update temporal_test.py, prediction_times.py and utils_for_testing.py

src/timeseriesflattener/processors/temporal_test.py

@@ -175,7 +175,7 @@ def test_slice_without_any_within_window():
         result.get_column("pred_is_null_within_0_to_2_days"),
         timedelta_frame.df.collect().get_column("is_null"),
         check_names=False,
-        check_dtype=False,
+        check_dtypes=False,
     )
 
 

src/timeseriesflattener/specs/prediction_times.py

@@ -36,7 +36,7 @@ def __post_init__(
         else:
             self.df: pl.LazyFrame = init_df  # type: ignore
 
-        self.df = self.df.with_columns(
+        self.df = self.df.with_columns(  # type: ignore
             pl.concat_str(
                 pl.col(self.entity_id_col_name), pl.lit("-"), pl.col(self.timestamp_col_name)
             ).alias(self.prediction_time_uuid_col_name)

src/timeseriesflattener/testing/utils_for_testing.py

@@ -1,4 +1,5 @@
 """Utilities for testing."""
+
 from __future__ import annotations
 
 from io import StringIO
@@ -121,15 +122,15 @@ def assert_flattened_data_as_expected(
     if expected_df:
         for col in expected_df.columns:
             assert_series_equal(
-                left=flattened_ds.get_df()[col], right=expected_df[col], check_dtype=False
-            )
+                left=flattened_ds.get_df()[col], right=expected_df[col], check_dtypes=False
+            )  # type: ignore
     elif expected_values:
         output_df = flattened_ds.get_df()
         value_cols = _get_value_cols_based_on_spec(output_df, output_spec)
         output = pd.Series(output_df[value_cols].values)
         expected = pd.Series(expected_values)
 
-        assert_series_equal(output, expected, check_dtype=False)
+        assert_series_equal(output, expected, check_dtypes=False)  # type: ignore
     else:
         raise ValueError("Must provide an expected set of data")