Added Type Hints to CVM module

src/UQpy/sensitivity/cramer_von_mises.py

@@ -15,14 +15,23 @@
 """
 
 import logging
+from typing import Union
 
 import numpy as np
+from beartype import beartype
 
 from UQpy.sensitivity.baseclass.sensitivity import Sensitivity
 from UQpy.sensitivity.baseclass.pickfreeze import generate_pick_freeze_samples
 from UQpy.sensitivity.sobol import compute_first_order as compute_first_order_sobol
 from UQpy.sensitivity.sobol import compute_total_order as compute_total_order_sobol
 from UQpy.utilities.UQpyLoggingFormatter import UQpyLoggingFormatter
+from UQpy.utilities.ValidationTypes import (
+    PositiveInteger,
+    PositiveFloat,
+    NumpyFloatArray,
+    NumpyIntArray,
+)
+
 
 # TODO: Sampling strategies
 
@@ -85,13 +94,14 @@ def __init__(
         self.num_vars = None
         "Number of input random variables, :class:`int`"
 
+    @beartype
     def run(
         self,
-        n_samples=1_000,
-        estimate_sobol_indices=False,
-        num_bootstrap_samples=None,
-        confidence_level=0.95,
-        disable_CVM_indices=False,
+        n_samples: PositiveInteger = 1_000,
+        estimate_sobol_indices: bool = False,
+        num_bootstrap_samples: PositiveInteger = None,
+        confidence_level: PositiveFloat = 0.95,
+        disable_CVM_indices: bool = False,
     ):
 
         """
@@ -243,7 +253,8 @@ def run(
         return computed_indices
 
     @staticmethod
-    def indicator_function(Y, W):
+    @beartype
+    def indicator_function(Y: Union[NumpyFloatArray, NumpyIntArray], w: float):
         """
         Vectorized version of the indicator function.
 
@@ -253,22 +264,28 @@ def indicator_function(Y, W):
         **Inputs:**
 
         * **Y** (`ndarray`):
-            Vector of values of the random variable.
+            Array of values of the random variable.
             Shape: `(N, 1)`
 
-        * **W** (`ndarray`):
-            Vector of values of the random variable.
-            Shape: `(N, 1)`
+        * **w** (`float`):
+            Value to compare with the array.
 
         **Outputs:**
 
         * **indicator** (`ndarray`):
+            Array of integers with truth values.
             Shape: `(N, 1)`
 
         """
-        return (Y <= W.T).astype(int)
+        return (Y <= w).astype(int)
 
-    def pick_and_freeze_estimator(self, A_model_evals, W_model_evals, C_i_model_evals):
+    @beartype
+    def pick_and_freeze_estimator(
+        self,
+        A_model_evals: Union[NumpyFloatArray, NumpyIntArray],
+        W_model_evals: Union[NumpyFloatArray, NumpyIntArray],
+        C_i_model_evals: Union[NumpyFloatArray, NumpyIntArray],
+    ):
 
         """
         Compute the first order Cramér-von Mises indices