From aa1fdab72551c719e085f2dc650fc55ae39ad7dc Mon Sep 17 00:00:00 2001
From: Peter Luenenschloss <peter.luenenschloss@ufz.de>
Date: Tue, 18 Jun 2019 15:27:37 +0200
Subject: [PATCH] Basflagger now returns categorical flags
---
flagger/baseflagger.py | 5 ++---
funcs/functions.py | 22 +++++++++++-----------
2 files changed, 13 insertions(+), 14 deletions(-)
diff --git a/flagger/baseflagger.py b/flagger/baseflagger.py
index 3c8a93121..7a441c0a2 100644
--- a/flagger/baseflagger.py
+++ b/flagger/baseflagger.py
@@ -50,9 +50,8 @@ class BaseFlagger:
return flags.values
def initFlags(self, data: pd.DataFrame) -> pd.DataFrame:
- out = data.copy().astype(self.flags)
- out.loc[:] = self.flags[0]
- return out
+ out = pd.DataFrame(data=self.flags[0], index=data.index, columns=data.columns)
+ return out.astype(self.flags)
def isFlagged(self, flags: ArrayLike, flag: T = None) -> ArrayLike:
if flag is None:
diff --git a/funcs/functions.py b/funcs/functions.py
index 5225ba0c6..4972c23fa 100644
--- a/funcs/functions.py
+++ b/funcs/functions.py
@@ -111,7 +111,8 @@ def flagSoilMoistureBySoilFrost(data, flags, field, flagger, soil_temp_reference
"""Function flags Soil moisture measurements by evaluating the soil-frost-level in the moment of measurement.
Soil temperatures below "frost_level" are regarded as denoting frozen soil state.
- :param data: The pandas dataframe holding the data-to-be flagged.
+ :param data: The pandas dataframe holding the data-to-be flagged, as well as the reference
+ series. Dataframe should be indexed by a datetime series.
:param flags: A dataframe holding the flags/flag-entries of "data"
:param field: Fieldname of the Soil moisture measurements field in data.
:param flagger: A flagger - object.
@@ -125,26 +126,25 @@ def flagSoilMoistureBySoilFrost(data, flags, field, flagger, soil_temp_reference
:param frost_level: Value level, the flagger shall check against, when evaluating soil frost level.
"""
-
-
# retrieve data series input:
- dataseries = pd.Series(data[field].values, index=pd.to_datetime(data.index))
+ dataseries = data[field]
# if reference series is part of input data frame, evaluate input data flags:
- flag_mask = flagger.isFlagged(flags)[soil_temp_reference]
+ # flag_mask = flagger.isFlagged(flags)[soil_temp_reference]
# retrieve reference series
- refseries = pd.Series(data[soil_temp_reference].values, index=pd.to_datetime(data.index))
+ refseries = data[soil_temp_reference]
# drop flagged values:
- refseries = refseries.loc[~np.array(flag_mask)]
+ # refseries = refseries.loc[~np.array(flag_mask)]
# make refseries index a datetime thingy
refseries.index = pd.to_datetime(refseries.index)
# drop nan values from reference series, since those are values you dont want to refer to.
refseries = refseries.dropna()
- # wrap around df.index.get_loc method to catch key error in case of empty tolerance window:
+ # wrap around df.index.get_loc method, to catch key error in case of empty tolerance window:
def check_nearest_for_frost(ref_date, ref_series, tolerance, check_level):
+
try:
- # if there is no reference value within tolerance margin, following line will rise key error and
+ # if there is no reference value within tolerance margin, following line will raise key error and
# trigger the exception
ref_pos = ref_series.index.get_loc(ref_date, method='nearest', tolerance=tolerance)
except KeyError:
@@ -156,11 +156,11 @@ def flagSoilMoistureBySoilFrost(data, flags, field, flagger, soil_temp_reference
# make temporal frame holding dateindex, since df.apply cant access index
temp_frame = pd.Series(dataseries.index)
- # get flagging mask
+ # get flagging mask ("False" denotes "bad"="test succesfull")
mask = temp_frame.apply(check_nearest_for_frost, args=(refseries,
tolerated_deviation, frost_level))
# apply calculated flags
- flags.loc[mask.values, field] = flagger.setFlag(flags.loc[mask, field], **kwargs)
+ flags.loc[mask.values, field] = flagger.setFlag(flags.loc[mask.values, field], **kwargs)
return data, flags
--
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