Renamed to from difference_qc to static_qc

src/pypromice/process/L1toL2.py

@@ -9,7 +9,7 @@
 import os
 import xarray as xr
 
-from pypromice.qc.difference import differenceQC
+from pypromice.qc.static_qc import staticQC
 from pypromice.qc.percentile import percentileQC
 
 
@@ -56,7 +56,7 @@ def toL2(L1, T_0=273.15, ews=1013.246, ei0=6.1071, eps_overcast=1.,
 
 
 
-    ds = differenceQC(ds)                                                      # Flag and Remove difference outliers     
+    ds = staticQC(ds)                                                      # Flag and Remove difference outliers
     ds = percentileQC(ds)                                                      # Flag and remove percentile outliers
 
     T_100 = _getTempK(T_0)  

src/pypromice/qc/difference.py → src/pypromice/qc/static_qc.py

@@ -3,8 +3,12 @@
 import xarray as xr
 
 
-def differenceQC(ds: xr.Dataset) -> xr.Dataset:
+def staticQC(ds: xr.Dataset) -> xr.Dataset:
     '''
+    Detect and filter data points that sems to be static within a certain period.
+
+    TODO: It could be nice to have a reference to the logger or description of the behaviour here.
+    The AWS logger program is know to return the last successfully read value if it fails reading from the sensor.
 
     Parameters
     ----------
@@ -22,7 +26,6 @@ def differenceQC(ds: xr.Dataset) -> xr.Dataset:
     # This is best done by running aws.py directly and setting 'test_station'
     # Plots will be shown before and after flag removal for each var
 
-    stid = ds.station_id
     df = ds.to_dataframe()  # Switch to pandas
 
     # Define threshold dict to hold limit values, and the difference values.

src/pypromice/qc/difference_test.py → src/pypromice/qc/static_qc_test.py

@@ -6,7 +6,7 @@
 import numpy as np
 import pandas as pd
 
-from pypromice.qc.difference import find_static_regions
+from pypromice.qc.static_qc import find_static_regions
 
 
 def get_random_datetime() -> datetime.datetime: