Needed to merge the two PRs here

README.md

@@ -1872,17 +1872,8 @@ The solver must minimize the force to lift the box while reaching the marker in
 It is designed to show how to use external forces. An example of external forces that depends on the state (for
 example, a spring) can be found at 'examples/torque_driven_ocp/spring_load.py'
 
-`Bioptim` expects `external_forces` to be a list (for each shooting node) of np.ndarray [6 x n],
-where the six components are [Mx, My, Mz, Fx, Fy, Fz], expressed at the origin of the global reference frame for each node n.
-Let us take a look at the definition of the external forces in
-this example :
-
-```python
-external_forces = [[["Seg1", (0, 0, 0, 0, 0, -2)], ["Test", (0, 0, 0, 0, 0, 5)]] for _ in range(n_shooting)]
-```
-
-`external_forces` is 30-element long, and each sub list array are composed of a string, the name of the segment, and a tuple, the external force.
-The tuple is [Mx, My, Mz, Fx, Fy, Fz] for each node (in this example, we take 30 shooting nodes).
+`Bioptim` expects `external_forces` to be a np.ndarray [6 x n x n_shooting], where the six components are 
+[Mx, My, Mz, Fx, Fy, Fz], expressed at the origin of the global reference frame for each node.
 
 ### The [example_implicit_dynamics.py](./bioptim/examples/getting_started/example_implicit_dynamics.py) file
 *#TODO*

bioptim/dynamics/configure_new_variable.py

@@ -418,7 +418,7 @@ def _declare_cx_and_plot(self):
                 )
                 if not self.skip_plot:
                     self.nlp.plot[f"{self.name}_states"] = CustomPlot(
-                        lambda t0, phases_dt, node_idx, x, u, p, a: (
+                        lambda t0, phases_dt, node_idx, x, u, p, a, d: (
                             x[self.nlp.states.key_index(self.name), :]
                             if x.any()
                             else np.ndarray((cx[0][0].shape[0], 1)) * np.nan
@@ -455,7 +455,7 @@ def _declare_cx_and_plot(self):
                 plot_type = PlotType.PLOT if self.nlp.control_type == ControlType.LINEAR_CONTINUOUS else PlotType.STEP
                 if not self.skip_plot:
                     self.nlp.plot[f"{self.name}_controls"] = CustomPlot(
-                        lambda t0, phases_dt, node_idx, x, u, p, a: (
+                        lambda t0, phases_dt, node_idx, x, u, p, a, d: (
                             u[self.nlp.controls.key_index(self.name), :]
                             if u.any()
                             else np.ndarray((cx[0][0].shape[0], 1)) * np.nan
@@ -518,6 +518,20 @@ def _declare_cx_and_plot(self):
                     self.nlp.variable_mappings[self.name],
                     node_index,
                 )
+                if not self.skip_plot:
+                    all_variables_in_one_subplot = True if self.name in ["m", "cov", "k"] else False
+                    self.nlp.plot[f"{self.name}_algebraic"] = CustomPlot(
+                        lambda t0, phases_dt, node_idx, x, u, p, a, d: (
+                            a[self.nlp.algebraic_states.key_index(self.name), :]
+                            if a.any()
+                            else np.ndarray((cx[0][0].shape[0], 1)) * np.nan
+                        ),
+                        plot_type=PlotType.STEP,
+                        axes_idx=self.axes_idx,
+                        legend=self.legend,
+                        combine_to=self.combine_name,
+                        all_variables_in_one_subplot=all_variables_in_one_subplot,
+                    )
 
 
 def _manage_fatigue_to_new_variable(
@@ -577,7 +591,7 @@ def _manage_fatigue_to_new_variable(
     legend = [f"{name}_{i}" for i in name_elements]
     fatigue_plot_name = f"fatigue_{name}"
     nlp.plot[fatigue_plot_name] = CustomPlot(
-        lambda t0, phases_dt, node_idx, x, u, p, a: (
+        lambda t0, phases_dt, node_idx, x, u, p, a, d: (
             x[:n_elements, :] if x.any() else np.ndarray((len(name_elements), 1))
         )
         * np.nan,
@@ -587,7 +601,7 @@ def _manage_fatigue_to_new_variable(
     )
     control_plot_name = f"{name}_controls" if not multi_interface and split_controls else f"{name}"
     nlp.plot[control_plot_name] = CustomPlot(
-        lambda t0, phases_dt, node_idx, x, u, p, a: (
+        lambda t0, phases_dt, node_idx, x, u, p, a, d: (
             u[:n_elements, :] if u.any() else np.ndarray((len(name_elements), 1))
         )
         * np.nan,
@@ -607,7 +621,7 @@ def _manage_fatigue_to_new_variable(
                 var_names_with_suffix[-1], name_elements, ocp, nlp, as_states, as_controls, skip_plot=True
             )
             nlp.plot[f"{var_names_with_suffix[-1]}_controls"] = CustomPlot(
-                lambda t0, phases_dt, node_idx, x, u, p, a, key: (
+                lambda t0, phases_dt, node_idx, x, u, p, a, d, key: (
                     u[nlp.controls.key_index(key), :] if u.any() else np.ndarray((len(name_elements), 1)) * np.nan
                 ),
                 plot_type=PlotType.STEP,
@@ -618,7 +632,7 @@ def _manage_fatigue_to_new_variable(
         elif i == 0:
             NewVariableConfiguration(f"{name}", name_elements, ocp, nlp, as_states, as_controls, skip_plot=True)
             nlp.plot[f"{name}_controls"] = CustomPlot(
-                lambda t0, phases_dt, node_idx, x, u, p, a, key: (
+                lambda t0, phases_dt, node_idx, x, u, p, a, d, key: (
                     u[nlp.controls.key_index(key), :] if u.any() else np.ndarray((len(name_elements), 1)) * np.nan
                 ),
                 plot_type=PlotType.STEP,
@@ -631,7 +645,7 @@ def _manage_fatigue_to_new_variable(
             name_tp = f"{var_names_with_suffix[-1]}_{params}"
             NewVariableConfiguration(name_tp, name_elements, ocp, nlp, True, False, skip_plot=True)
             nlp.plot[name_tp] = CustomPlot(
-                lambda t0, phases_dt, node_idx, x, u, p, a, key, mod: (
+                lambda t0, phases_dt, node_idx, x, u, p, a, d, key, mod: (
                     mod * x[nlp.states.key_index(key), :] if x.any() else np.ndarray((len(name_elements), 1)) * np.nan
                 ),
                 plot_type=PlotType.INTEGRATED,