Replaces older sol.value() with preferred sol() for consistency throu…

…ghout repository

aerosandbox/aerodynamics/aero_2D/ignore/viscous analysis.py

@@ -86,7 +86,7 @@ def logint(x):
 ### Solve
 sol = opti.solve()
 theta = sol(theta)
-H = sol.value(H)
+H = sol(H)
 
 ### Plot
 from aerosandbox.tools.pretty_plots import plt, show_plot

aerosandbox/aerodynamics/aero_3D/test_aero_3D/test_aero_buildup/test_fuselage_aerodynamics_optimization.py

@@ -32,14 +32,14 @@ def test_fuselage_aerodynamics_optimization():
 
     opti.minimize(-aero["L"] / aero["D"])
     sol = opti.solve(verbose=True)
-    print(sol.value(alpha))
-    assert sol.value(alpha) > 10 and sol.value(alpha) < 20
-    assert sol.value(beta) == pytest.approx(0, abs=1e-3)
+    print(sol(alpha))
+    assert sol(alpha) > 10 and sol(alpha) < 20
+    assert sol(beta) == pytest.approx(0, abs=1e-3)
 
     opti.minimize(aero["D"])
     sol = opti.solve(verbose=False)
-    assert sol.value(alpha) == pytest.approx(0, abs=1e-2)
-    assert sol.value(beta) == pytest.approx(0, abs=1e-2)
+    assert sol(alpha) == pytest.approx(0, abs=1e-2)
+    assert sol(beta) == pytest.approx(0, abs=1e-2)
 
 
 if __name__ == '__main__':

aerosandbox/aerodynamics/aero_3D/test_aero_3D/test_vlm/test_airplane_optimization.py

@@ -52,7 +52,7 @@ def get_aero(alpha, taper_ratio):
     opti.minimize(-LD)
     sol = opti.solve()
 
-    assert sol.value(alpha) == pytest.approx(5.5, abs=1)
+    assert sol(alpha) == pytest.approx(5.5, abs=1)
 
 
 if __name__ == '__main__':

aerosandbox/aerodynamics/aero_3D/test_aero_3D/test_vlm/test_operating_point_optimization.py

@@ -50,8 +50,8 @@ def test_vlm_optimization_operating_point():
         verbose=True,
         # callback=lambda _: print(f"alpha = {opti.debug.value(alpha)}")
     )
-    print(sol.value(alpha), sol.value(LD))
-    assert sol.value(alpha) == pytest.approx(5.85, abs=0.1)
+    print(sol(alpha), sol(LD))
+    assert sol(alpha) == pytest.approx(5.85, abs=0.1)
 
 
 if __name__ == '__main__':

aerosandbox/dynamics/point_mass/point_1D/test/test_block_move.py

@@ -107,8 +107,8 @@ def test_block_move_minimum_time():
     assert dyn.u_e[-1] == pytest.approx(0)
     assert np.max(dyn.u_e) == pytest.approx(1, abs=0.01)
     assert sol(u)[0] == pytest.approx(1, abs=0.05)
-    assert sol.value(u)[-1] == pytest.approx(-1, abs=0.05)
-    assert np.mean(np.abs(sol.value(u))) == pytest.approx(1, abs=0.01)
+    assert sol(u)[-1] == pytest.approx(-1, abs=0.05)
+    assert np.mean(np.abs(sol(u))) == pytest.approx(1, abs=0.01)
 
 
 if __name__ == '__main__':

aerosandbox/dynamics/rigid_body/rigid_2D/ignore/quadcopter - work in progress.py

@@ -114,7 +114,7 @@ def test_quadcopter_flip():
     sol = opti.solve(verbose=False)
     dyn = sol(dyn)
 
-    assert sol.value(time_final) == pytest.approx(0.824, abs=0.01)
+    assert sol(time_final) == pytest.approx(0.824, abs=0.01)
 
 
 if __name__ == '__main__':

aerosandbox/optimization/opti.py

@@ -23,7 +23,7 @@ class Opti(cas.Opti):
     >>> opti.subject_to(x > 3) # Adds a constraint to be enforced
     >>> opti.minimize(f) # Sets the objective function as f
     >>> sol = opti.solve() # Solves the problem using CasADi and IPOPT backend
-    >>> print(sol.value(x)) # Prints the value of x at the optimum.
+    >>> print(sol(x)) # Prints the value of x at the optimum.
     """
 
     def __init__(self,
@@ -1205,7 +1205,7 @@ def __init__(self,
             >>> sol = opti.solve()
             >>>
             >>> # Retrieve the value of the variable x in the solution:
-            >>> x_value = sol.value(x)
+            >>> x_value = sol(x)
             >>>
             >>> # Or, to be more concise:
             >>> x_value = sol(x)
@@ -1232,8 +1232,8 @@ def _value_scalar(self, x: Union[cas.MX, np.ndarray, float, int]) -> Union[float
         """
         Gets the value of a variable at the solution point. For developer use - see following paragraph.
 
-        This method is basically a less-powerful version of calling either `sol(x)` or `sol.value(x)` - if you're a
-            user and not a developer, you almost-certainly want to use one of those methods instead, as those are less
+        This method is basically a less-powerful version of calling `sol(x)` - if you're a
+            user and not a developer, you almost-certainly want to use that method instead, as those are less
             fragile with respect to various input data types. This method exists only as an abstraction to make it easier
             for other developers to subclass OptiSol, if they wish to intercept the variable substitution process.
 

aerosandbox/propulsion/ignore/propeller_model.py

@@ -245,21 +245,21 @@ def airfoil_CDp(alpha, Re, Ma, Cl):
     #     n_blades * 0.5 * air_density * (W ** 2) *
     #     cl * chord_local * blade_section
     # )
-    # dDrag = sol.value(
+    # dDrag = sol(
     #     n_blades * 0.5 * air_density * (W ** 2) *
     #     cd * chord_local * blade_section
     # )
-    dThrust = sol.value(
+    dThrust = sol(
         air_density * n_blades * gamma * (
                 W_t - W_a * cd / cl
         ) * blade_section
     )
-    dTorque = sol.value(
+    dTorque = sol(
         air_density * n_blades * gamma * (
                 W_a + W_t * cd / cl
         ) * radial_loc * blade_section
     )
-    # if sol.value(alpha_deg) <= 0:
+    # if sol(alpha_deg) <= 0:
     #     break
 
     thrust.append(dThrust)

aerosandbox/structures/tube_spar_bending.py

@@ -391,11 +391,11 @@ def draw(self, show=True):
 
     beam = sol(beam)
 
-    print(f"{sol.value(mass)} kg per half-wing")
+    print(f"{sol(mass)} kg per half-wing")
 
     beam.draw()
 
-    computed_spar_mass = 2 * sol.value(mass)
+    computed_spar_mass = 2 * sol(mass)
 
     vehicle_mass = lift / 9.81
     ultimate_load_factor = 2