Update README to make it clear that 'attributes' is associated with '…

…derived_variables'

README.md

@@ -371,10 +371,10 @@ The `inputs` section defines the source datasets to extract data from. Each sour
   - `rename`: simply rename the dimension to the new name
   - `stack`: stack the listed dimension to create the dimension in the output
   - `stack_variables_by_var_name`: stack the dimension into the new dimension, and also stack the variable name into the new variable name. This is useful when you have multiple variables with the same dimensions that you want to stack into a single variable.
-- `derived_variables`: defines the variables to be derived from the variables available in the source dataset. This should be a dictionary where each key is the variable to be derived and the value defines a dictionary with additional information.
-- `function`: the function to be used to derive a variable. This should be a string and may either be the full namespace of the function (e.g. `mllam_data_prep.derived_variables.calculate_toa_radiation`) or in case the function is included in the `mllam_data_prep.derived_variables` module it is enough with the function name only.
-- `kwargs`: arguments for the function used to derive a variable. This is a dictionary where each key is the variable name to select from the source dataset and each value is the named argument to `function`.
-- `attributes`: section where users can specify the attributes `units` and `long_name` as a dictionary (not included in the example config file), where the keys are the attribute names and the values are strings. If using a function defined in `mllam_data_prep.derived_variables` this section is optional as the attributes should already be defined. In this case, adding the attributes to the config file will overwrite the already-defined ones. If using an external function, where the attributes `units` and `long_name` are not set, this section is a requirement.
+- `derived_variables`: defines the variables to be derived from the variables available in the source dataset. This should be a dictionary where each key is the variable to be derived and the value defines a dictionary with the following additional information.
+  - `function`: the function to be used to derive a variable. This should be a string and may either be the full namespace of the function (e.g. `mllam_data_prep.derived_variables.calculate_toa_radiation`) or in case the function is included in the `mllam_data_prep.derived_variables` module it is enough with the function name only.
+  - `kwargs`: arguments for the function used to derive a variable. This is a dictionary where each key is the variable name to select from the source dataset and each value is the named argument to `function`.
+  - `attributes`: section where users can specify attributes (e.g. `units` and `long_name`) as a dictionary (not included in the example config file), where the keys are the attribute names and the values are strings. If using a function defined in `mllam_data_prep.derived_variables` this section is optional as the attributes should already be defined. In this case, adding the attributes to the config file will overwrite the already-defined ones. If using an external function, where the attributes `units` and `long_name` are not set, this section is a requirement.
 
 
 ### Config schema versioning