Monte Carlo Simulations

Running a Monte Carlo Simulation is similar to a normal calculation. You first call the simulate method with a calculation setup. This schedules a first simulation run. When the calculation is ready, the result can be queried like any other result. For each subsequent iteration you call the next method on that result which schedules a new iteration, that again returns a result that can be queried, and so on. Old iteration results are disposed automatically when a new iteration is started but you should dispose the last result when it is not needed anymore.

The API method for the first iteration is:

For each subsequent calculation, the API method is:

Examples

Python IPC

import random as rand
import statistics as stats

import olca_ipc as ipc
import olca_schema as o

client = ipc.Client()

# schedule a first iteration
print("run iteration 1")
result = client.simulate(
    o.CalculationSetup(
        target=o.Ref(
            ref_type=o.RefType.ProductSystem,
            id="7d1cbce0-b5b3-47ba-95b5-014ab3c7f569",
        ),
        impact_method=o.Ref(id="99b9d86b-ec6f-4610-ba9f-68ebfe5691dd"),
    )
)
result.wait_until_ready()

# get the result for some indicator from the first iteration
indicator = rand.choice(result.get_impact_categories())
val = lambda: result.get_total_impact_value_of(indicator).amount
xs: list[float] = [val()]

# collect the values from 99 more iterations
for i in range(0, 99):
    print(f"run iteration {i+2}")
    result.simulate_next()
    result.wait_until_ready()
    xs.append(val())

result.dispose()

# plot the results in a simple histogram
bucket_count = 15
quantiles = stats.quantiles(xs, n=bucket_count + 1, method="inclusive")
buckets = [0] * bucket_count
for x in xs:
    bucket = 0
    for i in range(1, bucket_count):
        if x <= quantiles[i]:
            bucket += 1
    buckets[bucket] += 1
for i in buckets:
    print(f"|{'#' * i}")

# ...
# run iteration 98
# run iteration 99
# run iteration 100
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