#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Step8 面板 - QAA 物理反演（非经验模型）
"""

import os
import sys

# 路径归一化 helper（与 pipeline.get_step_output_dir 互为表里）
_HERE = os.path.dirname(os.path.abspath(__file__))
if _HERE not in sys.path:
    sys.path.insert(0, _HERE)
from _step_path_resolver import resolve_subdir

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._on_run_single_clicked)
        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 = resolve_subdir(work_dir, '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 = resolve_subdir(self.work_dir, '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 _on_run_single_clicked(self):
        """通过 EventBus 发布单步执行请求（解耦面板与 PipelineExecutor）。"""
        from src.gui.core.event_bus import global_event_bus

        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

        config = {'step8_qaa': self.get_config()}
        global_event_bus.publish('RequestRunSingleStep', {
            'step_name': 'step8_qaa',
            'config': config,
        })

    def run_step(self):
        """独立运行 QAA 反演（旧版 parent 链上溯方式，保留兼容）。"""
        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 = resolve_subdir(work_dir, '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()),
        }