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pyomeca · Jul 18, 2023 · 377de7b · 377de7b
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diff --git a/OCP_equation.jpg b/OCP_equation.jpg
diff --git a/OCP_equation.png b/OCP_equation.png
diff --git a/README.md b/README.md
@@ -241,7 +241,7 @@ You can already enjoy bioptimizing!
 # Defining our optimal control problems
 Here we will detail our implementation of optimal control problems and some definitions.
 The mathematical transcription of the OCP is as follows:
-![](OCP_equation.png)
+![](OCP_equation.jpg)
 The optimization variables are the states (x = variables that represent the state of the system at each node and that 
 are subject to continuity constraints), controls (u = decision variables defined at each node that have an effect on the system),
 algebraic states (s = optimization variables that are defined at each node, but that are not subject to the 

diff --git a/bioptim/examples/stochastic_optimal_control/arm_reaching_muscle_driven.py b/bioptim/examples/stochastic_optimal_control/arm_reaching_muscle_driven.py
@@ -40,7 +40,7 @@
     MultinodeObjectiveList,
 )
 
-from bioptim.examples.stochastic_optimal_control.LeuvenArmModel import LeuvenArmModel
+from bioptim.examples.stochastic_optimal_control.leuven_arm_model import LeuvenArmModel
 
 
 def stochastic_forward_dynamics(

diff --git a/...chastic_optimal_control/LeuvenArmModel.py → ...astic_optimal_control/leuven_arm_model.py b/...chastic_optimal_control/LeuvenArmModel.py → ...astic_optimal_control/leuven_arm_model.py
diff --git a/bioptim/optimization/optimization_vector.py b/bioptim/optimization/optimization_vector.py
@@ -501,7 +501,7 @@ def to_dictionaries(ocp, data: np.ndarray | DM) -> tuple:
                     nlp.stochastic_variables.node_index = k
                     s_array = v_array[offset : offset + nstochastic].reshape(
                         (nlp.stochastic_variables.shape, -1), order="F"
-                    )  # @pariterre "F" seems like an interpolation?
+                    )
                     for key in nlp.stochastic_variables:
                         data_stochastic_variables[p_idx][key][:, k : k + 1] = s_array[
                             nlp.stochastic_variables[key].index, :