重构body api，性能分析，项目整理
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# CelerisLab/tests/postproc/run_exp_ctrl_matrix_vorticity.py
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"""Batch vorticity images for three-cylinder control matrix (exp_ctrl_matrix.md).
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Usage::
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# Single case
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conda run -n pycuda_3_10 python tests/postproc/run_exp_ctrl_matrix_vorticity.py \\
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--cases C0 --batch 10 --device-id 0
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# All cases
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conda run -n pycuda_3_10 python tests/postproc/run_exp_ctrl_matrix_vorticity.py \\
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--batch 10 --device-id 0
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# Full 100k steps, no batching (default)
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conda run -n pycuda_3_10 python tests/postproc/run_exp_ctrl_matrix_vorticity.py
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"""
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from __future__ import annotations
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import argparse
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import json
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import math
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import os
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import sys
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import tempfile
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from pathlib import Path
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from typing import Any, Dict, List, Tuple
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import numpy as np
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_REPO = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(_REPO / "src"))
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from CelerisLab import Simulation
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from CelerisLab.common.render import (
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compute_vorticity,
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render_vorticity_field,
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)
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from CelerisLab.common.preprocess import cylinders_from_triangle_layout
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INLET_U_PHYS_M_S = 0.009028
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CYLINDER_DIAMETER_M = 0.010
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CENTER_SPACING_M = 0.015
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DIAMETER_CELLS = 20.0
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RAMP_TIME_S = 5.0
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INITIAL_ACTIONS_M_S = (0.0, 0.0, 0.0)
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OMEGA_SIGN_FROM_ACTION = -1.0
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VORT_VMIN = -0.003
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VORT_VMAX = 0.003
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CONFIG_PATH = _REPO / "src/CelerisLab/configs/config_lbm_three_cylinder_triangle.json"
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DEFAULT_OUT = _REPO / "tests" / "output" / "exp_ctrl_matrix_vort_ny300"
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FIXED_STEPS = 100000 # keep constant while grid changes
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# Body order: 0=apex, 1=rear-lower(y_lower), 2=rear-upper(y_upper); swap action2/action3 targets.
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SWAP_ACTION23_BODIES = True
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# From tests/exp_ctrl_matrix.md (SIGNAL_FEATURES0 .. 6)
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CONTROL_CASES: List[Tuple[str, str, Dict[str, Any]]] = [
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(
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"C0",
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"no_ctrl",
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{
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"action1": {"mean": 0.0, "components": [(0.1354, 0.0, 1.600)]},
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"action2": {"mean": 0.0, "components": [(0.1354, 0.0, 2.099)]},
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"action3": {"mean": 0.0, "components": [(0.1354, 0.0, 1.639)]},
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},
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),
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(
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"C1",
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"stealth",
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{
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"action1": {"mean": 0.0, "components": [(0.1354, 0.0, 1.600)]},
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"action2": {"mean": -0.01806, "components": [(0.1354, 0.0, 2.099)]},
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"action3": {"mean": 0.01806, "components": [(0.1354, 0.0, 1.639)]},
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},
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),
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(
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"C2",
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"deceit",
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{
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"action1": {"mean": 0.0, "components": [(0.1354, 0.0026, 1.600)]},
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"action2": {
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"mean": -0.008730,
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"components": [(0.1354, 0.0045, 2.099), (0.2708, 0.0010, 0.612)],
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},
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"action3": {
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"mean": 0.008730,
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"components": [(0.1354, 0.0045, 1.639), (0.2708, 0.0010, -2.962)],
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},
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},
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),
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(
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"C3",
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"deceit_multi",
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{
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"action1": {
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"mean": 0.0,
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"components": [(0.1354, 0.0029, -2.619), (0.2708, 0.0008, 2.856)],
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},
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"action2": {
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"mean": -0.0140,
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"components": [
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(0.1354, 0.0050, -0.933),
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(0.2708, 0.0010, 0.801),
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(0.1806, 0.0003, 1.854),
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],
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},
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"action3": {
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"mean": 0.014,
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"components": [
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(0.1354, 0.0050, -1.398),
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(0.2708, 0.0010, 2.208),
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(0.1806, 0.0003, 1.810),
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],
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},
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},
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),
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(
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"C4",
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"deceit_f1p5",
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{
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"action1": {"mean": 0.0, "components": [(0.2031, 0.0026, 1.600)]},
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"action2": {
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"mean": -0.008730,
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"components": [(0.2031, 0.0045, 2.099), (0.4062, 0.0010, 0.612)],
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},
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"action3": {
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"mean": 0.008730,
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"components": [(0.2031, 0.0045, 1.639), (0.4062, 0.0010, -2.962)],
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},
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},
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),
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(
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"C5",
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"deceit_multi_f1p5",
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{
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"action1": {
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"mean": 0.0,
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"components": [(0.2031, 0.0029, -2.619), (0.4062, 0.0008, 2.856)],
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},
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"action2": {
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"mean": -0.0140,
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"components": [
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(0.2031, 0.0050, -0.933),
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(0.4062, 0.0010, 0.801),
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(0.2709, 0.0003, 1.854),
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],
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},
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"action3": {
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"mean": 0.014,
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"components": [
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(0.2031, 0.0050, -1.398),
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(0.4062, 0.0010, 2.208),
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(0.2709, 0.0003, 1.810),
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],
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},
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},
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),
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(
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"C6",
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"deceit_f2",
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{
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"action1": {
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"mean": 0.0,
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"components": [(0.2708, 0.0044, -2.619), (0.8124, 0.0012, 2.856)],
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},
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"action2": {
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"mean": -0.014,
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"components": [
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(0.2708, 0.0075, -0.933),
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(0.8124, 0.0015, 0.801),
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(0.5418, 0.0005, 1.854),
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],
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},
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"action3": {
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"mean": 0.014,
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"components": [
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(0.2708, 0.0075, -1.398),
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(0.8124, 0.0015, 2.208),
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(0.5418, 0.0005, 1.810),
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],
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},
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},
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),
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]
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def _ensure_compat_config(config_path: Path, preferred_scheme: str = "regularized") -> str:
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with config_path.open("r", encoding="utf-8") as f:
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cfg = json.load(f)
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method = cfg.setdefault("method", {})
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inlet = method.setdefault("inlet", {})
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outlet = method.setdefault("outlet", {})
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changed = False
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if "scheme" not in inlet:
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inlet["scheme"] = preferred_scheme
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changed = True
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if "regularized_neq_damp" not in inlet:
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inlet["regularized_neq_damp"] = 0.5
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changed = True
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if "blend_alpha" not in outlet:
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outlet["blend_alpha"] = 0.7
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changed = True
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if "backflow_clamp" not in outlet:
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outlet["backflow_clamp"] = True
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changed = True
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if not changed:
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return str(config_path)
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tmp = tempfile.NamedTemporaryFile(
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mode="w", suffix="_compat_lbm.json", delete=False, encoding="utf-8"
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)
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with tmp:
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json.dump(cfg, tmp, indent=4)
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return tmp.name
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def _triangle_layout(cfg) -> dict:
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dx_phys = CYLINDER_DIAMETER_M / DIAMETER_CELLS
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spacing_lb = CENTER_SPACING_M / dx_phys
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radius_lb = DIAMETER_CELLS / 2.0
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y_center = 0.5 * (cfg.ny - 1)
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x_cluster_center = cfg.nx / 3.0
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x_apex = x_cluster_center - (math.sqrt(3.0) / 3.0) * spacing_lb
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x_rear = x_apex + (math.sqrt(3.0) / 2.0) * spacing_lb
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return {
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"x_apex": x_apex,
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"x_rear": x_rear,
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"y_center": y_center,
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"y_upper": y_center + 0.5 * spacing_lb,
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"y_lower": y_center - 0.5 * spacing_lb,
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"radius_lb": radius_lb,
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}
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def _add_triangle_cylinders(sim: Simulation) -> dict:
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layout = _triangle_layout(sim.lbm_cfg)
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sim.add_body("circle", center=(layout["x_apex"], layout["y_center"]),
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radius=layout["radius_lb"])
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sim.add_body("circle", center=(layout["x_rear"], layout["y_lower"]),
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radius=layout["radius_lb"])
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sim.add_body("circle", center=(layout["x_rear"], layout["y_upper"]),
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radius=layout["radius_lb"])
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return layout
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def _generate_signal(t_phys: float, feature: dict) -> float:
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value = float(feature["mean"])
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for freq_hz, amp, phase in feature["components"]:
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value += amp * math.cos(2.0 * math.pi * freq_hz * t_phys + phase)
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return value
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def _ramp_factor(elapsed_s: float) -> float:
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if elapsed_s <= 0.0:
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return 0.0
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if elapsed_s >= RAMP_TIME_S:
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return 1.0
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x = elapsed_s / RAMP_TIME_S
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return 0.5 * (1.0 - math.cos(math.pi * x))
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def _actions_at_time(t_phys: float, features: dict) -> Tuple[float, float, float]:
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s1 = _generate_signal(t_phys, features["action1"])
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s2 = _generate_signal(t_phys, features["action2"])
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s3 = _generate_signal(t_phys, features["action3"])
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r = _ramp_factor(t_phys)
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a1 = INITIAL_ACTIONS_M_S[0] * (1.0 - r) + s1 * r
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a2 = INITIAL_ACTIONS_M_S[1] * (1.0 - r) + s2 * r
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a3 = INITIAL_ACTIONS_M_S[2] * (1.0 - r) + s3 * r
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return a1, a2, a3
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def _action_to_omega_lb(action_m_s: float, u_lb: float) -> float:
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u_surf_lb = action_m_s * (u_lb / INLET_U_PHYS_M_S)
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r_lb = DIAMETER_CELLS / 2.0
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return OMEGA_SIGN_FROM_ACTION * (u_surf_lb / r_lb)
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def _set_body_omegas(sim: Simulation, omega0: float, omega1: float, omega2: float) -> None:
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"""Set all three body rotation speeds using new API (implicit GPU upload)."""
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sim.set_body(0, omega=omega0)
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sim.set_body(1, omega=omega1)
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sim.set_body(2, omega=omega2)
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def _default_steps(nx: int, u_lb: float, step_multiplier: float) -> int:
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base = int(round(2.0 * float(nx) / (3.0 * float(u_lb))))
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return int(round(base * float(step_multiplier)))
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def run_case(
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case_id: str,
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slug: str,
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features: dict,
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*,
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out_dir: Path,
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steps: int,
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report_every: int,
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batch: int = 1,
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device_id: int = 0,
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) -> dict:
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compat = _ensure_compat_config(CONFIG_PATH)
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sim = Simulation(compat, device_id=device_id)
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layout = _add_triangle_cylinders(sim)
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sim.initialize()
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u_lb = float(sim.lbm_cfg.velocity)
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dx_phys = CYLINDER_DIAMETER_M / DIAMETER_CELLS
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dt_phys = dx_phys * (u_lb / INLET_U_PHYS_M_S)
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cylinders = cylinders_from_triangle_layout(layout)
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print(f"--- {case_id} {slug} steps={steps} u_lb={u_lb} dt_phys={dt_phys} batch={batch} ---")
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stream = sim.stream
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batch_size = max(1, int(batch))
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# Main loop: precompute actions, batch-step, read forces/sensors at intervals
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for batch_start in range(0, steps, batch_size):
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batch_end = min(batch_start + batch_size, steps)
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for j in range(batch_start, batch_end):
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t_phys = j * dt_phys
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a1, a2, a3 = _actions_at_time(t_phys, features)
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w1 = _action_to_omega_lb(a1, u_lb)
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w2 = _action_to_omega_lb(a2, u_lb)
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w3 = _action_to_omega_lb(a3, u_lb)
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if SWAP_ACTION23_BODIES:
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_set_body_omegas(sim, w1, w3, w2)
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else:
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_set_body_omegas(sim, w1, w2, w3)
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n = batch_end - batch_start
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sim.stepper.step(
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n,
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action_gpu=sim.bodies.action_gpu,
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obs_gpu=sim.bodies.obs_gpu,
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stream=stream,
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)
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if report_every > 0 and (batch_end % report_every == 0 or batch_end == steps):
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stream.synchronize()
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for bid in range(sim.bodies.count):
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fx = sim.bodies.read_force(bid)
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print(
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f" step={batch_end} body={bid}"
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f" fx={float(fx[0]):+.6f} fy={float(fx[1]):+.6f}",
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flush=True,
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)
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stream.synchronize()
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macro = sim.get_macroscopic()
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vort = compute_vorticity(macro["ux"], macro["uy"])
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png = out_dir / f"vorticity_{case_id}_{slug}.png"
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ckpt = out_dir / f"state_{case_id}_{slug}.h5"
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sim.save_checkpoint(str(ckpt))
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render_info = render_vorticity_field(
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vort,
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nx=int(sim.lbm_cfg.nx),
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ny=int(sim.lbm_cfg.ny),
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out_path=str(png),
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cylinders=cylinders,
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vmin=VORT_VMIN,
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vmax=VORT_VMAX,
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minimal_axes=True,
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)
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sim.close()
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summary = {
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"case_id": case_id,
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"slug": slug,
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"steps": int(steps),
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"batch": int(batch),
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"u_lb": u_lb,
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"dt_phys": dt_phys,
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"vort_png": str(png),
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"checkpoint": str(ckpt),
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"vort_range_data": [float(vort.min()), float(vort.max())],
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"vort_plot_range": [VORT_VMIN, VORT_VMAX],
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"swap_action23_bodies": bool(SWAP_ACTION23_BODIES),
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"render": render_info,
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}
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with (out_dir / f"summary_{case_id}_{slug}.json").open("w", encoding="utf-8") as f:
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json.dump(summary, f, indent=2)
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print(f" saved {png}")
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return summary
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def main() -> int:
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ap = argparse.ArgumentParser(description="exp_ctrl_matrix vorticity batch")
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ap.add_argument("--out-dir", type=str, default=str(DEFAULT_OUT))
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ap.add_argument(
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"--step-multiplier",
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type=float,
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default=2.0,
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help=(
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"Steps = multiplier * round(2*nx/(3*u_lb)); "
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"use 2.0 after halving nx/ny."
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),
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)
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ap.add_argument(
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"--steps",
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type=int,
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default=FIXED_STEPS,
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help=f"Total LBM steps (default {FIXED_STEPS}).",
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)
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ap.add_argument("--report-every", type=int, default=20000)
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ap.add_argument("--cases", type=str, default="",
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help="Comma list e.g. C0,C1 or empty=all.")
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ap.add_argument(
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"--batch", type=int, default=1,
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help=(
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"Batch N steps between action uploads. Default 1 (each step). "
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"With --batch 10, actions are computed and uploaded every 10 steps. "
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"Saves kernel launch overhead at the cost of control-signal interpolation."
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),
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)
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ap.add_argument("--device-id", type=int, default=0, help="GPU device id.")
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args = ap.parse_args()
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out_dir = Path(args.out_dir)
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out_dir.mkdir(parents=True, exist_ok=True)
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selected = {c.strip() for c in args.cases.split(",") if c.strip()} \
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if args.cases else None
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summaries = []
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with CONFIG_PATH.open("r", encoding="utf-8") as f:
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grid_cfg = json.load(f)["grid"]
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nx = int(grid_cfg["nx"])
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ny = int(grid_cfg["ny"])
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u_lb = 0.04
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base_steps = int(round(2.0 * nx / (3.0 * u_lb)))
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steps = int(args.steps) if int(args.steps) > 0 \
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||||
else _default_steps(nx, u_lb, args.step_multiplier)
|
||||
print(
|
||||
f"Output: {out_dir} | grid={nx}x{ny} | base_steps={base_steps} "
|
||||
f"x{args.step_multiplier} -> {steps} | batch={args.batch} | "
|
||||
f"device={args.device_id} | vort [{VORT_VMIN}, {VORT_VMAX}]"
|
||||
)
|
||||
|
||||
for case_id, slug, features in CONTROL_CASES:
|
||||
if selected and case_id not in selected:
|
||||
continue
|
||||
summaries.append(
|
||||
run_case(
|
||||
case_id,
|
||||
slug,
|
||||
features,
|
||||
out_dir=out_dir,
|
||||
steps=steps,
|
||||
report_every=int(args.report_every),
|
||||
batch=int(args.batch),
|
||||
device_id=int(args.device_id),
|
||||
)
|
||||
)
|
||||
|
||||
manifest = {
|
||||
"grid": {"nx": nx, "ny": ny},
|
||||
"base_steps": base_steps,
|
||||
"step_multiplier": float(args.step_multiplier),
|
||||
"steps": steps,
|
||||
"batch": int(args.batch),
|
||||
"device_id": int(args.device_id),
|
||||
"vort_vmin": VORT_VMIN,
|
||||
"vort_vmax": VORT_VMAX,
|
||||
"swap_action23_bodies": bool(SWAP_ACTION23_BODIES),
|
||||
"cases": summaries,
|
||||
}
|
||||
manifest_path = out_dir / "manifest.json"
|
||||
with manifest_path.open("w", encoding="utf-8") as f:
|
||||
json.dump(manifest, f, indent=2)
|
||||
print(f"Manifest: {manifest_path}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in new issue
Block a user