修复色域拖拽回弹、网格 GX 拖拽重排、均值滤波多阶与逐信号条目
1. 自定义颜色面板：拖动时鼠标移出控件不再"弹回去"
- 根因是选点位置每帧都从 RGB 反推：色相 1 与 0 是同一个颜色（红），拖到色相条底部
下一帧反推回 0，指示条弹到顶部；拖到色域方块右下角之外时明度为 0（黑色），
黑色的饱和度无定义，反推得 0，选点弹到最左边。
- 现在把"选点位置（饱和度/明度）与色相"作为独立状态保存，只由对应控件与外部改色维护；
越界一律夹取到最近边界，因此鼠标离开控件后选点停在边界上。
- 统一色域方块、色相条、预览色块与色号框的圆角；RGB 滑块适当加粗；
色域选点去掉外圈深色描边（只保留白色描边），色相指示器改为白色描边的圆角长方条、
底色取该位置的颜色；色相条改用分段线性渐变绘制。
2. 网格 GX 编号支持拖拽重排
- 按住网格图例左侧的 GX 徽标即可上下拖动，整块网格（连同波形与标尺）跟随鼠标移动；
途经的网格保持自身大小、整体让出一格，目标槽位有吸附高亮提示。
- 松开后真正重排并从下到上/从上到下重新编号，信号列表徽标、图例徽标、菜单与浮动面板
中所有 GX 编号同步更新；手动排布后切换为自定义划分，后续重建网格不会打乱次序。
- 拖动期间复用静态场景缓存，按网格条带平移贴图，因此大文件下依然流畅。
3. 均值滤波升级为"均值点数 X + 阶数 N"
- 原"均值点数 N"更名"均值点数 X"，新增"阶数 N"：预览曲线以 X 点均值滤波的结果为蓝本，
再重复同样的滤波共 N 阶（1 阶即与原来的单次滤波逐位一致）。
- 浮动面板改为逐信号条目：第一排是信号名与复选框，第二排是均值点数与阶数输入；
只有勾选的信号才绘制预览曲线，未勾选时不显示第二排；启用功能时默认只勾选第一个信号。
- 参数按信号保存（同一网格内不同信号可用不同参数），「构建为新信号」按各自参数构建。
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@@ -127,7 +127,7 @@ namespace jdi
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double edgeCenterPanelX = -1.0;
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double edgeCenterPanelY = -1.0;
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bool meanFilter = false;
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int meanFilterRadius = 5;
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std::vector<Lane::MeanFilterItem> meanFilterItems;
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bool meanFilterPanelOpen = true;
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double meanFilterPanelX = -1.0;
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double meanFilterPanelY = -1.0;
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@@ -152,7 +152,7 @@ namespace jdi
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carry.edgeCenterPanelX = lane.edgeCenterPanelX;
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carry.edgeCenterPanelY = lane.edgeCenterPanelY;
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carry.meanFilter = lane.meanFilter;
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carry.meanFilterRadius = lane.meanFilterRadius;
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carry.meanFilterItems = lane.meanFilterItems;
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carry.meanFilterPanelOpen = lane.meanFilterPanelOpen;
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carry.meanFilterPanelX = lane.meanFilterPanelX;
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carry.meanFilterPanelY = lane.meanFilterPanelY;
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@@ -188,7 +188,7 @@ namespace jdi
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lane.edgeCenterPanelX = carry.edgeCenterPanelX;
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lane.edgeCenterPanelY = carry.edgeCenterPanelY;
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lane.meanFilter = carry.meanFilter;
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lane.meanFilterRadius = carry.meanFilterRadius;
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lane.meanFilterItems = carry.meanFilterItems;
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lane.meanFilterPanelOpen = carry.meanFilterPanelOpen;
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lane.meanFilterPanelX = carry.meanFilterPanelX;
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lane.meanFilterPanelY = carry.meanFilterPanelY;
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@@ -969,6 +969,67 @@ namespace jdi
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}
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}
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void ComputeMovingAverageIterated(const std::vector<double>& values, int radius, int order,
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std::vector<float>& out)
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{
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const int stages = std::max(1, order);
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// 第 1 阶：从原始采样值出发。
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ComputeMovingAverage(values, radius, out);
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if (stages == 1)
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{
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return;
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}
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// 后续各阶以上一阶的结果为输入（double 中转，避免 float→double 的精度损失累积）。
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std::vector<double> buffer(values.size(), std::numeric_limits<double>::quiet_NaN());
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std::vector<float> stage;
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for (int iteration = 1; iteration < stages; ++iteration)
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{
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for (std::size_t index = 0; index < out.size(); ++index)
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{
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buffer[index] = std::isfinite(out[index]) ? static_cast<double>(out[index])
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: std::numeric_limits<double>::quiet_NaN();
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}
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ComputeMovingAverage(buffer, radius, stage);
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out.swap(stage);
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}
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}
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const Lane::MeanFilterItem* Lane::FindMeanFilterItem(const SignalRef& ref) const
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{
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for (const MeanFilterItem& item : meanFilterItems)
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{
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if (item.ref == ref)
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{
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return &item;
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}
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}
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return nullptr;
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}
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Lane::MeanFilterItem& Lane::EnsureMeanFilterItem(const SignalRef& ref)
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{
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for (MeanFilterItem& item : meanFilterItems)
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{
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if (item.ref == ref)
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{
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return item;
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}
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}
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MeanFilterItem item;
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item.ref = ref;
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meanFilterItems.push_back(item);
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return meanFilterItems.back();
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}
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void Lane::RemoveMeanFilterItem(const SignalRef& ref)
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{
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meanFilterItems.erase(
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std::remove_if(meanFilterItems.begin(), meanFilterItems.end(),
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[&ref](const MeanFilterItem& item) { return item.ref == ref; }),
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meanFilterItems.end());
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}
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int FindNearestSample(const DataFile& file, double time)
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{
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const std::size_t count = file.time.size();
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@@ -1192,6 +1253,23 @@ namespace jdi
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--ref.signalIndex;
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}
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}
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// 均值滤波条目同样持有信号引用，必须一起删除/前移。
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std::vector<Lane::MeanFilterItem>& items = lane.meanFilterItems;
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items.erase(std::remove_if(items.begin(), items.end(),
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[fileIndex, signalIndex](const Lane::MeanFilterItem& item)
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{
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return item.ref.fileIndex == fileIndex &&
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item.ref.signalIndex == signalIndex;
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}),
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items.end());
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for (Lane::MeanFilterItem& item : items)
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{
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if (item.ref.fileIndex == fileIndex && item.ref.signalIndex > signalIndex)
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{
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--item.ref.signalIndex;
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}
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}
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}
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// 被移除信号若独占了某个网格，该网格随之消失（与取消勾选的行为一致）。
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m_lanes.erase(std::remove_if(m_lanes.begin(), m_lanes.end(),
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@@ -1485,7 +1563,15 @@ namespace jdi
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lane.edgeCenterPanelX = source.edgeCenterPanelX;
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lane.edgeCenterPanelY = source.edgeCenterPanelY;
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lane.meanFilter = source.meanFilter;
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lane.meanFilterRadius = source.meanFilterRadius;
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// 拆分后每个子网格只保留自己那个信号的均值滤波条目。
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lane.meanFilterItems.clear();
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for (const Lane::MeanFilterItem& item : source.meanFilterItems)
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{
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if (item.ref == ref)
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{
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lane.meanFilterItems.push_back(item);
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}
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}
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lane.meanFilterPanelOpen = source.meanFilterPanelOpen;
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lane.meanFilterPanelX = source.meanFilterPanelX;
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lane.meanFilterPanelY = source.meanFilterPanelY;
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@@ -1529,6 +1615,14 @@ namespace jdi
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++ref.signalIndex;
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}
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}
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// 均值滤波条目同样持有信号引用，必须一起后移。
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for (Lane::MeanFilterItem& item : lane.meanFilterItems)
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{
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if (item.ref.fileIndex == fileIndex && item.ref.signalIndex >= insertPos)
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{
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++item.ref.signalIndex;
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}
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}
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}
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}
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@@ -1560,6 +1654,37 @@ namespace jdi
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return true;
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}
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bool TabConfig::MoveLane(int from, int target)
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{
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const int count = static_cast<int>(m_lanes.size());
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if (from < 0 || from >= count || target < 0 || target > count)
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{
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return false;
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}
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// `target` 语义 = 「插入到该下标之前」；向后拖时扣除自身占位后再比较，
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// 保证"拖到自己原来的位置"不会产生无意义的移动。
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int destination = target;
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if (destination > from)
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{
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--destination;
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}
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destination = std::max(0, std::min(destination, count - 1));
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if (destination == from)
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{
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return false;
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}
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Lane moved = std::move(m_lanes[static_cast<std::size_t>(from)]);
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m_lanes.erase(m_lanes.begin() + from);
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m_lanes.insert(m_lanes.begin() + destination, std::move(moved));
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// 手动排布 → 切换到自定义划分策略，避免后续重建网格时顺序被还原。
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m_laneMode = LaneMode::CUSTOM;
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RenameLanes();
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return true;
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}
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void TabConfig::RenameLanes()
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{
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for (std::size_t index = 0; index < m_lanes.size(); ++index)
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