DOC: remove forgotten type hints and update docstrings

rocketpy/mathutils/function.py

@@ -2988,22 +2988,15 @@ def _check_user_input(
         Returns
         -------
         tuple
-            A tuple containing the processed inputs, outputs, interpolation, and
-            extrapolation parameters.
+            A tuple containing the processed source, inputs, outputs,
+            interpolation, and extrapolation parameters.
 
         Raises
         ------
         ValueError
             If the dimensionality of the source does not match the combined
             dimensions of inputs and outputs. If the outputs list has more than
             one element.
-        TypeError
-            If the source is not a list, np.ndarray, Function object, str or
-            Path.
-        Warning
-            If inputs or outputs do not match for a Function source, or if
-            defaults are used for inputs, interpolation,and extrapolation for a
-            multidimensional source.
 
         Examples
         --------
@@ -3072,7 +3065,27 @@ def source_function(_):
         return source, inputs, outputs, interpolation, extrapolation
 
     @staticmethod
-    def _validate_inputs_outputs(inputs, outputs):  # None | st | list[str]
+    def _validate_inputs_outputs(inputs, outputs):
+        """Used to validate the inputs and outputs parameters for creating a
+        Function object. It sets default values if they are not provided.
+
+        Parameters
+        ----------
+        inputs : str, list of str, None
+            The name(s) of the input variable(s). If None, defaults to "Scalar".
+        outputs :
+            The name of the output variables. If None, defaults to "Scalar".
+
+        Returns
+        -------
+        tuple
+            A tuple containing the validated inputs and outputs parameters.
+
+        Raises
+        ------
+        ValueError
+            If the output has more than one element.
+        """
         if inputs is None:
             inputs = ["Scalar"]
         if outputs is None:
@@ -3091,10 +3104,41 @@ def _validate_inputs_outputs(inputs, outputs):  # None | st | list[str]
 
     @staticmethod
     def _validate_interpolation_and_extrapolation(
-        inputs: list[str], interpolation: str, extrapolation: str, source: np.ndarray
+        inputs, interpolation, extrapolation, source
     ):
+        """Used to validate the interpolation and extrapolation methods for
+        creating a Function object. It sets default values for interpolation
+        and extrapolation if they are not provided or if they are not supported
+        for the given source. The inputs and outputs may be modified if the
+        source is multidimensional.
+
+        Parameters
+        ----------
+        inputs : list of strings
+            List of inputs, each input is a string. Example: ['x', 'y']
+        interpolation : str, None
+            The type of interpolation to use. The default method is 'spline'.
+            Currently supported values are 'spline', 'linear', 'polynomial',
+            'akima', and 'shepard'.
+        extrapolation : str, None
+            The type of extrapolation to use. Currently supported values are
+            'constant', 'natural', and 'zero'. The default method is 'constant'.
+        source : np.ndarray
+            The source data of the Function object. This has to be a numpy
+            array.
+
+        Returns
+        -------
+        tuple
+            A tuple with the validated inputs, interpolation, and extrapolation
+            parameters (inputs, interpolation, extrapolation).
+
+        Raises
+        ------
+        ValueError
+            If the source has less than 2 dimensions.
+        """
         source_dim = source.shape[1]
-        # check interpolation and extrapolation
         ## single dimension (1D Functions)
         if source_dim == 2:
             # possible interpolation values: linear, polynomial, akima and spline
@@ -3124,7 +3168,6 @@ def _validate_interpolation_and_extrapolation(
 
         ## multiple dimensions
         elif source_dim > 2:
-            # check for inputs and outputs
             if inputs == ["Scalar"]:
                 inputs = [f"Input {i+1}" for i in range(source_dim - 1)]
 
@@ -3148,8 +3191,25 @@ def _validate_interpolation_and_extrapolation(
         return inputs, interpolation, extrapolation
 
     @staticmethod
-    def _validate_source_dimensions(inputs: list, outputs: list, source: np.ndarray):
-        # check input dimensions
+    def _validate_source_dimensions(inputs, outputs, source):
+        """Used to check whether the source dimensions match the inputs and
+        outputs.
+
+        Parameters
+        ----------
+        inputs : list of strings
+            List of inputs, each input is a string. Example: ['x', 'y']
+        outputs : list of strings
+            List of outputs, each output is a string. Example: ['z']
+        source : np.ndarray
+            The source data of the Function object. This has to be a numpy
+            array.
+
+        Raises
+        ------
+        ValueError
+            In case the source dimensions do not match the inputs and outputs.
+        """
         source_dim = source.shape[1]
         in_out_dim = len(inputs) + len(outputs)
         if source_dim != in_out_dim: