243 lines
11 KiB
Python
243 lines
11 KiB
Python
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import gc
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import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from statsmodels.tsa.stattools import acf, pacf
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from statsmodels.tsa.seasonal import seasonal_decompose
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from scipy.signal import spectrogram, periodogram
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def generate_time_series_plots(self):
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"""生成时间序列图"""
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try:
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self._log_step("Generating time series plots...")
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if not hasattr(self, 'data') or self.data is None or len(self.data.columns) == 0:
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self._log_step("No data available for time series plots", "warning")
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return None, "No data available", None
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# 准备数据
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n_plots = min(4, len(self.data.columns))
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plot_data = self.data.iloc[:, :n_plots].reset_index()
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# 将 timestamp 转为字符串，确保JSON可序列化
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if 'timestamp' in plot_data.columns:
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plot_data['timestamp'] = plot_data['timestamp'].astype(str)
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summary = f"Generated {n_plots} time series charts"
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# charts 模式：仅返回数据，不生成图片；保留绘图版在下方注释
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self._log_step("Time series data prepared", "success")
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return None, summary, plot_data
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# --- 绘图版保留参考 ---
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# fig, axes = plt.subplots(2, 2, figsize=(10, 8), dpi=100)
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# fig.suptitle('Time Series Analysis', fontsize=14)
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# axes = axes.flatten()
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# for i in range(n_plots):
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# try:
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# col = self.data.columns[i]
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# axes[i].plot(self.data.index, self.data[col], linewidth=1)
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# axes[i].set_title(f'{col}')
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# axes[i].tick_params(axis='x', rotation=45)
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# axes[i].grid(True, alpha=0.3)
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# except Exception as plot_err:
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# self._log_step(f"Plot {col} error: {plot_err}", "warning")
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# for i in range(n_plots, len(axes)):
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# fig.delaxes(axes[i])
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# plt.tight_layout()
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# img_path = os.path.join(self.temp_dir.name, 'time_series.png')
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# plt.savefig(img_path, dpi=100, bbox_inches='tight', format='png')
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# plt.close(fig)
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# self._log_step("Time series plots generated", "success")
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# return img_path, summary, plot_data
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except Exception as e:
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self._log_step(f"Time series plots failed: {str(e)[:100]}", "error")
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return None, f"Error: {e}", None
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def generate_acf_pacf_plots(self):
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"""生成自相关和偏自相关图"""
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try:
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self._log_step("Generating ACF and PACF plots...")
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if hasattr(self, 'data') and self.data is not None:
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numeric_cols = self.data.select_dtypes(include=[np.number]).columns
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n_cols = min(3, len(numeric_cols))
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# 计算ACF和PACF数据
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acf_pacf_results = {}
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for col in numeric_cols[:n_cols]:
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series = self.data[col].dropna()
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try:
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acf_vals = np.asarray(acf(series, nlags=min(40, len(series) // 4)))
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pacf_vals = np.asarray(pacf(series, nlags=min(20, len(series) // 5)))
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acf_pacf_results[col] = {
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'acf': acf_vals.tolist(),
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'pacf': pacf_vals.tolist(),
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}
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except Exception as e:
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self._log_step(f"Error calculating ACF/PACF for {col}: {e}", "warning")
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summary = f"生成 {n_cols} 个变量的ACF和PACF数据"
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self._log_step("ACF and PACF data generated", "success")
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return None, summary, acf_pacf_results
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# --- 绘图版保留参考 ---
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# fig, axes = plt.subplots(n_cols, 2, figsize=(12, 4 * n_cols))
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# fig.suptitle('自相关和偏自相关分析', fontsize=16)
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# if n_cols == 1:
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# axes = axes.reshape(1, -1)
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# for i, col in enumerate(numeric_cols[:n_cols]):
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# series = self.data[col].dropna()
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# plot_acf(series, ax=axes[i, 0], lags=min(40, len(series) // 4))
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# axes[i, 0].set_title(f'{col} - 自相关函数 (ACF)')
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# plot_pacf(series, ax=axes[i, 1], lags=min(20, len(series) // 5))
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# axes[i, 1].set_title(f'{col} - 偏自相关函数 (PACF)')
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# plt.tight_layout()
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# img_path = os.path.join(self.temp_dir.name, 'acf_pacf_plots.png')
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# plt.savefig(img_path, dpi=150, bbox_inches='tight')
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# plt.close()
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# self._log_step("ACF and PACF plots generated", "success")
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# return img_path, f"生成 {n_cols} 个变量的ACF和PACF图", acf_pacf_results
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self._log_step("No data available for ACF/PACF plots", "warning")
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return None, "数据不足，无法生成ACF/PACF图", None
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except Exception as e:
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self._log_step(f"ACF/PACF plots failed: {e}", "error")
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return None, f"ACF/PACF图生成失败: {e}", None
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def perform_seasonal_decomposition(self):
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"""执行季节性分解"""
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try:
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self._log_step("Performing seasonal decomposition...")
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if hasattr(self, 'data') and self.data is not None:
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numeric_cols = self.data.select_dtypes(include=[np.number]).columns
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# 选择第一个数值列进行分解
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if len(numeric_cols) > 0:
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col = numeric_cols[0]
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series = self.data[col].dropna()
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# 季节性分解
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result = seasonal_decompose(series, model='additive', period=min(24, len(series) // 2))
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decomposition_data = pd.DataFrame({
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'observed': result.observed,
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'trend': result.trend,
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'seasonal': result.seasonal,
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'resid': result.resid,
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})
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# 填充NaN以确保JSON序列化
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decomposition_data = decomposition_data.astype(object).where(
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pd.notnull(decomposition_data),
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None, # type: ignore[arg-type]
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)
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summary = f"季节性分解完成，变量: {col}"
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self._log_step("Seasonal decomposition completed (data only)", "success")
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return None, summary, decomposition_data
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# --- 绘图版保留参考 ---
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# fig, axes = plt.subplots(4, 1, figsize=(12, 10))
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# fig.suptitle(f'{col} - 季节性分解', fontsize=16)
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# result.observed.plot(ax=axes[0], title='原始序列')
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# result.trend.plot(ax=axes[1], title='趋势成分')
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# result.seasonal.plot(ax=axes[2], title='季节成分')
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# result.resid.plot(ax=axes[3], title='残差成分')
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# for ax in axes:
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# ax.tick_params(axis='x', rotation=45)
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# ax.grid(True, alpha=0.3)
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# plt.tight_layout()
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# img_path = os.path.join(self.temp_dir.name, 'seasonal_decomposition.png')
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# plt.savefig(img_path, dpi=150, bbox_inches='tight')
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# plt.close()
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# self._log_step("Seasonal decomposition completed", "success")
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# return img_path, summary, decomposition_data
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self._log_step("No numeric columns for decomposition", "warning")
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return None, "没有数值列可用于季节性分解", None
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self._log_step("No data available for seasonal decomposition", "warning")
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return None, "数据不足，无法进行季节性分解", None
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except Exception as e:
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self._log_step(f"Seasonal decomposition failed: {e}", "error")
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return None, f"季节性分解失败: {e}", None
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def perform_spectral_analysis(self):
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"""执行频谱分析"""
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try:
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self._log_step("Performing spectral analysis...")
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if hasattr(self, 'data') and self.data is not None:
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numeric_cols = self.data.select_dtypes(include=[np.number]).columns
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# 计算频谱数据（简化输出，避免数据量过大）
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spectral_results = {}
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for col in numeric_cols[:2]:
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try:
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series = self.data[col].dropna().values
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f, t, Sxx = spectrogram(series, fs=1.0, nperseg=min(256, len(series) // 4))
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f_p, Pxx_den = periodogram(series, fs=1.0)
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# 仅保留频谱的均值和形状，避免返回完整矩阵
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Sxx_log = 10 * np.log10(Sxx + 1e-12)
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spectral_results[col] = {
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'spectrogram': {
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'f': f.tolist(),
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't': t.tolist(),
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'Sxx_log10_mean': float(np.mean(Sxx_log)),
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'Sxx_shape': Sxx.shape,
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},
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'periodogram': {
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'f': f_p.tolist()[:20],
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'Pxx_den': Pxx_den.tolist()[:20],
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},
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}
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except Exception as e:
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self._log_step(f"Spectral calc failed for {col}: {e}", "warning")
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summary = "Spectral analysis completed"
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self._log_step("Spectral analysis completed (data only)", "success")
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return None, summary, spectral_results
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# --- 绘图版保留参考 ---
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# n_cols = min(2, len(numeric_cols))
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# fig, axes = plt.subplots(n_cols, 2, figsize=(15, 5 * n_cols))
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# fig.suptitle('频谱分析', fontsize=16)
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# if n_cols == 1:
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# axes = axes.reshape(1, -1)
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# for i, col in enumerate(numeric_cols[:n_cols]):
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# series = self.data[col].dropna().values
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# f, t, Sxx = spectrogram(series, fs=1.0, nperseg=min(256, len(series) // 4))
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# axes[i, 0].pcolormesh(t, f, 10 * np.log10(Sxx), shading='gouraud')
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# axes[i, 0].set_title(f'{col} - 频谱图')
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# axes[i, 0].set_ylabel('频率 [Hz]')
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# axes[i, 0].set_xlabel('时间')
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# f, Pxx_den = periodogram(series, fs=1.0)
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# axes[i, 1].semilogy(f, Pxx_den)
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# axes[i, 1].set_title(f'{col} - 周期图')
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# axes[i, 1].set_xlabel('频率 [Hz]')
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# axes[i, 1].set_ylabel('PSD [V**2/Hz]')
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# axes[i, 1].grid(True, alpha=0.3)
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# plt.tight_layout()
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# img_path = os.path.join(self.temp_dir.name, 'spectral_analysis.png')
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# plt.savefig(img_path, dpi=150, bbox_inches='tight')
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# plt.close()
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# self._log_step("Spectral analysis completed", "success")
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# return img_path, f"频谱分析完成，分析了 {n_cols} 个变量", spectral_results
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self._log_step("No data available for spectral analysis", "warning")
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return None, "数据不足，无法进行频谱分析", None
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except Exception as e:
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self._log_step(f"Spectral analysis failed: {e}", "error")
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return None, f"频谱分析失败: {e}", None
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