # target the min cumulative value
'weight': 1.0
},
]
#----------- STEP 3: Optimize (annealing over objective function) ---------#
best, optimal_stand_rxs, vars_over_time = schedule(stand_data,
axis_map,
valid_mgmts,
steps=250000,
report_interval=5000,
temp_min=0.0006,
temp_max=2,
live_plot=True)
#----------- STEP 4: output results ---------------------------------------#
print_results(axis_map, vars_over_time)
write_stand_mgmt_csv(optimal_stand_rxs, axis_map, filename="_results.csv")
if best < best_start:
best_start = best
best_mgmts = optimal_stand_rxs
# Now run the full schedule
best, optimal_stand_rxs, vars_over_time = schedule(
stand_data,
axis_map,
valid_mgmts,
steps=295000,
report_interval=5000,
temp_min=0.00005,
temp_max=20.0,
starting_mgmts=best_mgmts,
live_plot=True)
#----------- STEP 4: output results ---------------------------------------#,
print_results(axis_map, vars_over_time)
with open("results/results.csv", 'a') as fh:
for i, data in enumerate(vars_over_time.tolist()):
row = [2013 + i * 5, climate] + data
fh.write(",".join([str(x) for x in row]))
fh.write("\n")
write_stand_mgmt_csv(optimal_stand_rxs,
axis_map,
filename="results/%s_stands_rx.csv" % climate,
climate=climate)
#import ipdb; ipdb.set_trace()
temp_max=5.0
)
if best < best_start:
best_start = best
best_mgmts = optimal_stand_rxs
# Now run the full schedule
best, optimal_stand_rxs, vars_over_time = schedule(
stand_data,
axis_map,
valid_mgmts,
steps=295000,
report_interval=5000,
temp_min=0.00005,
temp_max=20.0,
starting_mgmts=best_mgmts,
live_plot=True
)
#----------- STEP 4: output results ---------------------------------------#,
print_results(axis_map, vars_over_time)
with open("results/results.csv", 'a') as fh:
for i, data in enumerate(vars_over_time.tolist()):
row = [2013 + i*5, climate] + data
fh.write(",".join([str(x) for x in row]))
fh.write("\n")
write_stand_mgmt_csv(optimal_stand_rxs, axis_map, filename="results/%s_stands_rx.csv" % climate, climate=climate)
#import ipdb; ipdb.set_trace()
),
'weight': 1.0
},
{
'name': 'carbon',
'strategy': 'cumulative_maximize', # target the max cumulative value
'weight': 1.0
},
{
'name': 'cost proxy',
'strategy': 'cumulative_minimize', # target the min cumulative value
'weight': 1.0
},
]
#----------- STEP 3: Optimize (annealing over objective function) ---------#
best, optimal_stand_rxs, vars_over_time = schedule(
stand_data,
axis_map,
valid_mgmts,
steps=250000,
report_interval=5000,
temp_min=0.0006,
temp_max=2,
live_plot=True
)
#----------- STEP 4: output results ---------------------------------------#
print_results(axis_map, vars_over_time)
write_stand_mgmt_csv(optimal_stand_rxs, axis_map, filename="_results.csv")