created detailed description of all data paths (which steps creates which data...

created detailed description of all data paths (which steps creates which data and where is it stored)

src/run_modules/README.md

+# On data handling
+
+This readme declares which function loads which data and where it is stored.
+
+## experiment setup
+
+*Data_path* is the destination where all downloaded data is locally stored. Data is downloaded from TOARDB either using 
+the JOIN interface or a direct connection to the underlying PostgreSQL DB. If data was already downloaded, no new 
+download will be started. Missing data will be downloaded on the fly and saved in data_path. 
+
+`data_path = src.helpers.prepare_host()`
+ 
+ Current implementation leads to following paths:
+ 
+ | hostname | path | comment |
+ | --- | --- | --- |
+ | ZAM144 | `/home/{user}/Data/toar_daily/` | notebook Felix |
+ | zam347 | `/home/{user}/Data/toar_daily/` | ESDE server |
+ | linux-gzsx | `/home/{user}/machinelearningtools/data/toar_daily/` | notebook Lukas |
+ | jureca | `/p/project/cjjsc42/{user}/DATA/toar_daily/` | JURECA |
+ | juwels | `/p/home/jusers/{user}/juwels/intelliaq/DATA/toar_daily/` | JUWELS |
+ | runner-6HmDp9Qd-project-2411-concurrent | `/home/{user}/machinelearningtools/data/toar_daily/` | gitlab-runner |
+
+*experiment_path* is the root folder in that all results from the experiment are saved. For each experiment there should
+be distinct folder. Experiment path is can be set in ExperimentSetup. `experiment_date` can be set by parser_args and 
+`experiment_path` (this argument is not the same as the internal stored experiment_path!) as args. The *experiment_path*
+is the combination of both given arguments `os.path.join(experiment_path, f"{experiment_date}_network")`. Inside this
+folder, several subfolders are created in the course of the program.
+
+```bash
+data_path
+    <station1>_<var1>_<var2>_..._<varx>.nc
+    <station1>_<var1>_<var2>_..._<varx>_meta.csv
+    <station2>_<var1>_<var2>_..._<varx>.nc
+    <station2>_<var1>_<var2>_..._<varx>_meta.csv
+------
+experiment_path
+|   history.json
+|   history_lr.json
+|   <experiment_name>_model.pdf
+|   <experiment_name>_model-best.h5
+|   <experiment_name>_my_model.h5
+├─── forecasts 
+|       forecasts_<station1>_test.nc
+|       forecasts_<station2>_test.nc
+|       ...
+└─── plots
+        conditional_quantiles_cali-ref_plot.pdf
+        conditional_quantiles_like-bas_plot.pdf
+        test_monthly_box.pdf
+        test_map_plot.pdf
+        <experiment_name>_history_learning_rate.pdf
+        <experiment_name>_history_loss.pdf
+        <experiment_name>_history_main_loss.pdf
+        <experiment_name>_history_main_mse.pdf
+        ...
+
+```
+
+*plot_path* includes all created plots. If not given, this is create into the experiment_path by default (as shown in 
+the folder structure above). Can be customised by `ExperimentSetup(plot_path=<path>)`.
+
+*forecast_path* is the place, where all forecasts are stored as netcdf file. Each file consists exactly one single
+station. If not given, this is create into the experiment_path by default (as shown in the folder structure above). Can 
+be customised by `ExperimentSetup(forecast_path=<path>)`.
+
+## pre-processing
+
+Each requested station is check whether it is already included in *data_path*. The files all following the naming 
+convention `<station_name>_<sorted_list_of_all_variables_split_by_underscore>.nc`. E.g. the station *DEBW013* with the
+variables cloudcover, NO, NO2, O3 and temp (all TOARDB short names) is saved as `DEBW013_cloudcover_no_no2_o3_temp.nc`, 
+whereas the same station with only O3 and temperature becomes `DEBW013_o3_temp.nc`. Although all data of the latter file
+is potentially also included in the former file, the program will always download the data specification for new and 
+save this data into a new file. Only if the exactly fitting file is available locally, no data is downloaded. **NOTE**:
+There is no check on data time range, only the name is compared. Set `overwrite_local_data=True` 
+in `experiment_setup.py` to overwrite local data by downloading new data.
+
+## model setup
+
+*checkpoint* is created inside *experiment_path* as `<experiment_name>_model-best.h5`.
+
+The architecture of the model is plotted into *experiment_path* as `<experiment_name>_model.pdf` 
+
+## training
+
+Training metrics are saved in `history.json` and `history_lr.json`.
+
+Best model is saved in `<experiment_name>_my_model.h5`.
+
+## post-processing
+
+During the *make_forecast* method, all calculated forecasts of the neural network, persistence, ordinary least squared
+and the target values with the regarding lead time are saved locally inside *forecast_path* as 
+`forecasts_<station>_test.nc`.
+
+All plots are created inside *plot_path*.
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