import numpy as np import matplotlib.pyplot as plt import os import sys import glob import h5py from datetime import datetime from tqdm import tqdm from onescience.utils.fcn.YParams import YParams from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['DejaVu Serif'] rcParams['mathtext.fontset'] = 'stix' rcParams['axes.linewidth'] = 0.9 rcParams['xtick.major.width'] = 0.9 rcParams['ytick.major.width'] = 0.9 def get_metadata(data_dir, channels): """从新版 h5 attrs 中读取变量列表和 time_step""" h5_files = sorted(glob.glob(os.path.join(data_dir, "data", "*.h5"))) with h5py.File(h5_files[0], "r") as f: ds = f["fields"] all_variables = [v.decode() if isinstance(v, bytes) else v for v in ds.attrs["variables"]] time_step = int(ds.attrs["time_step"]) channel_indices = [all_variables.index(v) for v in channels] total_files = [f for f in os.listdir('./result/output/') if f.endswith('.npy')] total_files.sort() return total_files, channel_indices, time_step def filename_to_index(filename, time_step): """将 YYYYMMDDHH 格式的文件名转换为年度 h5 文件中的时间步索引""" dt = datetime.strptime(filename, "%Y%m%d%H") year_start = datetime(dt.year, 1, 1) hours = (dt - year_start).total_seconds() / 3600 return int(hours / time_step) def get_result(total_files, channel_indices, time_step, data_dir, clim_mean): channel_rmse = np.zeros(len(channel_indices)) channel_acc = np.zeros(len(channel_indices)) clim_mean = clim_mean[0, :, :, :] if not os.path.exists('./result/rmse.npy') or not os.path.exists('result/acc.npy'): numerator = np.zeros(len(channel_indices)) pred_sq_sum = np.zeros(len(channel_indices)) label_sq_sum = np.zeros(len(channel_indices)) for file in tqdm(total_files, unit="files"): fname = file[:-4] # 去掉 .npy year = fname[:4] t_idx = filename_to_index(fname, time_step) with h5py.File(os.path.join(data_dir, 'data', f'{year}.h5'), "r") as f: label = f["fields"][t_idx] # [C, H, W] label = label[channel_indices] pred = np.load(f'result/output/{file}').squeeze() if pred.ndim == 2: pred = pred[np.newaxis] # 单通道时 squeeze 会压缩掉通道维，恢复为 [C, H, W] label_anom = label - clim_mean pred_anom = pred - clim_mean # 累加 numerator += np.sum(pred_anom * label_anom, axis=(1, 2)) pred_sq_sum += np.sum(pred_anom ** 2, axis=(1, 2)) label_sq_sum += np.sum(label_anom ** 2, axis=(1, 2)) channel_rmse += np.sqrt(np.mean((label - pred) ** 2, axis=(1, 2))) channel_rmse /= len(total_files) channel_acc = numerator / (np.sqrt(pred_sq_sum * label_sq_sum) + 1e-8) np.save('./result/acc.npy', channel_acc) np.save('./result/rmse.npy', channel_rmse) def show_result(): channel_rmse = np.load('./result/rmse.npy') channel_acc = np.load('./result/acc.npy') channels = [cfg_data.dataset.channels[i] for i in range(len(channel_indices))] w = 24 # 最长 channel 名宽度 # 表头 print(f"┌{'─' * (w + 2)}┬{'─' * 14}┬{'─' * 14}┐") print(f"│ {'Channel':<{w}} │ {'RMSE':>12} │ {'ACC':>12} │") print(f"├{'─' * (w + 2)}┼{'─' * 14}┼{'─' * 14}┤") # 数据行 for i, ch in enumerate(channels): print(f"│ {ch:<{w}} │ {channel_rmse[i]:>12.4f} | {channel_acc[i]:>12.4f} |") print(f"├{'─' * (w + 2)}┼{'─' * 14}┼{'─' * 14}┤") print(f"│ {'Average':<{w}} │ {np.mean(channel_rmse):>12.4f} │ {np.mean(channel_acc):>12.4f} │") print(f"└{'─' * (w + 2)}┴{'─' * 14}┴{'─' * 14}┘") def plot(label, pred, var, filename): fig, axes = plt.subplots(1, 3, figsize=(15, 4)) xtick_labels = ['180°W', '90°W', '0°', '90°E', '180°E'] ytick_labels = ['90°S', '45°S', '0°', '45°N', '90°N'] xticks = np.linspace(0, label.shape[-1] - 1, 5) yticks = np.linspace(0, label.shape[-2] - 1, 5) vmin = min(label.min(), pred.min()) vmax = max(label.max(), pred.max()) diff = label - pred rmse = np.sqrt(np.mean(diff ** 2)) diff_abs_max = np.abs(diff).max() plot_configs = [ {'data': label, 'title': 'Truth', 'cmap': 'viridis', 'vmin': vmin, 'vmax': vmax}, {'data': pred, 'title': 'Prediction', 'cmap': 'viridis', 'vmin': vmin, 'vmax': vmax}, {'data': diff, 'title': f'Difference (RMSE={rmse:.2f})', 'cmap': 'RdBu_r', 'vmin': -diff_abs_max, 'vmax': diff_abs_max}, ] for ax, cfg2 in zip(axes, plot_configs): im = ax.imshow(cfg2['data'], cmap=cfg2['cmap'], vmin=cfg2['vmin'], vmax=cfg2['vmax']) ax.set_title(cfg2['title'], fontsize=12, pad=4) ax.set_xlabel('Longitude') ax.set_ylabel('Latitude') ax.set_xticks(xticks) ax.set_xticklabels(xtick_labels) ax.set_yticks(yticks) ax.set_yticklabels(ytick_labels) plt.colorbar(im, ax=ax, orientation='horizontal') fig.suptitle(var, fontsize=14, fontweight='bold', y=0.98) plt.savefig(filename, dpi=300, bbox_inches='tight') plt.close() def plot_loss(train_loss, valid_loss): mask = ~(np.isnan(train_loss) | np.isnan(valid_loss)) train_loss = train_loss[mask] valid_loss = valid_loss[mask] fig, ax = plt.subplots(figsize=(5, 3.5)) colors = {'train': '#2563EB', 'valid': '#EA580C'} epochs = np.arange(1, len(train_loss) + 1) ax.plot(epochs, train_loss, color=colors['train'], linewidth=1.5, label='Train') ax.plot(epochs, valid_loss, color=colors['valid'], linewidth=1.5, label='Valid', linestyle='--') min_idx = np.argmin(valid_loss) ax.scatter(epochs[min_idx], valid_loss[min_idx], color=colors['valid'], s=40, zorder=5, edgecolors='white') ax.annotate(f'Best: {valid_loss[min_idx]:.3f}', xy=(epochs[min_idx], valid_loss[min_idx]), xytext=(10, 10), textcoords='offset points', fontsize=8, color=colors['valid'], arrowprops=dict(arrowstyle='-', color=colors['valid'], lw=0.5)) ax.set(xlabel='Epoch', ylabel='Loss', xlim=(0, len(train_loss) + 1)) ax.legend(frameon=False, loc='upper right') ax.grid(True, linestyle='--', alpha=0.3) ax.spines[['top', 'right']].set_visible(False) plt.tight_layout() plt.savefig('./result/loss.png', dpi=300, bbox_inches='tight') plt.close() if __name__ == "__main__": current_path = os.getcwd() sys.path.append(current_path) config_file_path = os.path.join(current_path, 'conf/config.yaml') cfg = YParams(config_file_path, 'model') cfg_data = YParams(config_file_path, "datapipe") train_loss = np.load('./data/checkpoints/trloss.npy') valid_loss = np.load('./data/checkpoints/valoss.npy') plot_loss(train_loss, valid_loss) data_dir = cfg_data.dataset.data_dir total_files, channel_indices, time_step = get_metadata(data_dir, cfg_data.dataset.channels) # Load data & Compute RMSE/ACC per channel h5_files = sorted(glob.glob(os.path.join(data_dir, "data", "*.h5"))) with h5py.File(h5_files[0], "r") as f: mu = f["global_means"][:] clim_mean = mu[:, channel_indices, :, :] get_result(total_files, channel_indices, time_step, data_dir, clim_mean) show_result() ##### 默认绘制第一个预测输出的时刻与全部通道，用户可自行指定 ##### eg_files = [total_files[0][:-4]] # 最多绘制 3 个通道的对比图 channel_index = list(range(min(3, len(cfg_data.dataset.channels)))) selected_var = [cfg_data.dataset.channels[int(i)] for i in channel_index] print(f"seleted date: {eg_files}") print(f"selected channels: {selected_var}") for file in eg_files: year = file[:4] t_idx = filename_to_index(file, time_step) with h5py.File(os.path.join(data_dir, 'data', f'{year}.h5'), "r") as f: label = f["fields"][t_idx] # [C, H, W] label = label[channel_indices] pred = np.load(f'result/output/{file}.npy').squeeze() if pred.ndim == 2: pred = pred[np.newaxis] # 单通道时 squeeze 会压缩掉通道维，恢复为 [C, H, W] for i in range(len(selected_var)): filename = f'./result/{file}_{selected_var[i]}.png' plot(label[channel_index[i]], pred[channel_index[i]], selected_var[i], filename) print(f'✅plot {filename}')