reuse util to seed error_mechanism

tab_err/error_mechanism/_ear.py

@@ -3,8 +3,6 @@
 import warnings
 from typing import TYPE_CHECKING
 
-import numpy as np
-
 from tab_err._utils import get_column, get_column_str
 
 from ._error_mechanism import ErrorMechanism
@@ -23,7 +21,7 @@ def _sample(self: EAR, data: pd.DataFrame, column: str | int, error_rate: float,
         if self.condition_to_column is None:
             col = get_column_str(data, column)
             column_selection = [x for x in data.columns if x != col]
-            condition_to_column = np.random.default_rng(self.seed).choice(column_selection)
+            condition_to_column = self._random_generator.choice(column_selection)
             warnings.warn(
                 "The user did not specify 'condition_to_column', the column on which the EAR Mechanism conditions the error distribution. "
                 + f"Randomly select column '{condition_to_column}'.",
@@ -46,7 +44,7 @@ def _sample(self: EAR, data: pd.DataFrame, column: str | int, error_rate: float,
 
         # we offset the upper bound of the lower_error_index by a) the existing number of errors in the row, and b) the number of errors to-be generated.
         upper_bound = len(se_data) - sum(se_mask) - n_errors
-        lower_error_index = np.random.default_rng(self.seed).integers(0, upper_bound) if upper_bound > 0 else 0
+        lower_error_index = self._random_generator.integers(0, upper_bound) if upper_bound > 0 else 0
         error_index_range = range(lower_error_index, lower_error_index + n_errors)
         selected_rows = data_column_error_free.sort_values(by=condition_to_column).iloc[error_index_range, :]
 

tab_err/error_mechanism/_ecar.py

@@ -3,8 +3,6 @@
 import warnings
 from typing import TYPE_CHECKING
 
-import numpy as np
-
 from tab_err._utils import get_column
 
 from ._error_mechanism import ErrorMechanism
@@ -30,6 +28,6 @@ def _sample(self: ECAR, data: pd.DataFrame, column: str | int, error_rate: float
             raise ValueError(msg)
 
         # randomly choose error-cells
-        error_indices = np.random.default_rng(seed=self.seed).choice(se_mask_error_free.index, n_errors, replace=False)
+        error_indices = self._random_generator.choice(se_mask_error_free.index, n_errors, replace=False)
         se_mask[error_indices] = True
         return error_mask

tab_err/error_mechanism/_enar.py

@@ -3,8 +3,6 @@
 import warnings
 from typing import TYPE_CHECKING
 
-import numpy as np
-
 from tab_err._utils import get_column
 
 from ._error_mechanism import ErrorMechanism
@@ -33,10 +31,7 @@ def _sample(self: ENAR, data: pd.DataFrame, column: str | int, error_rate: float
             msg += f"However, only {len(se_data_error_free)} error-free cells are available."
             raise ValueError(msg)
 
-        if len(se_data_error_free) != n_errors:  # noqa: SIM108
-            lower_error_index = np.random.default_rng(seed=self.seed).integers(0, len(se_data_error_free) - n_errors)
-        else:
-            lower_error_index = 0
+        lower_error_index = self._random_generator.integers(0, len(se_data_error_free) - n_errors) if len(se_data_error_free) != n_errors else 0
 
         error_index_range = range(lower_error_index, lower_error_index + n_errors)
         selected_rows = se_data_error_free.sort_values().iloc[error_index_range]

tab_err/error_mechanism/_error_mechanism.py

@@ -1,14 +1,26 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING
 
 import pandas as pd
 
+from tab_err._utils import seed_randomness
+
+if TYPE_CHECKING:
+    import numpy as np
+
 
 class ErrorMechanism(ABC):
     def __init__(self: ErrorMechanism, condition_to_column: int | str | None = None, seed: int | None = None) -> None:
+        if not (isinstance(seed, int) or seed is None):
+            msg = "'seed' need to be int or None."
+            raise TypeError(msg)
+
         self.condition_to_column = condition_to_column
-        self.seed = seed
+
+        self._seed = seed
+        self._random_generator: np.random.Generator
 
     def sample(
         self: ErrorMechanism,
@@ -22,17 +34,10 @@ def sample(
             error_rate_msg = "'error_rate' need to be float: 0 <= error_rate <= 1."
             raise ValueError(error_rate_msg)
 
-        if not (isinstance(self.seed, int) or self.seed is None):
-            msg = "'seed' need to be int or None."
-            raise TypeError(msg)
-
-        data_msg = "'data' needs to be a non-empty DataFrame."
-        if not isinstance(data, pd.DataFrame):
+        if not isinstance(data, pd.DataFrame) or data.empty:
+            data_msg = "'data' needs to be a non-empty DataFrame."
             raise TypeError(data_msg)
 
-        if data.empty:
-            raise ValueError(data_msg)
-
         # At least two columns are necessary if we condition to another
         if self.condition_to_column is not None and len(data.columns) < 2:  # noqa: PLR2004
             msg = "'data' need at least 2 columns if 'condition_to_column' is given."
@@ -45,6 +50,7 @@ def sample(
         if error_mask is None:  # initialize empty error_mask
             error_mask = pd.DataFrame(data=False, index=data.index, columns=data.columns)
 
+        self._random_generator = seed_randomness(self._seed)
         return self._sample(data, column, error_rate, error_mask)
 
     @abstractmethod