Xmod Co-simulation -
I'll write it as a with a small demo. """ xmod co-simulation — a lightweight modular co-simulation framework. Models exchange data via ports and advance with their own solvers. Master coordinates time steps and data exchange. """ import numpy as np from dataclasses import dataclass from typing import Dict, List, Callable from abc import ABC, abstractmethod ---------------------------------------------------------------------- Core xmod types ---------------------------------------------------------------------- @dataclass class XModPort: """Defines a data port for a model.""" name: str shape: tuple = () dtype: type = float
def __init__(self, name: str, Kp: float, x_ref: float = 0.0): super().__init__(name) self.Kp = Kp self.x_ref = x_ref self.input_ports = [XModPort("x_measured")] self.output_ports = [XModPort("F_cmd")] xmod co-simulation
def __init__(self, name: str): self.name = name self.input_ports: List[XModPort] = [] self.output_ports: List[XModPort] = [] I'll write it as a with a small demo
def step(self, t: float, dt: float, inputs: Dict[str, np.ndarray]) -> XModStep: x_meas = inputs.get("x_measured", np.array([0.0]))[0] F_cmd = self.Kp * (self.x_ref - x_meas) return XModStep( outputs={"F_cmd": np.array([F_cmd])}, new_time=t + dt ) Master coordinates time steps and data exchange
def __init__(self, name: str, m: float, k: float, c: float): super().__init__(name) self.m = m self.k = k self.c = c self.x = 0.0 self.v = 0.0 # Define ports self.input_ports = [XModPort("F_ext")] self.output_ports = [XModPort("x"), XModPort("v")]
