Run configuration

To handle shared information in the simulation, we can use the RunConfig class, which is a singleton initialized by RunConfigInit.

from exosim.utils import RunConfig

Parallel processing

Parallel processing is important for such demanding simulations. The number of parallel processes to use can be set using

from exosim.utils import RunConfig

RunConfig.n_job = N

This number is then set for both joblib and numba libraries.

Chunk size

The chunk size is the size of the chunk of the cached dataset (see Cached signals). This value can be set as

from exosim.utils import RunConfig

RunConfig.chunk_size = N

where N is the desired size of the chunk in MB, which will be set for the environment.

Random seed and Random generators

The initial random seed can be set as:

from exosim.utils import RunConfig

RunConfig.random_seed = N

where N is the desired seed number. By default, the seed is set to None, and therefore each simulation is unique.

ExoSim also provides a default random generator (numpy.random.Generator) already initialized with the set random seed. The random generator can be accessed as:

from exosim.utils import RunConfig

rng = RunConfig.random_generator

and it can be used as any other random generator.

from exosim.utils import RunConfig

# uniform distribution:
RunConfig.random_generator.uniform(-1,0,1000)

# normal distribution:
RunConfig.random_generator.normal(0,1,1000)

# Poisson distribution:
RunConfig.random_generator.poisson(5, 1000)

More examples are available in the numpy.random.Generator documentation.

Because ExoSim works with chunks of data and the generator may be used in loops, if the seed is not None, random_generator updates the seed at every call, by adding 1 to the given value.