Pandas dataframe error: matplotlib.axes._subplots.AxesSubplot 解决方案

本文介绍了解决在使用Pandas生成水平柱状图时遇到的matplotlib.axes._subplots.AxesSubplot错误的方法。通过在IPython环境中启用pylab模式和设置%matplotlibinline,可以成功避免此错误,实现数据可视化。

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Pandas dataframe error: matplotlib.axes._subplots.AxesSubplot


《利用Python进行数据分析》书中引言pandas计数例子,生成水平柱状图时报错,原因是没有打开pylab模式,IPython中执行

%pylab
%matplotlib inline

问题得到解决。

import tkinter as tk from tkinter import ttk, filedialog, messagebox import pandas as pd import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Dense, Lambda from tensorflow.keras.optimizers import Adam from sklearn.preprocessing import MinMaxScaler import os import time mpl.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS'] mpl.rcParams['axes.unicode_minus'] = False # 关键修复:使用 ASCII 减号 # 设置中文字体支持 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False class PINNModel(tf.keras.Model): def __init__(self, num_layers=4, hidden_units=32, **kwargs): super(PINNModel, self).__init__(**kwargs) self.dense_layers = [Dense(hidden_units, activation='tanh') for _ in range(num_layers)] self.final_layer = Dense(1, activation='linear') # 添加带约束的物理参数 self.k_raw = tf.Variable(0.01, trainable=True, dtype=tf.float32, name='k_raw') self.k = tf.math.sigmoid(self.k_raw) * 0.5 # 约束在0-0.5之间 def call(self, inputs): t, h = inputs x = tf.concat([t, h], axis=1) for layer in self.dense_layers: x = layer(x) return self.final_layer(x) def physics_loss(self, t, h_current): """计算物理损失(基于离散渗流方程)""" # 预测下一时刻的水位 h_next_pred = self([t, h_current]) # 离散渗流方程: h_{t+1} = h_t - k * h_t (时间步长=1) residual = h_next_pred - h_current * (1 - self.k) return tf.reduce_mean(tf.square(residual)) class DamSeepageModel: def __init__(self, root): self.root = root self.root.title("大坝渗流预测模型(PINNs)") self.root.geometry("1200x800") # 初始化数据 self.train_df = None #训练集 self.test_df = None #测试集 self.model = None self.scaler = MinMaxScaler(feature_range=(0, 1)) self.evaluation_metrics = {} # 创建主界面 self.create_widgets() def create_widgets(self): # 创建主框架 main_frame = ttk.Frame(self.root, padding=10) main_frame.pack(fill=tk.BOTH, expand=True) # 左侧控制面板 control_frame = ttk.LabelFrame(main_frame, text="模型控制", padding=10) control_frame.pack(side=tk.LEFT, fill=tk.Y, padx=5, pady=5) # 文件选择部分 file_frame = ttk.LabelFrame(control_frame, text="数据文件", padding=10) file_frame.pack(fill=tk.X, pady=5) # 训练集选择 ttk.Label(file_frame, text="训练集:").grid(row=0, column=0, sticky=tk.W, pady=5) self.train_file_var = tk.StringVar() ttk.Entry(file_frame, textvariable=self.train_file_var, width=30, state='readonly').grid(row=0, column=1, padx=5) ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("train")).grid(row=0, column=2) # 测试集选择 ttk.Label(file_frame, text="测试集:").grid(row=1, column=0, sticky=tk.W, pady=5) self.test_file_var = tk.StringVar() ttk.Entry(file_frame, textvariable=self.test_file_var, width=30, state='readonly').grid(row=1, column=1, padx=5) ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("test")).grid(row=1, column=2) # PINNs参数设置 param_frame = ttk.LabelFrame(control_frame, text="PINNs参数", padding=10) param_frame.pack(fill=tk.X, pady=10) # 验证集切分比例 ttk.Label(param_frame, text="验证集比例:").grid(row=0, column=0, sticky=tk.W, pady=5) self.split_ratio_var = tk.DoubleVar(value=0.2) ttk.Spinbox(param_frame, from_=0, to=1, increment=0.05, textvariable=self.split_ratio_var, width=10).grid(row=0, column=1, padx=5) # 隐藏层数量 ttk.Label(param_frame, text="网络层数:").grid(row=1, column=0, sticky=tk.W, pady=5) self.num_layers_var = tk.IntVar(value=4) ttk.Spinbox(param_frame, from_=2, to=8, increment=1, textvariable=self.num_layers_var, width=10).grid(row=1, column=1, padx=5) # 每层神经元数量 ttk.Label(param_frame, text="神经元数/层:").grid(row=2, column=0, sticky=tk.W, pady=5) self.hidden_units_var = tk.IntVar(value=32) ttk.Spinbox(param_frame, from_=16, to=128, increment=4, textvariable=self.hidden_units_var, width=10).grid(row=2, column=1, padx=5) # 训练轮次 ttk.Label(param_frame, text="训练轮次:").grid(row=3, column=0, sticky=tk.W, pady=5) self.epochs_var = tk.IntVar(value=500) ttk.Spinbox(param_frame, from_=100, to=2000, increment=100, textvariable=self.epochs_var, width=10).grid(row=3, column=1, padx=5) # 物理损失权重 ttk.Label(param_frame, text="物理损失权重:").grid(row=4, column=0, sticky=tk.W, pady=5) self.physics_weight_var = tk.DoubleVar(value=0.5) ttk.Spinbox(param_frame, from_=0.1, to=1.0, increment=0.1, textvariable=self.physics_weight_var, width=10).grid(row=4, column=1, padx=5) # 控制按钮 btn_frame = ttk.Frame(control_frame) btn_frame.pack(fill=tk.X, pady=10) ttk.Button(btn_frame, text="训练模型", command=self.train_model).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="预测结果", command=self.predict).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="保存结果", command=self.save_results).pack(side=tk.LEFT, padx=5) ttk.Button(btn_frame, text="重置", command=self.reset).pack(side=tk.RIGHT, padx=5) # 状态栏 self.status_var = tk.StringVar(value="就绪") status_bar = ttk.Label(control_frame, textvariable=self.status_var, relief=tk.SUNKEN, anchor=tk.W) status_bar.pack(fill=tk.X, side=tk.BOTTOM) # 右侧结果显示区域 result_frame = ttk.Frame(main_frame) result_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True, padx=5, pady=5) # 创建标签页 self.notebook = ttk.Notebook(result_frame) self.notebook.pack(fill=tk.BOTH, expand=True) # 损失曲线标签页 self.loss_frame = ttk.Frame(self.notebook) self.notebook.add(self.loss_frame, text="训练损失") # 预测结果标签页 self.prediction_frame = ttk.Frame(self.notebook) self.notebook.add(self.prediction_frame, text="预测结果") # 指标显示 self.metrics_var = tk.StringVar() metrics_label = ttk.Label( self.prediction_frame, textvariable=self.metrics_var, font=('TkDefaultFont', 10, 'bold'), relief='ridge', padding=5 ) metrics_label.pack(fill=tk.X, padx=5, pady=5) # 初始化绘图区域 self.fig, self.ax = plt.subplots(figsize=(10, 6)) self.canvas = FigureCanvasTkAgg(self.fig, master=self.prediction_frame) self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) # 损失曲线画布 self.loss_fig, self.loss_ax = plt.subplots(figsize=(10, 4)) self.loss_canvas = FigureCanvasTkAgg(self.loss_fig, master=self.loss_frame) self.loss_canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) def select_file(self, file_type): """选择Excel文件""" file_path = filedialog.askopenfilename( title=f"选择{file_type}集Excel文件", filetypes=[("Excel文件", "*.xlsx *.xls"), ("所有文件", "*.*")] ) if file_path: try: df = pd.read_excel(file_path) # 时间特征处理 time_features = ['year', 'month', 'day'] missing_time_features = [feat for feat in time_features if feat not in df.columns] if missing_time_features: messagebox.showerror("列名错误", f"Excel文件缺少预处理后的时间特征列: {', '.join(missing_time_features)}") return # 创建时间戳列 (增强兼容性) time_cols = ['year', 'month', 'day'] if 'hour' in df.columns: time_cols.append('hour') if 'minute' in df.columns: time_cols.append('minute') if 'second' in df.columns: time_cols.append('second') # 填充缺失的时间单位 for col in ['hour', 'minute', 'second']: if col not in df.columns: df[col] = 0 df['datetime'] = pd.to_datetime(df[time_cols]) # 设置时间索引 df = df.set_index('datetime') # 计算相对时间(天) df['days'] = (df.index - df.index[0]).days # 保存数据 if file_type == "train": self.train_df = df self.train_file_var.set(os.path.basename(file_path)) self.status_var.set(f"已加载训练集: {len(self.train_df)}条数据") else: self.test_df = df self.test_file_var.set(os.path.basename(file_path)) self.status_var.set(f"已加载测试集: {len(self.test_df)}条数据") except Exception as e: messagebox.showerror("文件错误", f"读取文件失败: {str(e)}") def calculate_metrics(self, y_true, y_pred): """计算评估指标""" from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score mse = mean_squared_error(y_true, y_pred) rmse = np.sqrt(mse) mae = mean_absolute_error(y_true, y_pred) non_zero_idx = np.where(y_true != 0)[0] if len(non_zero_idx) > 0: mape = np.mean(np.abs((y_true[non_zero_idx] - y_pred[non_zero_idx]) / y_true[non_zero_idx])) * 100 else: mape = float('nan') r2 = r2_score(y_true, y_pred) return { 'MSE': mse, 'RMSE': rmse, 'MAE': mae, 'MAPE': mape, 'R2': r2 } def train_model(self): """训练PINNs模型(带早停机制+训练指标监控,无指标绘图)""" if self.train_df is None: messagebox.showwarning("警告", "请先选择训练集文件") return try: self.status_var.set("正在预处理数据...") self.root.update() # 从训练集中切分训练子集和验证子集(时间顺序切分) split_ratio = 1 - self.split_ratio_var.get() split_idx = int(len(self.train_df) * split_ratio) train_subset = self.train_df.iloc[:split_idx] valid_subset = self.train_df.iloc[split_idx:] # 检查数据量是否足够 if len(train_subset) < 2 or len(valid_subset) < 2: messagebox.showerror("数据错误", "训练集数据量不足(至少需要2个时间步)") return # 数据预处理(训练子集拟合scaler,验证子集用相同scaler) train_subset_scaled = self.scaler.fit_transform(train_subset[['水位']]) valid_subset_scaled = self.scaler.transform(valid_subset[['水位']]) # 准备训练数据(原始值用于指标计算) t_train = train_subset['days'].values[1:].reshape(-1, 1).astype(np.float32) h_train = train_subset_scaled[:-1].astype(np.float32) h_next_train_scaled = train_subset_scaled[1:].astype(np.float32) # 归一化后的标签 h_next_train_true = train_subset['水位'].values[1:].reshape(-1, 1) # 原始真实值(反归一化前) # 准备验证数据(原始值用于指标计算) t_valid = valid_subset['days'].values[1:].reshape(-1, 1).astype(np.float32) h_valid = valid_subset_scaled[:-1].astype(np.float32) h_next_valid_scaled = valid_subset_scaled[1:].astype(np.float32) # 归一化后的标签 h_next_valid_true = valid_subset['水位'].values[1:].reshape(-1, 1) # 原始真实值 # 创建模型和优化器 self.model = PINNModel( num_layers=self.num_layers_var.get(), hidden_units=self.hidden_units_var.get() ) optimizer = Adam(learning_rate=0.001) # 构建训练/验证数据集 train_dataset = tf.data.Dataset.from_tensor_slices(((t_train, h_train), h_next_train_scaled)) train_dataset = train_dataset.shuffle(buffer_size=1024).batch(32) valid_dataset = tf.data.Dataset.from_tensor_slices(((t_valid, h_valid), h_next_valid_scaled)) valid_dataset = valid_dataset.batch(32) # 验证集无需shuffle # 损失记录(新增指标记录) train_data_loss_history = [] physics_loss_history = [] valid_data_loss_history = [] # 新增:训练集和验证集的指标历史(MSE, RMSE等) train_metrics_history = [] # 每个元素是字典(如{'MSE':..., 'RMSE':...}) valid_metrics_history = [] # 早停机制参数 patience = int(self.epochs_var.get() / 3) min_delta = 1e-4 best_valid_loss = float('inf') wait = 0 best_epoch = 0 best_weights = None start_time = time.time() # 自定义训练循环(新增指标计算) for epoch in range(self.epochs_var.get()): # 训练阶段 epoch_train_data_loss = [] epoch_physics_loss = [] # 收集训练预测值(归一化后) train_pred_scaled = [] for step, ((t_batch, h_batch), h_next_batch) in enumerate(train_dataset): with tf.GradientTape() as tape: h_pred = self.model([t_batch, h_batch]) data_loss = tf.reduce_mean(tf.square(h_next_batch - h_pred)) physics_loss = self.model.physics_loss(t_batch, h_batch) loss = data_loss + self.physics_weight_var.get() * physics_loss grads = tape.gradient(loss, self.model.trainable_variables) optimizer.apply_gradients(zip(grads, self.model.trainable_variables)) epoch_train_data_loss.append(data_loss.numpy()) epoch_physics_loss.append(physics_loss.numpy()) train_pred_scaled.append(h_pred.numpy()) # 保存训练预测值(归一化) # 合并训练预测值(归一化后) train_pred_scaled = np.concatenate(train_pred_scaled, axis=0) # 反归一化得到原始预测值 train_pred_true = self.scaler.inverse_transform(train_pred_scaled) # 计算训练集指标(使用原始真实值和预测值) train_metrics = self.calculate_metrics( y_true=h_next_train_true.flatten(), y_pred=train_pred_true.flatten() ) train_metrics_history.append(train_metrics) # 验证阶段 epoch_valid_data_loss = [] valid_pred_scaled = [] for ((t_v_batch, h_v_batch), h_v_next_batch) in valid_dataset: h_v_pred = self.model([t_v_batch, h_v_batch]) valid_data_loss = tf.reduce_mean(tf.square(h_v_next_batch - h_v_pred)) epoch_valid_data_loss.append(valid_data_loss.numpy()) valid_pred_scaled.append(h_v_pred.numpy()) # 保存验证预测值(归一化) # 合并验证预测值(归一化后) valid_pred_scaled = np.concatenate(valid_pred_scaled, axis=0) # 反归一化得到原始预测值 valid_pred_true = self.scaler.inverse_transform(valid_pred_scaled) # 计算验证集指标(使用原始真实值和预测值) valid_metrics = self.calculate_metrics( y_true=h_next_valid_true.flatten(), y_pred=valid_pred_true.flatten() ) valid_metrics_history.append(valid_metrics) # 计算平均损失 avg_train_data_loss = np.mean(epoch_train_data_loss) avg_physics_loss = np.mean(epoch_physics_loss) avg_valid_data_loss = np.mean(epoch_valid_data_loss) # 记录损失 train_data_loss_history.append(avg_train_data_loss) physics_loss_history.append(avg_physics_loss) valid_data_loss_history.append(avg_valid_data_loss) # 早停机制逻辑(与原代码一致) current_valid_loss = avg_valid_data_loss if current_valid_loss < best_valid_loss - min_delta: best_valid_loss = current_valid_loss best_epoch = epoch + 1 wait = 0 best_weights = self.model.get_weights() else: wait += 1 if wait >= patience: self.status_var.set(f"触发早停!最佳轮次: {best_epoch},最佳验证损失: {best_valid_loss:.4f}") if best_weights is not None: self.model.set_weights(best_weights) break # 更新状态(新增指标显示) if epoch % 10 == 0: # 提取当前训练/验证的关键指标(如RMSE) train_rmse = train_metrics['RMSE'] valid_rmse = valid_metrics['RMSE'] train_r2 = train_metrics['R2'] valid_r2 = valid_metrics['R2'] k_value = self.model.k.numpy() elapsed = time.time() - start_time self.status_var.set( f"训练中 | 轮次: {epoch + 1}/{self.epochs_var.get()} | " f"训练RMSE: {train_rmse:.4f} | 验证RMSE: {valid_rmse:.4f} | " f"训练R²: {train_r2:.4f} | 验证R²: {valid_r2:.4f} | " f"k: {k_value:.6f} | 时间: {elapsed:.1f}秒 | 早停等待: {wait}/{patience}" ) self.root.update() # 绘制损失曲线(仅保留原始损失曲线) self.loss_ax.clear() epochs_range = range(1, len(train_data_loss_history) + 1) self.loss_ax.plot(epochs_range, train_data_loss_history, 'b-', label='训练数据损失') self.loss_ax.plot(epochs_range, physics_loss_history, 'r--', label='物理损失') self.loss_ax.plot(epochs_range, valid_data_loss_history, 'g-.', label='验证数据损失') self.loss_ax.set_title('PINNs训练与验证损失') self.loss_ax.set_xlabel('轮次') self.loss_ax.set_ylabel('损失', rotation=0) self.loss_ax.legend() self.loss_ax.grid(True, alpha=0.3) self.loss_ax.set_yscale('log') self.loss_canvas.draw() # 训练完成提示(保留指标总结) elapsed = time.time() - start_time if wait >= patience: completion_msg = ( f"早停触发 | 最佳轮次: {best_epoch} | 最佳验证损失: {best_valid_loss:.4f} | " f"最佳验证RMSE: {valid_metrics_history[best_epoch - 1]['RMSE']:.4f} | " f"总时间: {elapsed:.1f}秒" ) else: completion_msg = ( f"训练完成 | 总轮次: {self.epochs_var.get()} | " f"最终训练RMSE: {train_metrics_history[-1]['RMSE']:.4f} | " f"最终验证RMSE: {valid_metrics_history[-1]['RMSE']:.4f} | " f"最终训练R²: {train_metrics_history[-1]['R2']:.4f} | " f"最终验证R²: {valid_metrics_history[-1]['R2']:.4f} | " f"总时间: {elapsed:.1f}秒" ) self.status_var.set(completion_msg) messagebox.showinfo("训练完成", f"PINNs模型训练成功完成!\n{completion_msg}") except Exception as e: messagebox.showerror("训练错误", f"模型训练失败:\n{str(e)}") self.status_var.set("训练失败") def predict(self): """使用PINNs模型进行预测(优化时间轴刻度与网格线)""" if self.model is None: messagebox.showwarning("警告", "请先训练模型") return if self.test_df is None: messagebox.showwarning("警告", "请先选择测试集文件") return try: self.status_var.set("正在生成预测...") self.root.update() # 预处理测试数据 test_scaled = self.scaler.transform(self.test_df[['水位']]) # 准备时间特征 t_test = self.test_df['days'].values.reshape(-1, 1).astype(np.float32) # 递归预测 predictions = [] for i in range(len(t_test)): h_current = np.array([[test_scaled[i][0]]]).astype(np.float32) h_pred = self.model([t_test[i:i + 1], h_current]) predictions.append(h_pred.numpy()[0][0]) # 反归一化 predictions = np.array(predictions).reshape(-1, 1) predictions = self.scaler.inverse_transform(predictions) actual_values = self.scaler.inverse_transform(test_scaled) # 创建时间索引(确保为DatetimeIndex) test_time = self.test_df.index # 假设为pandas DatetimeIndex类型 # 清除现有图表 self.ax.clear() # 绘制结果 self.ax.plot(test_time, actual_values, 'b-', label='真实值') self.ax.plot(test_time, predictions, 'r--', label='预测值') self.ax.set_title('大坝渗流水位预测结果(PINNs)') self.ax.set_xlabel('时间') self.ax.set_ylabel('测压管水位', rotation=0) self.ax.legend() # 添加网格和样式(优化时间轴) import matplotlib.dates as mdates # 导入日期刻度工具 # 设置x轴刻度:主刻度(年份)和次要刻度(每2个月) # 主刻度:每年11日(或数据起始年的第一个时间点) self.ax.xaxis.set_major_locator(mdates.YearLocator()) self.ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y')) # 仅显示年份 # 次要刻度:每2个月(如2月、4月、6月...) self.ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=2)) # 添加次要网格线(每2个月的竖直虚线) self.ax.grid(which='minor', axis='x', linestyle='--', color='gray', alpha=0.3) # 主要网格线(可选,保持原有水平网格) self.ax.grid(which='major', axis='y', linestyle='-', color='lightgray', alpha=0.5) # 优化刻度标签显示(避免重叠) self.ax.tick_params(axis='x', which='major', rotation=0, labelsize=10) self.ax.tick_params(axis='x', which='minor', length=3) # 次要刻度线长度 # 计算并显示评估指标(保持原有逻辑) self.evaluation_metrics = self.calculate_metrics( actual_values.flatten(), predictions.flatten() ) metrics_text = ( f"MSE: {self.evaluation_metrics['MSE']:.4f} | " f"RMSE: {self.evaluation_metrics['RMSE']:.4f} | " f"MAE: {self.evaluation_metrics['MAE']:.4f} | " f"MAPE: {self.evaluation_metrics['MAPE']:.2f}% | " f"R²: {self.evaluation_metrics['R2']:.4f}" ) # 更新文本标签 self.metrics_var.set(metrics_text) # 在图表上添加指标(位置调整,避免覆盖时间刻度) self.ax.text( 0.5, 1.08, metrics_text, # 略微上移避免与网格重叠 transform=self.ax.transAxes, ha='center', fontsize=10, bbox=dict(facecolor='white', alpha=0.8) ) # 调整布局(重点优化时间轴边距) plt.tight_layout(pad=2.0) # 增加底部边距避免刻度标签被截断 self.canvas.draw() # 保存预测结果(保持原有逻辑) self.predictions = predictions self.actual_values = actual_values self.test_time = test_time self.status_var.set("预测完成,结果已显示") except Exception as e: messagebox.showerror("预测错误", f"预测失败:\n{str(e)}") self.status_var.set("预测失败") def save_results(self): """保存预测结果""" if not hasattr(self, 'predictions'): messagebox.showwarning("警告", "请先生成预测结果") return save_path = filedialog.asksaveasfilename( defaultextension=".xlsx", filetypes=[("Excel文件", "*.xlsx"), ("所有文件", "*.*")] ) if not save_path: return try: # 创建结果DataFrame result_df = pd.DataFrame({ '时间': self.test_time, '实际水位': self.actual_values.flatten(), '预测水位': self.predictions.flatten() }) # 创建评估指标DataFrame metrics_df = pd.DataFrame([self.evaluation_metrics]) # 保存到Excel with pd.ExcelWriter(save_path) as writer: result_df.to_excel(writer, sheet_name='预测结果', index=False) metrics_df.to_excel(writer, sheet_name='评估指标', index=False) # 保存图表 chart_path = os.path.splitext(save_path)[0] + "_chart.png" self.fig.savefig(chart_path, dpi=300) self.status_var.set(f"结果已保存至: {os.path.basename(save_path)}") messagebox.showinfo("保存成功", f"预测结果和图表已保存至:\n{save_path}\n{chart_path}") except Exception as e: messagebox.showerror("保存错误", f"保存结果失败:\n{str(e)}") def reset(self): """重置程序状态""" self.train_df = None self.test_df = None self.model = None self.train_file_var.set("") self.test_file_var.set("") # 清除图表 if hasattr(self, 'ax'): self.ax.clear() if hasattr(self, 'loss_ax'): self.loss_ax.clear() # 重绘画布 if hasattr(self, 'canvas'): self.canvas.draw() if hasattr(self, 'loss_canvas'): self.loss_canvas.draw() # 清除状态 self.status_var.set("已重置,请选择新数据") # 清除预测结果 if hasattr(self, 'predictions'): del self.predictions # 清除指标文本 if hasattr(self, 'metrics_var'): self.metrics_var.set("") messagebox.showinfo("重置", "程序已重置,可以开始新的分析") if __name__ == "__main__": root = tk.Tk() app = DamSeepageModel(root) root.mainloop() 帮我修改文件保存,一个文件保存时间和实际及预测水位,增加一个文件保存轮次和五个评估指标还有三个损失
最新发布
07-20
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