将hexbin图一起添加

Question

我有四个hexbin图，它们都被标准化了。我如何将它们添加到一起以形成一个大发行版？ 我试图串联输入向量，然后创建hexbin情节，但这抛出了各自的分布正常化： 那么，如何添加个人hexbin分布，同时仍maintainging的induvidual正常化？

我的代码的相关部分是：

def hex_plot(x,y,max_v): 
    bounds = [0,max_v*m.exp(-(3**2)/2),max_v*m.exp(-2),max_v*m.exp(-0.5),max_v] # The sigma bounds 
    norm = mpl.colors.BoundaryNorm(bounds, ncolors=4) 
    hex_ = plt.hexbin(x, y, C=None, gridsize=gridsize,reduce_C_function=np.mean,cmap=cmap,mincnt=1,norm=norm) 
    print "Hex plot max: ",hex_.norm.vmax 
    return hex_ 

gridsize=50 
cmap = mpl.colors.ListedColormap(['grey','#6A92D4','#1049A9','#052C6E']) 

hex_plot(x_tot,y_tot,34840) 

谢谢。

Answer 1

我已经写了一些代码，可以完成你以后的工作。从你问题中的片段看来，你已经知道你的分配方案的分布高度（max_v），所以我在这个假设下工作。根据您应用的数据，实际情况可能并非如此，在这种情况下，以下情况将会失败（这与您对分布高度的猜测/知识一样好）。就我的示例数据而言，我刚刚对max_v1和max_v2的值进行了合理的猜测（基于快速绘图）。切换c1和c2我为注释版本定义应该会重现您的原始问题。

import scipy 
import matplotlib.pyplot as pyplot 
import matplotlib.colors 
import math 

#need to know the height of the distributions a priori 
max_v1 = 850 #approximate height of distribution 1 (defined below) with binning defined below 
max_v2 = 400 #approximate height of distribution 2 (defined below) with binning defined below 
max_v = max(max_v1,max_v2) 

#make 2 differently sized datasets (so will require different normalizations) 
#all normal distributions with assorted means/variances 
x1 = scipy.randn(50000)/6.0+0.5 
y1 = scipy.randn(50000)/3.0+0.5 
x2 = scipy.randn(100000)/2.0-0.5 
y2 = scipy.randn(100000)/2.0-0.5 
#c1 = scipy.ones(len(x1)) #I don't assign meaningful weights here 
#c2 = scipy.ones(len(x2)) #I don't assign meaningful weights here 
c1 = scipy.ones(len(x1))*(max_v/max_v1) #highest distribution: no net change in normalization here 
c2 = scipy.ones(len(x2))*(max_v/max_v2) #renormalized to same height as highest distribution 

#define plot boundaries 
xmin=-2.0 
xmax=2.0 
ymin=-2.0 
ymax=2.0 

#custom colormap 
cmap = matplotlib.colors.ListedColormap(['grey','#6A92D4','#1049A9','#052C6E']) 

#the bounds of 1sigma, 2sigma, etc. regions 
bounds = [0,max_v*math.exp(-(3**2)/2),max_v*math.exp(-2),max_v*math.exp(-0.5),max_v] 
norm = matplotlib.colors.BoundaryNorm(bounds, ncolors=4) 

#make the hexbin plot 
normalized = pyplot 
hexplot = normalized.subplot(111) 
normalized.hexbin(scipy.concatenate((x1,x2)), scipy.concatenate((y1,y2)), C=scipy.concatenate((c1,c2)), cmap=cmap, mincnt=1, extent=(xmin,xmax,ymin,ymax),gridsize=50, reduce_C_function=scipy.sum, norm=norm) #combine distributions and weights 
hexplot.axis([xmin,xmax,ymin,ymax]) 
cax = pyplot.axes([0.86, 0.1, 0.03, 0.85]) 
clims = cax.axis() 
cb = normalized.colorbar(cax=cax) 
cax.set_yticklabels([' ','3','2','1',' ']) 
normalized.subplots_adjust(wspace=0, hspace=0, bottom=0.1, right=0.78, top=0.95, left=0.12) 

normalized.show() 

这里的result不包含该修补（评论c1和c2使用），并且result带有fix（代码原样）; （似乎我的低代表阻止我发布实际图片）。

希望有所帮助。