符合图像的径向轮廓

Question

我一直在尝试用我在网上找到的修改过的脚本绘制一个fits图像的径向轮廓。我总是得到与预期完全不同的y轴单位。我甚至不知道y轴的单位是什么。我已经附加了配合文件和一个轮廓，我不断得到和正确的径向轮廓，我绘制了另一个程序。在

我对python很陌生，所以我不知道为什么会这样。如果有任何帮助，我们将不胜感激。在

这是我一直使用的代码：

import numpy as np
import pyfits
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def azimuthalAverage(image, center=None):
    """
    Calculate the azimuthally averaged radial profile.

    image - The 2D image
    center - The [x,y] pixel coordinates used as the center. The default is 
             None, which then uses the center of the image (including 
             fracitonal pixels).

    """
    # Calculate the indices from the image
    y, x = np.indices(image.shape)


    if not center:
        center = np.array([(x.max()-x.min())/2.0, (y.max()-y.min())/2.0])

    r = np.hypot(x - center[0], y - center[1])

    # Get sorted radii
    ind = np.argsort(r.flat)
    r_sorted = r.flat[ind]
    i_sorted = image.flat[ind]

    # Get the integer part of the radii (bin size = 1)
    r_int = r_sorted.astype(int)

    # Find all pixels that fall within each radial bin.
    deltar = r_int[1:] - r_int[:-1]  # Assumes all radii represented
    rind = np.where(deltar)[1]       # location of changed radius
    nr = rind[1:] - rind[:-1]        # number of radius bin

    # Cumulative sum to figure out sums for each radius bin
    csim = np.cumsum(i_sorted, dtype=float)
    tbin = csim[rind[1:]] - csim[rind[:-1]]

    radial_prof = tbin / nr
    print center
    print i_sorted
    print radial_prof
    return radial_prof

#read in image
hdulist = pyfits.open('cit6ndf2fitsexample.fits')
scidata = np.array(hdulist[0].data)[0,:,:]
center = None
radi = 10
rad = azimuthalAverage(scidata, center)

plt.xlabel('radius(pixels?)', fontsize=12)
plt.ylabel('image intensity', fontsize=12)
plt.xlim(0,10)
plt.ylim(0, 3.2)
plt.plot(rad[radi:])
plt.savefig('testfig1.png')
plt.show()

y轴单位错误的外形

使用Celtech光圈测光工具创建的具有预期正确单位的外形。在

Answer 1

from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator

minorLocator = AutoMinorLocator()


def radial_profile(data, center):
    x, y = np.indices((data.shape))
    r = np.sqrt((x - center[0])**2 + (y - center[1])**2)
    r = r.astype(np.int)

    tbin = np.bincount(r.ravel(), data.ravel())
    nr = np.bincount(r.ravel())
    radialprofile = tbin / nr
    return radialprofile 


fitsFile = fits.open('testfig.fits')
img = fitsFile[0].data[0]
img[np.isnan(img)] = 0

#center = np.unravel_index(img.argmax(), img.shape)
center = (-fitsFile[0].header['LBOUND2']+1, -fitsFile[0].header['LBOUND1']+1)
rad_profile = radial_profile(img, center)

fig, ax = plt.subplots()
plt.plot(rad_profile[0:22], 'x-')

ax.xaxis.set_minor_locator(minorLocator)

plt.tick_params(which='both', width=2)
plt.tick_params(which='major', length=7)
plt.tick_params(which='minor', length=4, color='r')
plt.grid()
ax.set_ylabel(fitsFile[0].header['Label'] + " (" + fitsFile[0].header['BUNIT'] + ")")
ax.set_xlabel("Pixels")
plt.grid(which="minor")
plt.show()

编辑：

我添加了一个注释行，用于从标题中检索中心。但是在选择使用argmax或头信息来查找中心之前，必须测试更多的fits文件。在

标题信息的第一部分：

^{pr2}$