change check fun name to check_timestamps_without_nans

CHANGELOG.md

@@ -2,7 +2,7 @@
 
 ### Improvements
 
-* Update util function `is_ascending_series` to discard nan values and add `check_timestamps_with_nans` fun to check if timestamps contain NaN values [#476](https://github.com/NeurodataWithoutBorders/nwbinspector/issues/476)
+* Update util function `is_ascending_series` to discard nan values and add `check_timestamps_without_nans` fun to check if timestamps contain NaN values [#476](https://github.com/NeurodataWithoutBorders/nwbinspector/issues/476)
 
 # v0.4.37
 

docs/best_practices/time_series.rst

@@ -98,7 +98,7 @@ Ensure that all timestamps are valid numerical values. If gaps in time need to b
 data into separate :ref:`nwb-schema:sec-TimeSeries` objects with appropriate ``starting_time`` or use the ``timestamps``
 vector to explicitly represent time gaps.
 
-Check function: :py:meth:`~nwbinspector.checks.time_series.check_timestamps_with_nans`
+Check function: :py:meth:`~nwbinspector.checks.time_series.check_timestamps_without_nans`
 
 
 

src/nwbinspector/checks/time_series.py

@@ -94,7 +94,7 @@ def check_timestamps_ascending(time_series: TimeSeries, nelems=200):
 
 
 @register_check(importance=Importance.BEST_PRACTICE_VIOLATION, neurodata_type=TimeSeries)
-def check_timestamps_with_nans(time_series: TimeSeries, nelems=200):
+def check_timestamps_without_nans(time_series: TimeSeries, nelems=200):
     """Check if there are NaN values in the timestamps array."""
     if time_series.timestamps is not None and np.isnan(time_series.timestamps[:nelems]).any():
         return InspectorMessage(message=f"{time_series.name} timestamps contain NaN values.")

tests/unit_tests/test_time_series.py

@@ -12,7 +12,7 @@
     check_data_orientation,
     check_timestamps_match_first_dimension,
     check_timestamps_ascending,
-    check_timestamps_with_nans,
+    check_timestamps_without_nans,
     check_missing_unit,
     check_resolution,
     check_timestamp_of_the_first_sample_is_not_negative,
@@ -261,19 +261,19 @@ def test_check_timestamps_ascending_with_nans_fail():
     )
 
 
-def test_check_timestamps_with_nans_pass():
+def test_check_timestamps_without_nans_pass():
     time_series = pynwb.TimeSeries(name="test_time_series", unit="test_units", data=[1, 2, 3], timestamps=[1, 2, 3])
-    assert check_timestamps_with_nans(time_series) is None
+    assert check_timestamps_without_nans(time_series) is None
 
 
-def test_check_timestamps_with_nans_fail():
+def test_check_timestamps_without_nans_fail():
     time_series = pynwb.TimeSeries(
         name="test_time_series", unit="test_units", data=[1, 2, 3], timestamps=[np.nan, 2, 3]
     )
-    assert check_timestamps_with_nans(time_series) == InspectorMessage(
+    assert check_timestamps_without_nans(time_series) == InspectorMessage(
         message="test_time_series timestamps contain NaN values.",
         importance=Importance.BEST_PRACTICE_VIOLATION,
-        check_function_name="check_timestamps_with_nans",
+        check_function_name="check_timestamps_without_nans",
         object_type="TimeSeries",
         object_name="test_time_series",
         location="/",