refactor: log the curve shift value sign in the warning message

Display the sign (+/-) of the curve shift value in the log warning
message for clarity of the actual shift rather than just displaying the
abs() value that is used when determining whether to raise a log warning

tests/test_ops_curve_shift.py

@@ -118,6 +118,7 @@ def test_calculate_power_curve_shift(
 
     if abs(expected) > CURVE_CONSTANTS[CurveTypes.POWER_CURVE.value]["warning_threshold"]:
         assert "Ops Curve Shift warning" in caplog.text
+        assert f": {actual:.3f}" in caplog.text  # check the actual value (including its +/- sign) is in the log message
 
     np.testing.assert_almost_equal(actual=actual, desired=expected)
 

wind_up/ops_curve_shift.py

@@ -208,7 +208,7 @@ def _calculate_curve_shift(curve_shift_input: CurveShiftInput) -> float:
     post_df = curve_shift_input.post_df
     wtg_name = curve_shift_input.turbine_name
 
-    bins = np.arange(0, pre_df[conf.x_col].max() + conf.x_bin_width, conf.x_bin_width) if conf.x_bin_width > 0 else 10  # type: ignore[operator]
+    bins = np.arange(0, pre_df[conf.x_col].max() + conf.x_bin_width, conf.x_bin_width) if conf.x_bin_width > 0 else 10  # type: ignore[operator,var-annotated]
 
     mean_curve = pre_df.groupby(pd.cut(pre_df[conf.x_col], bins=bins, retbins=False), observed=True).agg(
         x_mean=pd.NamedAgg(column=conf.x_col, aggfunc="mean"),
@@ -224,9 +224,7 @@ def _calculate_curve_shift(curve_shift_input: CurveShiftInput) -> float:
 
     # log warning
     if abs(result) > conf.warning_threshold:
-        warning_msg = (
-            f"{wtg_name} Ops Curve Shift warning: abs({conf.name}) > {conf.warning_threshold}: {abs(result):.3f}"
-        )
+        warning_msg = f"{wtg_name} Ops Curve Shift warning: abs({conf.name}) > {conf.warning_threshold}: {result:.3f}"
         result_manager.warning(warning_msg)
 
     return result