scripts/result.py

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, 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')

    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 result/output 中第一个预测结果，用户可自行指定日期和变量 #####
    eg_files = [total_files[0][:-4]]
    # DGMR 为单通道场，默认绘制该通道（后续帧的时刻可通过 h5 time_step 推算）
    channel_index = list(range(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}')