上传PythonModels文件

PythonModels/core/solver.py

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+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable
+
+
+@dataclass(frozen=True)
+class SolveIVPConfig:
+    t_start: float = 0.0
+    t_stop: float = 20.0
+    method: str = "BDF"
+    rtol: float = 1e-6
+    atol: float = 1e-8
+    max_step: float = 1e-3
+
+
+@dataclass(frozen=True)
+class ODESolution:
+    t: list[float]
+    y: list[list[float]]
+    success: bool
+    message: str
+
+
+def _vector_add(a: list[float], b: list[float], scale: float = 1.0) -> list[float]:
+    return [x + scale * y for x, y in zip(a, b)]
+
+
+def _runge_kutta_4(
+    rhs: Callable[[float, list[float]], list[float]],
+    initial_state: list[float],
+    config: SolveIVPConfig,
+    t_eval: list[float] | None,
+) -> ODESolution:
+    if t_eval is None:
+        point_count = max(
+            2,
+            int((config.t_stop - config.t_start) / max(config.max_step, 1e-6)) + 1,
+        )
+        step = (config.t_stop - config.t_start) / (point_count - 1)
+        t_eval = [config.t_start + index * step for index in range(point_count)]
+
+    state = list(initial_state)
+    states = [[value] for value in state]
+    times = [float(t_eval[0])]
+    current_time = float(t_eval[0])
+
+    for target_time in t_eval[1:]:
+        while current_time < target_time - 1e-15:
+            dt = min(config.max_step, target_time - current_time)
+            k1 = rhs(current_time, state)
+            k2 = rhs(current_time + 0.5 * dt, _vector_add(state, k1, 0.5 * dt))
+            k3 = rhs(current_time + 0.5 * dt, _vector_add(state, k2, 0.5 * dt))
+            k4 = rhs(current_time + dt, _vector_add(state, k3, dt))
+            state = [
+                value + (dt / 6.0) * (a + 2.0 * b + 2.0 * c + d)
+                for value, a, b, c, d in zip(state, k1, k2, k3, k4)
+            ]
+            current_time += dt
+
+        times.append(float(target_time))
+        for index, value in enumerate(state):
+            states[index].append(value)
+
+    return ODESolution(
+        t=times,
+        y=states,
+        success=True,
+        message="Integrated with built-in RK4 fallback because SciPy is unavailable.",
+    )
+
+
+def integrate_ode(
+    rhs: Callable[[float, list[float]], list[float]],
+    initial_state: list[float],
+    config: SolveIVPConfig,
+    t_eval: list[float] | None = None,
+):
+    """Thin wrapper around scipy.integrate.solve_ivp with a pure-Python fallback."""
+
+    if abs(config.t_stop - config.t_start) <= 1e-15:
+        return ODESolution(
+            t=[float(config.t_start)],
+            y=[[value] for value in initial_state],
+            success=True,
+            message="Skipped integration because t_start equals t_stop.",
+        )
+
+    try:
+        from scipy.integrate import solve_ivp
+    except ImportError:
+        return _runge_kutta_4(rhs, initial_state, config, t_eval)
+
+    return solve_ivp(
+        fun=rhs,
+        t_span=(config.t_start, config.t_stop),
+        y0=initial_state,
+        method=config.method,
+        rtol=config.rtol,
+        atol=config.atol,
+        t_eval=t_eval,
+    )