MagneticConstraintSet - remove and avoid a couple of pertinent deepcopies

bluemira/equilibria/optimisation/constraints.py

@@ -11,7 +11,6 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from copy import deepcopy
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -54,7 +53,7 @@ def _get_dummy_equilibrium(equilibrium: Equilibrium):
     # TODO @hsaunders1904: Add passive coil contributions here
     # 3579
     dummy = equilibrium.plasma
-    dummy.coilset = deepcopy(equilibrium.coilset)
+    dummy.coilset = equilibrium.coilset
     return dummy
 
 

bluemira/equilibria/run.py

@@ -449,7 +449,6 @@ def run_reference_equilibrium(self):
             gamma=self.eq_config.gamma,
         )
         program = PicardIterator(
-            eq,
             opt_problem,
             convergence=deepcopy(self.eq_config.convergence),
             relaxation=self.eq_config.relaxation,
@@ -470,7 +469,6 @@ def run_reference_equilibrium(self):
         )
 
         program = PicardIterator(
-            eq,
             opt_problem,
             convergence=deepcopy(self.eq_config.convergence),
             relaxation=self.eq_config.relaxation,
@@ -554,7 +552,7 @@ def get_sof_eof_opt_problems(
 
         return opt_problems
 
-    def converge_equilibrium(self, eq: Equilibrium, problem: CoilsetOptimisationProblem):
+    def converge_equilibrium(self, problem: EqCoilsetOptimisationProblem):
         """Converge an equilibrium problem from a 'frozen' plasma optimised state.
 
         Returns
@@ -563,7 +561,6 @@ def converge_equilibrium(self, eq: Equilibrium, problem: CoilsetOptimisationProb
             The iterator
         """
         program = PicardIterator(
-            eq,
             problem,
             fixed_coils=True,
             convergence=deepcopy(self.eq_config.convergence),
@@ -575,15 +572,19 @@ def converge_equilibrium(self, eq: Equilibrium, problem: CoilsetOptimisationProb
 
     def converge_and_snapshot(
         self,
-        sub_opt_problems: Iterable[CoilsetOptimisationProblem],
+        sub_opt_problems: Iterable[EqCoilsetOptimisationProblem],
         problem_names: Iterable[str] = (SOF, EOF),
     ):
         """Converge equilibrium optimisation problems and take snapshots."""
         for snap, problem in zip(problem_names, sub_opt_problems, strict=False):
-            eq = problem.eq
-            program = self.converge_equilibrium(eq, problem)
+            program = self.converge_equilibrium(problem)
             self.take_snapshot(
-                snap, eq, eq.coilset, problem, eq.profiles, iterator=program
+                snap,
+                problem.eq,
+                problem.eq.coilset,
+                problem,
+                problem.eq.profiles,
+                iterator=program,
             )
 
     def plot(self):

bluemira/equilibria/solve.py

@@ -30,8 +30,7 @@
 
     import numpy.typing as npt
 
-    from bluemira.equilibria.equilibrium import Equilibrium
-    from bluemira.equilibria.optimisation.problem import CoilsetOptimisationProblem
+    from bluemira.equilibria.optimisation.problem import EqCoilsetOptimisationProblem
     from bluemira.equilibria.optimisation.problem.base import CoilsetOptimiserResult
 
 __all__ = [
@@ -440,8 +439,6 @@ class PicardIterator:
 
     Parameters
     ----------
-    eq:
-        The equilibrium to solve for
     optimisation_problem:
         The optimisation problem to use when iterating
     convergence:
@@ -460,16 +457,15 @@ class PicardIterator:
 
     def __init__(
         self,
-        eq: Equilibrium,
-        optimisation_problem: CoilsetOptimisationProblem,
+        optimisation_problem: EqCoilsetOptimisationProblem,
         diagnostic_plotting: PicardDiagnosticOptions | None = None,
         convergence: ConvergenceCriterion | None = None,
         *,
         fixed_coils: bool = False,
         relaxation: float = 0,
         maxiter: int = 30,
     ):
-        self.eq = eq
+        self.eq = optimisation_problem.eq
         self.coilset = self.eq.coilset
         self.opt_prob = optimisation_problem
         if isinstance(convergence, ConvergenceCriterion):

eudemo/eudemo/equilibria/_designer.py

@@ -158,7 +158,6 @@ def run(self) -> Equilibrium:
         eq = self._make_equilibrium()
         opt_problem = self._make_opt_problem(eq)
         iterator_program = PicardIterator(
-            eq,
             opt_problem,
             convergence=DudsonConvergence(),
             relaxation=0.2,
@@ -722,7 +721,6 @@ def run(self) -> Equilibrium:
 
         iter_err_max = settings.pop("iter_err_max")
         iterator_program = PicardIterator(
-            eq,
             self.opt_problem,
             convergence=DudsonConvergence(iter_err_max),
             diagnostic_plotting=self.diagnostic_plotting,

examples/design/optimised_reactor.ex.py

@@ -192,7 +192,6 @@ def ref_eq(R_0, A) -> Equilibrium:  # noqa: D103
     )
     diagnostic_plotting = PicardDiagnosticOptions(plot=PicardDiagnostic.EQ)
     program = PicardIterator(
-        eq,
         current_opt_problem,
         fixed_coils=True,
         relaxation=0.2,

examples/equilibria/Toroidal_Harmonics_Optimisation_Set_Up.ex.py

@@ -160,7 +160,6 @@
 )
 
 program = PicardIterator(
-    th_current_opt_eq,
     current_opt_problem,
     fixed_coils=True,
     convergence=DudsonConvergence(1e-3),

examples/equilibria/coilset_opt_problem_tutorial.ex.py

@@ -294,7 +294,6 @@
     eq, magnetic_targets, gamma=1e-8
 )
 unconstrained_iterator = PicardIterator(
-    eq,
     unconstrained_cop,
     fixed_coils=True,
     relaxation=0.3,
@@ -375,7 +374,6 @@
     constraints=[field_constraints, x_point, lcfs_isoflux, legs_isoflux],
 )
 constrained_iterator = PicardIterator(
-    eq,
     opt_problem,
     fixed_coils=True,
     relaxation=0.1,

examples/equilibria/eudemo_2017.ex.py

@@ -298,7 +298,7 @@
     gamma=1e-7,
 )
 
-program = PicardIterator(reference_eq, ref_opt_problem, fixed_coils=True, relaxation=0.2)
+program = PicardIterator(ref_opt_problem, fixed_coils=True, relaxation=0.2)
 program()
 
 sof_psi_boundary = PsiBoundaryConstraint(
@@ -319,7 +319,6 @@
 )
 
 iterator = PicardIterator(
-    sof,
     sof_opt_problem,
     fixed_coils=True,
     relaxation=0.2,
@@ -345,7 +344,6 @@
 
 diagnostic_plotting = PicardDiagnosticOptions(plot=PicardDiagnostic.EQ)
 iterator = PicardIterator(
-    eof,
     eof_opt_problem,
     relaxation=0.2,
     fixed_coils=True,

examples/equilibria/single_null.ex.py

@@ -244,7 +244,6 @@
 )
 diagnostic_plotting = PicardDiagnosticOptions(plot=PicardDiagnostic.EQ)
 program = PicardIterator(
-    eq,
     current_opt_problem,
     convergence=DudsonConvergence(1e-3),
     fixed_coils=True,
@@ -294,7 +293,6 @@
     constraints=[x_point, field_constraints, force_constraints],
 )
 program = PicardIterator(
-    eq,
     current_opt_problem,
     fixed_coils=True,
     convergence=DudsonConvergence(1e-4),
@@ -326,7 +324,6 @@
 )
 
 program = PicardIterator(
-    minimal_current_eq,
     minimal_current_opt_problem,
     fixed_coils=True,
     convergence=DudsonConvergence(1e-4),
@@ -511,7 +508,6 @@
 
 # %%
 program = PicardIterator(
-    sof,
     current_opt_problem_sof,
     fixed_coils=True,
     convergence=DudsonConvergence(1e-4),
@@ -521,7 +517,6 @@
 program()
 
 program = PicardIterator(
-    eof,
     current_opt_problem_eof,
     fixed_coils=True,
     convergence=DudsonConvergence(1e-4),

examples/equilibria/spherical_harmonic_approximation_basic_use.ex.py

@@ -194,7 +194,6 @@
 
 # SOLVE
 program = PicardIterator(
-    sh_eq_solved,
     sh_con_len_opt,
     fixed_coils=True,
     convergence=DudsonConvergence(1e-2),

tests/equilibria/test_equilibrium.py

@@ -63,7 +63,7 @@ def setup_class(cls):
 
         opt_problem = UnconstrainedTikhonovCurrentGradientCOP(eq, targets, gamma=1e-8)
 
-        program = PicardIterator(eq, opt_problem, relaxation=0.1)
+        program = PicardIterator(opt_problem, relaxation=0.1)
         program()
         cls.eq = eq
 
@@ -266,7 +266,6 @@ def test_custom_profile(self):
             eq, self.targets, gamma=1e-8
         )
         program = PicardIterator(
-            eq,
             opt_problem,
             convergence=DudsonConvergence(1e-1),
             fixed_coils=True,
@@ -289,7 +288,6 @@ def test_betaip_profile(self, shape):
             eq, self.targets, gamma=1e-8
         )
         program = PicardIterator(
-            eq,
             opt_problem,
             convergence=DudsonConvergence(1e-1),
             fixed_coils=True,
@@ -324,7 +322,6 @@ def test_betapliip_profile(self, shape, o_point_fallback):
             eq, self.targets, gamma=1e-8
         )
         program = PicardIterator(
-            eq,
             opt_problem,
             convergence=DudsonConvergence(1e-2),
             fixed_coils=True,

tests/equilibria/test_optimisation.py

@@ -64,7 +64,7 @@ def test_isoflux_constrained_tikhonov_current_optimisation(request):
     opt_problem = TikhonovCurrentCOP(eq, targets, gamma=1e-8)
     diagnostic_plotting = PicardDiagnosticOptions(plot=PicardDiagnostic.EQ)
     program = add_plot_title(PicardIterator, request)(
-        eq, opt_problem, relaxation=0.1, diagnostic_plotting=diagnostic_plotting
+        opt_problem, relaxation=0.1, diagnostic_plotting=diagnostic_plotting
     )
     program()
     np.testing.assert_almost_equal(

tests/equilibria/test_st_equilibrium.py

@@ -193,7 +193,6 @@ def test_equilibrium(self):
         criterion = DudsonConvergence(build_tweaks["fbe_convergence_crit"])
 
         fbe_iterator = PicardIterator(
-            eq,
             opt_problem,
             relaxation=0.3,
             maxiter=400,