Edit TOML on the fly

[C3PO]

I would like to edit a local TOML file and save it again to be used in the same Python script. In this sense, to be able to change a given parameter in loop. You can see an example of file, here.

https://bitbucket.org/robmoss/particle-f...ation.toml

So far, I could load the file but I don't find how to change a parameter value.

import toml
data = toml.load("scenario.toml")

buran write Jan-27-2021, 02:07 PM:
Please, use proper tags when post code, traceback, output, etc. This time I have added tags for you.
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[buran]

What do you want to change? e.g. with the file from your link, after load, you get nested dict structure like this

Output:
{'components': {'model': 'pypfilt.examples.predation.LotkaVolterra',
                'summary': 'pypfilt.summary.HDF5',
                'time': 'pypfilt.Scalar'},
 'model': {'bounds': {'alpha': {'max': 2, 'min': 0},
                      'beta': {'max': 2, 'min': 0},
                      'delta': {'max': 2, 'min': 0},
                      'gamma': {'max': 2, 'min': 0},
                      'x': {'max': 2, 'min': 0},
                      'y': {'max': 2, 'min': 0}},
           'priors': {'alpha': {'args': {'high': 0.8, 'low': 0.6},
                                'function': 'uniform'},
                      'beta': {'args': {'high': 1.4, 'low': 1.2},
                               'function': 'uniform'},
                      'delta': {'args': {'high': 1.1, 'low': 0.9},
                                'function': 'uniform'},
                      'gamma': {'args': {'high': 1.1, 'low': 0.9},
                                'function': 'uniform'},
                      'x': {'args': {'high': 1.5, 'low': 0.5},
                            'function': 'uniform'},
                      'y': {'args': {'high': 0.4, 'low': 0.2},
                            'function': 'uniform'}}},
 'parameters': {'data_dir': '.',
                'fresh_cache': True,
                'max_days': 14,
                'out_dir': '.',
                'particles': 1000,
                'prng_seed': 42,
                'remove_cache': True,
                'resample': {'regularisation': True, 'threshold': 0.25},
                'steps_per_unit': 1,
                'summary': {'from_first_day': True,
                            'meta': {'packages': ['matplotlib']},
                            'tables': {'model_cints': {'credible_intervals': [0,
                                                                              50,
                                                                              95]}}},
                'time': {'start': 0.0, 'until': 15.0}},
 'scenario': {'example': {'name': 'Example Scenario',
                          'observations': {'x': {'file': 'predation-counts-x.ssv',
                                                 'format': {'sdev': '0.1f'},
                                                 'init': {'obs_period': 0},
                                                 'model': 'pypfilt.examples.predation.ObsModel',
                                                 'name': {'sdev': 'sdev'},
                                                 'parameters': {'sdev': 0.2}},
                                           'y': {'file': 'predation-counts-y.ssv',
                                                 'format': {'sdev': '0.1f'},
                                                 'init': {'obs_period': 0},
                                                 'model': 'pypfilt.examples.predation.ObsModel',
                                                 'name': {'sdev': 'sdev'},
                                                 'parameters': {'sdev': 0.2}}}}},
 'summary': {'monitors': {'expected_obs': {'model': 'pypfilt.summary.ExpectedObsMonitor'}},
             'tables': {'forecasts': {'init': {'exp_obs_monitor': 'expected_obs'},
                                      'model': 'pypfilt.summary.PredictiveCIs'},
                        'model_cints': {'model': 'pypfilt.summary.ModelCIs'},
                        'obs': {'model': 'pypfilt.summary.Obs'}}}}

[C3PO]

I would like to change all possible parameters since it will enrich the study. However, the main ones are particles, prng_seed and time (start and until).

[buran]

I would like to change all possible parameters since it will enrich the study.

again, it's a nested structure - dicts of dicts (or lists). Do you know how to work with dicts?


e.g.

import toml
data = toml.load('scenario.toml')

print(data['parameters']['particles'])
data['parameters']['particles'] = 2000
print(data['parameters']['particles'])

Output:
1000
2000