{ "cells": [ { "cell_type": "markdown", "id": "b35870ee", "metadata": {}, "source": [ "
George J. Bendo
\n",
"02 January 2022
This script converts a FITS image into greyscale and false colour png images. It is intended to be a demonstration of how professional astronomy images in general can be colourized.
\n", "\n", "The example used below is based on the mid-infrared (24 micron) image of M81 from http://ned.ipac.caltech.edu/uri/NED::Image/fits/2012MNRAS.423..197B/NGC_3031:I:MIPS24:bgm2012. This file should be downloaded to the same directory as the Jupyter Notebook before running the script.
\n", " \n", "The script will also work with other FITS files, although some of the lines would need to be modified. The locations of these lines are indicated in the comments within the code below using the words MODIFICATION OPTION.
\n", "\n", "The following packages need to be installed to use this script: astropy, matplotlib, numpy, and PIL. The matplotlib and numpy packages are standard python utilities. The astropy package contains the tools needed to import FITS files. PIL is used to export images as png files.
" ] }, { "cell_type": "markdown", "id": "d69637e7", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "id": "99635f0f", "metadata": {}, "source": [ "Perform a series of prepratory steps first." ] }, { "cell_type": "code", "execution_count": 1, "id": "392a84d9", "metadata": {}, "outputs": [], "source": [ "# Import packages.\n", "import numpy\n", "import matplotlib.pyplot as pp\n", "from astropy.io import fits\n", "from PIL import Image" ] }, { "cell_type": "code", "execution_count": 2, "id": "a008df5c", "metadata": {}, "outputs": [], "source": [ "# Set functions that describe the colours (r, g, and b). The input image will\n", "# be rescaled to values between 0 and 1000 before these conversions are applied.\n", "# These functions will then scale those pixel values into rgb colours that\n", "# range from 0 to 255. These conversion will produce a colours that change\n", "# from black through red and yellow to white, producing something like a \"heat\"\n", "# colour scale.\n", "#\n", "# [MODIFICATION OPTION: Change these lines to change the colours. This can be\n", "# quite complicated, but the plots in the next step can be used check how the\n", "# science valeus are mapped into red, green, and blue pixel values. Keep in\n", "# mind how these three colours combine to produce other colours.]\n", "x=numpy.asarray(range(1001))\n", "r=x*0.6\n", "g=(x-350)*0.52\n", "b=(x-650)*(255./350)\n", "\n", "# Perform some additional steps to adjust the ranges of the r, g, and b arrays\n", "# so that they stay between 0 and 255.\n", "r[r>255]=255\n", "g[g<0]=0\n", "g[g>255]=255\n", "b[b<0]=0\n", "b[b>255]=255" ] }, { "cell_type": "code", "execution_count": 3, "id": "9d68a59e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Output PNG Pixel Value')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "