Updated and renamed staticQC->apply_static_qc

* Added thresholds as input parameters with default values
* Cleaned up out commented code
* Applied black formatting on file

src/pypromice/process/L1toL2.py

@@ -9,7 +9,7 @@
 import os
 import xarray as xr
 
-from pypromice.qc.static_qc import staticQC
+from pypromice.qc.static_qc import apply_static_qc
 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 = staticQC(ds)                                                      # Flag and Remove difference outliers
+    ds = apply_static_qc(ds)                                                      # Flag and Remove difference outliers
     ds = percentileQC(ds)                                                      # Flag and remove percentile outliers
 
     T_100 = _getTempK(T_0)  

src/pypromice/qc/static_qc.py

@@ -1,10 +1,26 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
+from typing import Mapping, Optional
 
 
-def staticQC(ds: xr.Dataset) -> xr.Dataset:
-    '''
+__all__ = [
+    "apply_static_qc",
+    "find_static_regions",
+]
+
+DEFAULT_VARIABLE_THRESHOLDS = {
+    "t": {"static_limit": 0.001, "diff_period": 1},
+    "p": {"static_limit": 0.0001 / 24, "diff_period": 24},
+    "rh": {"static_limit": 0.0001 / 24, "diff_period": 24},
+}
+
+
+def apply_static_qc(
+    ds: xr.Dataset,
+    variable_thresholds: Optional[Mapping] = None,
+) -> 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.
@@ -13,13 +29,18 @@ def staticQC(ds: xr.Dataset) -> xr.Dataset:
     Parameters
     ----------
     ds : xr.Dataset
-         Level 1 datset
+        Level 1 datset
+    variable_thresholds : Mapping
+        Define threshold dict to hold limit values, and the difference values.
+        Limit values indicate how much a variable has to change to the previous value
+        diff_period is how many hours a value can stay the same without being set to NaN
+        * are used to calculate and define all limits, which are then applied to *_u, *_l and *_i
 
     Returns
     -------
     ds_out : xr.Dataset
             Level 1 dataset with difference outliers set to NaN
-    '''
+    """
 
     # the differenceQC is not done on the Windspeed
     # Optionally examine flagged data by setting make_plots to True
@@ -28,22 +49,17 @@ def staticQC(ds: xr.Dataset) -> xr.Dataset:
 
     df = ds.to_dataframe()  # Switch to pandas
 
-    # Define threshold dict to hold limit values, and the difference values.
-    # Limit values indicate how much a variable has to change to the previous value
-    # diff_period is how many hours a value can stay the same without being set to NaN
-    # * are used to calculate and define all limits, which are then applied to *_u, *_l and *_i
-
-    var_threshold = {
-        't': {'static_limit': 0.001, 'diff_period': 1},
-        'p': {'static_limit': 0.0001, 'diff_period': 24},
-        'rh': {'static_limit': 0.0001, 'diff_period': 24}
-    }
+    if variable_thresholds is None:
+        variable_thresholds = DEFAULT_VARIABLE_THRESHOLDS
 
-    for k in var_threshold.keys():
-
-        var_all = [k + '_u', k + '_l', k + '_i']  # apply to upper, lower boom, and instant
-        static_limit = var_threshold[k]['static_limit']  # loading static limit
-        diff_period = var_threshold[k]['diff_period']  # loading diff period
+    for k in variable_thresholds.keys():
+        var_all = [
+            k + "_u",
+            k + "_l",
+            k + "_i",
+        ]  # apply to upper, lower boom, and instant
+        static_limit = variable_thresholds[k]["static_limit"]  # loading static limit
+        diff_period = variable_thresholds[k]["diff_period"]  # loading diff period
 
         for v in var_all:
             if v in df:
@@ -56,9 +72,7 @@ def staticQC(ds: xr.Dataset) -> xr.Dataset:
     ds_out = ds_out.assign_attrs(ds.attrs)  # Dataset attrs
     for x in ds_out.data_vars:  # variable-specific attrs
         ds_out[x].attrs = ds[x].attrs
-    # equivalent to above:
-    # vals = [xr.DataArray(data=df_out[c], dims=['time'], coords={'time':df_out.index}, attrs=ds[c].attrs) for c in df_out.columns]
-    # ds_out = xr.Dataset(dict(zip(df_out.columns, vals)), attrs=ds.attrs)
+
     return ds_out