/**
 * 基于小程序Canvas API的核密度估计热力图
 * 实现类似test.html中的效果，但适配uni-app小程序环境
 */

/**
 * Epanechnikov核函数
 * @param {Number} bandwidth 带宽参数
 * @returns {Function} 核函数
 */
function kernelEpanechnikov(bandwidth) {
  return function (v) {
    const r = Math.sqrt(v[0] * v[0] + v[1] * v[1]);
    return r <= bandwidth
      ? (3 / (Math.PI * bandwidth * bandwidth)) *
          (1 - (r * r) / (bandwidth * bandwidth))
      : 0;
  };
}

/**
 * 核密度估计器 - 优化版本
 * @param {Function} kernel 核函数
 * @param {Array} range 范围[xmin, xmax]
 * @param {Number} samples 采样点数
 * @returns {Function} 密度估计函数
 */
function kernelDensityEstimator(kernel, range, samples) {
  return function (data) {
    const gridSize = (range[1] - range[0]) / samples;
    const densityData = [];
    const bandwidth = 0.8; // 从核函数中提取带宽
    
    // 预计算核函数值缓存（减少重复计算）
    const kernelCache = new Map();
    const maxDistance = Math.ceil(bandwidth * 2 / gridSize); // 最大影响范围
    
    for (let dx = -maxDistance; dx <= maxDistance; dx++) {
      for (let dy = -maxDistance; dy <= maxDistance; dy++) {
        const distance = Math.sqrt(dx * dx + dy * dy) * gridSize;
        if (distance <= bandwidth * 2) {
          kernelCache.set(`${dx},${dy}`, kernel([dx * gridSize, dy * gridSize]));
        }
      }
    }

    // 使用稀疏网格计算（只计算有数据点影响的区域）
    const affectedGridPoints = new Set();
    
    // 第一步：找出所有受影响的网格点
    data.forEach(point => {
      const centerX = Math.round((point[0] - range[0]) / gridSize);
      const centerY = Math.round((point[1] - range[0]) / gridSize);
      
      // 只考虑带宽范围内的网格点
      for (let dx = -maxDistance; dx <= maxDistance; dx++) {
        for (let dy = -maxDistance; dy <= maxDistance; dy++) {
          const gridX = centerX + dx;
          const gridY = centerY + dy;
          if (gridX >= 0 && gridX < samples && gridY >= 0 && gridY < samples) {
            affectedGridPoints.add(`${gridX},${gridY}`);
          }
        }
      }
    });

    // 第二步：只计算受影响的网格点
    affectedGridPoints.forEach(gridKey => {
      const [gridX, gridY] = gridKey.split(',').map(Number);
      const x = range[0] + gridX * gridSize;
      const y = range[0] + gridY * gridSize;
      
      let sum = 0;
      let validPoints = 0;
      
      // 只考虑附近的点（空间分割优化）
      data.forEach(point => {
        const dx = (x - point[0]) / gridSize;
        const dy = (y - point[1]) / gridSize;
        const cacheKey = `${Math.round(dx)},${Math.round(dy)}`;
        
        if (kernelCache.has(cacheKey)) {
          sum += kernelCache.get(cacheKey);
          validPoints++;
        }
      });
      
      if (validPoints > 0) {
        densityData.push([x, y, sum / data.length]);
      }
    });

    // 归一化
    if (densityData.length > 0) {
      const maxDensity = Math.max(...densityData.map((d) => d[2]));
      densityData.forEach((d) => {
        if (maxDensity > 0) d[2] /= maxDensity;
      });
    }

    return densityData;
  };
}

/**
 * 生成随机射箭数据点
 * @param {Number} centerCount 中心点数量
 * @param {Number} pointsPerCenter 每个中心点的箭数
 * @returns {Array} 箭矢坐标数组
 */
export function generateArcheryPoints(centerCount = 2, pointsPerCenter = 100) {
  const points = [];
  const range = 8; // 坐标范围 -4 到 4
  const spread = 3; // 分散度

  for (let i = 0; i < centerCount; i++) {
    const centerX = Math.random() * range - range / 2;
    const centerY = Math.random() * range - range / 2;

    for (let j = 0; j < pointsPerCenter; j++) {
      points.push([
        centerX + (Math.random() - 0.5) * spread,
        centerY + (Math.random() - 0.5) * spread,
      ]);
    }
  }

  return points;
}

/**
 * 颜色映射函数 - 将密度值映射到颜色
 * @param {Number} density 密度值 0-1
 * @returns {String} RGBA颜色字符串
 */
function getHeatColor(density) {
  // 绿色系热力图：从浅绿到深绿
  if (density < 0.1) return "rgba(0, 255, 0, 0)";

  const alpha = Math.min(density * 1.2, 1); // 增强透明度
  const intensity = density;

  if (intensity < 0.5) {
    // 低密度：浅绿色
    const green = Math.round(200 + 55 * intensity);
    const blue = Math.round(50 + 100 * intensity);
    return `rgba(${Math.round(50 * intensity)}, ${green}, ${blue}, ${alpha * 0.7})`;
  } else {
    // 高密度：深绿色
    const red = Math.round(50 * (intensity - 0.5) * 2);
    const green = Math.round(180 + 75 * (1 - intensity));
    const blue = Math.round(30 * (1 - intensity));
    return `rgba(${red}, ${green}, ${blue}, ${alpha * 0.7})`;
  }
}

// 添加缓存机制
const heatmapCache = new Map();

/**
 * 基于小程序Canvas API绘制核密度估计热力图 - 带缓存优化
 * @param {String} canvasId 画布ID
 * @param {Number} width 画布宽度
 * @param {Number} height 画布高度
 * @param {Array} points 箭矢坐标数组 [[x, y], ...]
 * @param {Object} options 可选参数
 * @returns {Promise} 绘制完成的Promise
 */
export function drawKDEHeatmap(canvasId, width, height, points, options = {}) {
  return new Promise((resolve, reject) => {
    try {
      const {
        bandwidth = 0.8,
        gridSize = 100,
        range = [-4, 4],
        showPoints = true,
        pointColor = "rgba(255, 255, 255, 0.9)",
      } = options;

      // 创建绘图上下文
      const ctx = uni.createCanvasContext(canvasId);

      // 清空画布
      ctx.clearRect(0, 0, width, height);

      // 如果没有数据，直接绘制
      if (!points || points.length === 0) {
        ctx.draw(false, () => resolve());
        return;
      }

      // 生成缓存key（基于参数和数据点的哈希）
      const cacheKey = `${bandwidth}-${gridSize}-${range.join(',')}-${points.length}-${JSON.stringify(points.slice(0, 10))}`;
      
      // 检查缓存
      if (heatmapCache.has(cacheKey)) {
        console.log('使用缓存的热力图数据');
        const cachedDensityData = heatmapCache.get(cacheKey);
        
        // 直接使用缓存数据绘制
        const cellWidth = width / gridSize;
        const cellHeight = height / gridSize;
        const xRange = range[1] - range[0];
        const yRange = range[1] - range[0];
        
        cachedDensityData.forEach((point) => {
          const [x, y, density] = point;
          const normalizedX = (x - range[0]) / xRange;
          const normalizedY = (y - range[0]) / yRange;
          const canvasX = normalizedX * width;
          const canvasY = normalizedY * height;
          const color = getHeatColor(density);
          
          ctx.setFillStyle(color);
          ctx.beginPath();
          ctx.arc(canvasX, canvasY, Math.min(cellWidth, cellHeight) * 0.6, 0, 2 * Math.PI);
          ctx.fill();
        });
        
        // 绘制原始数据点
        if (showPoints) {
          ctx.setFillStyle(pointColor);
          points.forEach((point) => {
            const [x, y] = point;
            const normalizedX = (x - range[0]) / xRange;
            const normalizedY = (y - range[0]) / yRange;
            const canvasX = normalizedX * width;
            const canvasY = normalizedY * height;
            
            ctx.beginPath();
            ctx.arc(canvasX, canvasY, 2.5, 0, 2 * Math.PI);
            ctx.fill();
          });
        }
        
        ctx.draw(false, () => {
          console.log("KDE热力图绘制完成（缓存）");
          resolve();
        });
        return;
      }

      // 计算核密度估计
      const kernel = kernelEpanechnikov(bandwidth);
      const kde = kernelDensityEstimator(kernel, range, gridSize);
      const densityData = kde(points);
      
      // 缓存结果（限制缓存大小）
      if (heatmapCache.size > 10) {
        const firstKey = heatmapCache.keys().next().value;
        heatmapCache.delete(firstKey);
      }
      heatmapCache.set(cacheKey, densityData);

      // 计算网格大小
      const cellWidth = width / gridSize;
      const cellHeight = height / gridSize;
      const xRange = range[1] - range[0];
      const yRange = range[1] - range[0];

      // 绘制热力图网格 - 批量绘制优化
      const colorGroups = new Map();
      
      // 按颜色分组，减少setFillStyle调用
      densityData.forEach((point) => {
        const [x, y, density] = point;
        const color = getHeatColor(density);
        
        if (!colorGroups.has(color)) {
          colorGroups.set(color, []);
        }
        colorGroups.get(color).push(point);
      });
      
      // 批量绘制相同颜色的点
      colorGroups.forEach((points, color) => {
        ctx.setFillStyle(color);
        points.forEach((point) => {
          const [x, y, density] = point;
          const normalizedX = (x - range[0]) / xRange;
          const normalizedY = (y - range[0]) / yRange;
          const canvasX = normalizedX * width;
          const canvasY = normalizedY * height;
          
          ctx.beginPath();
          ctx.arc(canvasX, canvasY, Math.min(cellWidth, cellHeight) * 0.6, 0, 2 * Math.PI);
          ctx.fill();
        });
      });

      // 绘制原始数据点 - 批量绘制优化
      if (showPoints) {
        ctx.setFillStyle(pointColor);
        ctx.beginPath(); // 开始批量路径
        points.forEach((point) => {
          const [x, y] = point;
          const normalizedX = (x - range[0]) / xRange;
          const normalizedY = (y - range[0]) / yRange;
          const canvasX = normalizedX * width;
          const canvasY = normalizedY * height;
          
          ctx.arc(canvasX, canvasY, 2.5, 0, 2 * Math.PI);
        });
        ctx.fill(); // 一次性填充所有圆点
      }

      // 执行绘制
      ctx.draw(false, () => {
        console.log("KDE热力图绘制完成");
        resolve();
      });
    } catch (error) {
      console.error("KDE热力图绘制失败:", error);
      reject(error);
    }
  });
}

/**
 * 生成热力图图片（类似原有的generateHeatmapImage函数）
 * 但使用核密度估计算法
 */
export function generateKDEHeatmapImage(
  canvasId,
  width,
  height,
  points,
  options = {}
) {
  return new Promise((resolve, reject) => {
    drawKDEHeatmap(canvasId, width, height, points, options)
      .then(() => {
        // 生成图片
        uni.canvasToTempFilePath({
          canvasId: canvasId,
          width: width,
          height: height,
          destWidth: width * 3, // 提高输出分辨率，让图像更细腻
          destHeight: height * 3,
          success: (res) => {
            console.log("KDE热力图图片生成成功:", res.tempFilePath);
            resolve(res.tempFilePath);
          },
          fail: (error) => {
            console.error("KDE热力图图片生成失败:", error);
            reject(error);
          },
        });
      })
      .catch(reject);
  });
}

/**
 * 清除热力图缓存
 * 在数据或参数需要强制更新时调用
 */
export function clearHeatmapCache() {
  heatmapCache.clear();
  console.log('热力图缓存已清除');
}

export const generateHeatMapData = (width, height, amount = 100) => {
  const data = [];
  const centerX = 0.5; // 中心点X坐标
  const centerY = 0.5; // 中心点Y坐标

  for (let i = 0; i < amount; i++) {
    let x, y;

    // 30%的数据集中在中心区域（高斯分布）
    if (Math.random() < 0.3) {
      // 使用正态分布生成中心区域的数据
      const angle = Math.random() * 2 * Math.PI;
      const radius = Math.sqrt(-2 * Math.log(Math.random())) * 0.15; // 标准差0.15
      x = centerX + radius * Math.cos(angle);
      y = centerY + radius * Math.sin(angle);
    } else {
      x = Math.random() * 0.8 + 0.1; // 0.1-0.9范围
      y = Math.random() * 0.8 + 0.1;
    }

    // 确保坐标在0-1范围内
    x = Math.max(0.05, Math.min(0.95, x));
    y = Math.max(0.05, Math.min(0.95, y));

    data.push({
      x: parseFloat(x.toFixed(3)),
      y: parseFloat(y.toFixed(3)),
      ring: Math.floor(Math.random() * 5) + 6, // 6-10环
    });
  }

  return data;
};