7.1.1.2.1.5. pta.sampling.uniform

Uniform sampling of the flux space of a metabolic network.

7.1.1.2.1.5.1. Module Contents

class pta.sampling.uniform.UniformSamplingModel(polytope: PolyRound.api.Polytope, reaction_ids: List[str])[source]

Bases: pta.sampling.primitives.FluxSpaceSamplingModel

Object holding the information necessary to run uniform sampling on a flux space.

Parameters:

  • polytope (Polytope) – Polytope object describing the flux space.

  • reaction_ids (List[str]) – Identifiers of the reactions in the flux space.

classmethod from_cobrapy_model(model: cobra.Model, infinity_flux_bound: float = default_flux_bound) → UniformSamplingModel[source]

Builds a uniform sampler model from a cobrapy model.

Parameters:

  • model (cobra.Model) – The input cobra model.

  • infinity_flux_bound (float, optional) – Default bound to use for unbounded fluxes.

Returns:

The constructed model.

Return type:

UniformSamplingModel

simulate(settings: _pta_python_binaries.SamplerSettings, initial_points: numpy.ndarray, directions_transform: numpy.ndarray) → numpy.ndarray[source]

Run the sampler with the given parameters.

Parameters:

  • settings (pb.SamplerSettings) – Sampling settings.

  • initial_points (np.ndarray) – The initial points for the chains.

  • directions_transform (np.ndarray) – The transform for the directions sampler.

compute_psrf(result: numpy.ndarray) → pandas.Series[source]

Compute the potential scale reduction factors for the variables of interest on a given set of chains.

Parameters:

result (np.ndarray) – The result of the sampling function.

Returns:

The computed potential scale reduction factors.

Return type:

pd.Series

get_chains(result: numpy.ndarray) → numpy.ndarray[source]

Extract the simulated chains from a given result.

Parameters:

result (np.ndarray) – The result of the native sampling function.

Returns:

The simulated chains.

Return type:

np.ndarray

pta.sampling.uniform.sample_flux_space_uniform(model: Union[cobra.Model, UniformSamplingModel], num_samples: int = default_num_samples, max_steps: int = -1, max_psrf: float = default_max_psrf, num_chains: int = -1, initial_points: numpy.array = None, num_initial_steps: int = -1, max_threads: int = default_max_threads, convergence_manager: pta.sampling.convergence_manager.ConvergenceManager = None) → pta.sampling.commons.SamplingResult[source]

Sample steady state fluxes in the given model. The sampler run until either max_steps or max_psrf is reached.

Parameters:

  • model (Union[cobra.Model, UniformSamplingModel]) – The model to sample.

  • num_samples (int, optional) – Number of samples to draw.

  • max_steps (int, optional) – The maximum number fo steps to simulate.

  • max_psrf (float, optional) – Maximum value of the PSRFs for convergence.

  • num_chains (int, optional) – The number of chains to simulate.

  • initial_points (np.array, optional) – The initial points for the chains.

  • num_initial_steps (int, optional) – Initial chains length.

  • max_threads (int, optional) – The maximum number of parallel threads to use.

  • convergence_manager (ConvergenceManager, optional) – The object to use to monitor and improve convergence.

Returns:

The sampling result.

Return type:

SamplingResult

Raises:

SamplingException – If sampling fails.