这个问题可能主要是针对天文学家或多或少的进步。

您知道如何将NVSS拟合文件转换为仅2个(非4！)轴的拟合吗？或者，当我尝试使用astropy和其他“ astro”库为Python尝试在光学DSS数据上重叠nvss countours时，如何处理具有4轴并在Python中产生以下错误的文件？(下面的代码)

我尝试这样做，并且当NVSS FITS有上述4个轴时，会出现错误消息和警告：

WARNING: FITSFixedWarning: The WCS transformation has more axes (4) than the image it is associated with (2) [astropy.wcs.wcs]

WARNING: FITSFixedWarning: 'datfix' made the change 'Invalid parameter value: invalid date '19970331''. [astropy.wcs.wcs]

https://stackoverflow.com/questions/33107224/re-sizing-a-fits-image-in-python

WARNING: FITSFixedWarning: 'datfix' made the change 'Invalid parameter value: invalid date '19970331''. [astropy.wcs.wcs]

Traceback (most recent call last):

File "p.py", line 118, in

cont2 = ax[Header2].contour(opt.data, [-8,-2,2,4], colors="r", linewidth = 10, zorder = 2)

File "/home/ela/anaconda2/lib/python2.7/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py", line 195, in contour

return self._contour("contour", *XYCL, **kwargs)

File "/home/ela/anaconda2/lib/python2.7/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py", line 167, in _contour

ny, nx = C.shape

ValueError: too many values to unpack

我还尝试过使用FITS_tools / match_images.py将NVSS FITS的大小首先调整为DSS文件的正常2轴大小，​​然后使用更正后的文件而不是原始文件，但这只会给我一个错误：

Traceback (most recent call last):

File "p.py", line 64, in

im1,im2 = FITS_tools.match_fits(to_be_projected,reference_fits)

File "/home/ela/anaconda2/lib/python2.7/site-packages/FITS_tools/match_images.py", line 105, in match_fits

image1_projected = project_to_header(fitsfile1, header, **kwargs)

File "/home/ela/anaconda2/lib/python2.7/site-packages/FITS_tools/match_images.py", line 64, in project_to_header

**kwargs)

File "/home/ela/anaconda2/lib/python2.7/site-packages/FITS_tools/hcongrid.py", line 49, in hcongrid

grid1 = get_pixel_mapping(header1, header2)

File "/home/ela/anaconda2/lib/python2.7/site-packages/FITS_tools/hcongrid.py", line 128, in get_pixel_mapping

csys2 = _ctype_to_csys(wcs2.wcs)

File "/home/ela/anaconda2/lib/python2.7/site-packages/FITS_tools/hcongrid.py", line 169, in _ctype_to_csys

raise NotImplementedError("Non-fk4/fk5 equinoxes are not allowed")

NotImplementedError: Non-fk4/fk5 equinoxes are not allowed

我不知道该怎么做。FIRST.FITS文件没有类似的问题。Python中的Imsize还告诉我，NVSS是唯一一个4维的(例如1、1、250、250)。因此，不能将其覆盖在适当的位置。你有什么主意吗？请帮助我，我可以捐赠您的项目。我花了几天时间尝试解决它，但它仍然无法正常工作，但是我迫切需要它。

码

# Import matplotlib modules

import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable

from matplotlib.axes import Axes

import matplotlib.cm as cm

from matplotlib.patches import Ellipse

import linecache

import FITS_tools

# Import numpy and scipy for filtering

import scipy.ndimage as nd

import numpy as np

import pyfits

import matplotlib.pyplot

import pylab

#Import astronomical libraries

from astropy.io import fits

import astropy.units as u

#from astroquery.ned import Ned

import pywcsgrid2

# Read and prepare the data

file1=open('/home/ela/file')

count=len(open('file', 'rU').readlines())

print count

for i in xrange(count):

wiersz=file1.readline()

title=str(wiersz)

print title

title2=title.strip("\n")

print title2

path = '/home/ela/'

fitstitle = path+title2+'_DSS.FITS'

fitstitle2 = path+title2+'_FIRST.FITS'

fitstitle3 = path+title2+'_NVSS.FITS'

datafile = path+title2

outtitle = path+title2+'.png'

print outtitle

print datafile

nvss = fits.open(fitstitle)[0]

first = fits.open(fitstitle2)[0]

opt = fits.open(fitstitle3)[0]

try:

fsock = fits.open(fitstitle3) #(2)

except IOError:

print "Plik nie istnieje"

print "Ta linia zawsze zostanie wypisana" #(3)

opt.verify('fix')

first.verify('fix')

nvss.verify('fix')

Header = nvss.header

Header2 = first.header

Header3 = opt.header

to_be_projected = path+title2+'_NVSS.FITS'

reference_fits = path+title2+'_DSS.FITS'

im1,im2 = FITS_tools.match_fits(to_be_projected,reference_fits)

print(opt.shape)

print(first.shape)

print(nvss.shape)

print(im2.shape)

#We select the range we want to plot

minmax_image = [np.average(nvss.data)-6.*np.std(nvss.data), np.average(nvss.data)+5.*np.std(nvss.data)] #Min and max value for the image

minmax_PM = [-500., 500.]

# PREPARE PYWCSGRID2 AXES AND FIGURE

params = {'text.usetex': True,'font.family': 'serif', 'font.serif': 'Times New Roman'}

plt.rcParams.update(params)

#INITIALIZE FIGURE

fig = plt.figure(1)

ax = pywcsgrid2.subplot(111, header=Header)

#CREATE COLORBAR AXIS

divider = make_axes_locatable(ax)

cax = divider.new_horizontal("5%", pad=0.1, axes_class=Axes)

#fig.add_axes(cax)

#Configure axis

ax.grid() #Will plot a grid in the figure

# ax.set_ticklabel_type("arcmin", center_pixel=[Header['CRPIX1'],Header['CRPIX2']]) #Coordinates centered at the galaxy

ax.set_ticklabel_type("arcmin") #Coordinates centered at the galaxy

ax.set_display_coord_system("fk5")

# ax.add_compass(loc=3) #Add a compass at the bottom left of the image

#Plot the u filter image

i = ax.imshow(nvss.data, origin="lower", interpolation="nearest", cmap=cm.bone_r, vmin= minmax_image[0], vmax = minmax_image[1], zorder = 0)

#Plot contours from the infrared image

cont = ax[Header2].contour(nd.gaussian_filter(first.data,4),2 , colors="r", linewidth = 20, zorder = 2)

# cont = ax[Header2].contour(first.data, [-2,0,2], colors="r", linewidth = 20, zorder = 1)

# cont2 = ax[Header2].contour(opt.data, [-8,-2,2,4], colors="r", linewidth = 10, zorder = 2)

#Plot PN positions with color coded velocities

# Velocities = ax['fk5'].scatter(Close_to_M31_PNs['RA(deg)'], Close_to_M31_PNs['DEC(deg)'], c = Close_to_M31_PNs['Velocity'], s = 30, cmap=cm.RdBu, edgecolor = 'none',

# vmin = minmax_PM[0], vmax = minmax_PM[1], zorder = 2)

f2=open(datafile)

count2=len(open('f2', 'rU').readlines())

print count2

for i in xrange(count):

xx=f2.readline()

# print xx

yy=f2.readline()

xxx=float(xx)

print xxx

yyy=float(yy)

print yyy

Velocities = ax['fk5'].scatter(xxx, yyy ,c=40, s = 200, marker='x', edgecolor = 'red', vmin = minmax_PM[0], vmax = minmax_PM[1], zorder = 1)

it2 = ax.add_inner_title(title2, loc=1)

# Plot the colorbar, with the v_los of the PN

# cbar = plt.colorbar(Velocities, cax=cax)

# cbar.set_label(r'$v_{los}[$m s$^{-1}]$')

# set_label('4444')

plt.show()

plt.savefig(outtitle)

#plt.savefig("image1.png")

解决方案

为了澄清一般用途：这是一个有关如何处理具有退化轴的FITS文件的问题，这些轴通常由CASA数据缩减程序和其他无线电数据缩减工具生成；简并轴是频率/波长和行程。一些天灾的附属工具知道如何处理这些工具(例如aplpy)，但许多工具却不知道。

最简单的答案是只使用squeeze将退化的轴放入数据中。但是，如果您要在执行此操作时保留元数据，则需要做更多的工作：

from astropy.io import fits

from astropy import wcs

fh = fits.open('file.fits')

data = fh[0].data.squeeze() # drops the size-1 axes

header = fh[0].header

mywcs = wcs.WCS(header).celestial

new_header = mywcs.to_header()

new_fh = fits.PrimaryHDU(data=data, header=new_header)

new_fh.writeto('new_file.fits')

该方法将为您提供一个带有有效天文(RA / Dec)标题的文件，但是它将丢失所有其他标题信息。

如果要保留其他标头信息，则可以使用该FITS_tools工具flatten_header代替上面的WCS操作：

new_header = FITS_tools.strip_headers.flatten_header(header)