Implement a new configuration system within the application that can handle multiple formats

[soaressgabriel]

Description

• Problem: The current Python console application relies on a *.ini file for configurations. This approach is limited and does not support the flexibility required in modern applications;

• Users have expressed a need for more versatile configuration options, such as using JSON files or Python dictionaries;

• This enhancement is critical for accommodating various deployment environments and user preferences.

• Why validate Input Raster Series?

  • Data Accuracy: Ensuring input raster data like ETp, rainfall, KP, etc., are accurate is crucial for reliable model results;
  • Spatial and Temporal Consistency: Models require consistent spatial and temporal resolution in input data to accurately represent hydrological processes;
  • Outlier and Anomaly Detection: Validating input data helps identify outliers or anomalies, crucial for maintaining model integrity;
  • Model Calibration and Validation: Prior to using a model for simulations, calibration and validation are essential, with data validation being a preliminary step;
  • Interpretability and Reliability of Results: Trust in model results depends on the transparency and accuracy of input data; validation enhances both;
  • Integration with Other Data and Models: Ensures smooth and accurate integration of the hydrological model with other models or external databases.

• Why validate Model Parameters?

  • Model Realism and Credibility: Parameters within realistic ranges ensure the model accurately represents real-world hydrological processes;
  • Calibration and Model Sensitivity: Validating parameters within acceptable ranges is crucial for model calibration and understanding its sensitivity to different parameters;
  • Prevention of Numerical Errors: Parameters within valid ranges prevent numerical instability or convergence errors in the model;
  • Comparability with Other Studies: Ensuring parameters are within accepted ranges enables comparison of results with other studies or models;
  • Conformity to Local Conditions: Parameters should reflect specific local hydrological conditions for accurate predictions and analyses;
  • Reliability in Model-Based Decisions: Accurate parameters are key for the reliability of decisions made based on the model's results.

Solution

• Implement a new configuration system within the application that can handle multiple formats: *.ini files, JSON files, and Python dictionaries. This system should include:

  1. Format Detection: Automatically detect the configuration format based on the file extension or input type;
  2. Unified Interface: A single interface/method to load configurations regardless of the format;
  3. Data Validation: Ensure robust validation of configuration data for each format to maintain the integrity and security of the application;
  4. Backward Compatibility: Ensure that the existing *.ini file configurations are still supported without any disruption to current users.

Alternative solution(s)

• Plugin System: Develop a plugin system where different configuration parsers (for *.ini, JSON, dictionaries) can be plugged in as needed. This could provide more flexibility but might increase complexity.

Additional context

• N/A

Acknowledgements

• @LINAMARIAOSORIO