Skip to content
Snippets Groups Projects
Commit c38034ff authored by Peter Lünenschloß's avatar Peter Lünenschloß
Browse files

made flagSoilMoistureByFrost parameter passing compatible to call by metadata.csv.

parent 73251908
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 16 additions and 65 deletions
funcs/functions.py
+ 16
− 65
View file @ c38034ff
......@@ -129,92 +129,43 @@ def flagMad(data, flags, field, flagger, length, z, deriv, **kwargs):
return data, flags
def flagSoilMoistureBySoilFrost(data, flags, field, flagger, time_stamp, tolerated_deviation, soil_temp_reference,
reference_field=None, reference_flags=None, reference_flagger=None,
reference_time_stamp=None, frost_level=0, **kwargs):
def flagSoilMoistureBySoilFrost(data, flags, field, flagger, soil_temp_reference, tolerated_deviation,
frost_level=0, **kwargs):
"""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 flags: A dataframe holding the flags/flag-entries of "data"
:param field: Fieldname of the Soil moisture measurements in data.
(Soil moisture measurement column should be accessible by "data[field]")
:param field: Fieldname of the Soil moisture measurements field in data.
:param flagger: A flagger - object.
:param time_stamp: (1)A STRING, denoting the data fields name, that holds the timestamp
series associated with the data,
(2) Pass None or 'index', if the input data dataframe is indexed with a
timestamp.
(3) Pass an array-like thingy, holding timestamp/datetime
like thingies that refer to the data(including datestrings).
:param tolerated_deviation: An offset alias, denoting the maximal temporal deviation,
the Soil frost states timestamp is allowed to have, relative to the
the soil frost states timestamp is allowed to have, relative to the
data point to-be-flagged.
:param soil_temp_reference: (1) A STRING, denoting the fields name in data,
:param soil_temp_reference: A STRING, denoting the fields name in data,
that holds the data series of soil temperature values,
the to-be-flagged values shall be checked against.
(2) A date indexed pandas.Series, holding the data series of soil
temperature values, the to-be-flagged values shall be checked against.
(3) A data frame (most likely refering to a loggers measurements), containing the
temperature values, the to-be-flagged values shall be checked against,
in one of its fields. (In this case, you have to pass
reference_field and reference_time_stamp as well)
:param reference_field: If a Dataframe is passed to soil_temp_reference, that parameter holds the
Fieldname refereing to the Soil temperature measurements.
:param reference_flag: If there are flags available for the reference series, pass them here
:param reference_flagger: If the flagger of the reference series is not the same as the one used
for the data-to-be-flagged, pass it here.
:param reference_time_stamp:
:param frost_level: Value level, the flagger shall check against, when evaluating soil frost level.
"""
# TODO: (To ASK):HOW TO FLAG nan values in input frame? general question: what should a test test?
# TODO: -> nan values with reference values that show frost, are flagged bad, nan values with reference value nan
# TODO: as well, are not flagged (test not applicable-> no flag)
# TODO: (To comment):PERFORMANCE COST OF NOT HARMONIZED
# TODO: Index = None input option
# TODO: puffer zone for intermediate/fluktuating frost state
# check and retrieve data series input:
if isinstance(time_stamp, str):
dataseries = pd.Series(data[field].values, index=pd.to_datetime(data[time_stamp].values))
else:
dataseries = pd.Series(data[field].values, index=pd.to_datetime(list(time_stamp)))
# retrieve data series input:
dataseries = pd.Series(data[field].values, index=pd.to_datetime(data.index))
# check and retrieve reference input:
#if reference is a string, it refers to data field
if isinstance(soil_temp_reference, str):
# if reference series is part of input data frame, evaluate input data flags:
flag_mask = flagger.isFlagged(flags)[soil_temp_reference]
# retrieve reference series
refseries = pd.Series(data[soil_temp_reference].values,
index=dataseries.index)
# drop flagged values:
refseries = refseries.loc[~np.array(flag_mask)]
# if reference is a series, it represents the soil temperature series-to-refer-to:
elif isinstance(soil_temp_reference, pd.Series):
refseries = soil_temp_reference
if reference_flags is not None:
if reference_flagger is None:
reference_flagger = flagger
reference_flag_mask = reference_flagger.isFlagged(reference_flags)
refseries = refseries.loc[~np.array(reference_flag_mask)]
# if reference is a dataframe, it contains the soil temperature series to-refer-to:
elif isinstance(soil_temp_reference, pd.DataFrame):
if isinstance(reference_time_stamp, str):
refseries = pd.Series(soil_temp_reference[reference_field].values,
index=pd.to_datetime(soil_temp_reference[reference_time_stamp].values))
else:
refseries = pd.Series(soil_temp_reference[reference_field].values,
index=pd.to_datetime(list(reference_time_stamp)))
if reference_flags is not None:
if reference_flagger is None:
reference_flagger = flagger
reference_flag_mask = reference_flagger.isFlagged(reference_flags)[reference_field]
refseries = refseries.loc[~np.array(reference_flag_mask)]
# retrieve reference input:
#if reference is a string, it refers to data field
# if reference series is part of input data frame, evaluate input data flags:
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))
# drop flagged values:
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.
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment