added information on transformation behaviour to README.md

README.md

@@ -21,3 +21,59 @@ add it to `src/join_settings.py` in the hourly data section. Replace the `TOAR_S
 value. To make sure, that this **sensitive** data is not uploaded to the remote server, use the following command to
 prevent git from tracking this file: `git update-index --assume-unchanged src/join_settings.py
 `
+
+# Customise your experiment
+
+This section summarises which parameters can be customised for a training.
+
+## Transformation
+
+There are two different approaches (called scopes) to transform the data:
+1) `station`: transform data for each station independently (somehow like batch normalisation)
+1) `data`: transform all data of each station with shared metrics
+
+Transformation must be set by the `transformation` attribute. If `transformation = None` is given to `ExperimentSetup`, 
+data is not transformed at all. For all other setups, use the following dictionary structure to specify the 
+transformation.
+```
+transformation = {"scope": <...>, 
+                  "method": <...>,
+                  "mean": <...>,
+                  "std": <...>}
+ExperimentSetup(..., transformation=transformation, ...)
+```
+
+### scopes
+
+**station**: mean and std are not used
+
+**data**: either provide already calculated values for mean and std (if required by transformation method), or choose 
+from different calculation schemes, explained in the mean and std section.
+
+### supported transformation methods
+Currently supported methods are:
+* standardise (default, if method is not given)
+* centre
+
+### mean and std
+`"mean"="accurate"`: calculate the accurate values of mean and std (depending on method) by using all data. Although, 
+this method is accurate, it may take some time for the calculation. Furthermore, this could potentially lead to memory 
+issue (not explored yet, but could appear for a very big amount of data)
+
+`"mean"="estimate"`: estimate mean and std (depending on method). For each station, mean and std are calculated and
+afterwards aggregated using the mean value over all station-wise metrics. This method is less accurate, especially 
+regarding the std calculation but therefore much faster.
+
+We recommend to use the later method *estimate* because of following reasons:
+* much faster calculation
+* real accuracy of mean and std is less important, because it is "just" a transformation / scaling
+* accuracy of mean is almost as high as in the *accurate* case, because of 
+$\bar{x_{ij}} = \bar{\left(\bar{x_i}\right)_j}$. The only difference is, that in the *estimate* case, each mean is 
+equally weighted for each station independently of the actual data count of the station.
+* accuracy of std is lower for *estimate* because of $\var{x_{ij}} \ne \bar{\left(\var{x_i}\right)_j}$, but still the mean of all 
+station-wise std is a decent estimate of the true std.
+
+`"mean"=<value, e.g. xr.DataArray>`: If mean and std are already calculated or shall be set manually, just add the
+scaling values instead of the calculation method. For method *centre*, std can still be None, but is required for the
+*standardise* method. **Important**: Format of given values **must** match internal data format of DataPreparation 
+class: `xr.DataArray` with `dims=["variables"]` and one value for each variable.
\ No newline at end of file

src/data_handling/data_generator.py

@@ -104,8 +104,8 @@ class DataGenerator(keras.utils.Sequence):
         method = transformation.get("method", "standardise")
         mean = transformation.get("mean", None)
         std = transformation.get("std", None)
-        if isinstance(mean, str):
         if scope == "data":
+            if isinstance(mean, str):
                 if mean == "accurate":
                     mean, std = self.calculate_accurate_transformation(method)
                 elif mean == "estimate":