# Specify a simulation algorithm¶

In this example we are going to parameterize a simulation algorithm:

• parameters linked to the number of points generated

• the precision of the probability estimator

• the sample storage strategy

• using callbacks to monitor progress and stopping criteria.

```import openturns as ot

ot.Log.Show(ot.Log.NONE)
```

Create the joint distribution of the parameters.

```distribution_R = ot.LogNormalMuSigma(300.0, 30.0, 0.0).getDistribution()
distribution_F = ot.Normal(75e3, 5e3)
marginals = [distribution_R, distribution_F]
distribution = ot.ComposedDistribution(marginals)
```

Create the model.

```model = ot.SymbolicFunction(["R", "F"], ["R-F/(pi_*100.0)"])
```

Create the event whose probability we want to estimate.

```vect = ot.RandomVector(distribution)
G = ot.CompositeRandomVector(model, vect)
event = ot.ThresholdEvent(G, ot.Less(), 0.0)
```

Create a Monte Carlo algorithm.

```experiment = ot.MonteCarloExperiment()
algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
```

Criteria 1: Define the Maximum Coefficient of variation of the probability estimator.

```algo.setMaximumCoefficientOfVariation(0.05)
```

Criteria 2: Define the number of iterations of the simulation.

```algo.setMaximumOuterSampling(int(1e4))
```

The block size parameter represents the number of samples evaluated per iteration, useful for parallelization.

```algo.setBlockSize(2)
```

HistoryStrategy to store the values of the probability used to draw the convergence graph.

Null strategy

```algo.setConvergenceStrategy(ot.Null())

# Full strategy
algo.setConvergenceStrategy(ot.Full())

# Compact strategy: N points
N = 1000
algo.setConvergenceStrategy(ot.Compact(N))
```

Use a callback to display the progress every 10%.

```def progress(p):
if p >= progress.t:
progress.t += 10.0
print("progress=", p, "%")
return False

progress.t = 10.0
algo.setProgressCallback(progress)
```

Use a callback to stop the simulation.

```def stop():
# here we never stop, but we could
return False

algo.setStopCallback(stop)
```
```algo.run()
```
```progress= 10.0 %
progress= 20.0 %
progress= 30.0 %
progress= 40.0 %
progress= 50.0 %
progress= 60.0 %
progress= 70.0 %
progress= 80.0 %
progress= 90.0 %
progress= 100.0 %
```

Retrieve results.

```result = algo.getResult()
probability = result.getProbabilityEstimate()
print("Pf=", probability)
```
```Pf= 0.027899999999999977
```