我试图对齐抖动图像，但我的代码一直给我以下错误:
AttributeError:"numpy。Float64 '对象没有属性'mask'
代码如下:
import numpy as np
from astropy.io import fits
import glob
from astropy.stats import sigma_clip
from reproject.mosaicking import find_optimal_celestial_wcs
from reproject import reproject_interp
# In the line below, you may have called "mydata" something else in Assignment 1
calibrated_dir = '../mydata/M52_calibrated/'
# Use glob to create file list that includes images from *all* bands
filelist = glob.glob(calibrated_dir, '*.fit')
# Find the optimal shape of the canvas that will fit all the images, as well as the optimal wcs coordinates
wcs_out, shape_out = find_optimal_celestial_wcs(filelist,auto_rotate=True)
print('Dimensions of new canvas:',shape_out) # Should be bigger than the original 2048x2048 images we started with.
bands = ['PhotB','PhotV','PhotR'] # This is the list of the three filter names
for band in bands: # Loop through the three bands
# Get the list of all the files that were exposed in the current band
filelist = glob.glob(calibrated_dir+'*'+band+'*')
filelist = sorted(filelist)
allexposures = [] # Declare an empty list. Each item of the list will hold the data array of each file in filelist.
airmass = [] # Declare an empty list. Will hold the airmass of each file in filelist.
texp = [] # Declare an empty list. Will hold the exposure times.
for f in filelist:
hdu = fits.open(f) #open the current file
texp.append(hdu[0].header['EXPTIME']) # get the exposure time
airmass.append(hdu[0].header['AIRMASS'])# get the air mass
# This line runs reproject_interp to map the pixels of the image to the pixels of the canvas we created above
# new_image_data below has the same dimensions as the larger canvas.
new_image_data = reproject_interp(f, wcs_out,shape_out=shape_out,return_footprint=False)
allexposures.append(new_image_data)
# Turn the list of arrays into a 3D array
allexposures = np.array(allexposures)
# We have now aligned all the exposures onto the same pixels. Combine them into a single image using sigma_clip and taking the mean.
images_masked = sigma_clip(allexposures, sigma=3.0) # Use sigma_clip to mask pixels more than 3 sigma from the mean of the exposures
combined_image = np.ma.mean(images_masked)
# np.ma.mean() sets pixels to 0 if there were no good pixels to take a mean. The following lines set them to NaN instead.
# NaN means "not a number" - easier to mask later on.
mask = combined_image.mask
combined_image = combined_image.data
combined_image[mask] = np.nan
...
这个错误来自于一行mask = images_combined.mask。我试过combined_image = np.ma.mean(images_masked.astype(float64))和其他类似的变体，但我无法摆脱错误。我在编程方面是新手，这是为了一门课，所以请原谅我真的不擅长这个。如有任何帮助，不胜感激。
我没有使用astropy，但显然使用sigma_clip会产生一个掩码数组。为了说明
In [18]: arr = np.ma.masked_equal([[1,2,3],[2,2,1]],2)
In [19]: arr
Out[19]:
masked_array(
data=[[1, --, 3],
[--, --, 1]],
mask=[[False, True, False],
[ True, True, False]],
fill_value=2)
对所有值取一个简单的平均值，得到一个数字:
In [20]: np.ma.mean(arr)
Out[20]: 1.6666666666666667
但是其中一个轴上的平均值产生另一个被屏蔽数组(被屏蔽时，行或列中的所有元素也被屏蔽)。
In [21]: np.ma.mean(arr, axis=0)
Out[21]:
masked_array(data=[1.0, --, 2.0],
mask=[False, True, False],
fill_value=1e+20)
In [22]: np.ma.mean(arr, axis=1)
Out[22]:
masked_array(data=[2.0, 1.0],
mask=[False, False],
fill_value=1e+20)
您尝试的替换工作:
In [36]: x=np.ma.mean(arr, axis=0)
In [37]: x
Out[37]:
masked_array(data=[1.0, --, 2.0],
mask=[False, True, False],
fill_value=1e+20)
In [38]: y = x.data
In [39]: y[x.mask] = np.nan
In [40]: y
Out[40]: array([ 1., nan, 2.])
更直接的方法:
In [47]: x=np.ma.mean(arr, axis=0)
In [48]: x.filled(np.nan)
Out[48]: array([ 1., nan, 2.])
allexposures是一个包含所有图像的3D数组，形状为(nbands, nx, ny)，因此要组合您想要沿第一个轴进行sigma裁剪的图像，并沿第一个轴取平均值:
images_masked = sigma_clip(allexposures, sigma=3.0, axis=0)
combined_image = np.ma.mean(images_masked, axis=0)
images_masked仍然是一个3D遮罩数组，其中被剪切的元素是遮罩的，combined_image将是一个2D遮罩数组。