codeant-ai-for-open-source[bot] commented on code in PR #42589:
URL: https://github.com/apache/superset/pull/42589#discussion_r3678757855
##########
tests/unit_tests/utils/test_core.py:
##########
@@ -359,6 +359,46 @@ def test_normalize_dttm_col_with_offset() -> None:
assert df["date_col"][2].strftime("%Y-%m-%d %H:%M:%S") == "2022-01-01
03:00:00"
+def test_normalize_dttm_col_second_precision_no_offset_matches_source() ->
None:
+ """Regression test for #37925: second-precision timestamps with no
+ dataset offset configured ("UTC", i.e. offset=0) and no time shift must
+ pass through ``normalize_dttm_col`` unchanged and identically to their
+ source values, with no per-row drift.
+
+ The issue reports charts showing datetimes shifted by inconsistent,
+ non-uniform amounts versus the same data in SQL Lab, with the reporter's
+ own examples showing each shift exactly equal to that row's own
+ time-of-day (e.g. 16:30:00 shifted by +16h30m, 10:00:00 by +10h,
+ 14:20:00 by +14h20m). ``normalize_dttm_col`` applies a single
+ ``_col.offset``/``_col.time_shift`` uniformly via ``timedelta(...)`` to
+ the whole column (see ``test_normalize_dttm_col_with_offset`` above,
+ already green), which cannot structurally produce a shift that varies
+ per row based on that row's own value, so this function is not the
+ mechanism the issue describes. This test locks in the specific
+ reported config (offset=0, no time_shift, second-level grain, multiple
+ distinct timestamps) end to end to make that explicit.
+ """
+ source_values = [
+ "2026-02-15 16:30:00",
+ "2026-02-15 10:00:00",
+ "2026-02-11 14:20:00",
+ ]
+ df = pd.DataFrame({"dttm": source_values})
+ dttm_cols = (
+ DateColumn(
+ col_label="dttm",
+ timestamp_format="%Y-%m-%d %H:%M:%S",
+ offset=0,
+ time_shift=None,
+ ),
+ )
Review Comment:
**Suggestion:** This test only exercises `normalize_dttm_col` with a
manually constructed, timezone-naive DataFrame and an explicitly supplied
format. The chart paths construct `DateColumn` from datasource metadata and may
provide an inferred format or already-native datetime values, so a defect in
that construction, format detection, or timezone-aware parsing can remain
undetected while this test passes. Add coverage through the relevant caller
path or use representative metadata/native datetime inputs before treating this
as a regression test for the reported chart behavior. [api mismatch]
<details>
<summary><b>Severity Level:</b> Major ⚠️</summary>
```mdx
- ❌ Chart normalization regressions can remain undetected.
- ⚠️ `Viz.get_df()` metadata handling is untested.
- ⚠️ Native timezone-aware datetime inputs are untested.
```
</details>
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<details>
<summary><b>Prompt for AI Agent 🤖 </b></summary>
```mdx
This is a comment left during a code review.
**Path:** tests/unit_tests/utils/test_core.py
**Line:** 386:394
**Comment:**
*Api Mismatch: This test only exercises `normalize_dttm_col` with a
manually constructed, timezone-naive DataFrame and an explicitly supplied
format. The chart paths construct `DateColumn` from datasource metadata and may
provide an inferred format or already-native datetime values, so a defect in
that construction, format detection, or timezone-aware parsing can remain
undetected while this test passes. Add coverage through the relevant caller
path or use representative metadata/native datetime inputs before treating this
as a regression test for the reported chart behavior.
Validate the correctness of the flagged issue. If correct, How can I resolve
this? If you propose a fix, implement it and please make it concise.
Once fix is implemented, also check other comments on the same PR, and ask
user if the user wants to fix the rest of the comments as well. if said yes,
then fetch all the comments validate the correctness and implement a minimal fix
```
</details>
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