perf: use numpy array lookup for solution unpacking

[MaykThewessen]

Summary

Replace pandas Series indexing with numpy array lookup in the solution unpacking loop (Model.solve(), lines 1570-1594).

Before:

sol = set_int_index(sol)
sol.loc[-1] = nan
for name, var in self.variables.items():
    idx = np.ravel(var.labels)
    vals = sol[idx].values.reshape(var.labels.shape)  # pandas indexing per variable
    var.solution = xr.DataArray(vals, var.coords)

After:

sol = set_int_index(sol)
# Build dense numpy lookup array once
sol_arr = np.full(sol_max_idx + 1, nan)
sol_arr[sol.index[sol.index >= 0]] = sol.values[sol.index >= 0]

for name, var in self.variables.items():
    idx = np.ravel(var.labels)
    vals = sol_arr[np.clip(idx, 0, sol_max_idx)]  # numpy indexing
    vals[idx < 0] = nan
    var.solution = xr.DataArray(vals.reshape(var.labels.shape), var.coords)

Same pattern applied to the dual values unpacking loop.

Motivation

After HiGHS solves, the solution is a pandas Series with integer labels. The unpacking loop accesses this Series once per variable type (~20 types in a typical PyPSA model). Each sol[idx].values call involves pandas' __getitem__ with index alignment overhead. Converting to a numpy array first and using direct array indexing eliminates this overhead.

Context

See #198 (comment) — item 5 in the priority list.

Test plan

• test_optimization.py highs-direct — 24/25 pass (one pre-existing failure in test_modified_model)
• Solution values verified correct via test_default_setting_sol_and_dual_accessor
• Expression solution accessor verified via test_default_setting_expression_sol_accessor
• Duplicated variables test passes

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