parpe.misc¶
Various helper functions
Functions
Compare parameter estimates to true parameters. |
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Concatenate two optimization trajectory matrices (numIteration x numStarts). |
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Generate correlation table by observable |
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Generate correlation table by observable |
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Read cost trajectory from HDF5 result file. |
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Read cost trajectory from file |
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Read cost trajectory from file |
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Read final cost from file |
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Load AMICI model module with given name from given path and return a model instance |
Find a local SBML parameter in kinetic laws and return {reaction_id}_{local_parameter_id} |
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Read result from simulations at final point from data file for all starts |
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Read result from simulations at final point from data file for all starts Returns: (measure, simulated)[startString][nCondition, nTimepoints, nObservables] |
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Put simulation results in new PEtab simulation df based on PEtab measurement df mes_df: petab measurement dataframe on which optimization was based sim: simulation results obtained by readSimulationsFromFile() result_file: HDF5-file with optimization results (or input HDF5-file) start: start for which simulation results should be taken observable_ids: observable ids which should be considered root_path: root path to where group fixedParameters is saved in HDF5file |
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Make unique, preserving order of first occurrence |
Classes
Interface to a parPE parameter estimation result file |
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Interface to a parPE parameter estimation summary file as created with misc/extractMultiStartParameters.py |
Functions
- parpe.misc.compare_optimization_results_to_true_parameters(filename, start_idx='0')[source]¶
Compare parameter estimates to true parameters. Print as table.
Used in example notebooks.
- Parameters:
filename (
str
) – Parameter estimation result file namestart_idx (
str
) – optimizer run index/name to use
- parpe.misc.concatenateStarts(a, b)[source]¶
Concatenate two optimization trajectory matrices (numIteration x numStarts). Dimensions can be different for a and b.
- parpe.misc.getCorrTable(mes, sim, minimum_number_datapoints=0)[source]¶
Generate correlation table by observable
- Parameters:
simulationResults – simulationResults as obtained from
parpe.readSimulationsFromFile –
- Returns:
ndarray with correlations of measurement and simulation for each start and observables in simulationResults
- parpe.misc.getCorrTableLegacy(simulationResults, minimum_number_datapoints=0)[source]¶
Generate correlation table by observable
Old data format
- Parameters:
simulationResults – simulationResults as obtained from
parpe.readSimulationsFromFile –
- Returns:
ndarray with correlations of measurement and simulation for each start and observables in simulationResults
- parpe.misc.getCostTrajectories(filename, starts=None)[source]¶
Read cost trajectory from HDF5 result file.
Arguments: filename: result file name starts: list with indices or None meaning all starts
- parpe.misc.getCostTrajectory2(filename)[source]¶
Read cost trajectory from file
#Arguments: #filename: HDF5 file as generated by misc/extractMultiStartParameters.py
Returns: ndarray(numIterations x numStarts): cost over iterations for all optimizations
- parpe.misc.getCostTrajectoryFromSummary(filename)[source]¶
Read cost trajectory from file
Arguments: filename: HDF5 file as generated by misc/extractMultiStartParameters.py
Returns: ndarray(numIterations x numStarts): cost over iterations for all optimizations
- parpe.misc.getFinalCostFromSummary(filename)[source]¶
Read final cost from file
Arguments: filename: HDF5 file as generated by misc/extractMultiStartParameters.py
Returns: ndarray(numStarts): cost at last iteration for all optimizations
- parpe.misc.get_amici_model(model_name, model_dir=None)[source]¶
Load AMICI model module with given name from given path and return a model instance
- Parameters:
model_name (
str
) – Name of the model modulemodel_dir (
Optional
[str
]) – Path to directory containing the AMICI model module
- Return type:
amici.Model
- Returns:
The given model instance
- parpe.misc.get_global_name_for_local_parameter(sbml_model, needle_parameter_id)[source]¶
Find a local SBML parameter in kinetic laws and return {reaction_id}_{local_parameter_id}
- Return type:
Optional
[str
]
- parpe.misc.readSimulationsFromFile(filename)[source]¶
Read result from simulations at final point from data file for all starts
- Returns:
- (measured, simulated, time, llh)[startString]
[nCondition][nTimepoints, nObservables]
- parpe.misc.readSimulationsFromFileLegacy(filename)[source]¶
Read result from simulations at final point from data file for all starts Returns: (measure, simulated)[startString][nCondition, nTimepoints, nObservables]
- parpe.misc.simulation_to_df(mes_df, sim, result_file, start, observable_ids, root_path='/')[source]¶
Put simulation results in new PEtab simulation df based on PEtab measurement df mes_df: petab measurement dataframe on which optimization was based sim: simulation results obtained by readSimulationsFromFile() result_file: HDF5-file with optimization results (or input HDF5-file) start: start for which simulation results should be taken observable_ids: observable ids which should be considered root_path: root path to where group fixedParameters is saved in HDF5file