BUG: Invalid Arguments on Two Dimensional Discretize (HOTFIX).

CHANGELOG.md

@@ -40,7 +40,7 @@ straightforward as possible.
 
 ### Fixed
 
--
+- BUG: Invalid Arguments on Two Dimensional Discretize. [#521](https://github.com/RocketPy-Team/RocketPy/pull/521)
 
 ## [v1.1.4] - 2023-12-07
 

rocketpy/mathutils/function.py

@@ -189,7 +189,7 @@ def set_source(self, source):
         self : Function
             Returns the Function instance.
         """
-        _ = self._check_user_input(
+        *_, interpolation, extrapolation = self._check_user_input(
             source,
             self.__inputs__,
             self.__outputs__,
@@ -277,10 +277,10 @@ def source_function(_):
                 self.source = source
             # Update extrapolation method
             if self.__extrapolation__ is None:
-                self.set_extrapolation()
+                self.set_extrapolation(extrapolation)
             # Set default interpolation for point source if it hasn't
             if self.__interpolation__ is None:
-                self.set_interpolation()
+                self.set_interpolation(interpolation)
             else:
                 # Updates interpolation coefficients
                 self.set_interpolation(self.__interpolation__)
@@ -560,14 +560,12 @@ def set_discrete(
             # Create nodes to evaluate function
             xs = np.linspace(lower[0], upper[0], sam[0])
             ys = np.linspace(lower[1], upper[1], sam[1])
-            xs, ys = np.meshgrid(xs, ys)
-            xs, ys = xs.flatten(), ys.flatten()
-            mesh = [[xs[i], ys[i]] for i in range(len(xs))]
+            xs, ys = np.array(np.meshgrid(xs, ys)).reshape(2, xs.size * ys.size)
             # Evaluate function at all mesh nodes and convert it to matrix
-            zs = np.array(self.get_value(mesh))
-            self.set_source(np.concatenate(([xs], [ys], [zs])).transpose())
+            zs = np.array(self.get_value(xs, ys))
             self.__interpolation__ = "shepard"
             self.__extrapolation__ = "natural"
+            self.set_source(np.concatenate(([xs], [ys], [zs])).transpose())
         return self
 
     def set_discrete_based_on_model(
@@ -664,11 +662,8 @@ def set_discrete_based_on_model(
             # Create nodes to evaluate function
             xs = model_function.source[:, 0]
             ys = model_function.source[:, 1]
-            xs, ys = np.meshgrid(xs, ys)
-            xs, ys = xs.flatten(), ys.flatten()
-            mesh = [[xs[i], ys[i]] for i in range(len(xs))]
             # Evaluate function at all mesh nodes and convert it to matrix
-            zs = np.array(self.get_value(mesh))
+            zs = np.array(self.get_value(xs, ys))
             self.set_source(np.concatenate(([xs], [ys], [zs])).transpose())
 
         interp = (
@@ -2860,6 +2855,8 @@ def _check_user_input(
 
         # check source for data type
         # if list or ndarray, check for dimensions, interpolation and extrapolation
+        if isinstance(source, Function):
+            source = source.get_source()
         if isinstance(source, (list, np.ndarray, str, Path)):
             # Deal with csv or txt
             if isinstance(source, (str, Path)):

tests/test_function.py

@@ -372,6 +372,55 @@ def test_multivariable_function_plot(mock_show):
     assert func.plot() == None
 
 
+def test_set_discrete_2d():
+    """Tests the set_discrete method of the Function for
+    two dimensional domains.
+    """
+    func = Function(lambda x, y: x**2 + y**2)
+    discretized_func = func.set_discrete([-5, -7], [8, 10], [50, 100])
+
+    assert isinstance(discretized_func, Function)
+    assert isinstance(func, Function)
+    assert discretized_func.source.shape == (50 * 100, 3)
+    assert np.isclose(discretized_func.source[0, 0], -5)
+    assert np.isclose(discretized_func.source[0, 1], -7)
+    assert np.isclose(discretized_func.source[-1, 0], 8)
+    assert np.isclose(discretized_func.source[-1, 1], 10)
+
+
+def test_set_discrete_2d_simplified():
+    """Tests the set_discrete method of the Function for
+    two dimensional domains with simplified inputs.
+    """
+    source = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    func = Function(source=source, inputs=["x", "y"], outputs=["z"])
+    discretized_func = func.set_discrete(-1, 1, 10)
+
+    assert isinstance(discretized_func, Function)
+    assert isinstance(func, Function)
+    assert discretized_func.source.shape == (100, 3)
+    assert np.isclose(discretized_func.source[0, 0], -1)
+    assert np.isclose(discretized_func.source[0, 1], -1)
+    assert np.isclose(discretized_func.source[-1, 0], 1)
+    assert np.isclose(discretized_func.source[-1, 1], 1)
+
+
+def test_set_discrete_based_on_2d_model(func_2d_from_csv):
+    """Tests the set_discrete_based_on_model method with a 2d model
+    Function.
+    """
+    func = Function(lambda x, y: x**2 + y**2)
+    discretized_func = func.set_discrete_based_on_model(func_2d_from_csv)
+
+    assert isinstance(discretized_func, Function)
+    assert isinstance(func, Function)
+    assert np.array_equal(
+        discretized_func.source[:, :2], func_2d_from_csv.source[:, :2]
+    )
+    assert discretized_func.__interpolation__ == func_2d_from_csv.__interpolation__
+    assert discretized_func.__extrapolation__ == func_2d_from_csv.__extrapolation__
+
+
 @pytest.mark.parametrize(
     "x,y,z_expected",
     [