downscaleml

First information first! - 'Installing the Package' The dist folder contains the ''downscaleml" package, install it using pip install downscaleml-0.1.0.tar.gz. Make sure you already have GDAL dependencies installed in your conda/venv/any_environments. If suppose you face problems with the GDAL installation in your system as well as in your environment, don't worry, I am here for you.

This package requires GDAL==3.4.3.

Follow this link to keep your GDAL installation clean and working: https://mothergeo-py.readthedocs.io/en/latest/development/how-to/gdal-ubuntu-pkg.html

You could also probably face a problem similar to what i faced, after installation of GDAL, it's the libstdc++.so.6 linkage problem. This is to do with either path linking or file missing in the directory. This file has to be found in your system and to be linked with the virtual environment you are working. This can be done by just following stackoverflow. I can drop a little clue, which can help you in linking, kindly change the paths relative to your system. ln -sf /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /home/anavani/anaconda3/envs/dmcgb/bin/../lib/libstdc++.so.6

You could also follow compatibility issue beterrn GDAL and Numpy or GDAL array now, you could arrest this issue by following the process:-

pip uninstall gdal pip install numpy pip install GDAL==$(gdal-config --version) --global-option=build_ext --global-option="-I/usr/include/gdal"

Recommended steps to get going!

1. Clone the git project in your local.
2. pip install poetry in your virtual environment
3. poetry install in the project local with the same local environment

How to effectively use the package?

The data is not provided in this package, the paths for the input-output data is provided in the downscaleml/main/config.py. You can make necessary changes here to reflect elsewhere in the project.

Modify and use the script /scripts/run_model.sh to have control with the important parameters and the model output.

To extract the best hyperparameter for any given downscaling model, run the downScaleML/downscaleml /main/grid_search_downScale.py, by tweaking the combination parameter in the downscaleml/main/config.py, one can control the running time to fetch the best hyperparameter. The combination parameter controls the randomised reduced grid size for optimised run time of hyperparameter search.

This script downscaleml/main/preprocessing/combined_preprocess_CERRA.py preprocesses CERRA (Copernicus European Regional Reanalysis) data by aggregating it to daily data. It supports reprojection and resampling to a target grid, with specific adjustments for temperature and precipitation data.

Example for running this scirpt: python combined_preprocess_CERRA.py --source /path/to/source --target /path/to/target --reproject --variable 2m_temperature

Similarly the script downscaleml/main/preprocessing/combined_preprocess_ERA5.py preprocesses ERA-5 (European Reanalysis) data by aggregating it to daily data. It supports reprojection and resampling to a target grid, with specific adjustments for temperature and precipitation data.

Example for running this scirpt: python preprocess_era5.py --source /path/to/source --target /path/to/target --reproject --variable 2m_temperature