Dev #4
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Dev #4
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…thout any random sampling and reproduce results
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This pull request introduces randomization options for data loading, implementing new methods for saving data and optimized integration, and extending utility functions for better modularity and usability.
Enhancements to
community.py:randomizeparameter to theload_datamethod, allowing random sampling for consumer preference and metabolic matrices. This change improves flexibility in data initialization. [1] [2]save_datamethod: Introduced a method to save the community's data (parameters, metabolic matrices, consumer preferences, and leakage coefficients) to specified paths, improving reproducibility and data management.optimized_integratemethod to perform numerical integration in batches, with species presence thresholds and early stopping to enhance computational efficiency.Enhancements to
utilities.py:extract_d_matrices: Added ause_dirichletparameter to allow flexibility in generating metabolic matrices, enabling or disabling Dirichlet sampling.next_experiment_pathfunction: Added a utility function to determine the next experiment folder path in a directory, aiding in experiment organization. The idea is to have 4-digit folder names, starting with 0001. This is used for batch_results directories containing simulation results with randomized matrices.These updates collectively improve the modularity, flexibility, and efficiency of the codebase, particularly for simulations and data management workflows.