MNT: addition of is_multidimensional to function.py

- MNT: Function.is_multidimensional property in function.py. It returns True when the function's internal domain dimension is greater than 1 and safely returns False on errors.

- MNT: Replaced the ad-hoc hasattr/len check in flight.py with a clearer check:
if isinstance and drag_function.is_multidimensional

Author: aZira371

rocketpy/mathutils/function.py

@@ -367,6 +367,18 @@ def set_extrapolation(self, method="constant"):
             self.__set_extrapolation_func()
         return self
 
+    @property
+    def is_multidimensional(self):
+        """Return True when the Function has domain dimension greater than 1.
+
+        This abstracts checks for multi-dimensionality so callers don't need
+        to inspect internal attributes like ``__inputs__`` or ``__dom_dim__``.
+        """
+        try:
+            return int(self.__dom_dim__) > 1
+        except Exception:
+            return False
+
     def __set_interpolation_func(self):  # pylint: disable=too-many-statements
         """Defines interpolation function used by the Function. Each
         interpolation method has its own function`.

rocketpy/simulation/flight.py

@@ -1398,8 +1398,10 @@ def __get_drag_coefficient(self, drag_function, mach, z, freestream_velocity_bod
         float
             Drag coefficient value
         """
-        # Check if drag function is multi-dimensional by examining its inputs
-        if hasattr(drag_function, "__inputs__") and len(drag_function.__inputs__) > 1:
+        # Check if drag function is multi-dimensional using Function API
+        if isinstance(drag_function, Function) and getattr(
+            drag_function, "is_multidimensional", False
+        ):
             # Multi-dimensional drag function - calculate additional parameters
 
             # Calculate Reynolds number

tests/integration/test_multidim_drag.py

@@ -106,7 +106,10 @@ def test_3d_drag_with_varying_alpha(example_plain_env, cesaroni_m1670):
     # Create a flat (alpha-agnostic) drag by averaging over alpha
     cd_flat = cd_data.mean(axis=2)
     drag_flat = Function.from_grid(
-        cd_flat, [mach, reynolds], inputs=["Mach", "Reynolds"], outputs="Cd",
+        cd_flat,
+        [mach, reynolds],
+        inputs=["Mach", "Reynolds"],
+        outputs="Cd",
     )
 
     # Use fixtures for environment and motor