implemented aggregation wrapper

docs/FunctionDescriptions.md

@@ -26,9 +26,9 @@ Main documentation of the implemented functions, their purpose and parameters an
  - [harmonize](#harmonize)
  - [deharmonize](#deharmonize)
  - [harmonize_shift2Grid](#harmonize_shift2grid)
- - [harmonize_shift2Grid](#harmonize_aggregate2grid)
- - [harmonize_shift2Grid](#harmonize_linear2grid)
- - [harmonize_shift2Grid](#harmonize_interpolate2grid)
+ - [harmonize_aggregate2Grid](#harmonize_aggregate2grid)
+ - [harmonize_linear2Grid](#harmonize_linear2grid)
+ - [harmonize_interpolate2Grid](#harmonize_interpolate2grid)
  
 
 ## range

saqc/funcs/harm_functions.py

@@ -829,7 +829,8 @@ def linear2Grid(data, field, flagger, freq, flag_assignment_method='nearest_agg'
 
 
 @register('harmonize_interpolate2Grid')
-def interpolate2Grid(data, field, flagger, freq, interpolation_method, interpolation_order=1, flag_assignment_method='nearest_agg', flag_agg_func=max, drop_flags=None, **kwargs):
+def interpolate2Grid(data, field, flagger, freq, interpolation_method, interpolation_order=1,
+                     flag_assignment_method='nearest_agg', flag_agg_func=max, drop_flags=None, **kwargs):
     return harmonize(
         data,
         field,
@@ -842,4 +843,61 @@ def interpolate2Grid(data, field, flagger, freq, interpolation_method, interpola
         drop_flags=drop_flags,
         **kwargs)
 
-#def aggregate(data, field, flagger, freq_base, freq_target, agg_func, **kwargs):
+
\ No newline at end of file
+def aggregate(data, field, flagger, source_freq, target_freq, agg_func=np.mean, sample_func=np.mean,
+              invalid_flags=None, max_invalid=np.inf, **kwargs):
+
+    # define the "fastest possible" aggregator
+    if sample_func is None:
+        if max_invalid < np.inf:
+
+            def aggregator(x):
+                if x.isna().sum() < max_invalid:
+                    return agg_func(x)
+                else:
+                    return np.nan
+        else:
+
+            def aggregator(x):
+                return agg_func(x)
+    else:
+
+        dummy_resampler = pd.Series(np.nan, index=[pd.Timedelta('1min')]).resample('1min')
+        if hasattr(dummy_resampler, sample_func.__name__):
+
+            sample_func_name = sample_func.__name__
+            if max_invalid < np.inf:
+                def aggregator(x):
+                    y = getattr(x.resample(source_freq), sample_func_name)()
+                    if y.isna().sum() < max_invalid:
+                        return agg_func(y)
+                    else:
+                        return np.nan
+
+            else:
+                def aggregator(x):
+                    agg_func(getattr(x.resample(source_freq), sample_func_name)())
+
+        else:
+            if max_invalid < np.inf:
+                def aggregator(x):
+                    y = x.resample(source_freq).apply(sample_func)
+                    if y.isna().sum() < max_invalid:
+                        agg_func(y)
+                    else:
+                        return np.nan
+            else:
+                def aggregator(x):
+                    return agg_func(x.resample(source_freq).apply(sample_func))
+
+    return harmonize(
+        data,
+        field,
+        flagger,
+        target_freq,
+        inter_method='bagg',
+        reshape_method='bagg',
+        inter_agg=aggregator,
+        reshape_agg=max,
+        drop_flags=invalid_flags,
+        **kwargs)