diff --git a/saqc/funcs/constants_detection.py b/saqc/funcs/constants_detection.py
index 8e6b2850a29f7ac3eaf9c236c4f532f0c93a195b..ff099f91ab8ddf6e6b98b893392c0e2f53135add 100644
--- a/saqc/funcs/constants_detection.py
+++ b/saqc/funcs/constants_detection.py
@@ -96,7 +96,7 @@ def flagConstant_varianceBased(
if plateaus.sum() == 0:
return data, flagger
- plateaus.fillna(method="bfill", limit=min_periods, inplace=True)
+ plateaus.fillna(method="bfill", limit=min_periods-1, inplace=True)
# result:
plateaus = (plateaus[plateaus == 1.0]).index
diff --git a/saqc/funcs/soil_moisture_tests.py b/saqc/funcs/soil_moisture_tests.py
index f9c232940d2310b722185fcff06b31ac0bf8bad9..37f9ad1de9c027f304ac9ef74eff3063afd008a8 100644
--- a/saqc/funcs/soil_moisture_tests.py
+++ b/saqc/funcs/soil_moisture_tests.py
@@ -9,6 +9,7 @@ from scipy.signal import savgol_filter
from saqc.funcs.break_detection import flagBreaks_spektrumBased
from saqc.funcs.spike_detection import flagSpikes_spektrumBased
+from saqc.funcs.constants_detection import flagConstant_varianceBased
from saqc.funcs.register import register
from saqc.lib.tools import (
estimateSamplingRate,
@@ -263,10 +264,12 @@ def flagSoilMoistureByConstantsDetection(
flagger,
plateau_window_min="12h",
plateau_var_limit=0.0005,
- rainfall_window="12h",
+ rainfall_window_range="12h",
filter_window_size="3h",
- i_start_infimum=0.0025,
- i_end_supremum=0,
+ var_total_nans=np.inf,
+ var_consec_nans=np.inf,
+ derivative_maximum_lb=0.0025,
+ derivative_minimum_ub=0,
data_max_tolerance=0.95,
**kwargs
):
@@ -274,126 +277,62 @@ def flagSoilMoistureByConstantsDetection(
"""Function is not ready to use yet: we are waiting for response from the author of [Paper] in order of getting
able to exclude some sources of confusion.
+ Note, function has to be harmonized to equidistant freq_grid
+
+ Note, in current implementation, it has to hold that: (rainfall_window_range >= plateau_window_min)
+
:param data: The pandas dataframe holding the data-to-be flagged.
Data must be indexed by a datetime series and be harmonized onto a
time raster with seconds precision (skips allowed).
:param field: Fieldname of the Soil moisture measurements field in data.
:param flagger: A flagger - object. (saqc.flagger.X)
"""
- dataseries, data_rate = retrieveTrustworthyOriginal(data, field, flagger)
-
- # abort processing if original series has no valid entries!
- if data_rate is np.nan:
- return data, flagger
-
- # get data max
- data_max = dataseries.max()
- min_periods = int(offset2periods(plateau_window_min, data_rate))
- # identify minimal plateaus:
- plateaus = dataseries.rolling(window=plateau_window_min).apply(
- lambda x: (x.var() > plateau_var_limit) | (x.size < min_periods), raw=False
- )
- plateaus = ~plateaus.astype(bool)
+ # get plateaus:
+ _, comp_flagger = flagConstant_varianceBased(data, field, flagger, plateau_window_min=plateau_window_min,
+ plateau_var_limit=plateau_var_limit, var_total_nans=var_total_nans,
+ var_consec_nans=var_consec_nans)
- # are there any candidates for beeing flagged plateau-ish
- if plateaus.sum() == 0:
- return data, flagger
-
- plateaus_reverse = pd.Series(np.flip(plateaus.values), index=plateaus.index)
- reverse_check = (
- plateaus_reverse.rolling(window=plateau_window_min)
- .apply(lambda x: True if True in x.values else False, raw=False)
- .astype(bool)
- )
- plateaus = pd.Series(np.flip(reverse_check.values), index=plateaus.index)
+ new_plateaus = (comp_flagger.getFlags(field)).eq(flagger.getFlags(field))
+ # get dataseries at its sampling freq:
+ dataseries, moist_rate = retrieveTrustworthyOriginal(data, field, flagger)
+ # get valuse referring to dataseries:
+ new_plateaus.resample(pd.Timedelta(moist_rate)).asfreq()
+ # cut out test_slices for min/max derivatives condition check:
+ # offset 2 periods:
+ rainfall_window_range = int(np.ceil(pd.Timedelta(rainfall_window_range) / moist_rate))
+ plateau_window_min = int(np.ceil(pd.Timedelta(plateau_window_min) / moist_rate))
+ period_diff = rainfall_window_range - plateau_window_min
+ # we cast plateua series to int - because replace has problems with replacing bools by "method".
+ new_plateaus = new_plateaus.astype(int)
+ # get plateau groups:
+ group_counter = new_plateaus.cumsum()
+ group_counter = group_counter[group_counter.diff() == 0]
+ group_counter.name = 'group_counter'
+ plateau_groups = pd.merge(group_counter, dataseries, left_index=True, right_index=True, how='inner')
+ # test mean-condition on plateau groups:
+ test_barrier = data_max_tolerance*dataseries.max()
+ plateau_group_drops = plateau_groups.groupby('group_counter').filter(lambda x: x[field].mean() <= test_barrier)
+ # discard values that didnt pass the test from plateau candidate series:
+ new_plateaus[plateau_group_drops.index] = 1
+
+ # we extend the plateaus to cover condition testing sets
+ # 1: extend backwards (with a technical "one" added):
+ cond1_sets = new_plateaus.replace(1, method='bfill', limit=(rainfall_window_range + plateau_window_min))
+ # 2. extend forwards:
+ if period_diff > 0:
+ cond1_sets = cond1_sets.replace(1, method='ffill', limit=period_diff)
+ # get first derivative
+ first_derivative = dataseries.diff()
+ # cumsum trick to seperate continous plateau groups from each other
+ group_counter = cond1_sets.cumsum()
+ group_counter = group_counter[group_counter.diff() == 0]
+ group_counter.name = 'group_counter'
+ group_frame = pd.merge(group_counter, first_derivative, left_index=True, right_index=True, how='inner')
+ group_frame = group_frame.groupby('group_counter')
+ condition_passed = group_frame.filter(
+ lambda x: (x[field].max() >= derivative_maximum_lb) & (x[field].min() <= derivative_minimum_ub))
+
+ flagger = flagger.setFlags(field, loc=condition_passed.index, **kwargs)
- # reverse the reversed ts and transform to dataframe, filter for consecutive timestamp values:
- plateaus = pd.DataFrame(
- {"date": dataseries.index, "mask": np.flip(plateaus.values)},
- index=dataseries.index,
- )
- plateaus = plateaus[plateaus["mask"] == True].drop("mask", axis=1)
- seperator_stair = plateaus["date"].diff() != pd.Timedelta(data_rate)
- plateaus["interval_nr"] = seperator_stair.cumsum()
- plateaus = plateaus["interval_nr"]
- invalids = pd.Series([])
- # loop through the intervals to be checked:
- for interval_2_check in range(1, plateaus.max() + 1):
- # how big is the interval?
- interval_delimeter = (
- plateaus[plateaus == interval_2_check].index[-1]
- - plateaus[plateaus == interval_2_check].index[0]
- )
-
- # slices of the area for the rainfallsearch
- check_start = (
- plateaus[plateaus == interval_2_check].index[0]
- - interval_delimeter
- - pd.Timedelta(rainfall_window)
- )
- check_end = (
- plateaus[plateaus == interval_2_check].index[-1]
- - interval_delimeter
- + pd.Timedelta(rainfall_window)
- )
-
- # slices to be smoothed and derivated
- smooth_start = check_start - pd.Timedelta(filter_window_size)
- smooth_end = check_end + pd.Timedelta(filter_window_size)
- data_slice = dataseries[smooth_start:smooth_end]
-
- # calculate first derivative of testing slice:
- smoothing_periods = int(np.ceil(offset2periods(filter_window_size, data_rate)))
- if smoothing_periods % 2 == 0:
- smoothing_periods += 1
-
- # check if the data slice to be checked is sufficiently big for smoothing options:
- if data_slice.size < smoothing_periods:
- smoothing_periods = data_slice.size
- if smoothing_periods % 2 == 0:
- smoothing_periods -= 1
-
- # calculate the derivative
- first_deri_series = pd.Series(
- data=savgol_filter(
- data_slice, window_length=smoothing_periods, polyorder=2, deriv=1
- ),
- index=data_slice.index,
- )
-
- # get test slice
- first_deri_series = first_deri_series[check_start:check_end]
- if first_deri_series.empty:
- continue
-
- # check some explicit and implicit conditions:
- rainfall_periods = int(offset2periods(rainfall_window, data_rate) * 2)
- if rainfall_periods % 2 == 0:
- rainfall_periods += 1
- maximums = first_deri_series.rolling(
- window=rainfall_periods, center=True, closed="left"
- ).max()
- minimums = first_deri_series.rolling(
- window=rainfall_periods, center=True, closed="left"
- ).min()
-
- maximums = maximums[maximums > i_start_infimum]
- minimums = minimums[minimums < i_end_supremum]
-
- if maximums.empty | minimums.empty:
- continue
-
- i_start_index = maximums.index[0]
- i_end_index = minimums.index[-1]
-
- if i_start_index > i_end_index:
- continue
-
- potential_invalid = data_slice[i_start_index:i_end_index]
- # test if the plateau is a high level plateau:
- if potential_invalid.mean() > data_max * data_max_tolerance:
- invalids = pd.concat([invalids, potential_invalid])
-
- flagger = flagger.setFlag(field, invalids.index, **kwargs)
return data, flagger
diff --git a/test/funcs/test_constants_detection.py b/test/funcs/test_constants_detection.py
index 07eaa4ff9753cd730c639a336573af689eee8506..b9b7d35450b436bdae2904cc85aa0175676e8ea6 100644
--- a/test/funcs/test_constants_detection.py
+++ b/test/funcs/test_constants_detection.py
@@ -5,6 +5,7 @@ import pytest
import numpy as np
from saqc.funcs.constants_detection import flagConstant_varianceBased
+from saqc.funcs.soil_moisture_tests import flagSoilMoistureByConstantsDetection
from test.common import TESTFLAGGER, initData
@@ -20,7 +21,7 @@ def data():
@pytest.mark.parametrize("flagger", TESTFLAGGER)
def test_flagConstants_varianceBased(data, flagger):
- data.iloc[5:25] = 0
+ data.iloc[5:25] = 200
expected = np.arange(5, 25)
field, *_ = data.columns
flagger = flagger.initFlags(data)
@@ -30,3 +31,5 @@ def test_flagConstants_varianceBased(data, flagger):
flag_result = flagger_result.getFlags(field)
test_sum = (flag_result[expected] == flagger.BAD).sum()
assert test_sum == len(expected)
+
+