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 group_files_by_year(total_files, time_step): """将输出文件按年份归组，减少重复打开年度 h5 文件的开销""" files_by_year = {} for file in total_files: fname = file[:-4] year = fname[:4] files_by_year.setdefault(year, []).append((file, filename_to_index(fname, time_step))) return files_by_year 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)) files_by_year = group_files_by_year(total_files, time_step) with tqdm(total=len(total_files), unit="files") as pbar: for year, year_files in files_by_year.items(): with h5py.File(os.path.join(data_dir, 'data', f'{year}.h5'), "r") as f: fields = f["fields"] for file, t_idx in year_files: label = fields[t_idx] # [C, H, W] label = label[channel_indices] pred = np.load(f'result/output/{file}').squeeze() 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))) pbar.update(1) 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): # 基础设置 fig, axes = plt.subplots(1, 3, figsize=(15, 4)) # 坐标轴标签 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) # 计算统一色条范围 vmin = min(label.min(), pred.min()) vmax = max(label.max(), pred.max()) # 计算差异和 RMSE diff = label - pred rmse = np.sqrt(np.mean(diff ** 2)) diff_abs_max = np.abs(diff).max() # 绘图配置 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}, ] # 统一绘制 for ax, cfg in zip(axes, plot_configs): im = ax.imshow(cfg['data'], cmap=cfg['cmap'], vmin=cfg['vmin'], vmax=cfg['vmax']) ax.set_title(cfg['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') # 总标题 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)) # 配置 colors = {'train': '#2563EB', 'valid': '#EA580C'} epochs = np.arange(1, len(train_loss) + 1) # 绑定曲线 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='--') # 标注最小值 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)) # 坐标轴 ax.set(xlabel='Epoch', ylabel='Loss', xlim=(0, len(train_loss) + 1)) # 样式 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 test_time 第一年的第一个时间步，用户可自行指定日期和变量 ##### test_year = cfg_data.dataset.test_time[0] eg_files = [f'{test_year}010206'] channel_index = [cfg_data.dataset.channels.index(v) for v in ['2m_temperature', 'geopotential_500', 'temperature_500']] 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() 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}')