getting a dark image using make_lupton_rgb

Question

I am using astropy visualization to make a colored image of M66 in this case. Before doing anything I learnt that I have to cast my RGB .fts array with numpy.float_()

forCasting = np.float_()

### READING
b = fits.open("data/"+"M66-Blue.fts")[0].data
r = fits.open("data/"+"M66-Red.fts")[0].data 
g = fits.open("data/"+"M66-Green.fts")[0].data

### CASTING
r = np.array(r,forCasting)
g = np.array(g,forCasting)
b = np.array(b,forCasting)

so that I could proceed with my stretch like :

stretch = SqrtStretch() + ZScaleInterval()

r = stretch(b)
g = stretch(r)
b = stretch(g)

plt.imshow(r, origin='lower')
plt.show()
plt.imshow(g, origin='lower')
plt.show()
plt.imshow(b, origin='lower')
plt.show()

Then I just use the method make_lupton_rgb from astropy.visualizaion as follow, but I have a super dark image that I cannot distinguish anything. Does anybody know why I have a dark final image here? Do you have any suggestions?

### SAVING
# rgb_default = make_lupton_rgb(r, g, b, minimum=1000, stretch=900, Q=100, filename="provafinale.png")
rgb_default = make_lupton_rgb(r, g, b, filename="provafinale.png")
plt.imshow(rgb_default, origin='lower')
plt.show()

Thanks!

user_4926757 · Answer

It looks like you have to set stretch and Q arguments of make_lupton_rgb.
The default values are stretch=5 and Q=8, that gives dark result.

I have no experience with astropy or with Astronomy.
I just played with the arguments, and got bright image using stretch=1 and Q=0.

rgb_default = make_lupton_rgb(r, g, b, minimum=0, stretch=1, Q=0, filename="provafinale.png")

I tried computing minimum and stretch using np.percentile, for linear stretching the output.

I tested the code using an m8_050507_9i9m image from index_fits.

Here is the code I used for testing:

import numpy as np
from astropy.io import fits
from astropy.visualization import SqrtStretch
from astropy.visualization import ZScaleInterval
from astropy.visualization import make_lupton_rgb
from matplotlib import pyplot as plt

forCasting = np.float_()

### READING
# http://www.mistisoftware.com/astronomy/index_fits.htm
r = fits.open("m8_050507_9i9m_R.FIT")[0].data 
g = fits.open("m8_050507_9i9m_G.FIT")[0].data
b = fits.open("m8_050507_9i9m_B.FIT")[0].data

# Crop the top and the right margin (contains black pixels)
r = r[:, :-200]
g = g[:, :-200]
b = b[:, :-200]

### CASTING
r = np.array(r,forCasting)
g = np.array(g,forCasting)
b = np.array(b,forCasting)

stretch = SqrtStretch() + ZScaleInterval()

r = stretch(b)
g = stretch(r)
b = stretch(g)

plt.imshow(r, origin='lower')
plt.imshow(g, origin='lower')
plt.imshow(b, origin='lower')

### SAVING
# https://docs.astropy.org/en/stable/api/astropy.visualization.make_lupton_rgb.html
# astropy.visualization.make_lupton_rgb(image_r, image_g, image_b, minimum=0, stretch=5, Q=8, fil/ename=None)[source]
# Return a Red/Green/Blue color image from up to 3 images using an asinh stretch.
# The input images can be int or float, and in any range or bit-depth.

lo_val, up_val = np.percentile(np.hstack((r.flatten(), g.flatten(), b.flatten())), (0.5, 99.5))  # Get the value of lower and upper 0.5% of all pixels

stretch_val = up_val - lo_val

rgb_default = make_lupton_rgb(r, g, b, minimum=lo_val, stretch=stretch_val, Q=0, filename="provafinale.png")

# Cut the top rows - contains black pixels
rgb_default = rgb_default[100:, :, :]

plt.imshow(rgb_default, origin='lower')
plt.show()

Result:

getting a dark image using make_lupton_rgb

1 Answers