Detections csv weak comment rejection

src/osekit/core/detection.py

@@ -12,6 +12,7 @@
 from matplotlib.transforms import TransformedBbox
 from pandas import Timedelta, Timestamp
 
+from osekit.config import global_logging_context as glc
 from osekit.core.event import Event
 from osekit.utils.core import is_empty_dataclass
 
@@ -28,6 +29,7 @@
     "min_frequency",
     "max_frequency",
     "annotation",
+    "label",
     "annotator",
     "annotator_expertise",
     "start_datetime",
@@ -617,7 +619,7 @@ def from_dict(cls, row: dict) -> Self:
 
         return cls(
             metadata=metadata,
-            label=row.get("annotation"),
+            label=row.get("label") or row.get("annotation"),
             detector_info=detector_info,
             begin=Timestamp(row["start_datetime"]),
             end=Timestamp(row["end_datetime"]),
@@ -764,7 +766,21 @@ def _from_csv(cls, csv: Path, **kwargs: Any) -> list[Self]:
             }
             for record in records
         ]
-        return [cls.from_dict(record) for record in records]
+        parsed_records = []
+        failed_records = []
+        for idx, record in enumerate(records):
+            try:
+                parsed_records.append(cls.from_dict(record))
+            except TypeError:  # Most probably a weak comment on the spectrogram
+                failed_records.append(idx)
+
+        if failed_records:
+            glc.logger.warning(
+                f"The following line(s) couldn't be parsed as detections: "
+                f"{failed_records}",
+            )
+
+        return parsed_records
 
     @classmethod
     def from_csv(cls, csv: Path | list[Path], **kwargs: Any) -> list[Self]:
@@ -792,4 +808,11 @@ def from_csv(cls, csv: Path | list[Path], **kwargs: Any) -> list[Self]:
         for csv_file in csv:
             output += cls._from_csv(csv_file, **kwargs)
 
+        if not output:
+            msg = (
+                "No valid Detection was found in the given csv file(s).\n"
+                "Tip: you can specify the used separator with the sep parameter."
+            )
+            raise ValueError(msg)
+
         return output

tests/_static/aplose_result.csv

@@ -1,4 +1,4 @@
-dataset,filename,annotation_id,is_update_of_id,start_time,end_time,start_frequency,end_frequency,min_frequency,max_frequency,annotation,annotator,annotator_expertise,start_datetime,end_datetime,is_box,type,confidence_indicator_label,confidence_indicator_level,comments,signal_quantity,signal_is_intensity_too_low,signal_does_overlap_other_signals,signal_start_frequency,signal_end_frequency,signal_relative_min_frequency_count,signal_relative_max_frequency_count,signal_steps_count,signal_has_harmonics,signal_trend,signal_sidebands,signal_subharmonics,signal_frequency_jumps,signal_deterministic_chaos,created_at_phase,lookaftering,bunyan
+dataset,filename,annotation_id,is_update_of_id,start_time,end_time,start_frequency,end_frequency,min_frequency,max_frequency,label,annotator,annotator_expertise,start_datetime,end_datetime,is_box,type,confidence_indicator_label,confidence_indicator_level,comments,signal_quantity,signal_is_intensity_too_low,signal_does_overlap_other_signals,signal_start_frequency,signal_end_frequency,signal_relative_min_frequency_count,signal_relative_max_frequency_count,signal_steps_count,signal_has_harmonics,signal_trend,signal_sidebands,signal_subharmonics,signal_frequency_jumps,signal_deterministic_chaos,created_at_phase,lookaftering,bunyan
 great_tit,990694,586654,,0.0,20.0,0.0,24000.0,0.0,24000.0,bird,vashti,NOVICE,2021-01-01T00:00:00.000+00:00,2021-01-01T00:00:20.000+00:00,0,WEAK,Sure,1/1,great tits |- vashti,MULTIPLE,,,,,,,,,,,,,,ANNOTATION,True,False
 great_tit,990694,586655,,1.412,3.651,2512.0,15661.0,2512.0,15661.0,bird,vashti,NOVICE,2021-01-01T00:00:01.412+00:00,2021-01-01T00:00:03.651+00:00,1,BOX,Not sure,0/1,fluffy-backed tit-babbler |- vashti,MULTIPLE,,,,,,,,,,,,,,ANNOTATION,False,False
 great_tit,990694,586673,,10.0,12.0,12000.0,13000.0,12000.0,13000.0,rain,heartleap,,2021-01-01T00:00:00.000+00:00,2021-01-01T00:00:20.000+00:00,0,BOX,,,,MULTIPLE,,,,,,,,,,,,,,ANNOTATION,True,True
@@ -9,3 +9,4 @@ great_tit,990694,586710,586655,1.412,3.651,2512.0,15660.0,2512.0,15660.0,bird,lo
 great_tit,994410,586671,,0.0,20.0,0.0,24000.0,0.0,24000.0,car,bunyan,EXPERT,2021-01-01T00:01:18.218+00:00,2021-01-01T00:01:38.218+00:00,0,WEAK,Sure,1/1,,MULTIPLE,,,,,,,,,,,,,,VERIFICATION,,
 great_tit,994410,586672,,0.0,20.0,0.0,24000.0,0.0,24000.0,bird,bunyan,EXPERT,2021-01-01T00:01:18.218+00:00,2021-01-01T00:01:38.218+00:00,0,WEAK,Sure,1/1,,MULTIPLE,,,,,,,,,,,,,,VERIFICATION,,
 ,,,,,,,,0.0,24000.0,,,,2021-01-01T00:01:18.218+00:00,2021-01-01T00:01:38.218+00:00,,,,,,,,,,,,,,,,,,,,,,
+great_tit,990694,,,,,,,,,,heartleap,,2021-01-01T00:00:00.000+00:00,2021-01-01T00:00:20.000+00:00,,,,,weak comment |- heartleap,,,,,,,,,,,,,,,,,

tests/_static/retro_compatibility_result.csv

@@ -0,0 +1,2 @@
+dataset,filename,annotation_id,is_update_of_id,start_time,end_time,start_frequency,end_frequency,min_frequency,max_frequency,annotation,annotator,annotator_expertise,start_datetime,end_datetime,is_box,type,confidence_indicator_label,confidence_indicator_level,comments,signal_quantity,signal_is_intensity_too_low,signal_does_overlap_other_signals,signal_start_frequency,signal_end_frequency,signal_relative_min_frequency_count,signal_relative_max_frequency_count,signal_steps_count,signal_has_harmonics,signal_trend,signal_sidebands,signal_subharmonics,signal_frequency_jumps,signal_deterministic_chaos,created_at_phase,lookaftering,bunyan
+great_tit,990694,586654,,0.0,20.0,0.0,24000.0,0.0,24000.0,bird,vashti,NOVICE,2021-01-01T00:00:00.000+00:00,2021-01-01T00:00:20.000+00:00,0,WEAK,Sure,1/1,great tits |- vashti,MULTIPLE,,,,,,,,,,,,,,ANNOTATION,True,False

tests/test_detection.py

@@ -1,9 +1,11 @@
+import logging
 from contextlib import AbstractContextManager, nullcontext
 from pathlib import Path
 from typing import Any, Literal
 
 import numpy as np
 import pytest
+from _pytest.logging import LogCaptureFixture
 from matplotlib.axes import Axes
 from matplotlib.patches import Rectangle
 from pandas import DataFrame, Timedelta, Timestamp
@@ -235,10 +237,14 @@ def test_confidence_indicator_from_relative_level_string(
         assert ci.maximum_level == e.maximum_level
 
 
-def test_detections_from_csv() -> None:
-    detections = Detection.from_csv(
-        csv=Path(__file__).parent / "_static" / "aplose_result.csv",
-    )
+def test_detections_from_csv(caplog: LogCaptureFixture) -> None:
+    with caplog.at_level(logging.WARNING):
+        detections = Detection.from_csv(
+            csv=Path(__file__).parent / "_static" / "aplose_result.csv",
+        )
+
+    # Failed parsed line should be logged (weak comment at idx 10)
+    assert "[10]" in caplog.text
 
     minimal_detection = next(d for d in detections if d.label is None)
     detections = [d for d in detections if d is not minimal_detection]
@@ -336,6 +342,21 @@ def test_detections_from_csv() -> None:
     assert len(minimal_detection.verifications) == 0
 
 
+def test_detections_from_csv_raises_if_no_detection(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    def mock_from_dict(*args, **kwargs) -> None:  # noqa: ANN002, ANN003
+        msg = r"Mock a TypeError while trying to parse a Detection object."
+        raise TypeError(msg)
+
+    monkeypatch.setattr(Detection, "from_dict", mock_from_dict)
+
+    with pytest.raises(ValueError, match="No valid Detection was found"):
+        Detection.from_csv(
+            csv=Path(__file__).parent / "_static/minimal_detector_result.csv",
+        )
+
+
 def test_detections_to_dict(tmp_path: Path) -> None:
     for result_file in ("aplose_result.csv", "minimal_detector_result.csv"):
         detections = Detection.from_csv(
@@ -393,6 +414,14 @@ def test_minimal_detector_result_file() -> None:
     assert len(detections) == 2
 
 
+def test_retro_compatibility() -> None:
+    detections = Detection.from_csv(
+        csv=Path(__file__).parent / "_static/retro_compatibility_result.csv",
+    )
+
+    assert detections[0].label == "bird"
+
+
 def test_detections_from_csv_list() -> None:
     detections = Detection.from_csv(
         [