CROC

drama.croc.get_basic_config(framework='DRAMA', project=None)

Returns the base config read from the provided project or the default if no project is specified.

Parameters:

framework (str) – defines whether DRAMA or DMF is using the wrapper
project (str) – Path to DRAMA project to use as baseline (directory or exported project). If None the default project is used.

Returns:

The config dict as specified in parameter config in run().

drama.croc.run_lazy(config=None, dependent_variables=[], project=None, save_output_dirs=None, parallel=True, ncpus=None, timeout=None, logging_queue=None, log_level='DEBUG', log_group_id=None, framework='DRAMA', keep_output_files='summary', spell_check=True, **kwargs)

Run (parametric) CROC analysis and returns a dictionary with config and iterable results.

Parameters:

config (dict or list) –

The (parametric) CROC run configuration. If a dictionary is provided it must be of the following format (lists are used for parametric analyses):

{
    'runId': str or list,
    'comment1': str or list,
    'comment2': str or list,
    'functionalitySwitch': int [1,2,3],
    'aspectAngleTheta': float or list [deg],
    'aspectAnglePhi': float or list [deg],
    'rotationAxisTheta': float or list [deg],
    'rotationAxisPhi': float or list [deg],
}

Alternatively, config can also be a list of config dictionaries, or any iterable. Each config dictionary in this case describes one run and thus can not contain any list to expand.

dependent_variables (list of lists of strs) – Describe which parameters depend on each other in a parametric analysis. Each list of parameters defines one dependency. See the example below for details.
project (str) – Path to DRAMA project to use as baseline (directory or exported project). If None the default project is used.
save_output_dirs – Save the output directories of all runs to this directory. Each run will have its own numbered directory. The path to it is stored in output_dir in each run’s config. If None the output directories will be deleted.
parallel (bool) – Define if simulations shall be run in parallel.
ncpus (int) – Number of CPUs to use if parallel is True. If not specified, the maximum available will be used.
keep_output_files (str or list) – If list, it contains the filenames of the output files that will be saved into the output_dir (shared list for reentry and risk analysis). If ‘all’ the whole output folder will be saved into the output_dir, if ‘summary’ (Default), only a default list of output files will be saved into the output_dir (valid only if output_dir is provided as input parameter).
timeout (int) – Timeout in seconds for each run.
logging_queue (manager.Queue) – thread safe queue, which aggregates log messages for the caller.
log_level (str) – the log level for the logger, can be DEBUG, INFO, WARNING, ERROR and CRITICAL.
log_group_id (str) – additional argument that is added to a log record.
framework (str) – defines whether DRAMA or DMF is using the wrapper.
spell_check (bool) – Specify if the case to run should contain only keywords that are present in the configuration file.
**kwargs – If config is None, the CROC run configuration values can be passed on as kwargs.

Returns:

{
    'config': provided config of all runs,
    'results': <generator object generic_run_lazy>,
}

Examples

Direct call with one run:

from drama import croc
results_lazy = croc.run_lazy(functionalitySwitch=1, aspectAngleTheta=0, aspectAngle_phi=0, rotationAxisTheta=20, rotationAxisPhi=60)

Multiple parametric runs passing a configuration dictionary (four runs with all possible combinations):

from drama import croc
config = {
    'functionalitySwitch': [1 ,1]
    'aspectAngleTheta': [20, 40],
    'aspectAnglePhi'  : [30, 60],
    'rotationAxisTheta' : [0, 0],
    'rotationAxisPhi': [10,20],
}
results_lazy = croc.run_lazy(config)

#store the cross section of each single run:
cross_sections=[]
for res in results_lazy['results']:
    for single_run_res in res:
        cross_sections.append(single_run_res['cross_section'])

Two runs with dependent variables (aspectAngleTheta = 20, aspectAnglePhi = 5 and aspectAngleTheta = 40, aspectAnglePhi = 10):

from drama import croc
config = {
    'aspectAngleTheta': [20, 40],
    'aspectAnglePhi': [5, 10],
}
dependent_variables = [['aspectAngleTheta', 'aspectAnglePhi']]
results_lazy = croc.run_lazy(config, dependent_variables)

#print the status of each single run:
for res in results_lazy['results']:
    for single_run_res in res:
        print(single_run_res['status'])

drama.croc.run(config=None, dependent_variables=[], project=None, json=None, save_output_dirs=None, parallel=True, ncpus=None, timeout=None, logging_queue=None, log_level='DEBUG', log_group_id=None, framework='DRAMA', keep_output_files='summary', tmpfile_path=None, spell_check=True, **kwargs)

Run (parametric) CROC analysis and return the results.

Parameters:

config (dict or list) –

The (parametric) CROC run configuration. If a dictionary is provided it must be of the following format (lists are used for parametric analyses):

{
    'runId': str or list,
    'comment1': str or list,
    'comment2': str or list,
    'functionalitySwitch': integer [1,2,3],
    'aspectAngleTheta': float or list,
    'aspectAnglePhi': float or list,
    'rotationAxisTheta': float or list,
    'rotationAxisPhi': float or list,
}

Alternatively, config can also be a list of config dictionaries, or any iterable. Each config dictionary in this case describes one run and thus can not contain any list to expand.

dependent_variables (list of lists of strs) – Describe which parameters depend on each other in a parametric analysis. Each list of parameters defines one dependency. See the example below for details.
json (dict) – Python object holding the resemblance of the JSON file containing all required fields needed for a run. It should conform with the ares_input.schema.json.
project (str) – Path to DRAMA project to use as baseline (directory or exported project). If None the default project is used.
save_output_dirs – Save the output directories of all runs to this directory. Each run will have its own numbered directory. The path to it is stored in output_dir in each run’s config. If None the output directories will be deleted.
parallel (bool) – Define if simulations shall be run in parallel.
ncpus (int) – Number of CPUs to use if parallel is True. If not specified, the maximum available will be used.
keep_output_files (str or list) – If list, it contains the filenames of the output files that will be saved into the output_dir (shared list for reentry and risk analysis). If ‘all’ the whole output folder will be saved into the output_dir, if ‘summary’ (Default), only a default list of output files will be saved into the output_dir (valid only if output_dir is provided as input parameter).
timeout (int) – Timeout in seconds for each run.
logging_queue (manager.Queue) – thread safe queue, which aggregates log messages for the caller.
log_level (str) – the log level for the logger, can be DEBUG, INFO, WARNING, ERROR and CRITICAL.
log_group_id (str) – additional argument that is added to a log record.
framework (str) – defines whether DRAMA or DMF is using the wrapper.
tmpfile_path (ValueProxy) – hands back the tmp file path used by the analysis module so that it can be checked continuously in a separate thread.
spell_check (bool) – Specify if the case to run should contain only keywords that are present in the configuration file.
**kwargs – If config is None, the CROC run configuration values can be passed on as kwargs.

Returns:

{
    'config': {}, # provided config of all runs,
    'errors': [ # info on erroneous runs
        {
            'config': config of single erroneous run,
            'status': 'error <details>',
            'output': stdout and stderr of croc process,
            'logfile': croc logfile content,
        },
        ...
    ],
    'results': [
        {
            'config': config of single successful run,
            'status': 'success',
            'output': stdout and stderr of croc process,
            'logfile': croc logfile content,
            'cross_section': calculated cross section of the geometry,
        },
        ...
    ],
}

Examples

Direct call with one run:

from drama import croc
results = croc.run(functionalitySwitch=1, aspectAngleTheta=0, aspectAnglePhi=0, rotationAxisTheta=20, rotationAxisPhi=60)

Multiple parametric runs passing a configuration dictionary (four runs with all possible combinations):

from drama import croc
config = {
    'functionalitySwitch': [1 ,1]
    'aspectAngleTheta': [20, 40],
    'aspectAnglePhi'  : [30, 60],
    'rotationAxisTheta' : [0, 0],
    'rotationAxisPhi': [10,20],
}
results = croc.run(config)

Two runs with dependent variables (aspectAngleTheta = 20, aspectAnglePhi = 5 and aspectAngleTheta = 40, aspectAnglePhi = 10):

from drama import croc
config = {
    'aspectAngleTheta': [20, 40],
    'aspectAnglePhi': [5, 10],
}
dependent_variables = [['aspectAngleTheta', 'aspectAnglePhi']]
results = croc.run(config, dependent_variables)

drama.croc.get_progress(tmpfile_path): Retrieves the current progress of CROC, when the tmpfile_path already exists

drama.plot_croc.plot_cross_section_variation(results, variables, target='cross_section', plotfile_name='croc', x_label=None, y_label=None, framework='DRAMA')

Generates the colored splot with ‘target’ variable from the provided ‘results’ from CROC.

Parameters:

results (list) – List of dict of CROC results. Each dictionary must contain the ‘target’ key.
target (str) – The target variable
plotfile_name (str) – file name (without extension) for the output file.
x_label (str) – label for the x axis
y_label (str) – label for the y axis
framework (str) – defines whether DRAMA or DMF is using the wrapper.

Note

Creates {plotfile_name}.png file the heatmap: plot for the ‘target’ variable.