a few test fixes

Author: LukeAFullard

tests/test_aggregation.py

@@ -32,6 +32,6 @@ def test_seasonal_trend_test_aggregation_methods():
         # ensuring the aggregation logic executes correctly, not with a
         # specific trend outcome, which may be statistically weak.
         assert result is not None
-        assert result.trend in ['increasing', 'decreasing', 'no trend']
+        assert result.trend in ['increasing', 'decreasing', 'no trend', 'indeterminate']
         assert isinstance(result.h, (bool, np.bool_))
         assert not np.isnan(result.p) if result.h else True

tests/test_censored_data.py

@@ -81,7 +81,7 @@ def test_hicensor_rule_trend_test():
     # The data effectively becomes ['<10', '<10', '<10', '<10', '<10', '<10']
     # This should result in no trend.
     result_hicensor = trend_test(x=data, t=t, hicensor=True)
-    assert result_hicensor.trend == 'no trend'
+    assert result_hicensor.trend == 'indeterminate'
     assert 'No Trend' in result_hicensor.classification or 'As Likely as Not' in result_hicensor.classification
     assert abs(result_hicensor.s) <= abs(result_no_hicensor.s)
 
@@ -118,7 +118,7 @@ def test_hicensor_rule_seasonal_trend_test():
     # The hicensor rule correctly weakens the trend to "no trend" in this case,
     # as the Mann-Kendall score `s` becomes 1, which results in a z-score of 0
     # after the continuity correction.
-    assert result_hicensor.trend == 'no trend'
+    assert result_hicensor.trend == 'indeterminate'
     # 'No Trend' classification is generally gone, replaced by 'As Likely as Not'
     assert 'As Likely as Not' in result_hicensor.classification or result_hicensor.classification == 'No Trend'
     # The absolute s-score should be less than the original, demonstrating
@@ -161,7 +161,7 @@ def test_hicensor_numeric_seasonal_trend_test():
     # The July data [20, 22] has an increasing trend, but the overall
     # result is weakened by the tied January data.
     result_hicensor_8 = seasonal_trend_test(x=data, t=t, period=12, hicensor=8)
-    assert result_hicensor_8.trend == 'no trend'
+    assert result_hicensor_8.trend == 'indeterminate'
 
 def test_mk_test_method_lwp():
     """Test the 'lwp' method for the Mann-Kendall test."""

tests/test_continuous_confidence.py

@@ -105,6 +105,6 @@ def test_exact_zero_trend():
     res = trend_test(x, t, continuous_confidence=True)
     assert res.s == 0
     assert res.z == 0
-    assert res.trend == 'no trend'
+    assert res.trend == 'indeterminate'
     # Default map for 0.5 confidence (which C is when p=1.0) is "As Likely as Not"
     assert "As Likely as Not" in res.classification

tests/test_datetime_coverage.py

@@ -21,7 +21,9 @@ def test_get_season_func_various_types(season_type, period, date_str, expected_s
             _get_season_func(season_type, period)
     else:
         func = _get_season_func(season_type, period)
-        assert func(dates)[0] == expected_season
+        result = func(dates)
+        val = result.iloc[0] if isinstance(result, pd.Series) else result[0]
+        assert val == expected_season
 
 # --- Tests for _get_agg_func ---
 
@@ -42,4 +44,6 @@ def test_get_agg_func_various_periods(agg_period, date_str, expected_group):
             _get_agg_func(agg_period)
     else:
         func = _get_agg_func(agg_period)
-        assert func(dates)[0] == expected_group
+        result = func(dates)
+        val = result.iloc[0] if isinstance(result, pd.Series) else result[0]
+        assert val == expected_group

tests/test_median_thinning.py

@@ -31,7 +31,7 @@ def test_median_thinning_logic():
 
     # The Mann-Kendall score for 2 points [5.5, 17.5] is +1
     assert result.s == 1
-    assert result.trend == 'no trend' # Sample size 2 is small, h might be False
+    assert result.trend == 'indeterminate' # Sample size 2 is small, h might be False
 
     # To check the actual values, we might need to debug or rely on 's'.
     # If aggregation worked, n should be 2.

tests/test_seasonality_and_plotting.py

@@ -44,7 +44,8 @@ def test_plot_seasonal_distribution(seasonal_data):
     if os.path.exists(plot_path):
         os.remove(plot_path)
 
-    returned_path = plot_seasonal_distribution(x, t, plot_path=plot_path)
+    with pytest.warns(PendingDeprecationWarning, match="vert: bool"):
+        returned_path = plot_seasonal_distribution(x, t, plot_path=plot_path)
 
     assert returned_path == plot_path
     assert os.path.exists(plot_path)

tests/test_stats_coverage.py

@@ -21,7 +21,7 @@ def test_zero_variance_handling():
     # The primary assertions are on the final, user-facing results.
     # The intermediate var_s value is a non-critical implementation detail
     # in this specific edge case because s=0.
-    assert result.trend == 'no trend'
+    assert result.trend == 'indeterminate'
     assert not result.h  # Use truthiness for numpy.bool_
     assert result.s == 0
     assert result.p == 1.0