Renamed static_qc -> persistent_qc

.github/workflows/unit_test.yml

@@ -30,4 +30,4 @@ jobs:
         shell: bash
         run: |
           cd $GITHUB_WORKSPACE/src/pypromice
-          python3 -m unittest -v process/aws.py get.py tx/tx.py qc/static_qc_test.py
+          python3 -m unittest -v process/aws.py get.py tx/tx.py qc/persistence_test.py

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="pypromice",
-    version="1.2.1",
+    version="1.3.0",
     author="GEUS Glaciology and Climate",
     description="PROMICE/GC-Net data processing toolbox",
     long_description=long_description,

src/pypromice/process/L1toL2.py

@@ -11,7 +11,7 @@
 import os
 import xarray as xr
 
-from pypromice.qc.static_qc import apply_static_qc
+from pypromice.qc.persistence import persistence_qc
 from pypromice.process.value_clipping import clip_values
 
 logger = logging.getLogger(__name__)
@@ -62,7 +62,7 @@ def toL2(
     except Exception:
         logger.exception('Flagging and fixing failed:')
     if ds.attrs['format'] == 'TX':
-        ds = apply_static_qc(ds)                                               # Detect and filter data points that seems to be static
+        ds = persistence_qc(ds)                                               # Detect and filter data points that seems to be static
 
     T_100 = _getTempK(T_0)
     ds['rh_u_cor'] = correctHumidity(ds['rh_u'], ds['t_u'],

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

@@ -6,9 +6,9 @@
 from typing import Mapping, Optional, Union
 
 __all__ = [
-    "apply_static_qc",
-    "find_static_regions",
-    "count_consecutive_static_values",
+    "persistence_qc",
+    "find_persistent_regions",
+    "count_consecutive_persistent_values",
     "count_consecutive_true",
 ]
 
@@ -22,12 +22,12 @@
 }
 
 
-def apply_static_qc(
+def persistence_qc(
     ds: xr.Dataset,
     variable_thresholds: Optional[Mapping] = None,
 ) -> xr.Dataset:
     """
-    Detect and filter data points that seems to be static within a certain period.
+    Detect and filter data points that seems to be persistent 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.
@@ -58,24 +58,24 @@ def apply_static_qc(
     if variable_thresholds is None:
         variable_thresholds = DEFAULT_VARIABLE_THRESHOLDS
 
-    logger.debug(f"Running apply_static_qc using {variable_thresholds}")
+    logger.debug(f"Running persistence_qc using {variable_thresholds}")
 
     for k in variable_thresholds.keys():
         var_all = [
             k + "_u",
             k + "_l",
             k + "_i",
         ]  # apply to upper, lower boom, and instant
-        max_diff = variable_thresholds[k]["max_diff"]  # loading static limit
+        max_diff = variable_thresholds[k]["max_diff"]  # loading persistent limit
         period = variable_thresholds[k]["period"]  # loading diff period
 
         for v in var_all:
             if v in df:
-                mask = find_static_regions(df[v], period, max_diff)
+                mask = find_persistent_regions(df[v], period, max_diff)
                 n_masked = mask.sum()
                 n_samples = len(mask)
                 logger.debug(
-                    f"Applying static QC in {v}. Filtering {n_masked}/{n_samples} samples"
+                    f"Applying persistent QC in {v}. Filtering {n_masked}/{n_samples} samples"
                 )
                 # setting outliers to NaN
                 df.loc[mask, v] = np.nan
@@ -89,22 +89,22 @@ def apply_static_qc(
     return ds_out
 
 
-def find_static_regions(
+def find_persistent_regions(
     data: pd.Series,
     min_repeats: int,
     max_diff: float,
 ) -> pd.Series:
     """
     Algorithm that ensures values can stay the same within the outliers_mask
     """
-    consecutive_true_df = count_consecutive_static_values(data, max_diff)
-    static_regions = consecutive_true_df >= min_repeats
+    consecutive_true_df = count_consecutive_persistent_values(data, max_diff)
+    persistent_regions = consecutive_true_df >= min_repeats
     # Ignore entries which already nan in the input data
-    static_regions[data.isna()] = False
-    return static_regions
+    persistent_regions[data.isna()] = False
+    return persistent_regions
 
 
-def count_consecutive_static_values(
+def count_consecutive_persistent_values(
     data: pd.Series,
     max_diff: float,
 ) -> pd.Series:

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

@@ -4,11 +4,11 @@
 import numpy.testing
 import pandas as pd
 
-from pypromice.qc.static_qc import find_static_regions
+from pypromice.qc.persistence import find_persistent_regions
 
 
-class StaticQATestCase(unittest.TestCase):
-    def test_1_hour_static(self):
+class PersistenceQATestCase(unittest.TestCase):
+    def test_1_hour_persistent(self):
         self._test_1_hour_repeat(10)
 
     def test_1_hour_second_index(self):
@@ -28,27 +28,27 @@ def _test_1_hour_repeat(self, index: int):
         expected_output = input_series.map(lambda _: False)
         expected_output[index + 1] = True
 
-        static_mask = find_static_regions(
+        persistent_mask = find_persistent_regions(
             input_series, min_repeats=min_repeats, max_diff=0.001
         )
 
-        pd.testing.assert_series_equal(expected_output, static_mask, check_names=False)
+        pd.testing.assert_series_equal(expected_output, persistent_mask, check_names=False)
 
-    def test_no_static_period(self):
+    def test_no_persistent_period(self):
         time_range = pd.date_range(
             start="2023-01-26", end="2023-01-27", freq="h", tz="utc", inclusive="left"
         )
         input_series = pd.Series(index=time_range, data=np.arange(0, len(time_range)))
         min_repeats = 1
         expected_output = input_series.map(lambda _: False)
 
-        static_mask = find_static_regions(
+        persistent_mask = find_persistent_regions(
             input_series, min_repeats=min_repeats, max_diff=0.001
         )
 
-        pd.testing.assert_series_equal(expected_output, static_mask, check_names=False)
+        pd.testing.assert_series_equal(expected_output, persistent_mask, check_names=False)
 
-    def test_static_period_longer_than_period_threshold(self):
+    def test_persistent_period_longer_than_period_threshold(self):
         time_range = pd.date_range(
             start="2023-01-26", end="2023-01-28", freq="h", tz="utc", inclusive="left"
         )
@@ -62,13 +62,13 @@ def test_static_period_longer_than_period_threshold(self):
         expected_output = input_series.map(lambda _: False)
         expected_output[expected_filter_start:expected_filter_end] = True
 
-        static_mask = find_static_regions(
+        persistent_mask = find_persistent_regions(
             input_series, min_repeats=min_repeats, max_diff=0.001
         )
 
-        pd.testing.assert_series_equal(expected_output, static_mask, check_names=False)
+        pd.testing.assert_series_equal(expected_output, persistent_mask, check_names=False)
 
-    def test_period_threshold_longer_than_static_period(self):
+    def test_period_threshold_longer_than_persistent_period(self):
         time_range = pd.date_range(
             start="2023-01-26", end="2023-01-28", freq="h", tz="utc", inclusive="left"
         )
@@ -79,13 +79,13 @@ def test_period_threshold_longer_than_static_period(self):
         input_series[index_start:index_end] = input_series[index_start]
         expected_output = input_series.map(lambda _: False)
 
-        static_mask = find_static_regions(
+        persistent_mask = find_persistent_regions(
             input_series, min_repeats=min_repeats, max_diff=0.001
         )
 
-        pd.testing.assert_series_equal(expected_output, static_mask, check_names=False)
+        pd.testing.assert_series_equal(expected_output, persistent_mask, check_names=False)
 
-    def test_static_period_at_the_end(self):
+    def test_persistent_period_at_the_end(self):
         time_range = pd.date_range(
             start="2023-01-26", end="2023-01-28", freq="h", tz="utc", inclusive="left"
         )
@@ -97,11 +97,11 @@ def test_static_period_at_the_end(self):
         expected_output = input_series.map(lambda _: False)
         expected_output[expected_filter_start:] = True
 
-        static_mask = find_static_regions(
+        persistent_mask = find_persistent_regions(
             input_series, min_repeats=min_repeats, max_diff=0.001
         )
 
-        pd.testing.assert_series_equal(expected_output, static_mask, check_names=False)
+        pd.testing.assert_series_equal(expected_output, persistent_mask, check_names=False)
 
     def test_dont_filter_nan_values(self):
         time_range = pd.date_range(
@@ -119,13 +119,13 @@ def test_dont_filter_nan_values(self):
         # The output mask shouldn't filter nan values.
         expected_output = input_series.map(lambda _: False)
 
-        static_mask = find_static_regions(
+        persistent_mask = find_persistent_regions(
             input_series, min_repeats=min_repeats, max_diff=0.001
         )
 
-        pd.testing.assert_series_equal(expected_output, static_mask, check_names=False)
+        pd.testing.assert_series_equal(expected_output, persistent_mask, check_names=False)
 
-    def test_series_with_nan_values_between_static_values(self):
+    def test_series_with_nan_values_between_persistent_values(self):
         time_range = pd.date_range(
             start="2023-01-26", end="2023-01-27", freq="h", tz="utc", inclusive="left"
         )
@@ -141,6 +141,10 @@ def test_series_with_nan_values_between_static_values(self):
         # Note: The station region mask shall not filter nan values
         expected_mask[16] = True
 
-        output_mask = find_static_regions(series, min_repeats=period, max_diff=0.01)
+        output_mask = find_persistent_regions(series, min_repeats=period, max_diff=0.01)
 
         np.testing.assert_equal(expected_mask, output_mask)
+
+
+if __name__ == "__main__":
+    unittest.main()