remove all Union type hints

src/mrtool/core/data.py

@@ -8,7 +8,7 @@
 
 import warnings
 from dataclasses import dataclass, field
-from typing import Any, Union
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -208,9 +208,7 @@ def _assert_not_empty(self):
         if self.is_empty():
             raise ValueError("MRData object is empty.")
 
-    def is_cov_normalized(
-        self, covs: Union[list[str], str, None] = None
-    ) -> bool:
+    def is_cov_normalized(self, covs: list[str] | str | None = None) -> bool:
         """Return true when covariates are normalized."""
         if covs is None:
             covs = list(self.covs.keys())
@@ -237,11 +235,11 @@ def reset(self):
     def load_df(
         self,
         data: pd.DataFrame,
-        col_obs: Union[str, None] = None,
-        col_obs_se: Union[str, None] = None,
-        col_covs: Union[list[str], None] = None,
-        col_study_id: Union[str, None] = None,
-        col_data_id: Union[str, None] = None,
+        col_obs: str | None = None,
+        col_obs_se: str | None = None,
+        col_covs: list[str] | None = None,
+        col_study_id: str | None = None,
+        col_data_id: str | None = None,
     ):
         """Load data from data frame."""
         self.reset()
@@ -273,10 +271,10 @@ def load_df(
     def load_xr(
         self,
         data,
-        var_obs: Union[str, None] = None,
-        var_obs_se: Union[str, None] = None,
-        var_covs: Union[list[str], None] = None,
-        coord_study_id: Union[str, None] = None,
+        var_obs: str | None = None,
+        var_obs_se: str | None = None,
+        var_covs: list[str] | None = None,
+        coord_study_id: str | None = None,
     ):
         """Load data from xarray."""
         self.reset()
@@ -314,11 +312,11 @@ def to_df(self) -> pd.DataFrame:
 
         return df
 
-    def has_covs(self, covs: Union[list[str], str]) -> bool:
+    def has_covs(self, covs: list[str] | str) -> bool:
         """If the data has the provided covariates.
 
         Args:
-            covs (Union[list[str], str]):
+            covs (list[str] | str):
                 list of covariate names or one covariate name.
 
         Returns:
@@ -330,11 +328,11 @@ def has_covs(self, covs: Union[list[str], str]) -> bool:
         else:
             return all([cov in self.covs for cov in covs])
 
-    def has_studies(self, studies: Union[list[Any], Any]) -> bool:
+    def has_studies(self, studies: list[Any] | Any) -> bool:
         """If the data has provided study_id
 
         Args:
-            studies Union[list[Any], Any]:
+            studies list[Any] | Any:
                 list of studies or one study.
 
         Returns:
@@ -346,7 +344,7 @@ def has_studies(self, studies: Union[list[Any], Any]) -> bool:
         else:
             return all([study in self.studies for study in studies])
 
-    def _assert_has_covs(self, covs: Union[list[str], str]):
+    def _assert_has_covs(self, covs: list[str] | str):
         """Assert has covariates otherwise raise ValueError."""
         if not self.has_covs(covs):
             covs = to_list(covs)
@@ -355,7 +353,7 @@ def _assert_has_covs(self, covs: Union[list[str], str]):
                 f"MRData object do not contain covariates: {missing_covs}."
             )
 
-    def _assert_has_studies(self, studies: Union[list[Any], Any]):
+    def _assert_has_studies(self, studies: list[Any] | Any):
         """Assert has studies otherwise raise ValueError."""
         if not self.has_studies(studies):
             studies = to_list(studies)
@@ -366,11 +364,11 @@ def _assert_has_studies(self, studies: Union[list[Any], Any]):
                 f"MRData object do not contain studies: {missing_studies}."
             )
 
-    def get_covs(self, covs: Union[list[str], str]) -> np.ndarray:
+    def get_covs(self, covs: list[str] | str) -> np.ndarray:
         """Get covariate matrix.
 
         Args:
-            covs (Union[list[str], str]):
+            covs (list[str] | str):
                 list of covariate names or one covariate name.
 
         Returns:
@@ -385,11 +383,11 @@ def get_covs(self, covs: Union[list[str], str]) -> np.ndarray:
                 [self.covs[cov_names][:, None] for cov_names in covs]
             )
 
-    def get_study_data(self, studies: Union[list[Any], Any]) -> "MRData":
+    def get_study_data(self, studies: list[Any] | Any) -> "MRData":
         """Get study specific data.
 
         Args:
-            studies (Union[list[Any], Any]): list of studies or  one study.
+            studies (list[Any] | Any): list of studies or  one study.
 
         Returns
             MRData: Data object contains the study specific data.
@@ -399,7 +397,7 @@ def get_study_data(self, studies: Union[list[Any], Any]) -> "MRData":
         index = np.array([study in studies for study in self.study_id])
         return self._get_data(index)
 
-    def normalize_covs(self, covs: Union[list[str], str, None] = None):
+    def normalize_covs(self, covs: list[str] | str | None = None):
         """Normalize covariates by the largest absolute value for each covariate."""
         if covs is None:
             covs = list(self.covs.keys())

src/mrtool/core/model.py

@@ -7,7 +7,6 @@
 """
 
 from copy import deepcopy
-from typing import Union
 
 import numpy as np
 import pandas as pd
@@ -453,7 +452,7 @@ def __init__(
         data: MRData,
         ensemble_cov_model: CovModel,
         ensemble_knots: NDArray,
-        cov_models: Union[list[CovModel], None] = None,
+        cov_models: list[CovModel] | None = None,
         inlier_pct: float = 1.0,
     ):
         """Constructor of `MRBeRT`
@@ -462,7 +461,7 @@ def __init__(
             data (MRData): Data for meta-regression.
             ensemble_cov_model (CovModel):
                 Covariates model which will be used with ensemble.
-            cov_models (Union[list[CovModel], None], optional):
+            cov_models (list[CovModel] | None, optional):
                 Other covariate models, assume to be mutual exclusive with ensemble_cov_mdoel.
             inlier_pct (float): A float number between 0 and 1 indicate the percentage of inliers.
         """

src/mrtool/core/utils.py

@@ -5,7 +5,7 @@
 `utils` module of the `mrtool` package.
 """
 
-from typing import Any, Union
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -294,7 +294,7 @@ def avg_integral(mat, spline=None, use_spline_intercept=False):
 def sample_knots(
     num_knots: int,
     knot_bounds: np.ndarray,
-    min_dist: Union[float, np.ndarray],
+    min_dist: float | np.ndarray,
     num_samples: int = 1,
 ) -> np.ndarray:
     """Sample knot vectors given a set of rules.
@@ -367,9 +367,7 @@ def _check_knot_bounds(num_knots: int, knot_bounds: np.ndarray) -> np.ndarray:
     return knot_bounds
 
 
-def _check_min_dist(
-    num_knots: int, min_dist: Union[float, np.ndarray]
-) -> np.ndarray:
+def _check_min_dist(num_knots: int, min_dist: float | np.ndarray) -> np.ndarray:
     """Check knot min_dist."""
     if np.isscalar(min_dist):
         min_dist = np.tile(min_dist, num_knots + 1)

src/mrtool/cov_selection/covfinder.py

@@ -6,7 +6,6 @@
 
 import warnings
 from copy import deepcopy
-from typing import Union
 
 import numpy as np
 
@@ -23,7 +22,7 @@ def __init__(
         self,
         data: MRData,
         covs: list[str],
-        pre_selected_covs: Union[list[str], None] = None,
+        pre_selected_covs: list[str] | None = None,
         normalized_covs: bool = True,
         num_samples: int = 1000,
         laplace_threshold: float = 1e-5,
@@ -34,7 +33,7 @@ def __init__(
         beta_gprior: dict[str, np.ndarray] = None,
         beta_gprior_std: float = 1.0,
         bias_zero: bool = False,
-        use_re: Union[dict, None] = None,
+        use_re: dict | None = None,
     ):
         """Covariate Finder.
 
@@ -59,7 +58,7 @@ def __init__(
             beta_gprior_std (float, optional): Loose beta Gaussian prior standard deviation. Default to 1.
             bias_zero (bool, optional):
                 If `True`, fit when specify the Gaussian prior it will be mean zero. Default to `False`.
-            use_re (Union[dict, None], optional):
+            use_re (dict | None, optional):
                 A dictionary of use_re for each covariate. When `None` we have an uninformative prior
                 for the random effects variance. Default to `None`.
         """