优化仿真求解性能并修复流量闭合问题（初版）
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@@ -12,6 +12,36 @@ from app.simulation.solvers.solver import (
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)
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def _counting_fixed_step_solver(
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step_size: float,
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dense_output_times: list[float],
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):
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import numpy as np
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class FixedStepSolver:
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def __init__(self, _fun, t0, y0, t_bound, **_kwargs):
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self.t = float(t0)
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self.y = np.asarray(y0, dtype=float)
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self.t_bound = float(t_bound)
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self.status = "running"
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self.nfev = 0
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self.njev = 0
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self.nlu = 0
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def step(self):
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self.t = min(self.t + step_size, self.t_bound)
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self.y = np.asarray([self.t], dtype=float)
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if self.t >= self.t_bound:
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self.status = "finished"
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return None
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def dense_output(self):
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dense_output_times.append(self.t)
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return lambda time: np.asarray([float(time)], dtype=float)
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return FixedStepSolver
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class IntegrateOdeTests(unittest.TestCase):
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def test_generic_solver_keeps_canonical_default_tolerance(self) -> None:
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self.assertEqual(SolveIVPConfig().atol, 1.0e-8)
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@@ -159,6 +189,151 @@ class IntegrateOdeTests(unittest.TestCase):
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self.assertEqual(result.status, "cancelled")
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self.assertEqual(result.t, [0.0])
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def test_stepwise_solvers_build_dense_output_only_when_crossing_a_sample(
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self,
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) -> None:
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import scipy.integrate
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for method in ("BDF", "Radau", "RK45"):
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with self.subTest(method=method):
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dense_output_times: list[float] = []
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fixed_step_solver = _counting_fixed_step_solver(
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0.2,
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dense_output_times,
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)
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with patch.object(scipy.integrate, method, fixed_step_solver):
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result = integrate_ode(
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rhs=lambda _time, _state: [1.0],
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initial_state=[0.0],
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config=SolveIVPConfig(
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t_start=0.0,
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t_stop=1.0,
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method=method,
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max_step=1.0,
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),
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t_eval=[0.0, 0.75, 1.0],
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cancel_check=lambda: False,
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)
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self.assertTrue(result.success, result.message)
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self.assertEqual(result.t, [0.0, 0.75, 1.0])
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self.assertEqual(result.y, [[0.0, 0.75, 1.0]])
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self.assertEqual(dense_output_times, [0.8, 1.0])
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self.assertEqual(
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result.solver_segments[0].accepted_step_count,
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5,
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)
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def test_stepwise_solvers_keep_dense_output_for_state_event_detection(
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self,
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) -> None:
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import scipy.integrate
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for method in ("BDF", "Radau", "RK45"):
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with self.subTest(method=method):
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dense_output_times: list[float] = []
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inspected_steps = 0
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fixed_step_solver = _counting_fixed_step_solver(
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0.25,
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dense_output_times,
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)
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def inspect_state_event(
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previous_time,
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_previous_state,
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current_time,
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_current_state,
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dense_state,
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):
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nonlocal inspected_steps
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inspected_steps += 1
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midpoint = 0.5 * (previous_time + current_time)
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self.assertAlmostEqual(dense_state(midpoint)[0], midpoint)
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return None
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with patch.object(scipy.integrate, method, fixed_step_solver):
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result = integrate_ode(
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rhs=lambda _time, _state: [1.0],
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initial_state=[0.0],
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config=SolveIVPConfig(
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t_start=0.0,
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t_stop=1.0,
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method=method,
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max_step=1.0,
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),
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t_eval=[0.0, 1.0],
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state_transition_handler=inspect_state_event,
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)
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self.assertTrue(result.success, result.message)
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self.assertEqual(len(dense_output_times), 4)
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self.assertEqual(inspected_steps, 4)
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def test_stepwise_dense_output_preserves_adjacent_float_samples(self) -> None:
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import scipy.integrate
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dense_output_times: list[float] = []
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adjacent_time = math.nextafter(0.5, math.inf)
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half_interval_solver = _counting_fixed_step_solver(
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0.5,
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dense_output_times,
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)
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with patch.object(scipy.integrate, "RK45", half_interval_solver):
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result = integrate_ode(
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rhs=lambda _time, _state: [1.0],
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initial_state=[0.0],
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config=SolveIVPConfig(
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t_start=0.0,
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t_stop=1.0,
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method="RK45",
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max_step=1.0,
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),
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t_eval=[0.0, 0.5, adjacent_time, 1.0],
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cancel_check=lambda: False,
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)
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self.assertTrue(result.success, result.message)
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self.assertEqual(result.t, [0.0, 0.5, adjacent_time, 1.0])
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self.assertEqual(result.y[0], result.t)
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self.assertEqual(dense_output_times, [0.5, 1.0])
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def test_dense_output_pruning_preserves_cancelled_partial_samples(self) -> None:
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import scipy.integrate
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cancellation_requested = False
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dense_output_times: list[float] = []
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fixed_step_solver = _counting_fixed_step_solver(
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0.2,
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dense_output_times,
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)
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def request_cancel(time: float) -> None:
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nonlocal cancellation_requested
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cancellation_requested = time >= 0.4
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with patch.object(scipy.integrate, "BDF", fixed_step_solver):
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result = integrate_ode(
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rhs=lambda _time, _state: [1.0],
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initial_state=[0.0],
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config=SolveIVPConfig(
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t_start=0.0,
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t_stop=1.0,
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method="BDF",
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max_step=1.0,
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),
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t_eval=[0.0, 0.3, 0.8, 1.0],
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cancel_check=lambda: cancellation_requested,
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accepted_step_callback=request_cancel,
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)
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self.assertFalse(result.success)
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self.assertEqual(result.status, "cancelled")
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self.assertEqual(result.t, [0.0, 0.3, 0.4])
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self.assertEqual(result.y, [[0.0, 0.3, 0.4]])
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self.assertEqual(dense_output_times, [0.4])
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def test_segmented_bdf_uses_left_limit_and_restarts_at_event(self) -> None:
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import scipy.integrate
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