black applied

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())

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
-

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

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

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
     )
-

saqc/lib/types.py

 #! /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