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refactor: load eq_param_index of BundleSolvers correctly #212

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fixes: #211

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codecov-commenter commented Dec 31, 2023

Codecov Report

Attention: 1 lines in your changes are missing coverage. Please review.

Comparison is base (1b6d8c5) 84.46% compared to head (b90ab42) 84.46%.

Files Patch % Lines
neurodiffeq/solvers.py 0.00% 1 Missing ⚠️

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@@           Coverage Diff           @@
##           master     #212   +/-   ##
=======================================
  Coverage   84.46%   84.46%           
=======================================
  Files          21       21           
  Lines        3695     3695           
=======================================
  Hits         3121     3121           
  Misses        574      574           

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@@ -1357,7 +1357,7 @@ def __init__(self, ode_system, conditions, t_min, t_max,

def _diff_eqs_wrapper(*variables):
funcs_and_coords = variables[:N_FUNCTIONS + N_COORDS]
eq_params = tuple(variables[idx] for idx in eq_param_index)
eq_params = tuple(variables[idx] for idx in self.eq_param_index)
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Is there a particular reason we want to use self.eq_param_index instead of eq_param_index here? Both ways should have the same effect most of the time, but I don't want self.diff_eqs to be a closure that depends on self. This would make certain operations complicated, such as recreating a solver

solver1 = BundleSolver1D(..., eq_param_index1)
solver2 = BundleSolver1D(..., eq_param_index2)
solver2.diff_eqs = solver1.diff_eqs

If eq_param_index1 and eq_param_index2 are different, the above won't work, as solver1.diff_eqs will always depend on solver1.

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hmm.. my motivation to use self.eq_param_index was to use this internal variable when loading a saved solver as otherwise we have to recompute eq_param_index again and pass it in the constructor.

@@ -530,7 +530,9 @@ def load(cls,
t_min=t_min,
t_max=t_max,
theta_min=tuple(load_dict['solver'].r_min[1:]),
theta_max=tuple(load_dict['solver'].r_max[1:]))
theta_max=tuple(load_dict['solver'].r_max[1:]),
eq_param_index=load_dict['solver'].eq_param_index
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At a certain point we should consider refactoring the solver_utils package. A lot of the code seems to be a little ad-hoc. They are fine for now.

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Yes, I think we are due to think about refactoring and redesigning these pieces as they are pretty hard coded. I also have some thoughts on how parameters are saved - https://github.com/NeuroDiffGym/neurodiffeq/blob/master/neurodiffeq/solvers_utils.py#L99 which ties back to how parameters are used inside the function of the differential equation. I will open up an issue on this and we can discuss these things in detail.

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Missing eq_param_index when loading BundleSolution
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