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removed one liner jl.seval #7

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Merged
blegat merged 1 commit into from
Jul 13, 2026
Merged

removed one liner jl.seval #7

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23 changes: 5 additions & 18 deletions src/jumpy/bridge_juliacall.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,21 +39,6 @@ def _define_helpers(jl):
return
# jl.Any[...] is broken in PythonCall with Julia 1.12+
_any_vec = jl.seval("(args...) -> Any[args...]")
jl.seval("""
function _jumpy_scalar_affine(terms, constant)
return MOI.ScalarAffineFunction(terms, constant)
end

function _jumpy_affine_term(coef, var)
return MOI.ScalarAffineTerm(coef, var)
end

function _jumpy_set_objective!(optimizer, sense, func)
MOI.set(optimizer, MOI.ObjectiveSense(), sense)
F = typeof(func)
MOI.set(optimizer, MOI.ObjectiveFunction{F}(), func)
end
""")
jl.seval("""
function _jumpy_add_variables!(optimizer, count, lower, upper)
vars = MOI.add_variables(optimizer, count)
Expand Down Expand Up @@ -141,7 +126,9 @@ def build_moi_model(jl, model: Model) -> list[float]:
else jl.MOI.MAX_SENSE
)
obj_func = _expr_to_moi(jl, all_jl_vars, model._objective.expr)
jl._jumpy_set_objective_b(optimizer, sense, obj_func)

@blegat blegat Jul 10, 2026

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Wait, where is the function with the _b suffix ?

@blegat blegat Jul 10, 2026

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Where is it defined?

@Sutekinaa Sutekinaa Jul 10, 2026

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I remove it and replaced it by lines 130 and 131. Previously the function was defined in the julia helper as "_jumpy_set_objective!" but since python doesn't support "!" in variable name the juliacall convention is to put "_b" on the python side


jl.MOI.set(optimizer, jl.MOI.ObjectiveSense(), sense)
jl.MOI.set(optimizer, jl.MOI.ObjectiveFunction[jl.typeof(obj_func)](), obj_func)

# Optimize and extract solution
jl.MOI.optimize_b(optimizer)
Expand Down Expand Up @@ -340,9 +327,9 @@ def _expr_to_moi_linear(jl, all_jl_vars, expr):
jl_terms = jl.seval("MOI.ScalarAffineTerm{Float64}[]")
for coef, var_idx in terms:
jl_var = _get_jl_var_by_index(jl, all_jl_vars, var_idx)
jl.push_b(jl_terms, jl._jumpy_affine_term(float(coef), jl_var))
jl.push_b(jl_terms, jl.MOI.ScalarAffineTerm(float(coef), jl_var))

return jl._jumpy_scalar_affine(jl_terms, float(constant))
return jl.MOI.ScalarAffineFunction(jl_terms, float(constant))


def _expr_to_moi(jl, all_jl_vars, expr):
Expand Down
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