diff --git a/saqc/core/modules/noise.py b/saqc/core/modules/noise.py
index f162e5808f4ec71bf37d2aa9ff4c3c9011344468..bfba8074fbc16c2e9e9d7b0fb571eca58ef37a26 100644
--- a/saqc/core/modules/noise.py
+++ b/saqc/core/modules/noise.py
@@ -11,14 +11,15 @@ from saqc.lib.types import ColumnName, FreqString, PositiveInt, PositiveFloat
class Noise(ModuleBase):
- def flagByStatLowPass(self,
- field: ColumnName,
- stat: Callable[[numpy.array, pd.Series], float],
- winsz: FreqString,
- thresh: PositiveFloat,
- sub_winsz: FreqString = None,
- sub_thresh: PositiveFloat = None,
- min_periods: PositiveInt = None,
- flag: float = BAD
+ def flagByStatLowPass(
+ self,
+ field: ColumnName,
+ stat: Callable[[numpy.array, pd.Series], float],
+ winsz: FreqString,
+ thresh: PositiveFloat,
+ sub_winsz: FreqString = None,
+ sub_thresh: PositiveFloat = None,
+ min_periods: PositiveInt = None,
+ flag: float = BAD,
) -> SaQC:
return self.defer("flagByStatLowPass", locals())
diff --git a/saqc/funcs/constants.py b/saqc/funcs/constants.py
index bc311aae08ff8ddde4347b7ebcb4ecb507503c80..f4f36cae168b8fb691bd95199922f6c8de8ac5c8 100644
--- a/saqc/funcs/constants.py
+++ b/saqc/funcs/constants.py
@@ -143,9 +143,14 @@ def flagByVariance(
min_periods = int(np.ceil(pd.Timedelta(window) / pd.Timedelta(delta)))
window = pd.Timedelta(window)
- to_set = statPass(dataseries, lambda x: varQC(x, max_missing, max_consec_missing),
- window, thresh, min_periods=min_periods, comparator=operator.lt)
+ to_set = statPass(
+ dataseries,
+ lambda x: varQC(x, max_missing, max_consec_missing),
+ window,
+ thresh,
+ min_periods=min_periods,
+ comparator=operator.lt,
+ )
flags[to_set[to_set].index, field] = flag
return data, flags
-
diff --git a/saqc/funcs/noise.py b/saqc/funcs/noise.py
index 7bc5404dc2100596ef3034ff03cee32b4f5dcf21..9427eb262b1813d74eea8342bc574da75d0e3c0e 100644
--- a/saqc/funcs/noise.py
+++ b/saqc/funcs/noise.py
@@ -11,18 +11,20 @@ from saqc.lib.types import ColumnName, FreqString, PositiveInt, PositiveFloat, L
from saqc.lib.tools import statPass
-@register(masking='field', module="noise")
-def flagByStatLowPass(data: DictOfSeries,
- field: ColumnName,
- flags: Flags,
- stat: Callable[[np.array, pd.Series], float],
- winsz: FreqString,
- thresh: PositiveFloat,
- sub_winsz: FreqString = None,
- sub_thresh: PositiveFloat = None,
- min_periods: PositiveInt = None,
- flag: float = BAD,
- **kwargs):
+@register(masking="field", module="noise")
+def flagByStatLowPass(
+ data: DictOfSeries,
+ field: ColumnName,
+ flags: Flags,
+ stat: Callable[[np.array, pd.Series], float],
+ winsz: FreqString,
+ thresh: PositiveFloat,
+ sub_winsz: FreqString = None,
+ sub_thresh: PositiveFloat = None,
+ min_periods: PositiveInt = None,
+ flag: float = BAD,
+ **kwargs
+):
"""
Flag *chunks* of length, `winsz`:
@@ -61,6 +63,15 @@ def flagByStatLowPass(data: DictOfSeries,
winsz = pd.Timedelta(winsz)
if sub_winsz:
sub_winsz = pd.Timedelta(sub_winsz)
- to_set = statPass(datcol, stat, winsz, thresh, sub_winsz, sub_thresh, min_periods, comparator=operator.gt)
+ to_set = statPass(
+ datcol,
+ stat,
+ winsz,
+ thresh,
+ sub_winsz,
+ sub_thresh,
+ min_periods,
+ comparator=operator.gt,
+ )
flags[to_set[to_set].index, field] = flag
- return data, flags
\ No newline at end of file
+ return data, flags
diff --git a/saqc/lib/tools.py b/saqc/lib/tools.py
index 7816dc985615b5bd8da36517f8db09d66f152220..d5560c37fc023ce60bbedf9274161001f2b06962 100644
--- a/saqc/lib/tools.py
+++ b/saqc/lib/tools.py
@@ -3,7 +3,7 @@
import re
import datetime
-from typing import Sequence, Union, Any, Iterator,Callable
+from typing import Sequence, Union, Any, Iterator, Callable
import operator
import itertools
import numpy as np
@@ -570,7 +570,7 @@ def getFreqDelta(index):
return delta
-def getAttrOrApply(in_obj, apply_obj, attr_access='__name__', attr_or='apply'):
+def getAttrOrApply(in_obj, apply_obj, attr_access="__name__", attr_or="apply"):
"""
For the repeating task of applying build in (accelerated) methods/funcs (`apply_obj`),
of rolling/resampling - like objects (`in_obj`) ,
@@ -585,14 +585,16 @@ def getAttrOrApply(in_obj, apply_obj, attr_access='__name__', attr_or='apply'):
return out
-def statPass(datcol: pd.Series,
- stat: Callable[[np.array, pd.Series], float],
- winsz: pd.Timedelta,
- thresh: PositiveFloat,
- comparator: Callable[[float, float], bool],
- sub_winsz: pd.Timedelta = None,
- sub_thresh: PositiveFloat = None,
- min_periods: PositiveInt = None):
+def statPass(
+ datcol: pd.Series,
+ stat: Callable[[np.array, pd.Series], float],
+ winsz: pd.Timedelta,
+ thresh: PositiveFloat,
+ comparator: Callable[[float, float], bool],
+ sub_winsz: pd.Timedelta = None,
+ sub_thresh: PositiveFloat = None,
+ min_periods: PositiveInt = None,
+):
"""
Check `datcol`, if it contains chunks of length `winsz`, exceeding `thresh` with
regard to `stat` and `comparator`:
@@ -608,13 +610,13 @@ def statPass(datcol: pd.Series,
if sub_winsz:
stat_sub = datcol.rolling(sub_winsz)
stat_sub = getAttrOrApply(stat_sub, stat)
- min_stat = stat_sub.rolling(winsz - sub_winsz, closed='both').min()
+ min_stat = stat_sub.rolling(winsz - sub_winsz, closed="both").min()
exceeding_sub = comparator(min_stat, sub_thresh)
exceeds = exceeding_sub & exceeds
to_set = pd.Series(False, index=exceeds.index)
for g in exceeds.groupby(by=exceeds.values):
if g[0]:
- to_set[g[1].index[0] - winsz:g[1].index[-1]] = True
+ to_set[g[1].index[0] - winsz : g[1].index[-1]] = True
- return to_set
\ No newline at end of file
+ return to_set
diff --git a/saqc/lib/ts_operators.py b/saqc/lib/ts_operators.py
index af0c67b99a755bc40f47170dcf5d0fe1783afac8..3969229ab4112bce550776b772280675ac252b5a 100644
--- a/saqc/lib/ts_operators.py
+++ b/saqc/lib/ts_operators.py
@@ -361,12 +361,13 @@ def shift2Freq(
)
-def butterFilter(x, cutoff, nyq=0.5, filter_order=2,
- fill_method='linear', filter_type='low'):
+def butterFilter(
+ x, cutoff, nyq=0.5, filter_order=2, fill_method="linear", filter_type="low"
+):
"""
Applies butterworth filter.
`x` is expected to be regularly sampled.
-
+
Parameters
----------
x: pd.Series
@@ -377,13 +378,13 @@ def butterFilter(x, cutoff, nyq=0.5, filter_order=2,
The niquist-frequency. relates to multiples if the sampling rate.
fill_method: Literal[‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’]
Any method keyword, accepted by pandas.Series.interpolate.
-
+
Returns
-------
"""
na_mask = x.isna()
- x = x.interpolate(fill_method).interpolate('ffill').interpolate('bfill')
+ x = x.interpolate(fill_method).interpolate("ffill").interpolate("bfill")
b, a = butter(N=filter_order, Wn=cutoff / nyq, btype=filter_type)
y = pd.Series(filtfilt(b, a, x), x.index, name=x.name)
y[na_mask] = np.nan
@@ -493,4 +494,3 @@ def polynomialInterpolation(data, inter_limit=2, inter_order=2):
return interpolateNANs(
data, "polynomial", inter_limit=inter_limit, order=inter_order
)
-
diff --git a/saqc/lib/types.py b/saqc/lib/types.py
index dced4a5df1faf13e52b20e3115b9472b8eae0f41..34c28291439cbc991fc8645c7f4eefe1268715d0 100644
--- a/saqc/lib/types.py
+++ b/saqc/lib/types.py
@@ -1,18 +1,18 @@
#! /usr/bin/env python
# -*- coding: utf-8 -*-
__all__ = [
- 'T',
- 'ArrayLike',
- 'PandasLike',
- 'DiosLikeT',
- 'FuncReturnT',
- 'FreqString',
- 'ColumnName',
- 'IntegerWindow',
- 'TimestampColumnName',
- 'CurveFitter',
- 'PositiveFloat',
- 'PositiveInt'
+ "T",
+ "ArrayLike",
+ "PandasLike",
+ "DiosLikeT",
+ "FuncReturnT",
+ "FreqString",
+ "ColumnName",
+ "IntegerWindow",
+ "TimestampColumnName",
+ "CurveFitter",
+ "PositiveFloat",
+ "PositiveInt",
]
from typing import TypeVar, Union, NewType