修改以下代码，虽然运行正常，但显示不正确，要求使两幅图的尺寸大致相等。import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LogNorm, Normalize from matplotlib.gridspec import GridSpec import os import xarray as xr from scipy.stats import pearsonr from scipy.stats import spearmanr, kendalltau '''比较各个模型与标准模型（CSRM、GLDAS）的偏离程度，以标准线的形式给出''' def plot_model_comparisons(file_path: str, base_model: str = "CSRM", compare_models: list = ["RAPM", "TDAPM"],\ param_type: str = "trend", area_col: str = "area", save_path=r'D:\ill_pose\verify\outdata'): try: df = pd.read_excel(file_path) except FileNotFoundError: raise ValueError(f"文件不存在：{file_path}") # 验证参数有效性 valid_params = ["trend", "annual", 'semiannual'] if param_type not in valid_params: raise ValueError(f"参数类型必须为 {valid_params}") if param_type == 'trend': units = '(cm/yr)' else: units = '(cm)' # 自动生成列名模板 base_col = f"{param_type}-{base_model}" compare_cols = [f"{param_type}-{model}" for model in compare_models] # 检查列存在性 required_cols = [base_col, area_col] + compare_cols missing_cols = [col for col in required_cols if col not in df.columns] if missing_cols: raise KeyError(f"缺失必要列：{missing_cols}") # 创建子图布局 n_models = len(compare_models) ncols = min(3, n_models) # 每行最多3个子图 nrows = int(np.ceil(n_models / ncols)) fig = plt.figure(figsize=(12, 3 * nrows), dpi=500) gs = GridSpec(nrows, ncols, figure=fig) axes = [] # cmap = 'viridis' cmap = plt.get_cmap('RdYlBu_r') # 反转色带以符合常见地理可视化习惯 norm = Normalize(vmin=1e5, vmax=6e6) # norm = LogNorm(vmin=1e5, vmax=6e6) # 对数归一化 # 遍历每个对比模型 for i, model in enumerate(compare_models): # 创建子图 ax = fig.add_subplot(gs[i // ncols, i % ncols]) axes.append(ax) # 提取对比数据列 compare_col = f"{param_type}-{model}" df_plot = df[[base_col, compare_col, area_col]].copy()