48d17ef0ca
=== 自动填入过于激进（幽灵路径级联）=== - 所有面板 update_from_config 移除 os.makedirs()，目录创建留给 pipeline 执行 - 输出路径仅 widget 为空时填入默认值，不覆盖用户已选 - 输入路径从上游读取后添加 os.path.exists() 检查，阻断幽灵路径级联 - panel_factory._replay_live_panel_inputs 广播前校验文件确实存在 - step10 update_from_config 添加 os.path.isfile() 存在性检查 - 清理 step8/9/11 中冗余局部 import os（修复 UnboundLocalError） === 输出目录缺失 === - step7 新增 output_file FileSelectWidget，默认路径 7_Water_Quality_Indices/ - step9 output_file 从文件模式改为目录模式 (Directories) === 空目录自动创建 === - step12 _setup_prediction_output_dirs 移除 mkdir() 调用，改为只读日志 === 过期依赖与缺失 import === - panel_registry Step10 依赖 bsq_file→sampling_csv_file（匹配 CSV 模式重构） - step7 添加缺失的 import pandas as pd（修复 NameError） === 导航与 UI 一致性 === - water_quality_gui_v2 新增 _select_first_nav_item()，启动时默认选中第一项 - step1 输出卡片对齐 step8 风格 - step8 补充缺失的样式表和统一边距 === Step4 交互式光谱探针重构 === - 左右分栏 QSplitter 布局：左侧控制区 + 右侧 Matplotlib 视图 - 1x2 子图：ax1 散点图 + ax2 光谱曲线 - Hover 悬停 Annotation 显示坐标，Click 点击高亮+绘制光谱 - NavigationToolbar2QT 工具栏（保存/缩放/平移） - 自动检测坐标列和波段列，完善异常处理
711 lines
30 KiB
Python
711 lines
30 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Step4 面板 - 采样点布设（内嵌交互式光谱探针视图）
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2026-06-30 重构：
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- 左右分栏布局 (QSplitter)：左侧控制区 + 右侧嵌入式 Matplotlib 视图
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- 1×2 子图：ax1 散点图 + ax2 光谱曲线
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- Hover 悬停显示坐标提示，Click 点击绘制该点光谱曲线
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- NavigationToolbar2QT 工具栏自带保存/缩放/平移
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"""
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import os
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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_HERE = os.path.dirname(os.path.abspath(__file__))
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if _HERE not in sys.path:
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sys.path.insert(0, _HERE)
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from _step_path_resolver import resolve_subdir
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from PyQt5.QtCore import QTimer, Qt
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from PyQt5.QtWidgets import (
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QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QFormLayout,
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QPushButton, QSpinBox, QCheckBox, QMessageBox, QLabel, QFrame,
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QSplitter, QSizePolicy,
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)
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from matplotlib.backends.backend_qt5agg import (
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FigureCanvasQTAgg as FigureCanvas,
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NavigationToolbar2QT as NavigationToolbar,
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)
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from matplotlib.figure import Figure
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from src.gui.components.custom_widgets import FileSelectWidget
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from src.gui.dialogs import SamplingViewerDialog
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from src.gui.styles import ModernStylesheet
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class Step4SamplingPanel(QWidget):
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def __init__(self, parent=None):
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super().__init__(parent)
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# 交互状态
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self._df = None
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self._x_col = None
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self._y_col = None
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self._band_cols = []
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self._scatter = None
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self._highlight_idx = None
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self._annot = None
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self._cid_hover = None
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self._cid_click = None
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self._last_render_path = None
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self.init_ui()
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# ═══════════════════════════════════════════════════════════════
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# UI 构建
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# ═══════════════════════════════════════════════════════════════
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def init_ui(self):
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self.setStyleSheet(ModernStylesheet.get_main_stylesheet())
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# ── 顶层：水平分栏 (QSplitter) ──
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splitter = QSplitter(Qt.Horizontal)
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splitter.setChildrenCollapsible(False)
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# ═══════════════════════════════════════════════
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# 左侧：控制区（原封保留原有三张卡片）
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# ═══════════════════════════════════════════════
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left_widget = QWidget()
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left_layout = QVBoxLayout()
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left_layout.setContentsMargins(24, 24, 12, 24)
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left_layout.setSpacing(20)
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# --- 卡片 1：输入数据 ---
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input_group = QGroupBox("📁 输入数据")
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input_layout = QVBoxLayout()
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input_layout.setSpacing(16)
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input_layout.setContentsMargins(20, 24, 20, 20)
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self.deglint_img_file = FileSelectWidget(
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"去耀斑影像:",
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"Image Files (*.bsq *.dat *.tif);;All Files (*.*)"
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)
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self.water_mask_file = FileSelectWidget(
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"水域掩膜图:",
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"Mask Files (*.dat *.tif);;All Files (*.*)"
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)
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input_layout.addWidget(self.deglint_img_file)
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input_layout.addWidget(self.water_mask_file)
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input_group.setLayout(input_layout)
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left_layout.addWidget(input_group)
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# --- 卡片 2：采样参数 ---
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params_group = QGroupBox("⚙️ 采样参数")
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params_layout = QFormLayout()
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params_layout.setSpacing(16)
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params_layout.setContentsMargins(20, 24, 20, 20)
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self.interval = QSpinBox()
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self.interval.setRange(10, 500)
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self.interval.setValue(50)
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self.interval.setSuffix(" px")
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self.interval.setMinimumWidth(120)
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params_layout.addRow("采样点间隔:", self.interval)
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self.sample_radius = QSpinBox()
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self.sample_radius.setRange(1, 50)
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self.sample_radius.setValue(5)
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self.sample_radius.setSuffix(" px")
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self.sample_radius.setMinimumWidth(120)
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params_layout.addRow("中心采样半径:", self.sample_radius)
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self.chunk_size = QSpinBox()
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self.chunk_size.setRange(100, 10000)
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self.chunk_size.setValue(1000)
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self.chunk_size.setSuffix(" px")
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self.chunk_size.setMinimumWidth(120)
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params_layout.addRow("内存处理块大小:", self.chunk_size)
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self.use_adaptive_sampling = QCheckBox("启用自适应边缘采样")
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self.use_adaptive_sampling.setChecked(True)
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params_layout.addRow("智能模式:", self.use_adaptive_sampling)
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params_group.setLayout(params_layout)
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left_layout.addWidget(params_group)
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# --- 卡片 3：输出与执行 ---
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output_group = QGroupBox("🚀 输出与执行")
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output_layout = QVBoxLayout()
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output_layout.setSpacing(16)
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output_layout.setContentsMargins(20, 24, 20, 20)
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self.output_file = FileSelectWidget(
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"结果保存至:",
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"CSV Files (*.csv);;All Files (*.*)",
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mode="save"
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)
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self.output_file.line_edit.setPlaceholderText("sampling_spectra.csv")
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output_layout.addWidget(self.output_file)
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action_layout = QHBoxLayout()
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action_layout.addStretch()
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self.refresh_btn = QPushButton("🔄 刷新视图")
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self.refresh_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('normal'))
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self.refresh_btn.setEnabled(False)
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self.refresh_btn.setMinimumWidth(140)
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self.refresh_btn.setToolTip("重新加载 CSV 并渲染采样点散点图")
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self.refresh_btn.clicked.connect(self._on_refresh_clicked)
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self.run_btn = QPushButton("独立运行步骤")
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self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet('primary'))
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self.run_btn.setMinimumWidth(140)
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self.run_btn.clicked.connect(self._on_run_single_clicked)
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action_layout.addWidget(self.refresh_btn)
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action_layout.addWidget(self.run_btn)
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output_layout.addLayout(action_layout)
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output_group.setLayout(output_layout)
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left_layout.addWidget(output_group)
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left_layout.addStretch()
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left_widget.setLayout(left_layout)
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left_widget.setMinimumWidth(360)
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# ═══════════════════════════════════════════════
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# 右侧：可视化区（嵌入式 Matplotlib 视图）
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# ═══════════════════════════════════════════════
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right_widget = QWidget()
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right_layout = QVBoxLayout()
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right_layout.setContentsMargins(12, 24, 24, 24)
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right_layout.setSpacing(0)
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viz_group = QGroupBox("📊 采样点交互式探索")
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viz_layout = QVBoxLayout()
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viz_layout.setContentsMargins(8, 20, 8, 8)
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viz_layout.setSpacing(0)
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# Matplotlib 画布（1×2 子图：散点图 + 光谱曲线）
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self._fig = Figure(figsize=(9, 5))
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self._ax_scatter = self._fig.add_subplot(121)
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self._ax_spectrum = self._fig.add_subplot(122)
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self._canvas = FigureCanvas(self._fig)
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self._canvas.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
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# 设置子图初始状态
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self._ax_scatter.set_title("采样点分布", fontsize=11, fontweight='bold')
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self._ax_scatter.set_xlabel("X 坐标")
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self._ax_scatter.set_ylabel("Y 坐标")
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self._ax_scatter.text(0.5, 0.5, "等待采样数据生成...\n\n请先配置参数并运行步骤\n或选择已有的 CSV 后刷新",
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ha='center', va='center', transform=self._ax_scatter.transAxes,
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fontsize=12, color='#888888')
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self._ax_scatter.grid(False)
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self._ax_spectrum.set_title("光谱曲线", fontsize=11, fontweight='bold')
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self._ax_spectrum.set_xlabel("波长 (nm)")
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self._ax_spectrum.set_ylabel("反射率")
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self._ax_spectrum.text(0.5, 0.5, "点击左侧散点\n查看光谱曲线",
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ha='center', va='center', transform=self._ax_spectrum.transAxes,
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fontsize=12, color='#888888')
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self._ax_spectrum.grid(False)
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self._fig.tight_layout(pad=2.0)
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# 工具栏（自带保存/缩放/平移）
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self._toolbar = NavigationToolbar(self._canvas, self)
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viz_layout.addWidget(self._toolbar)
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viz_layout.addWidget(self._canvas)
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viz_group.setLayout(viz_layout)
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right_layout.addWidget(viz_group)
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right_widget.setLayout(right_layout)
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# ── 组装分栏 ──
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splitter.addWidget(left_widget)
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splitter.addWidget(right_widget)
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splitter.setSizes([420, 680]) # 初始比例 ≈ 38:62
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splitter.setStretchFactor(0, 1)
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splitter.setStretchFactor(1, 2)
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top_layout = QHBoxLayout()
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top_layout.setContentsMargins(0, 0, 0, 0)
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top_layout.addWidget(splitter)
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self.setLayout(top_layout)
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# ── 事件绑定 ──
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self._cid_hover = self._canvas.mpl_connect('motion_notify_event', self._on_hover)
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self._cid_click = self._canvas.mpl_connect('button_press_event', self._on_click)
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# ── 定时器：降低频率，仅用于自动发现新生成的 CSV ──
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self._status_timer = QTimer(self)
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self._status_timer.timeout.connect(self._check_csv_and_auto_render)
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self._status_timer.start(5000)
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self.output_file.line_edit.textChanged.connect(self._on_output_changed)
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# ═══════════════════════════════════════════════════════════════
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# 配置读写（保持不变）
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# ═══════════════════════════════════════════════════════════════
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def get_config(self):
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config = {
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'interval': self.interval.value(),
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'sample_radius': self.sample_radius.value(),
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'chunk_size': self.chunk_size.value(),
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'use_adaptive_sampling': self.use_adaptive_sampling.isChecked(),
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}
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deglint_img_path = self.deglint_img_file.get_path()
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if deglint_img_path:
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config['deglint_img_path'] = deglint_img_path
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water_mask_path = self.water_mask_file.get_path()
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if water_mask_path:
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config['water_mask_path'] = water_mask_path
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return config
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def set_config(self, config):
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if 'interval' in config:
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self.interval.setValue(config['interval'])
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if 'sample_radius' in config:
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self.sample_radius.setValue(config['sample_radius'])
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if 'chunk_size' in config:
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self.chunk_size.setValue(config['chunk_size'])
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if 'use_adaptive_sampling' in config:
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self.use_adaptive_sampling.setChecked(config['use_adaptive_sampling'])
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if 'deglint_img_path' in config:
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self.deglint_img_file.set_path(config['deglint_img_path'])
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if 'water_mask_path' in config:
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self.water_mask_file.set_path(config['water_mask_path'])
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def update_from_config(self, work_dir=None, pipeline=None):
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if work_dir:
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self.work_dir = work_dir
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elif hasattr(self, 'work_dir') and self.work_dir:
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pass
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else:
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self.work_dir = None
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main_window = self.window()
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deglint_path = None
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if pipeline and hasattr(pipeline, 'step_outputs'):
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step3_outputs = getattr(pipeline, 'step_outputs', {}).get('step3', {})
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deglint_path = (
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step3_outputs.get('deglint_image') or step3_outputs.get('output_path') or
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step3_outputs.get('output_file') or step3_outputs.get('deglint_img_path')
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)
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if not deglint_path and hasattr(main_window, 'step3_panel'):
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deglint_path = main_window.step3_panel.output_file.get_path()
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if deglint_path:
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if not os.path.isabs(deglint_path):
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deglint_path = os.path.join(self.work_dir or '', deglint_path).replace('\\', '/')
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if os.path.exists(deglint_path):
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self.deglint_img_file.set_path(deglint_path)
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water_mask_path = None
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if pipeline and hasattr(pipeline, 'step_outputs'):
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step1_outputs = getattr(pipeline, 'step_outputs', {}).get('step1', {})
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water_mask_path = (
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step1_outputs.get('water_mask') or step1_outputs.get('output_path') or step1_outputs.get(
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'output_file')
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)
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if not water_mask_path and hasattr(main_window, 'step1_panel'):
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water_mask_path = main_window.step1_panel.output_file.get_path()
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if not water_mask_path and self.work_dir:
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mask_dir = resolve_subdir(self.work_dir, 'water_mask')
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if os.path.isdir(mask_dir):
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dat_files = [f for f in os.listdir(mask_dir) if f.lower().endswith('.dat')]
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if dat_files:
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water_mask_path = os.path.join(mask_dir, dat_files[0]).replace('\\', '/')
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if not water_mask_path and self.work_dir:
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input_test_dir = os.path.join(self.work_dir, "input-test")
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if os.path.isdir(input_test_dir):
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dat_files = [f for f in os.listdir(input_test_dir) if f.lower().endswith('.dat')]
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for f in dat_files:
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if 'water_mask_from_shp' in f.lower():
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water_mask_path = os.path.join(input_test_dir, f).replace('\\', '/')
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break
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if not water_mask_path and dat_files:
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water_mask_path = os.path.join(input_test_dir, dat_files[0]).replace('\\', '/')
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if water_mask_path:
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if not os.path.isabs(water_mask_path):
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water_mask_path = os.path.join(self.work_dir or '', water_mask_path).replace('\\', '/')
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if os.path.exists(water_mask_path):
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self.water_mask_file.set_path(water_mask_path)
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if self.work_dir and not self.output_file.get_path():
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output_path = resolve_subdir(self.work_dir, 'sampling_csv_path')
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self.output_file.set_path(output_path.replace('\\', '/'))
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self._check_csv_exists()
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# 若 CSV 已存在，尝试自动渲染
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csv_path = self.output_file.get_path()
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if csv_path and os.path.isfile(csv_path):
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self._render_inline_plot()
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# ═══════════════════════════════════════════════════════════════
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# 执行
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# ═══════════════════════════════════════════════════════════════
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def _on_run_single_clicked(self):
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from src.gui.core.event_bus import global_event_bus
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deglint_img_path = self.deglint_img_file.get_path()
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if not deglint_img_path:
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QMessageBox.warning(self, "输入错误", "请选择去耀斑影像文件！")
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return
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config = {'step4_sampling': self.get_config()}
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global_event_bus.publish('RequestRunSingleStep', {
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'step_name': 'step4_sampling',
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'config': config,
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})
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# ═══════════════════════════════════════════════════════════════
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# CSV 状态检测
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# ═══════════════════════════════════════════════════════════════
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def _check_csv_exists(self):
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csv_path = self.output_file.get_path()
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enabled = bool(csv_path and os.path.isabs(csv_path) and os.path.exists(csv_path))
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self.refresh_btn.setEnabled(enabled)
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return enabled
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def _on_output_changed(self, _text=None):
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self._check_csv_exists()
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def _check_csv_and_auto_render(self):
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"""定时器回调：检测到新 CSV 出现时自动渲染一次。"""
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csv_path = self.output_file.get_path()
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if csv_path and os.path.isfile(csv_path):
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if csv_path != self._last_render_path:
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self.refresh_btn.setEnabled(True)
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self._render_inline_plot()
|
||
|
||
def _on_refresh_clicked(self):
|
||
"""手动点击刷新按钮。"""
|
||
csv_path = self.output_file.get_path()
|
||
if not csv_path or not os.path.exists(csv_path):
|
||
QMessageBox.warning(self, "文件不存在", f"采样点 CSV 文件不存在：{csv_path}\n请先运行生成数据。")
|
||
return
|
||
self._render_inline_plot()
|
||
|
||
# ═══════════════════════════════════════════════════════════════
|
||
# 核心渲染
|
||
# ═══════════════════════════════════════════════════════════════
|
||
|
||
def _detect_coordinate_columns(self, df: pd.DataFrame):
|
||
"""检测坐标列，返回 (x_col, y_col) 或 (None, None)。
|
||
|
||
优先级：pixel_x/pixel_y → longitude/latitude → lon/lat →
|
||
X/Y → UTM_X/UTM_Y → 任何含 'x'/'y' 关键字的列
|
||
"""
|
||
cols_lower = {c.lower(): c for c in df.columns}
|
||
|
||
priority_pairs = [
|
||
('pixel_x', 'pixel_y'),
|
||
('longitude', 'latitude'),
|
||
('lon', 'lat'),
|
||
('x', 'y'),
|
||
('utm_x', 'utm_y'),
|
||
]
|
||
|
||
for x_key, y_key in priority_pairs:
|
||
if x_key in cols_lower and y_key in cols_lower:
|
||
return cols_lower[x_key], cols_lower[y_key]
|
||
|
||
# 最后尝试：找名字中含 x / y 的数值列
|
||
x_candidates = [c for c in df.columns if 'x' in c.lower() and pd.api.types.is_numeric_dtype(df[c])]
|
||
y_candidates = [c for c in df.columns if 'y' in c.lower() and pd.api.types.is_numeric_dtype(df[c])]
|
||
if x_candidates and y_candidates:
|
||
return x_candidates[0], y_candidates[0]
|
||
|
||
return None, None
|
||
|
||
def _detect_band_columns(self, df: pd.DataFrame):
|
||
"""检测光谱波段列（纯数字列名，值域在 200–3000 nm 之间）。
|
||
|
||
优先使用列名可解析为 float 且在波长范围内的列；
|
||
否则回退到位置索引（跳过坐标列和已知元数据列）。
|
||
"""
|
||
band_cols = []
|
||
for col in df.columns:
|
||
try:
|
||
val = float(str(col).strip())
|
||
if 200.0 <= val <= 3000.0:
|
||
band_cols.append(col)
|
||
except (ValueError, TypeError):
|
||
continue
|
||
|
||
if band_cols:
|
||
# 按波长数值排序
|
||
band_cols.sort(key=lambda c: float(str(c).strip()))
|
||
return band_cols
|
||
|
||
# 回退：跳过坐标列和已知元数据列，取数值列
|
||
skip_keywords = ('x', 'y', 'lon', 'lat', 'utm', 'id', 'sample', 'index', 'pixel')
|
||
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
|
||
if self._x_col:
|
||
numeric_cols = [c for c in numeric_cols if c != self._x_col]
|
||
if self._y_col:
|
||
numeric_cols = [c for c in numeric_cols if c != self._y_col]
|
||
return [c for c in numeric_cols if not any(k in c.lower() for k in skip_keywords)]
|
||
|
||
def _render_inline_plot(self):
|
||
"""读取 CSV → 检测坐标/波段列 → 绘制 1×2 子图。"""
|
||
csv_path = self.output_file.get_path()
|
||
if not csv_path or not os.path.isfile(csv_path):
|
||
self._show_empty_state("等待采样数据生成...\n\n请先配置参数并运行步骤")
|
||
return
|
||
|
||
# 读取数据
|
||
try:
|
||
df = pd.read_csv(csv_path)
|
||
except Exception as e:
|
||
self._show_empty_state(f"读取 CSV 失败:\n{str(e)[:200]}")
|
||
return
|
||
|
||
if df.empty:
|
||
self._show_empty_state("CSV 文件为空")
|
||
return
|
||
|
||
# 检测坐标列
|
||
x_col, y_col = self._detect_coordinate_columns(df)
|
||
if x_col is None or y_col is None:
|
||
self._show_empty_state(
|
||
"缺少坐标列\n\n"
|
||
f"可用列: {', '.join(str(c) for c in df.columns[:15])}\n"
|
||
"期望: pixel_x/pixel_y, longitude/latitude, X/Y 等"
|
||
)
|
||
return
|
||
|
||
# 检测光谱列
|
||
band_cols = self._detect_band_columns(df)
|
||
if not band_cols:
|
||
# 仅显示散点图，光谱子图留空
|
||
pass
|
||
|
||
# 缓存
|
||
self._df = df
|
||
self._x_col = x_col
|
||
self._y_col = y_col
|
||
self._band_cols = band_cols
|
||
self._highlight_idx = None
|
||
self._last_render_path = csv_path
|
||
|
||
# 清除 Annotation
|
||
if self._annot is not None:
|
||
try:
|
||
self._annot.remove()
|
||
except Exception:
|
||
pass
|
||
self._annot = None
|
||
|
||
# ── 绘制 ax1：散点图 ──
|
||
self._ax_scatter.clear()
|
||
x = df[x_col].values
|
||
y = df[y_col].values
|
||
|
||
self._scatter = self._ax_scatter.scatter(
|
||
x, y,
|
||
c='#0078D7', alpha=0.7, edgecolors='white',
|
||
linewidth=0.5, s=40, picker=True, zorder=3
|
||
)
|
||
self._ax_scatter.set_xlabel(str(x_col), fontsize=10)
|
||
self._ax_scatter.set_ylabel(str(y_col), fontsize=10)
|
||
self._ax_scatter.set_title(f"采样点分布 (共 {len(df)} 个点)", fontsize=11, fontweight='bold')
|
||
self._ax_scatter.grid(True, alpha=0.3, linestyle='--')
|
||
self._ax_scatter.set_facecolor('#F8F9FA')
|
||
|
||
# ── 绘制 ax2：光谱曲线（初始状态）──
|
||
self._ax_spectrum.clear()
|
||
if band_cols:
|
||
self._ax_spectrum.set_title("光谱曲线（点击左侧散点查看）", fontsize=11, fontweight='bold')
|
||
self._ax_spectrum.set_xlabel("波长 (nm)", fontsize=10)
|
||
self._ax_spectrum.set_ylabel("反射率", fontsize=10)
|
||
self._ax_spectrum.grid(True, alpha=0.3, linestyle='--')
|
||
self._ax_spectrum.text(0.5, 0.5, "点击左侧散点\n查看光谱曲线",
|
||
ha='center', va='center', transform=self._ax_spectrum.transAxes,
|
||
fontsize=12, color='#999999')
|
||
else:
|
||
self._ax_spectrum.text(0.5, 0.5, "缺少光谱数据列",
|
||
ha='center', va='center', transform=self._ax_spectrum.transAxes,
|
||
fontsize=12, color='#999999')
|
||
|
||
self._fig.tight_layout(pad=2.0)
|
||
self._canvas.draw()
|
||
|
||
def _show_empty_state(self, message: str):
|
||
"""在右侧画布显示提示信息。"""
|
||
self._ax_scatter.clear()
|
||
self._ax_scatter.set_title("采样点分布", fontsize=11, fontweight='bold')
|
||
self._ax_scatter.text(0.5, 0.5, message, ha='center', va='center',
|
||
transform=self._ax_scatter.transAxes,
|
||
fontsize=12, color='#888888')
|
||
self._ax_scatter.grid(False)
|
||
|
||
self._ax_spectrum.clear()
|
||
self._ax_spectrum.set_title("光谱曲线", fontsize=11, fontweight='bold')
|
||
self._ax_spectrum.text(0.5, 0.5, "等待数据...",
|
||
ha='center', va='center', transform=self._ax_spectrum.transAxes,
|
||
fontsize=12, color='#888888')
|
||
self._ax_spectrum.grid(False)
|
||
|
||
self._fig.tight_layout(pad=2.0)
|
||
self._canvas.draw()
|
||
self._df = None
|
||
self._last_render_path = None
|
||
|
||
# ═══════════════════════════════════════════════════════════════
|
||
# 交互事件 (Hover & Click)
|
||
# ═══════════════════════════════════════════════════════════════
|
||
|
||
def _on_hover(self, event):
|
||
"""悬停：在散点旁显示坐标 + ID 提示框。"""
|
||
if event.inaxes != self._ax_scatter:
|
||
# 鼠标离开 ax1 → 隐藏 annotation
|
||
if self._annot is not None:
|
||
try:
|
||
self._annot.set_visible(False)
|
||
self._canvas.draw_idle()
|
||
except Exception:
|
||
pass
|
||
return
|
||
|
||
if self._df is None or self._scatter is None:
|
||
return
|
||
|
||
# 检测是否悬停在散点上
|
||
contains, info = self._scatter.contains(event)
|
||
if not contains or info is None or 'ind' not in info or len(info['ind']) == 0:
|
||
if self._annot is not None:
|
||
try:
|
||
self._annot.set_visible(False)
|
||
self._canvas.draw_idle()
|
||
except Exception:
|
||
pass
|
||
return
|
||
|
||
idx = info['ind'][0]
|
||
row = self._df.iloc[idx]
|
||
x_val = row[self._x_col]
|
||
y_val = row[self._y_col]
|
||
|
||
# 创建或更新 Annotation
|
||
text = f"#{idx}\n({x_val:.4f}, {y_val:.4f})"
|
||
if self._annot is None:
|
||
self._annot = self._ax_scatter.annotate(
|
||
text,
|
||
xy=(x_val, y_val),
|
||
xytext=(12, 12),
|
||
textcoords='offset points',
|
||
bbox=dict(boxstyle='round,pad=0.4', facecolor='#FFFFFF',
|
||
edgecolor='#0078D7', alpha=0.9),
|
||
fontsize=9,
|
||
zorder=10,
|
||
)
|
||
else:
|
||
self._annot.xy = (x_val, y_val)
|
||
self._annot.set_text(text)
|
||
self._annot.set_visible(True)
|
||
|
||
self._canvas.draw_idle()
|
||
|
||
def _on_click(self, event):
|
||
"""点击：高亮散点 + 绘制光谱曲线。"""
|
||
if event.inaxes != self._ax_scatter:
|
||
return
|
||
|
||
if self._df is None or self._scatter is None:
|
||
return
|
||
|
||
# 检测是否点击在散点上
|
||
contains, info = self._scatter.contains(event)
|
||
if not contains or info is None or 'ind' not in info or len(info['ind']) == 0:
|
||
return
|
||
|
||
idx = info['ind'][0]
|
||
row = self._df.iloc[idx]
|
||
self._highlight_idx = idx
|
||
|
||
# ── 高亮选中点 ──
|
||
x = self._df[self._x_col].values
|
||
y = self._df[self._y_col].values
|
||
|
||
colors = ['#0078D7'] * len(self._df)
|
||
sizes = [40] * len(self._df)
|
||
colors[idx] = '#E74C3C'
|
||
sizes[idx] = 80
|
||
|
||
self._ax_scatter.clear()
|
||
self._scatter = self._ax_scatter.scatter(
|
||
x, y, c=colors, s=sizes,
|
||
alpha=0.7, edgecolors='white', linewidth=0.5,
|
||
picker=True, zorder=3
|
||
)
|
||
# 将选中点提升到顶层
|
||
self._ax_scatter.scatter(
|
||
[x[idx]], [y[idx]],
|
||
c='#E74C3C', s=100,
|
||
alpha=0.9, edgecolors='white', linewidth=1.5,
|
||
zorder=5
|
||
)
|
||
self._ax_scatter.set_xlabel(str(self._x_col), fontsize=10)
|
||
self._ax_scatter.set_ylabel(str(self._y_col), fontsize=10)
|
||
self._ax_scatter.set_title(f"采样点分布 (共 {len(self._df)} 个点)", fontsize=11, fontweight='bold')
|
||
self._ax_scatter.grid(True, alpha=0.3, linestyle='--')
|
||
self._ax_scatter.set_facecolor('#F8F9FA')
|
||
|
||
# ── 绘制光谱曲线 ──
|
||
self._ax_spectrum.clear()
|
||
if self._band_cols:
|
||
wavelengths = []
|
||
reflectance = []
|
||
for col in self._band_cols:
|
||
try:
|
||
wl = float(str(col).strip())
|
||
val = row[col]
|
||
if pd.notna(val):
|
||
wavelengths.append(wl)
|
||
reflectance.append(float(val))
|
||
except (ValueError, TypeError):
|
||
continue
|
||
|
||
if wavelengths:
|
||
# 按波长排序
|
||
pairs = sorted(zip(wavelengths, reflectance), key=lambda p: p[0])
|
||
wavelengths, reflectance = zip(*pairs) if pairs else ([], [])
|
||
self._ax_spectrum.plot(
|
||
wavelengths, reflectance,
|
||
color='#0078D7', lw=1.5, marker='.', markersize=3, alpha=0.8
|
||
)
|
||
self._ax_spectrum.fill_between(wavelengths, reflectance, alpha=0.1, color='#0078D7')
|
||
self._ax_spectrum.set_xlabel("波长 (nm)", fontsize=10)
|
||
self._ax_spectrum.set_ylabel("反射率", fontsize=10)
|
||
self._ax_spectrum.set_title(f"样本 #{idx} 的光谱曲线 ({len(wavelengths)} 个波段)",
|
||
fontsize=11, fontweight='bold')
|
||
self._ax_spectrum.grid(True, alpha=0.3, linestyle='--')
|
||
else:
|
||
self._ax_spectrum.text(0.5, 0.5, "该样本无有效光谱数据",
|
||
ha='center', va='center',
|
||
transform=self._ax_spectrum.transAxes,
|
||
fontsize=12, color='#999999')
|
||
else:
|
||
self._ax_spectrum.text(0.5, 0.5, "缺少光谱波段列\n无法绘制光谱",
|
||
ha='center', va='center',
|
||
transform=self._ax_spectrum.transAxes,
|
||
fontsize=12, color='#999999')
|
||
|
||
self._fig.tight_layout(pad=2.0)
|
||
self._canvas.draw()
|
||
|
||
# ═══════════════════════════════════════════════════════════════
|
||
# 旧版弹窗查看器（保留，供外部调用）
|
||
# ═══════════════════════════════════════════════════════════════
|
||
|
||
def _open_sampling_viewer(self):
|
||
"""打开独立的 SamplingViewerDialog 弹窗（保留兼容）。"""
|
||
csv_path = self.output_file.get_path()
|
||
if not csv_path or not os.path.exists(csv_path):
|
||
QMessageBox.warning(self, "文件不存在", f"采样点 CSV 文件不存在：{csv_path}\n请先运行生成数据。")
|
||
return
|
||
dialog = SamplingViewerDialog(csv_path, self)
|
||
dialog.exec_()
|
||
self._check_csv_exists()
|