Added test for correctness of generated datetime-intervals based on f…

…ilenames

tests/disabled_utils.py

@@ -78,14 +78,14 @@ def test_save_dataset_to_zarr(self):  # noqa D102
         # test function but save_dataset_to_zarr depends on a dataset being loaded,
         # we have to reload the dataset here. This means that this test can theoretically
         # fail for two reasons: Either the data-loading failed, or the data-saving failed.
-        cloudmask_dataset = load_cloudmask_to_dataset(
-            Path(self.cloud_mask_filename), temp_directory=Path(os.getcwd()), area="UK"
+        rss_dataset, _ = load_native_to_dataset(
+            Path(self.rss_filename), temp_directory=Path(os.getcwd()), area="UK"
         )
 
         zarr_path = os.path.join(os.getcwd(), "tmp.zarr")
 
         save_dataset_to_zarr(
-            cloudmask_dataset,
+            rss_dataset,
             zarr_path=zarr_path,
             compressor_name="bz2",
             zarr_mode="w",

tests/test_download.py

@@ -4,7 +4,11 @@
 
 import pandas as pd
 
-from satip.download import _determine_datetimes_to_download_files, download_eumetsat_data
+from satip.download import (
+    _determine_datetimes_to_download_files,
+    _get_missing_datetimes_from_list_of_files,
+    download_eumetsat_data,
+)
 from satip.utils import format_dt_str
 
 
@@ -49,3 +53,32 @@ def test_determine_datetime_to_download_files(self):
         self.assertEqual(datetimes[0][1], pd.to_datetime("2020-03-09 11:58:00"))
         self.assertEqual(datetimes[1][0], pd.to_datetime("2020-03-09 11:59:00"))
         self.assertEqual(datetimes[1][1], pd.to_datetime("2020-03-10 11:58:00"))
+
+    def test_get_missing_datetimes_from_list_of_files(self):
+        """Tests padding of datetimes if files present are missing data for given days."""
+        # Assume we only have two files present, as something went very wrong
+        # with the download.
+        filenames = [
+            "MSG3-SEVI-MSG15-0100-NA-20190308114036.810000000Z-NA.nat",
+            "MSG3-SEVI-MSG15-0100-NA-20220308133711.810000000Z-NA.nat",
+        ]
+
+        # We then expect the function to pad this by adding missing timeranges
+        # to fill up everything from the beginning to the first day to the timestamp
+        # of the first day (case A), then everything between both files (case B)
+        # and finally fill up the rest until the end of the second day (case C).
+        expected_timestamps = [
+            (pd.to_datetime("2019-03-08 00:00:00"), pd.to_datetime("2019-03-08 11:40:00")),
+            (pd.to_datetime("2019-03-08 11:40:00"), pd.to_datetime("2022-03-08 13:37:00")),
+            (pd.to_datetime("2022-03-08 13:37:00"), pd.to_datetime("2022-03-08 23:58:00")),
+        ]
+
+        # Generate the list of missing datetime interval boundaries:
+        res = _get_missing_datetimes_from_list_of_files(filenames)
+
+        for i, interval in enumerate(expected_timestamps):
+            for b, boundary in enumerate(interval):
+                # Note that the function returns datetime-objects, but we defined the expected
+                # values as pd-datetime. Though their str-repr is different, they still pass
+                # the equality-check when compared for same dates.
+                self.assertEqual(boundary, res[i][b])