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热力图
2019-07-10 14:39:14HeatMap(热力图) 热力图主要通过颜色去表现数值的大小,必须要配合 visualMap 组件使用。直角坐标系上必须要使用两个类目轴。 import random from pyecharts import HeatMap x_axis = [ "12a", "1a", "2a", "3a...HeatMap(热力图)
热力图主要通过颜色去表现数值的大小,必须要配合 visualMap 组件使用。直角坐标系上必须要使用两个类目轴。
import random from pyecharts import HeatMap x_axis = [ "12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a", "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"] y_axis = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"] data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)] heatmap = HeatMap() heatmap.add("热力图直角坐标系", x_axis, y_axis, data, is_visualmap=True, visual_text_color="#000", visual_orient="horizontal", visual_pos="left", visual_bottom="20%") heatmap
<div id="f8c7f3083e2f4ea6935340bcecf27797" style="width:800px;height:400px;"></div>
import datetime import random from pyecharts import HeatMap begin, end = datetime.date(2017, 1, 1), datetime.date(2017, 12, 31) data = [[str(begin + datetime.timedelta(days=i)), random.randint(1000, 25000)] for i in range((end - begin).days + 1)] heatmap = HeatMap("日历热力图示例", "某人 2017 年微信步数情况", width=1000) heatmap.add("", data, is_calendar_heatmap=True, is_visualmap=True, visual_orient="horizontal", visual_pos="center", visual_top="70%", visual_text_color='#000', visual_range_text=['', ''], visual_range=[1000, 25000], calendar_cell_size=['auto', 30], visual_split_number=3, # 分段型中分割的段数,在设置为分段型时生效。默认分为 5 段。 calendar_date_range="2017", is_piecewise=True, # 是否将组件转换为分段型(默认为连续型),默认为 False ) heatmap
<div id="89d31bdf784d4b148aa0f697e2b4d22a" style="width:1000px;height:400px;"></div>
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一、python可视化——热力图
2018-04-04 10:41:57热力图 1、利用热力图可以看数据表里多个特征两两的相似度。参考官方API参数及地址: seaborn.heatmap(data, vmin=None, vmax=None,cmap=None, center=None, robust=False, annot=None, fmt=’.2g’, annot_kws=...热力图
1、利用热力图可以看数据表里多个特征两两的相似度。参考官方API参数及地址:
seaborn.heatmap(data, vmin=None, vmax=None,cmap=None, center=None, robust=False, annot=None, fmt=’.2g’, annot_kws=None,linewidths=0, linecolor=’white’, cbar=True, cbar_kws=None, cbar_ax=None,square=False, xticklabels=’auto’, yticklabels=’auto’, mask=None, ax=None,**kwargs)
(1)热力图输入数据参数:
data:矩阵数据集,可以是numpy的数组(array),也可以是pandas的DataFrame。如果是DataFrame,则df的index/column信息会分别对应到heatmap的columns和rows,即df.index是热力图的行标,df.columns是热力图的列标
(2)热力图矩阵块颜色参数:
vmax,vmin:分别是热力图的颜色取值最大和最小范围,默认是根据data数据表里的取值确定
cmap:从数字到色彩空间的映射,取值是matplotlib包里的colormap名称或颜色对象,或者表示颜色的列表;改参数默认值:根据center参数设定
center:数据表取值有差异时,设置热力图的色彩中心对齐值;通过设置center值,可以调整生成的图像颜色的整体深浅;设置center数据时,如果有数据溢出,则手动设置的vmax、vmin会自动改变
robust:默认取值False;如果是False,且没设定vmin和vmax的值,热力图的颜色映射范围根据具有鲁棒性的分位数设定,而不是用极值设定(3)热力图矩阵块注释参数:
annot(annotate的缩写):默认取值False;如果是True,在热力图每个方格写入数据;如果是矩阵,在热力图每个方格写入该矩阵对应位置数据
fmt:字符串格式代码,矩阵上标识数字的数据格式,比如保留小数点后几位数字
annot_kws:默认取值False;如果是True,设置热力图矩阵上数字的大小颜色字体,matplotlib包text类下的字体设置;官方文档:(4)热力图矩阵块之间间隔及间隔线参数:
linewidths:定义热力图里“表示两两特征关系的矩阵小块”之间的间隔大小
linecolor:切分热力图上每个矩阵小块的线的颜色,默认值是’white’(5)热力图颜色刻度条参数:
cbar:是否在热力图侧边绘制颜色刻度条,默认值是True
cbar_kws:热力图侧边绘制颜色刻度条时,相关字体设置,默认值是None
cbar_ax:热力图侧边绘制颜色刻度条时,刻度条位置设置,默认值是None(6)square:设置热力图矩阵小块形状,默认值是False
xticklabels, yticklabels:xticklabels控制每列标签名的输出;yticklabels控制每行标签名的输出。默认值是auto。如果是True,则以DataFrame的列名作为标签名。如果是False,则不添加行标签名。如果是列表,则标签名改为列表中给的内容。如果是整数K,则在图上每隔K个标签进行一次标注。 如果是auto,则自动选择标签的标注间距,将标签名不重叠的部分(或全部)输出
mask:控制某个矩阵块是否显示出来。默认值是None。如果是布尔型的DataFrame,则将DataFrame里True的位置用白色覆盖掉
ax:设置作图的坐标轴,一般画多个子图时需要修改不同的子图的该值
**kwargs:All other keyword arguments are passed to ax.pcolormesh热力图矩阵块颜色参数
#cmap(颜色) import matplotlib.pyplot as plt import seaborn as sns % matplotlib inline f, (ax1,ax2) = plt.subplots(figsize = (6,4),nrows=2) # cmap用cubehelix map颜色 cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True) pt = df.corr() # pt为数据框或者是协方差矩阵 sns.heatmap(pt, linewidths = 0.05, ax = ax1, vmax=900, vmin=0, cmap=cmap) ax1.set_title('cubehelix map') ax1.set_xlabel('') ax1.set_xticklabels([]) #设置x轴图例为空值 ax1.set_ylabel('kind') # cmap用matplotlib colormap sns.heatmap(pt, linewidths = 0.05, ax = ax2, vmax=900, vmin=0, cmap='rainbow') # rainbow为 matplotlib 的colormap名称 ax2.set_title('matplotlib colormap') ax2.set_xlabel('region') ax2.set_ylabel('kind')
#center的用法(颜色) f, (ax1,ax2) = plt.subplots(figsize = (6, 4),nrows=2) cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True) sns.heatmap(pt, linewidths = 0.05, ax = ax1, cmap=cmap, center=None ) ax1.set_title('center=None') ax1.set_xlabel('') ax1.set_xticklabels([]) #设置x轴图例为空值 ax1.set_ylabel('kind') # 当center设置小于数据的均值时,生成的图片颜色要向0值代表的颜色一段偏移 sns.heatmap(pt, linewidths = 0.05, ax = ax2, cmap=cmap, center=200) ax2.set_title('center=3000') ax2.set_xlabel('region') ax2.set_ylabel('kind')
#robust的用法(颜色) f, (ax1,ax2) = plt.subplots(figsize = (6,4),nrows=2) cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True) sns.heatmap(pt, linewidths = 0.05, ax = ax1, cmap=cmap, center=None, robust=False ) ax1.set_title('robust=False') ax1.set_xlabel('') ax1.set_xticklabels([]) #设置x轴图例为空值 ax1.set_ylabel('kind') sns.heatmap(pt, linewidths = 0.05, ax = ax2, cmap=cmap, center=None, robust=True ) ax2.set_title('robust=True') ax2.set_xlabel('region') ax2.set_ylabel('kind')
热力图矩阵块注释参数
#annot(矩阵上数字),annot_kws(矩阵上数字的大小颜色字体)matplotlib包text类下的字体设置 import numpy as np np.random.seed(20180316) x = np.random.randn(4, 4) f, (ax1, ax2) = plt.subplots(figsize=(6,6),nrows=2) sns.heatmap(x, annot=True, ax=ax1) sns.heatmap(x, annot=True, ax=ax2, annot_kws={'size':9,'weight':'bold', 'color':'blue'}) # Keyword arguments for ax.text when annot is True. http://stackoverflow.com/questions/35024475/seaborn-heatmap-key-words
#fmt(字符串格式代码,矩阵上标识数字的数据格式,比如保留小数点后几位数字) import numpy as np np.random.seed(0) x = np.random.randn(4,4) f, (ax1, ax2) = plt.subplots(figsize=(6,6),nrows=2) sns.heatmap(x, annot=True, ax=ax1) sns.heatmap(x, annot=True, fmt='.1f', ax=ax2)
热力图矩阵块之间间隔及间隔线参数
#linewidths(矩阵小块的间隔),linecolor(切分热力图矩阵小块的线的颜色) import matplotlib.pyplot as plt f, ax = plt.subplots(figsize = (6,4)) cmap = sns.cubehelix_palette(start = 1, rot = 3, gamma=0.8, as_cmap = True) sns.heatmap(pt, cmap = cmap, linewidths = 0.05, linecolor= 'red', ax = ax) ax.set_title('Amounts per kind and region') ax.set_xlabel('region') ax.set_ylabel('kind')
#xticklabels,yticklabels横轴和纵轴的标签名输出 import matplotlib.pyplot as plt f, (ax1,ax2) = plt.subplots(figsize = (5,5),nrows=2) cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True) p1 = sns.heatmap(pt, ax=ax1, cmap=cmap, center=None, xticklabels=False) ax1.set_title('xticklabels=None',fontsize=8) p2 = sns.heatmap(pt, ax=ax2, cmap=cmap, center=None, xticklabels=2, yticklabels=list(range(5))) ax2.set_title('xticklabels=2, yticklabels is a list',fontsize=8) ax2.set_xlabel('region')
#mask对某些矩阵块的显示进行覆盖 f, (ax1,ax2) = plt.subplots(figsize = (5,5),nrows=2) cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True) p1 = sns.heatmap(pt, ax=ax1, cmap=cmap, xticklabels=False, mask=None) ax1.set_title('mask=None') ax1.set_ylabel('kind') p2 = sns.heatmap(pt, ax=ax2, cmap=cmap, xticklabels=True, mask=(pt<800)) #mask对pt进行布尔型转化,结果为True的位置用白色覆盖 ax2.set_title('mask: boolean DataFrame') ax2.set_xlabel('region') ax2.set_ylabel('kind')
用mask实现:突出显示某些数据
f,(ax1,ax2) = plt.subplots(figsize=(4,6),nrows=2) x = np.array([[1,2,3],[2,0,1],[-1,-2,0]]) sns.heatmap(x, annot=True, ax=ax1) sns.heatmap(x, mask=x < 1, ax=ax2, annot=True, annot_kws={"weight": "bold"}) #把小于1的区域覆盖掉
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Python如何绘制日历图和热力图_用表格做热力图
2020-11-22 21:44:00Python如如何何绘绘制制日日历历图图和和热热力力图图 这篇文章主要介绍了Python如何绘制日历图和热力图帮助大家更好的理解和学习Python感兴趣的朋友可以了 解下 文以2019年全国各城市的空气质量观测数据为例利用... -
热力图插件
2018-03-09 10:38:39openlayer热力图插件openlayer热力图插件openlayer热力图插件 -
python 百度地图api热力图_【热力图】区域地图热力图,百度地图api
2021-02-09 13:07:07最简单的地图热力图,用的是百度的api,所以取经纬度的时候最好也用百度地图。这里已经填好百度api的key了,但还是建议大家注册使用自己的key,因为有每日额度,超过访问次数了就不可用了。效果展示:下面是完整代码...最简单的地图热力图,用的是百度的api,所以取经纬度的时候最好也用百度地图。
这里已经填好百度api的key了,但还是建议大家注册使用自己的key,因为有每日额度,超过访问次数了就不可用了。
效果展示:
下面是完整代码,注释已经标得很清楚了,直接保存为html文件用浏览器打开就可以了。
XXXXXul,li{list-style: none;margin:0;padding:0;float:left;}
html{height:100%}
body{height:100%;margin:0px;padding:0px;font-family:"微软雅黑";}
#allmap{height:100%;width:100%;}
// 百度地图API功能
var map = new BMap.Map("allmap");
//设置打开后的中心位置,这里设置的是北京
var point = new BMap.Point(116.512885,39.847469);
//设置打开后的缩放大小,这里设置的大致为中国版图的大小
map.centerAndZoom(point, 5);
// 编写自定义函数,创建标注
function addMarker(point){
var marker = new BMap.Marker(point);
map.addOverlay(marker);
}
//lat是纬度,lng是经度,count是对应的数值大小
//下列的经纬度基本覆盖了中国各个省份及重要城市
var res = [{"count": 61, "lat": 30.5984667364009, "lng":114.311581554732},
{"count": 18, "lat": 30.9306892270182, "lng":113.922510077336},
{"count": 70, "lat": 30.4593588576181, "lng":114.878490484107},
{"count": 2, "lat": 31.6965167723283, "lng":113.389450018221},
{"count": 47, "lat": 30.3408421077429, "lng":112.245522629261},
{"count": 59, "lat": 32.0147968046692, "lng":112.128537201002},
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{"count": 33, "lat": 30.6974464844923, "lng":111.292549210354},
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heatmapOverlay = new BMapLib.HeatmapOverlay({"radius":20});
map.addOverlay(heatmapOverlay);
//调整max的值效果会不一样,但count值一定要在max值之内
heatmapOverlay.setDataSet({data: res,max:100});
-
热力图数据
2019-02-13 18:11:00热力图点数据和示例,arcgis for jsapi 4.10。用于展示热力图效果。数据格式为JSON。 -
leaflet热力图
2019-02-13 15:58:52基于leaflet地图实现类似百度地图里的热力图(谷歌地图里的热图) -
Echart 实现热力图 调用echart里的百度地图 再进行热力图渲染 地图和热力图分开渲染
2020-04-19 21:27:16在应用echart实现热力图时 出现了一个疑问,可以使地图和热力图渲染分开进行么 实现一次地图显示 多次不同数据渲染 就不用每次在option加bmap进行地图显示 bmap: { center: [120.13066322374, 30.240018034923], ...在应用echart实现热力图时 出现了一个疑问,可以使地图和热力图渲染分开进行么 实现一次地图显示 多次不同数据渲染 就不用每次在option加bmap进行地图显示
bmap: {
center: [120.13066322374, 30.240018034923],
zoom: 14,
roam: true
},首先在public/index.html里加上 百度地图api
<script type="text/javascript" src="https://api.map.baidu.com/api?v=3.0&ak=你的key"></script>
定义容器放地图 和热力图渲染
<div ref="map" style="width: 100%;height: 100%"></div>
然后methods里定义函数 创建地图
methods: { //创建百度地图 creatMap(){ const myChart = echarts.init(this.$refs.map); const option = { bmap: { center: [120.13066322374, 30.240018034923], zoom: 8, mapStyleV2: { styleId: '50aa27f5dc9a72aff522ccefb0da0e83' }, roam: true }, } myChart.setOption(option) } }
在mounted里初始化地图
mounted() { this.creatMap() }
效果:
再进行热力图渲染
直接使用echart热力图例子 https://www.echartsjs.com/examples/zh/editor.html?c=heatmap-bmap
//渲染热力图 drawHeat(){ const points = [].concat.apply([], data.map(function (track) { return track.map(function (seg) { return seg.coord.concat([1]); }); })); const myChart = echarts.getInstanceByDom(this.$refs.map) const option = { animation: false, visualMap: { show: false, top: 'top', min: 0, max: 5, seriesIndex: 0, calculable: true, inRange: { color: ['blue', 'blue', 'green', 'yellow', 'red'] } }, series: [{ type: 'heatmap', coordinateSystem: 'bmap', data: points, pointSize: 5, blurSize: 6, gradientColors: [{ offset: 0.4, color: 'green' }, { offset: 0.5, color: 'yellow' }, { offset: 0.8, color: 'orange' }, { offset: 1, color: 'red' }] }] } myChart.setOption(option) }
这里没有加bmap 因为前面creatMap函数已经渲染了地图,里面的data是官方的json数据 我下载到本地同级目录了
json数据地址:https://www.echartsjs.com/examples/data/asset/data/hangzhou-tracks.json
echarts.getInstanceByDom(this.$refs.map)是获取当前echart实例 在该实例上进行热力图操作
添加了点击按钮 点击就渲染热力图 效果如图:
这样 方便渲染其他数据下的热力图 或者刷新
其中如果想对地图进行其他操作 百度地图API里的操作 只需获取到该地图实例就可以了
也可以算是echarts里调用百度地图
//获取当前echart实例 const myChart=echarts.getInstanceByDom(this.$refs.map) // 添加百度地图插件 map为当前地图实例 const map = myChart.getModel().getComponent('bmap').getBMap();
代码结构:
遇到什么问题,可留言,看到会尽快回复。
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2018-01-23 15:36:531.百度热力图 前提:百度地图的热力图目前只支持有canvas支持的浏览器 步骤: 1.建立地图图层 var map=new BMap.map('mapconElement') /*2.设置地图的中心点和缩放(建议设置 默认的是 lng:0,... -
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2018-10-15 16:51:54本代码简单介绍python热力图如何绘制的,仅供大家分享和参考。 -
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2020-12-28 23:31:23热力图是数据可视化项目中,比较常用的显示方式。通过颜色变化程度,他可以直观反应出热点分布,区域聚集等数据信息。屏幕快照 2017-02-10 下午3.45.52.png项目概述我们的项目任务是统计场馆中参观者的实时区域分布... -
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