matplotlib：如何将XYZ散点转换为像素图像？ [英] matplotlib: how can I convert a XYZ scatter to a pixel image?

问题描述

我正在寻找一种方法来将散点图（X与Y，由Z归一化的颜色）转换为2D像素图像。也就是说如何绘制像素化图像，其中像素根据第三个变量着色？

I'm looking for some way in to convert a scatter plot (X vs Y, color normalized by Z) into a 2D "pixel" image. I.e. how can I plot a pixelized image where the pixels are colored according to a third variable?

在我的例子中，我有一个星系的列表，每个星系都有天空坐标（X，Y）和距离（Z）。我想制作X对Y的像素化图像，像素颜色根据Z归一化（例如，该像素中的星系的中值Z值）。

In my case, I have a list of galaxies, each a with sky coordinate (X,Y) and a distance (Z). I want to make a pixelized image of X vs Y, with the pixels color normalized according to Z (e.g. the median Z value for the galaxies in that pixel).

I知道我可以做这样的事情与hexbin，但我想有方形像素，而不是六边形。 （更像imshow产生的东西）。

I know I could do something like this with hexbin, but I would like to have square pixels, not hexagons. (Something more like what imshow produces).

我还在学习python，所以如果有一个简单/快速的方法来做这个

I'm still learning python, so if there is a simple/quick way to do this (or clear instructions on how to do it the complicated way!) that'd be great.

任何帮助都会非常感激。

Any help would be much appreciated!

推荐答案

好的 - 有两种方法可以做到这一点。一个是你有一个离散的数量的仓位（如d <10pc，10pc 20pc）。这是比较容易，所有你需要做的是几个循环 - 这里是一个例子与3：

Okay - there are two ways that you can do this. One would be for you to have a discreet number of bins for the distances (like d < 10pc, 10pc < d < 20pc, d> 20pc). This is relatively easy, all you need to do are a few loops - here is an example with 3:

raclose = []
ramid = []
rafar = []
decdlose = []
decmid = []
decfar = []

for ii in range(len(dist)):
  if dist[ii] < 10.:
    raclose.append(ra[ii])
    decclose.append(dec[ii])
  elif dist[ii] > 20.:
    rafar.append(ra[ii])
    decfar.append(dec[ii])
  else:
    ramid.append(ra[ii])
    decmid.append(dec[ii])

plt.clf
ax1 = scatter(raclose, decclose, marker='o', s=20, color="darkgreen", alpha=0.6)
ax2 = scatter(ramid, decmid, marker='o', s=20, color="goldenrod", alpha=0.6)
ax3 = scatter(rafar, decfar, marker='o', s=20, color="firebrick", alpha=0.6)
line1 = Line2D(range(10), range(10), marker='o', color="darkgreen")
line2 = Line2D(range(10), range(10), marker='o',color="goldenrod")
line3 = Line2D(range(10), range(10), marker='o',color="firebrick")
plt.legend((line1,line2,line3),('d < 10pc','20pc > d > 10pc', 'd > 20pc'),numpoints=1, loc=3)
show()

绘图，使您在x轴上规定RA，在y轴上规定Dec，并用距离填充绘图。 RA和Dec都是具有相应坐标的1D数组。然后你做一个二维数组与距离。确定距离的中值/平均值，然后将二维数组除以该值，以将其归一化。最后，使用等高线图（使用contourf或imshow）绘图，如：

Or you can do a contour plot, such that you stipulate RA on the x-axis and Dec on the y-axis and fill in the plot with the distances. Both RA and Dec are 1D arrays with the respective coordinates. Then you make a 2D array with the distance. Determine what the median/mean value of the distances are and then divide the 2D array by that value to normalize it. Finally, plot using a contour plot (using contourf or imshow), like:

import matplotlib.pyplot as plt
from matplotlib import cm
ax = pylab.contourf(RA,Dec,dists, levels=[1, 5, 10, 15], cmap=plt.cm.spectral)
cbar=pylab.colorbar()

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