import gymnasium as gym import numpy as np from gymnasium import spaces import ctypes from collections import deque from typing import Tuple import sys import os import matplotlib.pyplot as plt import queue os.environ["OMP_NUM_THREADS"] = "1" os.environ["MKL_NUM_THREADS"] = "1" current_dir = os.path.dirname(os.path.abspath("__file__")) parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir)) sys.path.append(parent_dir) from CelerisLab import FlowField from CelerisLab import utils config_cuda = utils.load_cuda_config( os.path.join(parent_dir, "configs", "config_cuda.json") ) config_field = utils.load_flow_field_config( os.path.join(parent_dir, "configs", "config_flowfield.json") ) S_DIM, A_DIM = 12, 3 U0 = config_field.velocity T0 = 1000 SAMPLE_INTERVAL = 800 FIFO_LEN = 120 CONV_LEN = 60 MAX_STEPS = 500 if config_field.data_type == "FP32": DATA_TYPE = np.float32 else: raise ValueError(f"Unsupported data type {config_field.data_type}.") class CustomEnv(gym.Env): """Custom Environment that follows gym interface.""" metadata = {"render_modes": ["human"], "render_fps": T0 / SAMPLE_INTERVAL} def __init__(self, device_id=0): super().__init__() self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE) self.observation_space = spaces.Box( low=-1, high=1, shape=(S_DIM,), dtype=DATA_TYPE ) self.fifo_states = deque(maxlen=FIFO_LEN) self.target_states = np.empty((0, 6), dtype=DATA_TYPE) self.force_norm_fact = 1.0 self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE) self.sens_deviation = np.zeros(6, dtype=DATA_TYPE) self.reward_cd = 0.0 self.reward_cl = 0.0 self.reward_sim = 0.0 self.current_step = 0 self.flow_field = FlowField(config_field, config_cuda, device_id) L0 = 20 U0 = config_field.velocity NX = self.flow_field.FIELD_SHAPE[0] NY = self.flow_field.FIELD_SHAPE[1] center: Tuple[float, float, float] = (10 * L0, (NY - 1) / 2, 0) self.flow_field.add_cylinder(center, L0) center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 + 2 * L0, 0) self.flow_field.add_sensor(center, L0 / 4) center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2, 0) self.flow_field.add_sensor(center, L0 / 4) center: Tuple[float, float, float] = (40 * L0, (NY - 1) / 2 - 2 * L0, 0) self.flow_field.add_sensor(center, L0 / 4) self.flow_field.run(int(4*NX/U0), np.zeros(4, dtype=DATA_TYPE)) for i in range(FIFO_LEN): self.flow_field.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE)) new_state = self.flow_field.obs.copy()[2:8] self.target_states = np.vstack((self.target_states, new_state)) self.flow_field.apply_ddf() center: Tuple[float, float, float] = (30 * L0, (NY - 1) / 2, 0) self.flow_field.add_cylinder(center, L0 / 2) center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 + 0.75 * L0, 0) self.flow_field.add_cylinder(center, L0 / 2) center: Tuple[float, float, float] = (31.3 * L0, (NY - 1) / 2 - 0.75 * L0, 0) self.flow_field.add_cylinder(center, L0 / 2) self.flow_field.run(int(4*NX/U0), np.zeros(7, dtype=DATA_TYPE)) self.flow_field.get_ddf() for i in range(FIFO_LEN): self.flow_field.run(SAMPLE_INTERVAL, np.zeros(7, dtype=DATA_TYPE)) self.fifo_states.append(self.flow_field.obs.copy()[2:14]) self.save_states = self.fifo_states.copy() self.flow_field.get_ddf() temp_states = np.array(self.fifo_states) self.force_norm_fact = 6 * np.max(np.abs(temp_states[:, 6:12])) for i in range(6): self.sens_deviation[i] = np.mean(temp_states[:, i]) self.sens_norm_fact[i] = 5 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i])) self.target_states[:, i] = (self.target_states[:, i] - self.sens_deviation[i]) / self.sens_norm_fact[i] def step(self, action): assert self.action_space.contains(action), "%r (%s) invalid" % ( action, type(action), ) # barrier = threading.Barrier(2) result_queue = queue.Queue() def run_flow_field(action): self.flow_field.context.push() U0 = config_field.velocity try: temp = np.zeros(7, dtype=DATA_TYPE) temp[4:7] = np.array((action*8+[0,-4,4])*U0, dtype=DATA_TYPE) self.flow_field.run(SAMPLE_INTERVAL, temp) finally: self.flow_field.context.pop() # barrier.wait() self.fifo_states.append(self.flow_field.obs.copy()[2:14]) def proc_data(): states = np.array(self.fifo_states) forces = states[-1, 6:12] / self.force_norm_fact cd = (forces[0] + forces[2] + forces[4]) / 3 cl = (forces[1] + forces[3] + forces[5]) / 3 sens = (states[-1, 0:6] - self.sens_deviation) / self.sens_norm_fact similarities = 0.0 def calc_lag(target, state): target_mean = np.mean(target) state_mean = np.mean(state) correlation = np.correlate(target - target_mean, state - state_mean, "full") lags = np.arange(-len(target) + 1, len(target)) max_lag = lags[np.argmax(correlation)] return max_lag def calc_sim(target, state, lag): target_mean = np.mean(target) state_mean = np.mean(state) target_std = np.std(target) aligned_state = np.roll(state, lag) if lag >= 0: # seq_target = target[-CONV_LEN:]-target_mean # seq_state = aligned_state[-CONV_LEN:]-state_mean seq_target = target[-CONV_LEN:] seq_state = aligned_state[-CONV_LEN:] else: # seq_target = target[:CONV_LEN]-target_mean # seq_state = aligned_state[:CONV_LEN]-state_mean seq_target = target[:CONV_LEN] seq_state = aligned_state[:CONV_LEN] def dtw(target, state): n = len(target) m = len(state) dtw_matrix = np.full((n + 1, m + 1), np.inf) dtw_matrix[0, 0] = 0 for i in range(1, n + 1): for j in range(1, m + 1): cost = abs(target[i - 1] - state[j - 1]) last_min = min(dtw_matrix[i - 1, j], dtw_matrix[i, j - 1], dtw_matrix[i - 1, j - 1]) dtw_matrix[i, j] = cost + last_min return 1 - (dtw_matrix[n, m] / len(target)) # seq_diff = seq_target - seq_state # sim_cor = 10*(np.corrcoef(seq_target, seq_state)[0, 1] - 1) # sim_div = -np.abs((target_mean - state_mean) / target_std * 0.75) # sim_amp = -np.abs(np.std(seq_diff) / target_std * 2) # return np.exp((sim_cor + sim_div + sim_amp) / 3) return dtw(seq_target, seq_state) id_sens = 0 target_seq = self.target_states[:, id_sens] state_seq = (states[:, id_sens] - self.sens_deviation[id_sens]) / self.sens_norm_fact[id_sens] lag = calc_lag(target_seq, state_seq) # similarities += calc_sim(target_seq, state_seq, lag) / 6 similarities += calc_sim(target_seq*self.sens_norm_fact[id_sens]+self.sens_deviation[id_sens], state_seq*self.sens_norm_fact[id_sens]+self.sens_deviation[id_sens], lag) / 6 for i in range(1, 6): target_seq = self.target_states[:, i] state_seq = (states[:, i] - self.sens_deviation[i]) / self.sens_norm_fact[i] # similarities += calc_sim(target_seq, state_seq, lag) / 6 similarities += calc_sim(target_seq*self.sens_norm_fact[id_sens]+self.sens_deviation[id_sens], state_seq*self.sens_norm_fact[id_sens]+self.sens_deviation[id_sens], lag) / 6 self.reward_cd = np.exp(-np.abs(cd * 20)) self.reward_cl = np.exp(-np.abs(cl * 80)) self.reward_sim = similarities reward = np.minimum(0.3 * self.reward_cd + 0.4 * self.reward_cl + 0.5 * self.reward_sim, 1.0) # barrier.wait() result_queue.put((np.hstack([forces, sens]), reward)) run_flow_field(action) proc_data() observation, reward = result_queue.get() truncated = bool(np.any(observation > 1) or np.any(observation < -1)) observation = np.clip(observation, -1, 1) self.current_step += 1 done = self.current_step >= MAX_STEPS return observation, float(reward), done, truncated, {} def reset(self, seed=None): self.flow_field.apply_ddf() self.fifo_states = self.save_states.copy() self.current_step = 0 return np.zeros(S_DIM, dtype=np.float32), {} def render(self, mode="human"): NX = self.flow_field.FIELD_SHAPE[0] NY = self.flow_field.FIELD_SHAPE[1] self.flow_field.get_ddf() ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0) ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0 uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0 speed = np.sqrt(ux**2 + uy**2) plt.figure(figsize=(10, 5)) plt.imshow(speed.T, origin='lower', cmap='viridis', extent=[0, NX, 0, NY]) plt.colorbar(label='Speed') plt.title('Scalar Velocity Field') plt.xlabel('X') plt.ylabel('Y') plt.tight_layout() plt.show() def save_field(self, filename): NX = self.flow_field.FIELD_SHAPE[0] NY = self.flow_field.FIELD_SHAPE[1] self.flow_field.get_ddf() ddf_plot = self.flow_field.ddf.copy().reshape((9, NY, NX)).transpose(2, 1, 0) flag_plot = self.flow_field.flag.copy().reshape((NY, NX)).transpose(1, 0) ux = (ddf_plot[:, :, 1] + ddf_plot[:, :, 5] + ddf_plot[:, :, 8] - ddf_plot[:, :, 3] - ddf_plot[:, :, 6] - ddf_plot[:, :, 7]) / U0 uy = (ddf_plot[:, :, 2] + ddf_plot[:, :, 5] + ddf_plot[:, :, 6] - ddf_plot[:, :, 4] - ddf_plot[:, :, 7] - ddf_plot[:, :, 8]) / U0 with open(os.path.join(parent_dir, "output", filename), "w") as f: f.write("Title= \"LBM 2D\"\r\n") f.write("VARIABLES= \"X\",\"Y\",\"flag\",\"U\",\"V\",\r\n") f.write(f"ZONE T= \"BOX\",I= {NX},J= {NY},F=POINT\r\n") for j in range(NY): for i in range(NX): f.write(f"{i},{j},{flag_plot[i, j]},{ux[i, j]},{uy[i, j]}\r\n") def close(self): self.flow_field.__del__()