Improve stdataset

[SarahAlidoost]

closes #75

In this PR:

• data preparation is moved from STDataset class to util data preparation function
• util data preparation function has an option to save preprocessed data to zarr --> but currently this is not used in our workflow
• STDataset class has an option to keep data lazy in init and instead load it in getitem: save memory but slower --> data of training should not be lazy
• loop over year is added to train and predict
• data classes are created grouping input arguments
• Scripts to run data preparation on HPC in scripts folder using dask distributed

I'm still testing the example_hourly notebook on HPC. but this PR can be reviewed.

TODO:

• after merging this, we can finalize 25 with 3 years of data
• after merging this PR, we can finalize Fix remaining issues with tuning #79

[meiertgrootes]

This is a duplication of the class methos also used in predict.py. Given the very nice work you've done in cleaning and structuring one could consider moving it to utils and reusing in both modules. However, this is a most a suggestion - feel free to ignore

[meiertgrootes]

Very nice and clean organization! One very minor comment and a response to @rogerkuou 's . Works locally. if/when levante test is complete feel free to merge!