feat(step8): 新增 Step8 水色指数反演 GUI 面板 step8_waterindex_panel

src/gui/panels/step8_qaa_panel.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Step8 面板 - QAA 物理反演（非经验模型）
+"""
+
+import os
+
+from PyQt5.QtWidgets import (
+    QWidget, QVBoxLayout, QGroupBox, QFormLayout, QGridLayout,
+    QHBoxLayout, QLabel, QLineEdit, QComboBox, QCheckBox,
+    QPushButton, QFileDialog, QMessageBox,
+)
+from PyQt5.QtGui import QFont
+from PyQt5.QtCore import Qt
+
+from src.gui.components.custom_widgets import FileSelectWidget
+from src.gui.styles import ModernStylesheet
+from src.utils.water_owt_config import (
+    get_all_lake_names,
+    get_lake_config,
+    get_lambda_0,
+    get_default_lake,
+)
+from src.core.algorithms.qaa import QAABaselineSolver
+
+
+class Step8QAAPanel(QWidget):
+    """步骤8：QAA 物理反演（非经验模型）"""
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        self.init_ui()
+
+    def init_ui(self):
+        layout = QVBoxLayout()
+
+        title = QLabel("步骤8：QAA 物理反演（非经验模型）")
+        title.setFont(QFont("Arial", 12, QFont.Bold))
+        layout.addWidget(title)
+
+        # 光谱 CSV 文件输入
+        self.spectrum_csv_file = FileSelectWidget(
+            "光谱 CSV 文件:",
+            "CSV Files (*.csv);;All Files (*.*)"
+        )
+        self.spectrum_csv_file.line_edit.setPlaceholderText(
+            "选择实测光谱或采样点光谱 CSV（含波长列）"
+        )
+        layout.addWidget(self.spectrum_csv_file)
+
+        # 水域类型选择
+        lake_group = QGroupBox("水域类型配置")
+        lake_layout = QFormLayout()
+
+        self.lake_combo = QComboBox()
+        lake_names = get_all_lake_names()
+        self.lake_combo.addItems(lake_names)
+        default_lake = get_default_lake()
+        if default_lake in lake_names:
+            self.lake_combo.setCurrentText(default_lake)
+        self.lake_combo.currentTextChanged.connect(self._on_lake_changed)
+        lake_layout.addRow("水域选择:", self.lake_combo)
+
+        # 参考波长显示
+        self.lambda_0_label = QLabel()
+        self.lambda_0_label.setStyleSheet(
+            f"color: {ModernStylesheet.COLORS['accent']}; "
+            f"font-weight: bold;"
+        )
+        lake_layout.addRow("参考波长 λ₀:", self.lambda_0_label)
+
+        # 算法提示
+        self.hint_label = QLabel()
+        self.hint_label.setWordWrap(True)
+        self.hint_label.setStyleSheet(
+            f"color: {ModernStylesheet.COLORS['text_secondary']}; "
+            "font-size: 12px;"
+        )
+        lake_layout.addRow("算法提示:", self.hint_label)
+
+        lake_group.setLayout(lake_layout)
+        layout.addWidget(lake_group)
+
+        # 输出路径
+        self.output_path = FileSelectWidget(
+            "输出文件:",
+            "CSV Files (*.csv);;All Files (*.*)",
+            mode="save"
+        )
+        self.output_path.line_edit.setPlaceholderText(
+            "自动生成到 8_QAA_Inversion，或手动指定..."
+        )
+        self.output_path.browse_btn.clicked.disconnect()
+        self.output_path.browse_btn.clicked.connect(self.browse_output_path)
+        layout.addWidget(self.output_path)
+
+        # 启用步骤
+        self.enable_checkbox = QCheckBox("启用此步骤")
+        self.enable_checkbox.setChecked(False)
+        layout.addWidget(self.enable_checkbox)
+
+        # 独立运行按钮
+        self.run_btn = QPushButton("执行 QAA 反演")
+        self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('success'))
+        self.run_btn.clicked.connect(self.run_step)
+        layout.addWidget(self.run_btn)
+
+        layout.addStretch()
+        self.setLayout(layout)
+
+        self._on_lake_changed(self.lake_combo.currentText())
+
+    def _on_lake_changed(self, lake_name: str):
+        """当用户切换水域时更新显示"""
+        cfg = get_lake_config(lake_name)
+        if cfg:
+            self.lambda_0_label.setText(
+                f"{cfg['lambda_0']} nm（{cfg['qaa_version']}）"
+            )
+            self.hint_label.setText(cfg.get('notes', ''))
+        else:
+            self.lambda_0_label.setText("—")
+            self.hint_label.setText("")
+
+    def _get_default_work_dir(self) -> str:
+        """获取 work_dir，优先用 panel 自身缓存的，否则尝试从主窗口取"""
+        if hasattr(self, 'work_dir') and self.work_dir:
+            return str(self.work_dir)
+        mw = self.window()
+        if mw and hasattr(mw, 'work_dir') and mw.work_dir:
+            return str(mw.work_dir)
+        return ""
+
+    def browse_output_path(self):
+        """浏览输出文件路径"""
+        current = self.output_path.get_path().strip()
+        if current:
+            initial_dir = os.path.dirname(current)
+            initial_file = os.path.basename(current)
+        else:
+            initial_dir = ""
+            initial_file = ""
+
+        if not initial_dir or not os.path.isdir(initial_dir):
+            work_dir = self._get_default_work_dir()
+            initial_dir = os.path.join(work_dir, "8_QAA_Inversion") if work_dir else ""
+            if initial_dir and not os.path.isdir(initial_dir):
+                os.makedirs(initial_dir, exist_ok=True)
+
+        file_path, _ = QFileDialog.getSaveFileName(
+            self, "保存输出文件",
+            os.path.join(initial_dir, initial_file) if initial_file else initial_dir,
+            "CSV Files (*.csv);;All Files (*.*)"
+        )
+        if file_path:
+            self.output_path.set_path(file_path)
+
+    def get_config(self) -> dict:
+        """获取面板配置"""
+        config = {
+            'lake_name': self.lake_combo.currentText(),
+            'lambda_0': get_lambda_0(self.lake_combo.currentText()),
+            'spectrum_csv_path': self.spectrum_csv_file.get_path(),
+        }
+        output_path = self.output_path.get_path()
+        if output_path:
+            config['output_path'] = output_path
+        return config
+
+    def set_config(self, config: dict):
+        """设置面板配置"""
+        if 'lake_name' in config:
+            lake_name = config['lake_name']
+            idx = self.lake_combo.findText(lake_name)
+            if idx >= 0:
+                self.lake_combo.setCurrentIndex(idx)
+        if 'spectrum_csv_path' in config:
+            self.spectrum_csv_file.set_path(config['spectrum_csv_path'])
+        if 'output_path' in config:
+            self.output_path.set_path(config['output_path'])
+
+    def update_from_config(self, work_dir=None, pipeline=None):
+        """从全局配置自动填充训练数据和输出路径"""
+        if work_dir:
+            self.work_dir = work_dir
+        elif hasattr(self, 'work_dir') and self.work_dir:
+            pass
+        else:
+            self.work_dir = None
+
+        main_window = self.window()
+
+        if main_window and hasattr(main_window, 'step5_panel'):
+            step5_output = main_window.step5_panel.output_file.get_path()
+            if step5_output:
+                if not os.path.isabs(step5_output):
+                    step5_output = os.path.join(self.work_dir or '', step5_output).replace('\\', '/')
+                self.spectrum_csv_file.set_path(step5_output)
+
+        if self.work_dir:
+            qaa_dir = os.path.join(self.work_dir, "8_QAA_Inversion")
+            os.makedirs(qaa_dir, exist_ok=True)
+            output_path = os.path.join(qaa_dir, "a_lambda_results.csv").replace('\\', '/')
+            self.output_path.set_path(output_path)
+        else:
+            self.output_path.set_path("")
+
+    def run_step(self):
+        """独立运行 QAA 反演"""
+        spectrum_path = self.spectrum_csv_file.get_path()
+        if not spectrum_path:
+            QMessageBox.warning(self, "输入错误", "请选择光谱 CSV 文件！")
+            return
+
+        main_window = self.window()
+        if hasattr(main_window, 'run_single_step'):
+            config = {'step8_qaa': self.get_config()}
+            main_window.run_single_step('step8_qaa', config)
+        else:
+            self._run_qaa_direct()
+
+    def _run_qaa_direct(self):
+        """直接执行 QAA 反演（不依赖主窗口流水线）"""
+        spectrum_path = self.spectrum_csv_file.get_path()
+        output_path = self.output_path.get_path()
+        lake_name = self.lake_combo.currentText()
+        lambda_0 = get_lambda_0(lake_name)
+
+        if not output_path:
+            work_dir = self._get_default_work_dir()
+            qaa_dir = os.path.join(work_dir, "8_QAA_Inversion") if work_dir else ""
+            if qaa_dir and not os.path.isdir(qaa_dir):
+                os.makedirs(qaa_dir, exist_ok=True)
+            output_path = os.path.join(qaa_dir, "a_lambda_results.csv").replace('\\', '/')
+
+        try:
+            import numpy as np
+            import pandas as pd
+            df = pd.read_csv(spectrum_path, encoding="utf-8-sig")
+            col_names = df.columns.tolist()
+
+            wavelength_col_idx = None
+            for i, col in enumerate(col_names):
+                try:
+                    float(col)
+                    wavelength_col_idx = i
+                    break
+                except (ValueError, TypeError):
+                    pass
+
+            if wavelength_col_idx is None:
+                QMessageBox.warning(
+                    self, "解析错误",
+                    "无法从 CSV 列名中识别波长信息，请确保列名包含数值型波长（nm）"
+                )
+                return
+
+            wavelengths = np.array(
+                [float(c) for c in col_names[wavelength_col_idx:]], dtype=np.float64
+            )
+            data_matrix = df.iloc[:, wavelength_col_idx:].values.astype(np.float64)
+
+            if data_matrix.ndim == 1:
+                data_matrix = data_matrix[np.newaxis, :]
+
+            solver = QAABaselineSolver()
+            raw_result = solver.run_inversion(wavelengths, data_matrix, lambda_0)
+
+            # run_inversion 返回：单样本 → dict，多样本 → list[dict]
+            if isinstance(raw_result, list):
+                sample_results = raw_result
+                aw_0 = raw_result[0].get('aw_0', 0)
+                bbw_0 = raw_result[0].get('bbw_0', 0)
+            else:
+                sample_results = [raw_result]
+                aw_0 = raw_result.get('aw_0', 0)
+                bbw_0 = raw_result.get('bbw_0', 0)
+
+            rows_out = []
+            for i, sample_result in enumerate(sample_results):
+                wl_arr = wavelengths
+                a_arr = sample_result['a_lambda']
+                bb_arr = sample_result['bb_lambda']
+                for j, wl in enumerate(wl_arr):
+                    rows_out.append({
+                        'sample_id': f"sample_{i}",
+                        'Wavelength': wl,
+                        'a_lambda': a_arr[j],
+                        'bb_lambda': bb_arr[j],
+                    })
+
+            result_df = pd.DataFrame(rows_out)
+            os.makedirs(os.path.dirname(output_path) or '.', exist_ok=True)
+            result_df.to_csv(output_path, index=False, float_format='%.8f')
+
+            QMessageBox.information(
+                self, "执行成功",
+                f"QAA 反演完成！\n"
+                f"水域: {lake_name}\n"
+                f"参考波长 λ₀: {lambda_0} nm\n"
+                f"λ₀ 处 aw: {aw_0:.6f} m⁻¹\n"
+                f"λ₀ 处 bbw: {bbw_0:.6f} m⁻¹\n"
+                f"结果已保存到:\n{output_path}"
+            )
+
+        except Exception as e:
+            QMessageBox.critical(self, "执行错误", f"QAA 反演失败:\n{str(e)}")
+
+    def get_training_params(self) -> dict:
+        """获取反演参数"""
+        return {
+            'pipeline_type': 'qaa_non_empirical',
+            'lake_name': self.lake_combo.currentText(),
+            'lambda_0': get_lambda_0(self.lake_combo.currentText()),
+        }