271 lines
7.6 KiB
JavaScript
271 lines
7.6 KiB
JavaScript
/**
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* 基于小程序Canvas API的核密度估计热力图
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* 实现类似test.html中的效果，但适配uni-app小程序环境
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*/
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/**
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* Epanechnikov核函数
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* @param {Number} bandwidth 带宽参数
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* @returns {Function} 核函数
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*/
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function kernelEpanechnikov(bandwidth) {
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return function (v) {
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const r = Math.sqrt(v[0] * v[0] + v[1] * v[1]);
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return r <= bandwidth
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? (3 / (Math.PI * bandwidth * bandwidth)) *
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(1 - (r * r) / (bandwidth * bandwidth))
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: 0;
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};
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}
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/**
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* 核密度估计器
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* @param {Function} kernel 核函数
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* @param {Array} range 范围[xmin, xmax]
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* @param {Number} samples 采样点数
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* @returns {Function} 密度估计函数
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*/
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function kernelDensityEstimator(kernel, range, samples) {
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return function (data) {
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const gridSize = (range[1] - range[0]) / samples;
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const densityData = [];
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for (let x = range[0]; x <= range[1]; x += gridSize) {
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for (let y = range[0]; y <= range[1]; y += gridSize) {
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let sum = 0;
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for (const point of data) {
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sum += kernel([x - point[0], y - point[1]]);
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}
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densityData.push([x, y, sum / data.length]);
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}
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}
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// 归一化
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const maxDensity = Math.max(...densityData.map((d) => d[2]));
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densityData.forEach((d) => {
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if (maxDensity > 0) d[2] /= maxDensity;
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});
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return densityData;
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};
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}
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/**
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* 生成随机射箭数据点
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* @param {Number} centerCount 中心点数量
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* @param {Number} pointsPerCenter 每个中心点的箭数
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* @returns {Array} 箭矢坐标数组
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*/
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export function generateArcheryPoints(centerCount = 2, pointsPerCenter = 100) {
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const points = [];
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const range = 8; // 坐标范围 -4 到 4
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const spread = 3; // 分散度
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for (let i = 0; i < centerCount; i++) {
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const centerX = Math.random() * range - range / 2;
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const centerY = Math.random() * range - range / 2;
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for (let j = 0; j < pointsPerCenter; j++) {
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points.push([
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centerX + (Math.random() - 0.5) * spread,
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centerY + (Math.random() - 0.5) * spread,
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]);
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}
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}
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return points;
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}
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/**
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* 颜色映射函数 - 将密度值映射到颜色
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* @param {Number} density 密度值 0-1
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* @returns {String} RGBA颜色字符串
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*/
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function getHeatColor(density) {
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// 绿色系热力图：从浅绿到深绿
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if (density < 0.1) return "rgba(0, 255, 0, 0)";
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const alpha = Math.min(density * 1.2, 1); // 增强透明度
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const intensity = density;
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if (intensity < 0.5) {
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// 低密度：浅绿色
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const green = Math.round(200 + 55 * intensity);
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const blue = Math.round(50 + 100 * intensity);
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return `rgba(${Math.round(50 * intensity)}, ${green}, ${blue}, ${alpha * 0.7})`;
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} else {
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// 高密度：深绿色
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const red = Math.round(50 * (intensity - 0.5) * 2);
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const green = Math.round(180 + 75 * (1 - intensity));
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const blue = Math.round(30 * (1 - intensity));
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return `rgba(${red}, ${green}, ${blue}, ${alpha * 0.7})`;
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}
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}
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/**
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* 基于小程序Canvas API绘制核密度估计热力图
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* @param {String} canvasId 画布ID
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* @param {Number} width 画布宽度
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* @param {Number} height 画布高度
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* @param {Array} points 箭矢坐标数组 [[x, y], ...]
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* @param {Object} options 可选参数
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* @returns {Promise} 绘制完成的Promise
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*/
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export function drawKDEHeatmap(canvasId, width, height, points, options = {}) {
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return new Promise((resolve, reject) => {
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try {
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const {
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bandwidth = 0.8,
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gridSize = 100,
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range = [-4, 4],
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showPoints = true,
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pointColor = "rgba(255, 255, 255, 0.9)",
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} = options;
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// 创建绘图上下文
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const ctx = uni.createCanvasContext(canvasId);
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// 清空画布
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ctx.clearRect(0, 0, width, height);
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// 如果没有数据，直接绘制
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if (!points || points.length === 0) {
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ctx.draw(false, () => resolve());
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return;
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}
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// 计算核密度估计
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const kernel = kernelEpanechnikov(bandwidth);
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const kde = kernelDensityEstimator(kernel, range, gridSize);
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const densityData = kde(points);
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// 计算网格大小
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const cellWidth = width / gridSize;
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const cellHeight = height / gridSize;
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const xRange = range[1] - range[0];
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const yRange = range[1] - range[0];
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// 绘制热力图网格
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densityData.forEach((point) => {
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const [x, y, density] = point;
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// 将逻辑坐标转换为画布坐标
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const normalizedX = (x - range[0]) / xRange;
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const normalizedY = (y - range[0]) / yRange;
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const canvasX = normalizedX * width;
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const canvasY = normalizedY * height;
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// 获取颜色
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const color = getHeatColor(density);
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// 绘制单元格（使用圆形绘制，边缘更平滑）
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ctx.setFillStyle(color);
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ctx.beginPath();
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ctx.arc(
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canvasX,
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canvasY,
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Math.min(cellWidth, cellHeight) * 0.6,
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0,
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2 * Math.PI
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);
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ctx.fill();
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});
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// 绘制原始数据点
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if (showPoints) {
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ctx.setFillStyle(pointColor);
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points.forEach((point) => {
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const [x, y] = point;
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const normalizedX = (x - range[0]) / xRange;
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const normalizedY = (y - range[0]) / yRange;
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const canvasX = normalizedX * width;
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const canvasY = normalizedY * height;
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// 绘制小圆点
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ctx.beginPath();
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ctx.arc(canvasX, canvasY, 2.5, 0, 2 * Math.PI);
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ctx.fill();
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});
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}
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// 执行绘制
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ctx.draw(false, () => {
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console.log("KDE热力图绘制完成");
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resolve();
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});
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} catch (error) {
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console.error("KDE热力图绘制失败:", error);
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reject(error);
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}
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});
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}
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/**
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* 生成热力图图片（类似原有的generateHeatmapImage函数）
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* 但使用核密度估计算法
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*/
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export function generateKDEHeatmapImage(
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canvasId,
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width,
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height,
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points,
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options = {}
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) {
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return new Promise((resolve, reject) => {
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drawKDEHeatmap(canvasId, width, height, points, options)
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.then(() => {
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// 生成图片
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uni.canvasToTempFilePath({
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canvasId: canvasId,
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width: width,
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height: height,
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destWidth: width * 3, // 提高输出分辨率，让图像更细腻
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destHeight: height * 3,
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success: (res) => {
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console.log("KDE热力图图片生成成功:", res.tempFilePath);
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resolve(res.tempFilePath);
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},
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fail: (error) => {
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console.error("KDE热力图图片生成失败:", error);
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reject(error);
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},
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});
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})
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.catch(reject);
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});
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}
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export const generateHeatMapData = (width, height, amount = 100) => {
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const data = [];
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const centerX = 0.5; // 中心点X坐标
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const centerY = 0.5; // 中心点Y坐标
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for (let i = 0; i < amount; i++) {
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let x, y;
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// 30%的数据集中在中心区域（高斯分布）
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if (Math.random() < 0.3) {
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// 使用正态分布生成中心区域的数据
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const angle = Math.random() * 2 * Math.PI;
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const radius = Math.sqrt(-2 * Math.log(Math.random())) * 0.15; // 标准差0.15
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x = centerX + radius * Math.cos(angle);
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y = centerY + radius * Math.sin(angle);
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} else {
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x = Math.random() * 0.8 + 0.1; // 0.1-0.9范围
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y = Math.random() * 0.8 + 0.1;
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}
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// 确保坐标在0-1范围内
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x = Math.max(0.05, Math.min(0.95, x));
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y = Math.max(0.05, Math.min(0.95, y));
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data.push({
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x: parseFloat(x.toFixed(3)),
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y: parseFloat(y.toFixed(3)),
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ring: Math.floor(Math.random() * 5) + 6, // 6-10环
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});
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}
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return data;
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};
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