==== Quick Image Check ====
An easy way to check the quality of taken images during the observation is an indispensable tool. In this page, I will show a quick way of checking the images for (1) saturation check and (2) aperture photometry. 

=== Using Ginga ===
Install Ginga: (1) conda update --all  (2) pip install ginga


=== Standalone Python script ===
{{tag>radprofile}}
  * install Python 3.12.x
  * install astropy -> pip install astropy
  * install photutils via pip
  * install scikit-image via pip
  * ipython

Use the following python script to quickly check a radial profile of a source in a JPEG image.
<grid>
<col md="6">
<code python>
from scipy.optimize import leastsq
from skimage import io
from astropy.nddata import Cutout2D
from photutils.centroids import centroid_com
import numpy as np
import matplotlib.pyplot as plt

# calculate radial distances from a given point (x0,y0)
def radial_dist ( x_size, y_size, x_center, y_center):
    x = np.arange(x_size) - x_center
    y1 = np.ones(y_size)
    dx = np.outer(y1,x)**2

    y = np.arange(y_size) - y_center
    x1 = np.ones(x_size)
    dy = np.outer(y,x1)**2
    d = np.sqrt(dx + dy)

    return d

# For a given image data and source position (xc,yc),
# create a radial profile plot with an inset of the source
def radplot(im,xc0,yc0,boxsize=30,fwhm=4.0):
    # make a cutout of boxsize x boxsize
    imcut = Cutout2D(im,[xc0,yc0],boxsize)
    # let's do a more precise source position estimation
    xc,yc = centroid_com(imcut.data)
    y_size,x_size = imcut.data.shape
    d2 = radial_dist(x_size, y_size, xc, yc)

    iarr = imcut.data.reshape(-1)
    darr  = d2.reshape(-1)

    dfull = np.concatenate( (-darr[::-1],darr) )
    ifull = np.concatenate( ( iarr[::-1],iarr) )

    # Fit with a gaussian
    fitfunc = lambda p, x: p[0]*np.exp( -(x-p[1])**2/(2.0*p[2]**2)) + p[3]
    errfunc = lambda p, x, y: fitfunc(p,x) - y

    pinit = [ifull.max(),0.0,fwhm/2.3548,np.median(iarr)]
    pfinal, success = leastsq(errfunc,pinit,args=(dfull,ifull))
    fit = fitfunc(pfinal,darr)

    return darr,iarr,fit,pfinal,imcut

if __name__ == "__main__":
   filename = 'img0037.jpg'
   img = io.imread(filename)
   im0 = img[:,:,0]  # take only the first color channel as a test
   yc, xc  = 277, 3400   # approx position of the source in the main image
   darr,iarr,fit,pfinal,imcut = radplot(im0, xc, yc)
   # Make a plot
   fig, ax = plt.subplots()
   ax.plot(darr,iarr,'r.',ms=1.0)
   ax.plot(darr,fit,'k:')
   ax.set_title(f'Source at {xc,yc} in {filename}')
   ax.set_xlabel("Distance from (%.1f,%.1f) in pixels" % (xc,yc))
   ax.set_ylabel("Data Number")
   ax.axvline(pfinal[2]*2.3548*0.5)
   ax.text(0.25,0.5,f'HWHM = {pfinal[2]*2.3548*0.5:.2f} pixels',transform=ax.transAxes)
   ax_inset = ax.inset_axes([0.6,0.6,0.35,0.35])
   ax_inset.plot(xc,yc,'r+',ms=15.0)
   ax_inset.imshow(imcut.data,aspect='equal', origin='lower')
   ax_inset.get_xaxis().set_visible(False)
   ax_inset.get_yaxis().set_visible(False)
   plt.show()
</code>
</col>

<col md="6">
{{proposals:megagrant:projects:low-cost-telescope:radprofile.png?nolink&600|}}
</col>
</grid>

=== I/O and view DNG file ===
DNG file is a RAW file known as "Digital NeGative" file. Using the following information, one can read a DNG file in Python.

<code python>
# Install relevant packages
# `pip install process_raw`
#
# get a sample RAW file from https://github.com/yl-data/yl-data.github.io/raw/master/2201.process_raw/raw-12bit-GBRG.dng
from process_raw import DngFile
dng = DngFile.read('raw-12bit-GBRG.dng')
rgb = dng.postprocess()
rgb.shape
Out[6]: (3036, 4024, 3)
</code>