# Source code for cobra.sampling.sampling

```
"""Provide a wrapper function for performing flux sampling of cobra models."""
from typing import TYPE_CHECKING, Optional
import pandas as pd
from .achr import ACHRSampler
from .optgp import OptGPSampler
if TYPE_CHECKING:
from cobra import Model
[docs]def sample(
model: "Model",
n: int,
method: str = "optgp",
thinning: int = 100,
processes: int = 1,
seed: Optional[int] = None,
) -> pd.DataFrame:
"""Sample valid flux distributions from a cobra model.
Currently, two methods are supported:
1. 'optgp' (default) which uses the OptGPSampler that supports parallel
sampling. Requires large numbers of samples to be performant
(`n` > 1000). For smaller samples, 'achr' might be better suited.
For details, refer [1]_ .
2. 'achr' which uses artificial centering hit-and-run. This is a single
process method with good convergence. For details, refer [2]_ .
Parameters
----------
model : cobra.Model
The model from which to sample flux distributions.
n : int
The number of samples to obtain. When using 'optgp', this must be a
multiple of `processes`, otherwise a larger number of samples will
be returned.
method : {"optgp", "achr"}, optional
The sampling algorithm to use (default "optgp").
thinning : int, optional
The thinning factor of the generated sampling chain. A thinning of
10 means samples are returned every 10 steps. Defaults to 100 which
in benchmarks gives approximately uncorrelated samples. If set to 1
will return all iterates (default 100).
processes : int, optional
Only used for 'optgp'. The number of processes used to generate
samples (default 1).
seed : int > 0, optional
Sets the random number seed. Initialized to the current time stamp
if None (default None).
Returns
-------
pandas.DataFrame
The generated flux samples. Each row corresponds to a sample of the
fluxes and the columns are the reactions.
Notes
-----
The samplers have a correction method to ensure equality feasibility for
long-running chains, however this will only work for homogeneous models,
meaning models with no non-zero fixed variables or constraints (
right-hand side of the equalities are zero).
References
----------
.. [1] Megchelenbrink W, Huynen M, Marchiori E (2014)
optGpSampler: An Improved Tool for Uniformly Sampling the Solution-Space
of Genome-Scale Metabolic Networks.
PLoS ONE 9(2): e86587.
https://doi.org/10.1371/journal.pone.0086587
.. [2] Direction Choice for Accelerated Convergence in Hit-and-Run Sampling
David E. Kaufman, Robert L. Smith
Operations Research 199846:1 , 84-95
https://doi.org/10.1287/opre.46.1.84
"""
if method == "optgp":
sampler = OptGPSampler(model, processes=processes, thinning=thinning, seed=seed)
elif method == "achr":
sampler = ACHRSampler(model, thinning=thinning, seed=seed)
else:
raise ValueError(
f'Invalid value: "{method}" for method used. '
'The value must be "optgp" or "achr".'
)
return pd.DataFrame(
columns=[rxn.id for rxn in model.reactions], data=sampler.sample(n)
)
```