only assign to a new variable if the new flag parameter 'assign_to' is

explicitly given

README.md

@@ -13,10 +13,10 @@
   + test name and parameter object/dictionary need to be seperated by comma
 - Example: `limits, {min: 0, max: 100}`
 #### Optional Test Parameters
-- flag:
+- `flag`:
   The value to set (more precisely the value to pass to the flagging component) if the tests
   does not pass
-- flag_period:
+- `flag_period`:
   + if a value is flagged, so is the given time period following the timestamp of that value
   + Number followed by a frequency specification, e.g. '5min', '6D'.
     A comprehensive list of the supported frequies can be found in the table 'Offset Aliases' in the [Pandas Docs](http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects "Pandas Docs"). The (probably) most common options are also listed below:
@@ -27,10 +27,12 @@
     | `H`              | one hour    |
     | `T` or `min`     | one minute  |
     | `S`              | one second  |
-- flag_values:
+- `flag_values`:
   + Number
   + if a value is flagged, so are the next n previously unflagged values
-
+- `assign_to`:
+  + String
+  + Assign the test result to a new columns given as a value to assign
 ### Predefined Tests
 
 | name  | required parameters | optional parameters | description                             |

core.py

@@ -12,6 +12,13 @@ from dsl import parseFlag
 from flagger import PositionalFlagger, BaseFlagger
 
 
+class FlagParams:
+    FLAG = "flag"
+    PERIODE = "flag_period"
+    VALUES = "flag_values"
+    ASSIGN = "assign_to"
+
+
 def _inferFrequency(data):
     return pd.tseries.frequencies.to_offset(pd.infer_freq(data.index))
 
@@ -67,15 +74,21 @@ def runner(meta, flagger, data, flags=None, nodata=np.nan):
             if pd.isnull(flag_test):
                 continue
 
+            func_name, flag_params = parseFlag(flag_test)
+
+            assign_to = flag_params.get(FlagParams.ASSIGN)
+            if assign_to:
+                dummy = pd.DataFrame(index=data.index, columns=[assign_to])
+                flags = flags.join(flagger.emptyFlags(dummy))
+
             varname, start_date, end_date = configrow[fields]
             if varname not in data:
-                # add a new variable
-                dummy = pd.DataFrame(index=data.index, columns=[varname])
-                flags = flags.join(flagger.emptyFlags(dummy))
+                continue
 
             dchunk = data.loc[start_date:end_date]
             if dchunk.empty:
                 continue
+
             # NOTE:
             # within the activation period of a variable, the flag will
             # be initialized if necessary
@@ -83,7 +96,6 @@ def runner(meta, flagger, data, flags=None, nodata=np.nan):
                       .loc[start_date:end_date]
                       .fillna({varname: flagger.no_flag}))
 
-            func_name, flag_params = parseFlag(flag_test)
             try:
                 dchunk, fchunk = flagDispatch(func_name,
                                               dchunk, fchunk, varname,