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  • 密度图绘制

    2013-03-28 20:24:17
    可以绘制节理等密度图,各种岩土工程制图等
  • CNN-人群密度检测-密度图制作-数据集-标注工具等,这里的内容主要是本人在学习过程中在网络上收集所得。也有部分自己的改写。费了半天劲儿整理。绝对物超所值。
  • R中的直方图和核密度图绘制

    万次阅读 2017-08-12 20:05:57
    直方图 核密度图

    1.直方图

    # simple histogram                                                        
    hist(mtcars$mpg) #未指定任何选项时的默认图形

    这里写图片描述

    # colored histogram with specified number of bins        
    hist(mtcars$mpg, 
         breaks=12, #分组个数
         col="red",  #红色
         xlab="Miles Per Gallon", # x轴标签
         main="Colored histogram with 12 bins")# 标题

    这里写图片描述

    # colored histogram with rug plot, frame, and specified number of bins 
    hist(mtcars$mpg, 
         freq=FALSE, 
         breaks=12, 
         col="red", 
         xlab="Miles Per Gallon", 
         main="Histogram, rug plot, density curve")  
    rug(jitter(mtcars$mpg)) 
    lines(density(mtcars$mpg), col="blue", lwd=2)

    这里写图片描述

    # histogram with superimposed normal curve (Thanks to Peter Dalgaard)  
    x <- mtcars$mpg 
    h<-hist(x, 
            breaks=12, 
            col="red", 
            xlab="Miles Per Gallon", 
            main="Histogram with normal curve and box") 
    xfit<-seq(min(x),max(x),length=40) 
    yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) 
    yfit <- yfit*diff(h$mids[1:2])*length(x) 
    lines(xfit, yfit, col="blue", lwd=2)
    box()

    这里写图片描述

    2.核密度图

    d <- density(mtcars$mpg) # returns the density data  
    plot(d) # plots the results 

    这里写图片描述

    d <- density(mtcars$mpg)                                  
    plot(d, main="Kernel Density of Miles Per Gallon")       
    polygon(d, col="red", border="blue")                     
    rug(mtcars$mpg, col="brown") 

    这里写图片描述

    参考:《R in action–Data Analysis and Graphics with R》

    展开全文
  • 1.复现了上边网址的代码,成功出现密度图 链接:https://pan.baidu.com/s/1sqNLNrBsnBC5IdlzgXsl3Q 提取码:aki0 将自己的弄好的路径文件(windows下的放入百度网盘,在jupyternotebook下直接运行即可) ...

    https://blog.csdn.net/sinat_35406909/article/details/83548694

    1.复现了上边网址的代码,成功出现密度图

    链接:https://pan.baidu.com/s/1sqNLNrBsnBC5IdlzgXsl3Q 
    提取码:aki0 
    将自己的弄好的路径文件(windows下的放入百度网盘,在jupyternotebook下直接运行即可)

    2.Linux下路径文件

    路径改为

    Path='/home/***/shanghaitech/part_A_final/tarin_data/ground_truth/GT_IMG_'

    ipath='/home/***/ shanghaitech/part_A_final/tarin_data/images/IMG_1'

    mp=sio.loadmat('/home/***/map.mat')

    3.        mat文件分享

    链接:https://pan.baidu.com/s/1mYUOtWwA2zP_QEE9ys2tdg 
    提取码:78dc 
     

    展开全文
  • DDMaker执行局部密度分析,并从RGB颜色,灰度或二进制图像开始,生成2D图像中成像的细胞结构的空间分布的伪彩色。 DDMaker局部密度分析允许选择性地对信号进行降噪,可视化和量化其分布,并基于局部密度对图像进行...
  • python绘制直方图和密度图的实例更新时间:2019年07月08日 10:29:13 作者:ColiYin今天小编就为大家分享一篇python绘制直方图和密度图的实例,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧对于...

    python绘制直方图和密度图的实例

    更新时间:2019年07月08日 10:29:13 作者:ColiYin

    今天小编就为大家分享一篇python绘制直方图和密度图的实例,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧

    对于pandas的dataframe,绘制直方图方法如下:

    //pdf是pandas的dataframe, delta_time是其中一列

    //xlim是x轴的范围,bins是分桶个数

    pdf.delta_time.plot(kind='hist', xlim=(-50,300), bins=500)

    20190708102759.jpg

    对于pandas的dataframe,绘制概率密度图方法如下:

    //pdf是pandas的dataframe, delta_time是其中一列

    pdf.delta_time.dropna().plot(kind='kde', xlim=(-50,300))

    20190708102808.jpg

    以上这篇python绘制直方图和密度图的实例就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持脚本之家。

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    最新评论

    展开全文
  • 研究生期间写的绘制两幅图像的散点密度图,从蓝色到红色密度逐渐增大,论文绘图简单好用,只需要输入两幅图像即可
  • Matlab绘制散点密度图

    千次阅读 2020-11-01 16:54:24
    使用Matlab绘制散点密度图,代码简单(附代码)。

    关于使用Matlab绘制散点密度图,以前有一些文章,比如博客园文章MATLAB实例:散点密度图,这里面使用了Matlab社区中的scatplot.m文件,代码长度(含注释)为226行。

    下图为博客园MATLAB实例:散点密度图中的输出结果,其所用数据源于博客园文章MATLAB中“fitgmdist”的用法及其GMM聚类算法

    在这里插入图片描述

    在本文中所用数据同样源于MATLAB中“fitgmdist”的用法及其GMM聚类算法,或者参考文中末尾部分,由于数据共有1000条,在示例代码中数据的省略部分用…代替;或者将数据放在Excel中,直接读取Excel文件即可。

    主要的思路为:
    读入数据(此处只取前两列作为X和Y,第三列未考虑) -> 设置搜索半径 -> 记录目标点所在的半径内的点数 -> 使用scatter函数(scatter函数详细说明)进行显示,参考代码如下:

    %%
    clc;
    clear;
    
    %%
    XYZ = [
        -0.752713846442762  2.48140797998545    1
        -0.798625575507672  2.14835001132099    1
        2.82002920206994    1.97084621196340    1
        0.913856576539988   2.24942313999122    1
        1.57243525115195    2.68322568351427    1
        0.241170005783610   1.89791938743627    1
        2.10634746115858    2.20631449410867    1
        1.61173455443266    2.69163655587553    1
        1.39436249281445    1.28104472307183    1
        1.65267727628557    1.85771163664832    1
        ...
        0.580602656916380   -1.38403756935533   2
        -1.27308082491469   -2.20514718975902   2
        -2.92790668922674   -1.98439888443983   2
        -1.62181912776895   -0.851506362212275  2
        ];
    
    datamin = min(min(XYZ(:, 1)), min(XYZ(:, 2)));
    datamax = max(max(XYZ(:, 1)), max(XYZ(:, 2)));
    datamin = floor(datamin);
    datamax = ceil(datamax);
    
    Length = 600;
    Width = 600;
    
    axismin = datamin;
    axismax = datamax;
    
    % 绘制密度图
    XYZ(:, 3) = XYZ(:, 3) - XYZ(:, 3);
    sizeXYZ = size(XYZ);
    searchR = 1.0;
    for i = 1 : sizeXYZ(1)
        index_i = find(XYZ(:, 1) > XYZ(i, 1) - searchR & XYZ(:, 1) < XYZ(i, 1) + searchR ...
            & XYZ(:, 2) > XYZ(i, 2) - searchR & XYZ(:, 2) < XYZ(i, 2) + searchR);
        sizeIndexI = size(index_i);
        XYZ(i, 3) = sizeIndexI(1);
    end
    [sortXYZ, sortI] = sort(XYZ(:, 3));
    
    scatter(XYZ(sortI, 1), XYZ(sortI, 2), [], XYZ(sortI, 3), 'filled');
    hold on;
    axis([axismin axismax axismin axismax]);
    gc = get(gca);
    set(gcf, 'position', [300, 100, Length, Width]);
    

    此处的搜索为矩形搜索,大家可以根据自己的需求自行修改,绘制的结果如下:

    在这里插入图片描述
    参考的数据如下:

    -0.752713846442762  2.48140797998545    1
    -0.798625575507672  2.14835001132099    1
    2.82002920206994    1.97084621196340    1
    0.913856576539988   2.24942313999122    1
    1.57243525115195    2.68322568351427    1
    0.241170005783610   1.89791938743627    1
    2.10634746115858    2.20631449410867    1
    1.61173455443266    2.69163655587553    1
    1.39436249281445    1.28104472307183    1
    1.65267727628557    1.85771163664832    1
    0.0927741368750946  2.00698799954306    1
    2.79887910552062    1.70868183551872    1
    0.652907219449091   1.88702134773695    1
    3.43327629572431    2.17571612839302    1
    1.96527202098605    2.34069549818768    1
    0.867813825335363   1.68534433959204    1
    2.08252259376894    1.02114736190308    1
    0.613863159235077   2.17081564512242    1
    0.399552396654452   2.11342763957560    1
    2.91186118166440    1.82639334702902    1
    0.377524852838774   3.08573945644000    1
    1.98881806018168    1.98460069084178    1
    2.68223790603780    1.96134300697451    1
    -0.622058850926772  3.30007822155342    1
    0.275231442237888   1.59279139683080    1
    3.34643957013717    1.68055674830698    1
    1.03601109129843    2.70541249651070    1
    0.752332013138056   2.49601620599903    1
    0.508824726005269   2.57436514443626    1
    1.73027562632102    2.19502286037081    1
    -0.713230300478002  3.73262960861479    1
    0.00814731258125789 1.52233440802986    1
    4.86084534117603    2.64339300870922    1
    2.82851799598685    1.79094004967764    1
    0.725784406602703   1.32609248276320    1
    0.147485590657029   1.49033710171378    1
    0.601129894478657   3.58795933263211    1
    1.32767044246750    1.73683867424831    1
    1.27723837775742    2.62061783710240    1
    0.240726647338080   1.43127112737987    1
    0.387110343064517   2.39576239976627    1
    -0.735171242460893  2.21884994425795    1
    0.821208540380224   2.28230651108960    1
    2.84910757299470    2.40958045875721    1
    -0.00305938218238255    1.36758622169689    1
    0.664256129846708   1.22550082896155    1
    -0.178114240634728  2.30408704591301    1
    2.00786623874121    2.03129722636611    1
    1.20272622506596    2.22475373629028    1
    2.50250207292945    2.98770457384530    1
    -0.874927698714206  1.71654280942273    1
    2.39750117925196    1.84040401876970    1
    0.369966892118764   2.74170598529279    1
    2.15279953669453    1.58546917422906    1
    0.591444584760838   2.29854661364821    1
    2.57828408797099    2.37460697014727    1
    0.816618335677021   1.11574196519408    1
    1.86043373148875    2.20777177264008    1
    0.100606202686330   3.21045350847405    1
    2.53445397100630    0.854302259627375   1
    1.39168420124415    1.49609940831950    1
    1.93929865340817    1.15181790512326    1
    -0.727792104505467  1.79285231965211    1
    0.646252982052485   1.03986431564848    1
    1.19673169055003    1.93448155511090    1
    1.12984878643876    2.22883744483371    1
    4.02666426647567    1.86649614448613    1
    1.27117265547696    2.03774514826000    1
    1.00533765244964    1.17061188123361    1
    -0.414741919400961  2.28828102447677    1
    0.495317556413179   1.83288455064957    1
    2.76823223857300    2.97934893222715    1
    1.08058855707478    2.26487370266768    1
    1.38546325331279    1.40278788558597    1
    1.52167644900484    2.69183023331115    1
    1.67505702182493    2.85222287490608    1
    4.10199034200796    2.18231282334728    1
    2.40054022033848    2.07878116194682    1
    1.49836399169312    1.92754270979332    1
    1.52768214037169    0.604144369658773   1
    1.48106369736206    1.89716183115311    1
    1.88582351601666    2.33794971444330    1
    2.26705524676369    1.71381451846972    1
    1.60866449353269    1.49135394247519    1
    -0.115055715038331  1.12849347288791    1
    3.57622395160265    1.10664344270162    1
    2.26630255633806    1.22083515972890    1
    1.33163191466841    1.44619768873473    1
    -1.45842213709207   2.22949005602252    1
    1.70411565663116    3.47971193248190    1
    1.48213175474953    2.96603725956335    1
    1.72101076041395    3.01672973472638    1
    -1.35146166439553   2.85558221164039    1
    0.380649241141038   2.56729542762187    1
    1.88667156332277    2.85630884112937    1
    1.97758943297860    1.70558017430484    1
    3.24821546740318    1.82804963183289    1
    0.843823793198429   2.64758378735128    1
    0.0405183480549609  1.71670699774521    1
    0.976813370248264   2.49595625636074    1
    -0.962694637418230  2.58074576675867    1
    1.82963248497625    1.90436660429995    1
    -0.0311001170696965 2.55990131230256    1
    1.78671558216327    1.30722487774421    1
    -0.0804210798437657 1.50783793009072    1
    0.592128283430208   0.755659320204709   1
    1.95365332837982    2.16200348956491    1
    1.36682968081700    1.73744055959892    1
    0.979475411390431   2.42632494823554    1
    1.39902046293800    1.73826197272758    1
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    2.18596583418406    1.73505362502330    1
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    -0.209010836036135  1.61369208932597    1
    0.320019483215927   1.82234886961005    1
    2.22606598804851    2.92553777460368    1
    -1.34847806650935   1.73833614701202    1
    -1.10055386975634   1.73836801433344    1
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    0.417690253375689   2.64425612559673    1
    2.21840601718359    1.86213274353155    1
    -1.80189093986379   1.88663190741782    1
    2.18214937211589    2.81206394431873    1
    0.0239657716321194  2.37810261200744    1
    1.48366333882258    1.88062228076692    1
    -1.50485783728876   1.75467615653968    1
    -0.156597929943039  2.92699154393160    1
    1.00641966523359    1.82300801832677    1
    0.229462398563604   0.218216163076399   1
    3.08788315084484    2.44977421952063    1
    -2.75317240661940   1.47607778387816    1
    1.57689796637766    1.14148728120332    1
    0.289637818858192   1.37410983329226    1
    1.80546824398122    1.11123227831127    1
    2.05342064550405    2.59331992761210    1
    -0.908793900499392  1.78719275353105    1
    0.366987870101896   0.201547399427021   1
    0.449481220306850   3.37425663694842    1
    1.77118120808653    1.75664778049838    1
    -0.418491981240371  1.67286506452260    1
    3.52677560506185    2.36946900535883    1
    1.02163690286654    2.47962254560261    1
    1.41689244102377    2.38217492483121    1
    1.13691599064491    0.957013586442155   1
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    0.416289186862122   1.39483608724636    1
    -0.0282160104049050 2.45786458707630    1
    0.0521611900408511  2.82924447860124    1
    1.24447082399573    3.12144145109146    1
    2.55665751225502    2.59582682074224    1
    -1.72738716416235   2.87567939582205    1
    0.818034818369206   2.08879327658097    1
    0.665710432632597   3.02252628314314    1
    -0.830517107648893  1.19084685791838    1
    -0.738848145854706  1.25759468066556    1
    0.884558977595650   2.40820510111672    1
    0.588696730035120   0.647436434761032   1
    0.458454700841834   2.31765414523682    1
    1.64997736187883    3.05662030735942    1
    1.39057520552050    1.64617201786334    1
    0.694081545245352   2.15199590331699    1
    0.0806501948918665  1.80117182589078    1
    -0.184406277418025  1.67502632215727    1
    0.775990550072731   2.43159523244796    1
    0.442197809595544   2.42563275519744    1
    0.408665021617116   1.66059134779610    1
    0.587376671955379   1.86069409237464    1
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    展开全文
  • 原标题:核密度图绘制我们先看一下别人家文章中的核密度图。 那么,怎么做出这样的图呢?这种核密度图到底想表达或表现什么呢?核密度图绘制所谓“眼过千遍不如手过一遍”,数据我们这里用Wikipedia文中的这6个...
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  • Python数据可视化之密度图绘制

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  • python绘制密度图

    2021-06-16 11:26:56
  • matplotlib绘制简单密度图 import matplotlib.pyplot as plt import pandas as pd import numpy as np import matplotlib.mlab as mlab # 加载数据 data = pd.read_csv("birth-rate.csv") # 删除不需要的空数据 data...
  • python绘制直方图和密度图

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  • 绘制 直方图和核密度图

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  • GWAS分析之绘制 SNP 密度图

    千次阅读 2020-01-17 09:46:02
    绘制 SNP 密度图 一般使用 CMplot 绘制SNP密度图: 包链接:https://github.com/YinLiLin/R-CMplot 要绘制 SNP 密度图,仅仅需要三列即可: a. 第一列是 SNP 名称 b. 第二列是染色体 c. 第三列是 SNP 的位置 d. 第四...
  • R语言绘制二维密度图

    2021-05-10 09:59:55
    R语言绘制二维密度图 二维密度图显示了两个数值变量之间的关系,一个在x轴上表示,另一个在Y轴上表示,与散点图类似,然后计算二维空间中特定区域内的观测数,并用颜色梯度表示。二维密度图有几种类型,以下主要展示...
  • matplotlib绘制简单密度图与直方图结合 import matplotlib.pyplot as plt import pandas as pd import numpy as np import matplotlib.mlab as mlab # 加载数据 data = pd.read_csv("birth-rate.csv") # 删除空数据 ...

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