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import json |
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import os |
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import tempfile |
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from pathlib import Path |
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from typing import Dict, List |
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import basico |
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import pandas as pd |
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import typer |
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from vcell_opt.data import OptProblem, Vcellopt, VcelloptStatus, OptResultSet |
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from vcell_opt.optUtils import get_reference_data, get_fit_parameters, get_copasi_opt_method_settings, \ |
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result_set_from_fit, get_progress_report |
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def run_command(opt_file: Path = typer.Argument(..., file_okay=True, dir_okay=False, exists=True, |
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help="optimization input json file"), |
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result_file: Path = typer.Argument(..., file_okay=True, dir_okay=False, |
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help="optimization result output json file"), |
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report_file: Path = typer.Argument(..., file_okay=True, dir_okay=False, |
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help="report file with intermediate results")) -> None: |
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if opt_file is None or result_file is None: |
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print("use --help for help") |
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return typer.Exit(-1) |
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os.chdir(tempfile.gettempdir()) |
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with open(opt_file, "rb") as f_optfile: |
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opt_file_json = json.load(f_optfile) |
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opt_problem: OptProblem = OptProblem.from_json_data(opt_file_json) |
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basico.load_model_from_string(opt_problem.math_model_sbml_contents) |
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exp_data = get_reference_data(opt_problem) |
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basico.add_experiment('exp1', data=exp_data) |
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task_settings = basico.get_task_settings('Parameter Estimation') |
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task_settings['method'] = get_copasi_opt_method_settings(opt_problem) |
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basico.set_task_settings('Parameter Estimation', task_settings) |
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basico.add_report('parest report', task=basico.T.PARAMETER_ESTIMATION, |
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body=[ |
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'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Function Evaluations', |
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'\\\t', |
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'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Best Value', |
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'\\\t', |
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'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Best Parameters' |
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], |
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) |
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with open(report_file, 'w') as f_report_file: |
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param_names: List[str] = [param_desc.name for param_desc in opt_problem.parameter_description_list] |
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f_report_file.write(json.dumps(param_names)+"\n") |
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basico.assign_report("parest report", task=basico.T.PARAMETER_ESTIMATION, filename=str(report_file), append=True) |
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fit_items = get_fit_parameters(opt_problem) |
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basico.set_fit_parameters(fit_items) |
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results: pd.DataFrame = basico.run_parameter_estimation(update_model=True) |
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assert results is not None |
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fit_solution: pd.DataFrame = basico.task_parameterestimation.get_parameters_solution() |
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opt_parameter_values: Dict[str, float] = result_set_from_fit(fit_solution) |
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fit_statistics: Dict[str, float] = basico.task_parameterestimation.get_fit_statistic(include_parameters=True, |
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include_experiments=True, |
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include_fitted=True) |
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objective_function = fit_statistics['obj'] |
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num_function_evaluations = fit_statistics['f_evals'] |
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opt_progress_report = get_progress_report(report_file) |
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result_set = OptResultSet(num_function_evaluations=int(num_function_evaluations), |
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objective_function=objective_function, |
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opt_parameter_values=opt_parameter_values, |
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opt_progress_report=opt_progress_report) |
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status_message = str(basico.task_parameterestimation.get_fit_statistic( |
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include_parameters=True, include_experiments=True, include_fitted=True)) |
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opt_run: Vcellopt = Vcellopt(opt_problem=opt_problem, opt_result_set=result_set, status=VcelloptStatus.COMPLETE, |
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status_message=status_message) |
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with open(result_file, "w") as f_result_file: |
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json_data = opt_run.to_json_data() |
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json.dump(json_data, f_result_file) |
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if __name__ == "__main__": |
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typer.run(run_command) |
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