diff --git a/saqc/funcs/harm_functions.py b/saqc/funcs/harm_functions.py
index 9c50bfaba7f5c52253f1ccd467b00135c58d6782..afa30e616483150869a31af7402c610be9bf83d5 100644
--- a/saqc/funcs/harm_functions.py
+++ b/saqc/funcs/harm_functions.py
@@ -7,7 +7,7 @@ import logging
from saqc.funcs.functions import flagMissing
from saqc.funcs.register import register
-from saqc.lib.tools import toSequence
+from saqc.lib.tools import toSequence, funcInput_2_func
# todo: frequencie estimation function
@@ -44,10 +44,10 @@ def harmWrapper(heap={}):
freq,
inter_method,
reshape_method,
- inter_agg=np.mean,
+ inter_agg="mean",
inter_order=1,
inter_downcast=False,
- reshape_agg=max,
+ reshape_agg="max",
reshape_missing_flag=None,
reshape_shift_comment=False,
drop_flags=None,
@@ -55,6 +55,10 @@ def harmWrapper(heap={}):
**kwargs
):
+ # get funcs from strings:
+ inter_agg = funcInput_2_func(inter_agg)
+ reshape_agg = funcInput_2_func(reshape_agg)
+
# for some tingle tangle reasons, resolving the harmonization will not be sound, if not all missing/np.nan
# values get flagged initially:
data, flagger = flagMissing(
@@ -830,7 +834,7 @@ 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):
+ flag_assignment_method='nearest_agg', flag_agg_func="max", drop_flags=None, **kwargs):
return harmonize(
data,
field,
@@ -845,9 +849,14 @@ def interpolate2Grid(data, field, flagger, freq, interpolation_method, interpola
@register('harmonize_downsample')
-def downsample(data, field, flagger, sample_freq, agg_freq, sample_func=np.mean, agg_func=np.mean,
+def downsample(data, field, flagger, sample_freq, agg_freq, sample_func="mean", agg_func="mean",
invalid_flags=None, max_invalid=np.inf, **kwargs):
+ agg_func = funcInput_2_func(agg_func)
+
+ if sample_func is not None:
+ sample_func = funcInput_2_func(sample_func)
+
# define the "fastest possible" aggregator
if sample_func is None:
if max_invalid < np.inf:
diff --git a/saqc/lib/tools.py b/saqc/lib/tools.py
index a3950cc91c6137145c5c4d8e28ea283c3069942d..321b59c2aaf2718d3a0cb7a73919d93f71097376 100644
--- a/saqc/lib/tools.py
+++ b/saqc/lib/tools.py
@@ -10,6 +10,15 @@ import numba as nb
from saqc.lib.types import T
+STRING_2_FUNC = {
+ 'sum': np.sum,
+ 'mean': np.mean,
+ 'median': np.median,
+ 'min': np.min,
+ 'max': np.max,
+ 'first': pd.Series(np.nan, index=pd.DatetimeIndex([])).resample('0min').first,
+ 'last': pd.Series(np.nan, index=pd.DatetimeIndex([])).resample('0min').last
+}
def assertScalar(name, value, optional=False):
if (not np.isscalar(value)) and (value is not None) and (optional is True):
@@ -286,3 +295,19 @@ def assertSingleColumns(df, argname=""):
raise TypeError(
f"given pd.DataFrame {argname} is not allowed to have a muliindex on columns"
)
+
+def funcInput_2_func(func):
+ """
+ Aggregation functions passed by the user, are selected by looking them up in the STRING_2_DICT dictionary -
+ But since there are wrappers, that dynamically generate aggregation functions and pass those on ,the parameter
+ interfaces must as well be capable of processing real functions passed. This function does that.
+
+ :param func: A key to the STRING_2_FUNC dict, or an actual function
+ """
+ # if input is a callable - than just pass it:
+ if hasattr(func, "__call__"):
+ return func
+ elif func in STRING_2_FUNC.keys():
+ return STRING_2_FUNC[func]
+ else:
+ raise ValueError("Function input not a callable nor a known key to internal the func dictionary.")
diff --git a/test/funcs/test_harm_funcs.py b/test/funcs/test_harm_funcs.py
index 1170981b10589db94c89ef4937c019fccea22bc1..d6e34a3e5dc64a37b1d0a5d25d3a1a6ead007ed1 100644
--- a/test/funcs/test_harm_funcs.py
+++ b/test/funcs/test_harm_funcs.py
@@ -161,7 +161,7 @@ def test_harmSingleVarInterpolations(data, flagger, interpolation, freq):
interpolation,
"fshift",
reshape_shift_comment=False,
- inter_agg=np.sum,
+ inter_agg="sum",
)
if interpolation is "fshift":
@@ -261,8 +261,8 @@ def test_multivariatHarmonization(multi_data, flagger, shift_comment):
freq,
"bagg",
"bshift",
- inter_agg=sum,
- reshape_agg=max,
+ inter_agg="sum",
+ reshape_agg="max",
reshape_shift_comment=shift_comment,
)
@@ -295,14 +295,14 @@ def test_gridInterpolation(data, method):
data = data.squeeze()
# we are just testing if the interpolation gets passed to the series without causing an error:
_interpolateGrid(
- data, freq, method, order=1, agg_method=sum, downcast_interpolation=True
+ data, freq, method, order=1, agg_method="sum", downcast_interpolation=True
)
if method == "polynomial":
_interpolateGrid(
- data, freq, method, order=2, agg_method=sum, downcast_interpolation=True
+ data, freq, method, order=2, agg_method="sum", downcast_interpolation=True
)
_interpolateGrid(
- data, freq, method, order=10, agg_method=sum, downcast_interpolation=True
+ data, freq, method, order=10, agg_method="sum", downcast_interpolation=True
)
data = _insertGrid(data, freq)
_interpolate(data, method, inter_limit=3)
@@ -335,10 +335,10 @@ def test_wrapper(data, flagger):
field = data.columns[0]
freq = '15min'
flagger = flagger.initFlags(data)
- downsample(data, field, flagger, '15min', '30min', agg_func=np.sum, sample_func=np.mean)
+ downsample(data, field, flagger, '15min', '30min', agg_func="sum", sample_func="mean")
- linear2Grid(data, field, flagger, freq, flag_assignment_method='nearest_agg', flag_agg_func=max,
+ linear2Grid(data, field, flagger, freq, flag_assignment_method='nearest_agg', flag_agg_func="max",
drop_flags=None)
- aggregate2Grid(data, field, flagger, freq, agg_func=sum, agg_method='nearest_agg',
- flag_agg_func=max, drop_flags=None)
+ aggregate2Grid(data, field, flagger, freq, agg_func="sum", agg_method='nearest_agg',
+ flag_agg_func="max", drop_flags=None)
shift2Grid(data, field, flagger, freq, shift_method='nearest_shift', drop_flags=None)