[RTR] Stochastic variables should not be inputs of OCP

[Ipuch]

It always confuses me and it broke all my older ocps.

• should Stochastic variables be merged into control and states ? #711

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[codecov]

Codecov Report

Attention: 51 lines in your changes are missing coverage. Please review.

Files	Coverage Δ
bioptim/dynamics/dynamics_functions.py	86.53% <ø> (ø)
...ptim/optimization/receding_horizon_optimization.py	95.98% <100.00%> (ø)
bioptim/optimization/optimal_control_program.py	85.82% <89.47%> (+0.58%)	⬆️
...optimization/stochastic_optimal_control_program.py	31.49% <27.11%> (+5.49%)	⬆️

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[pariterre]

• Add a method dynamic_eval in ocp so we can have a signature for the custom dynamic function that changes depending on the type of ocp (for instance including (or not) stochatic_variable for problem where it makes sense to have them)
• Add a super.init() in StochasticOCP to ensure a sane ocp
• Factor common methods of StochasticsOCP and OCP and intercept what should not be performed in SOCP by an empty method

[Ipuch]

• Add a method dynamic_eval in ocp so we can have a signature for the custom dynamic function that changes depending on the type of ocp (for instance including (or not) stochatic_variable for problem where it makes sense to have them)
• Add a super.init() in StochasticOCP to ensure a sane ocp
• Factor common methods of StochasticsOCP and OCP and intercept what should not be performed in SOCP by an empty method

I propose to let the first bullet point for another PR to not grow this PR.

[pariterre]

Reviewed 1 of 3 files at r1, 3 of 3 files at r2, 6 of 6 files at r3, all commit messages.
Reviewable status: all files reviewed, 1 unresolved discussion (waiting on @Ipuch)

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bioptim/optimization/optimal_control_program.py line 318 at r3 (raw file):

            s_init,
            s_scaling,
        )

self._prepare_all_decision_variables should prepare them, but I don't see the point returning them (I know that you did not introduce that, but since we are refactoring...). If we don't return them, it allows an overriden method to actually prepare what they want (and need) without worrying of the return signature of the method.

And if an inherited class need to change the behavior of a particular value, they can call super on this and change the specific one they need

[Ipuch]

Reviewable status: all files reviewed, 1 unresolved discussion (waiting on @pariterre)

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bioptim/optimization/optimal_control_program.py line 318 at r3 (raw file):

Previously, pariterre (Pariterre) wrote…

self._prepare_all_decision_variables should prepare them, but I don't see the point returning them (I know that you did not introduce that, but since we are refactoring...). If we don't return them, it allows an overriden method to actually prepare what they want (and need) without worrying of the return signature of the method.

And if an inherited class need to change the behavior of a particular value, they can call super on this and change the specific one they need

I need a small talk. I took the easiest path, but maybe i can do better.

[pariterre]

Reviewable status: complete! all files reviewed, all discussions resolved (waiting on @Ipuch)