Fix how list types are specified in type hints for type checking

When specifying that an input can be a list of some type, you don't use the list object, you use the List type. This has been fixed across all functions in the repo

src/rc_gpfs/process/factory.py

@@ -3,7 +3,7 @@ import pandas as pd
 import dask.dataframe as dd
 import dask_cudf
 from .utils import as_timedelta
-from typing import Literal
+from typing import Literal, List
 from typeguard import typechecked
 
 __all__ = ['get_aggregator']
@@ -21,8 +21,8 @@ class Aggregator:
     def _cut(
         self,
         ser: pd.Series | cudf.Series,
-        bins: list[int | pd.Timestamp],
-        labels: list[str] | None = None,
+        bins: List[int | pd.Timestamp],
+        labels: List[str] | None = None,
         **kwargs
     ) -> pd.Series | cudf.Series:
         right = kwargs.pop('right',False)
@@ -40,10 +40,10 @@ class Aggregator:
 
     def create_timedelta_cutoffs(
         self,
-        delta_vals: int | list[int],
+        delta_vals: int | List[int],
         delta_unit: Literal['D','W','M','Y'],
         run_date: pd.Timestamp
-    ) -> list[int | pd.Timestamp]:
+    ) -> List[int | pd.Timestamp]:
         deltas = pd.Series([as_timedelta(c,delta_unit) for c in delta_vals])
         cutoffs = pd.to_datetime(run_date - deltas)
         cutoffs = (
@@ -60,9 +60,9 @@ class Aggregator:
     
     def create_timedelta_labels(
         self,
-        delta_vals: list[int],
+        delta_vals: List[int],
         delta_unit: Literal['D','W','M','Y'],
-    ) -> list[str]:
+    ) -> List[str]:
         delta_vals.sort(reverse=True)
         deltas = [f'{d}{delta_unit}' for d in delta_vals]
         labels = [f'>{deltas[0]}'] + [f'{deltas[i+1]}-{deltas[i]}' for i in range(len(deltas)-1)] + [f'<{deltas[-1]}']
@@ -82,9 +82,9 @@ class PandasAggregator(Aggregator):
     def aggregate(
         self,
         df: cudf.DataFrame,
-        col: str | list[str],
-        grps: str | list[str],
-        funcs: str | list[str]
+        col: str | List[str],
+        grps: str | List[str],
+        funcs: str | List[str]
     ) -> pd.DataFrame:
         
         df_agg = (
@@ -110,9 +110,9 @@ class CUDFAggregator(Aggregator):
     def aggregate(
         self,
         df: cudf.DataFrame,
-        col: str | list[str],
-        grps: str | list[str],
-        funcs: str | list[str]
+        col: str | List[str],
+        grps: str | List[str],
+        funcs: str | List[str]
     ) -> pd.DataFrame:
         df_agg = (
             df.groupby(grps,observed = True)[col]
@@ -135,9 +135,9 @@ class DaskAggregator(Aggregator):
     def aggregate(
         self,
         df: dd.DataFrame,
-        col: str | list[str],
-        grps: str | list[str],
-        funcs: str | list[str]
+        col: str | List[str],
+        grps: str | List[str],
+        funcs: str | List[str]
     ) -> pd.DataFrame:
         df_agg = (
             df.groupby(grps,observed = True)[col]
@@ -164,9 +164,9 @@ class DaskCUDFAggregator(Aggregator):
     def aggregate(
         self,
         df: dask_cudf.DataFrame,
-        col: str | list[str],
-        grps: str | list[str],
-        funcs: str | list[str]
+        col: str | List[str],
+        grps: str | List[str],
+        funcs: str | List[str]
     ) -> pd.DataFrame:
         df_agg = (
             df.groupby(grps,observed = True)[col]