{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "adjustable-greece", "slideshow": { "slide_type": "slide" } }, "source": [ "Astronomical data analysis using Python\n", "=======\n", "\n", "Lecture 9\n", "-----------------\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tutorial for Assignment 2\n", "\n", "I hope all of you have downloaded Assignment 2 from the Moodle platform. Given that this is a much tougher assignment than the first one, we will have two tutorial session devoted to it on Dec 16, and Dec 20. I strongly recommend you to attempt the questions by the 16th, so that if you have any doubts, these can be resolved on the 16th. In the second tutorial, Preetish will present the full solutions for the questions. " ] }, { "cell_type": "markdown", "metadata": { "id": "baking-medicine", "slideshow": { "slide_type": "slide" } }, "source": [ "Table Sorting\n", "-----" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "generic-blogger", "outputId": "c19862bf-a3f9-4844-f6cb-e6b80d1ded17", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name obs_date mag_b mag_v\n", "---- ---------- ----- -----\n", " M31 2012-01-02 17.0 17.5\n", " M31 2012-01-02 17.1 17.4\n", "M101 2012-01-02 15.1 13.5\n", " M82 2012-02-14 16.2 14.5\n", " M31 2012-02-14 16.9 17.3\n", " M82 2012-02-14 15.2 15.5\n", "M101 2012-02-14 15.0 13.6\n", " M82 2012-03-26 15.7 16.5\n", "M101 2012-03-26 15.1 13.5\n", "M101 2012-03-26 14.8 14.3\n" ] } ], "source": [ "from astropy.table import Table\n", "demo_table = Table.read(\"demo.txt\", format=\"ascii\")\n", "print (demo_table)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "thrown-shock", "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "demo_table.sort([\"name\", \"mag_b\"]) # sort by name, then magb" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "through-wheel", "outputId": "b334c9e2-74ed-456e-e19b-5adca1b316c9", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name obs_date mag_b mag_v\n", "---- ---------- ----- -----\n", "M101 2012-03-26 14.8 14.3\n", "M101 2012-02-14 15.0 13.6\n", "M101 2012-01-02 15.1 13.5\n", "M101 2012-03-26 15.1 13.5\n", " M31 2012-02-14 16.9 17.3\n", " M31 2012-01-02 17.0 17.5\n", " M31 2012-01-02 17.1 17.4\n", " M82 2012-02-14 15.2 15.5\n", " M82 2012-03-26 15.7 16.5\n", " M82 2012-02-14 16.2 14.5\n" ] } ], "source": [ "print (demo_table)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "compatible-massage", "outputId": "9dc6ee11-0770-470b-8c69-1a713542e64b", "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name obs_date mag_b mag_v\n", "---- ---------- ----- -----\n", " M82 2012-02-14 16.2 14.5\n", " M82 2012-03-26 15.7 16.5\n", " M82 2012-02-14 15.2 15.5\n", " M31 2012-01-02 17.1 17.4\n", " M31 2012-01-02 17.0 17.5\n", " M31 2012-02-14 16.9 17.3\n", "M101 2012-03-26 15.1 13.5\n", "M101 2012-01-02 15.1 13.5\n", "M101 2012-02-14 15.0 13.6\n", "M101 2012-03-26 14.8 14.3\n" ] } ], "source": [ "demo_table.reverse() # Reverse existing table. Descending order!\n", "print (demo_table)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Writing a table" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "demo_table.write('reversesortedtable.fits', format='fits', overwrite=True) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "Many output formats are available: ascii (many variants like csv, with and without headers), hdf5, votable, fits, latex etc. All these formats can be read from as well. " ] }, { "cell_type": "markdown", "metadata": { "id": "public-reliance", "slideshow": { "slide_type": "slide" } }, "source": [ "Table Groups\n", "----\n", "\n", "* It is possible to organize the table into groups. \n", "* For example, all entries for object M101 can be selected as a single group.\n", "* One can access individual groups for various operations.\n", "* Also supported \"group-wise reductions\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "cheap-prayer", "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "demo_table = Table.read(\"demo.txt\", format=\"ascii\")\n", "grouped_table = demo_table.group_by(\"name\") " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "complex-hospital", "outputId": "699060f8-7d5d-4c1d-afd7-96afc486412b", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name obs_date mag_b mag_v\n", "---- ---------- ----- -----\n", "M101 2012-01-02 15.1 13.5\n", "M101 2012-02-14 15.0 13.6\n", "M101 2012-03-26 15.1 13.5\n", "M101 2012-03-26 14.8 14.3\n" ] } ], "source": [ "# To access groups.\n", "print (grouped_table.groups[0]) # first group" ] }, { "cell_type": "markdown", "metadata": { "id": "particular-evaluation", "slideshow": { "slide_type": "slide" } }, "source": [ "Group-wise Reductions (e.g. group-wise mean)\n", "----" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "amber-density", "outputId": "f2d09910-e55e-4904-c4b1-f2b213efc027", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Cannot aggregate column 'obs_date' with type 'Table length=3\n", "\n", "\n", "\n", "\n", "\n", "\n", "
namemag_bmag_v
str4float64float64
M10115.00000000000000213.725000000000001
M3117.017.400000000000002
M8215.69999999999999815.5
" ], "text/plain": [ "\n", "name mag_b mag_v \n", "str4 float64 float64 \n", "---- ------------------ ------------------\n", "M101 15.000000000000002 13.725000000000001\n", " M31 17.0 17.400000000000002\n", " M82 15.699999999999998 15.5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy\n", "grouped_table.groups.aggregate(numpy.mean)" ] }, { "cell_type": "markdown", "metadata": { "id": "attended-korean", "slideshow": { "slide_type": "slide" } }, "source": [ "Filters\n", "----\n", "\n", "* Define a function some_filter( TableObject, KeyColumns ) .\n", "* The function returns `True` or `False`.\n", "* Then use the function to remove rows which satisfy some condition.\n", "\n", "The following will select all table groups with only positive values in the non- key columns:\n", "\n", "\n", "def all_positive(table, key_colnames):\n", " colnames = [name for name in table.colnames if name not in key_colnames]\n", " for colname in colnames:\n", " if np.any(table[colname] <= 0):\n", " return False\n", " return True\n", "t_positive = tg.groups.filter(all_positive)`\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "parliamentary-mortality", "slideshow": { "slide_type": "slide" } }, "source": [ "Stuff For You To Explore On Your Own\n", "====\n", "\n", "Stacks - vstack, hstack\n", "---\n", "\n", "\"joins\"\n", "---\n", "\n", "https://docs.astropy.org/en/stable/table/operations.html\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Useful generic tools for handling astronomical data\n", "\n", "* **Topcat**: Interactive viewer and editor for tabular data. Can handle data in many formats, and includes tools for making many different kinds of plots.\n", "* **Aladin**: interactive sky atlas allowing the user to visualize digitized astronomical images or full surveys, \n", "superimpose entries from astronomical catalogues\n", "* **DS9**: is an alternative to Aladin. It provides many of the features that Aladin has in a fast, lightweight interface.\n", "\n", "All of these tools are tightly integrated with each other, all follow **Virtual Obervatory protocols** and can exchange data with each other. Even if you are going to process all your data through a Python program, it is important that you become proficient in the use of these tools, so that you can easily do quality checks on your analysis." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# SkyCoord - generic object for coordinates" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "10d41m04.488s\n", "0.7123053333333335\n", "hms_tuple(h=0.0, m=42.0, s=44.299200000000525)\n", "41.26917\n", "0.7202828960652683\n", "\n", "\n" ] } ], "source": [ "from astropy.coordinates import SkyCoord\n", "import astropy.units as u\n", "c = SkyCoord(ra=10.68458*u.degree, dec=41.26917*u.degree)\n", "print (c)\n", "print (c.ra)\n", "print (c.ra.hour)\n", "print (c.ra.hms)\n", "print (c.dec.degree)\n", "print (c.dec.radian)\n", "print (c.galactic)\n", "print (c.fk4)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 3D and angular separation" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.5228602415117987 pc\n", "1d24m16.34302012s\n" ] } ], "source": [ "c1 = SkyCoord(ra=10*u.degree, dec=9*u.degree, distance=10*u.pc, frame='icrs')\n", "c2 = SkyCoord(ra=11*u.degree, dec=10*u.degree, distance=11.5*u.pc, frame='icrs')\n", "print (c1.separation_3d(c2))\n", "print (c1.separation(c2))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Radial velocity correction" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/latex": [ "$-22.332862 \\; \\mathrm{\\frac{km}{s}}$" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from astropy.coordinates import EarthLocation\n", "from astropy.time import Time\n", "obstime = Time('2017-2-14')\n", "target = SkyCoord.from_name('M31') # needs internet connection\n", "pune = EarthLocation.of_address('Pune, India') # needs internet connection\n", "target.radial_velocity_correction(obstime=obstime, location=pune).to('km/s') \n", "# apply correction to get heliocentric radial velocity accurate to about 3 m/s" ] }, { "cell_type": "markdown", "metadata": { "id": "upper-spirit", "slideshow": { "slide_type": "slide" } }, "source": [ "FITS Files in Python\n", "=====================\n", "\n", "Again, if this talk was being given few years ago, we would cover \n", "\n", "PyFITS\n", "------\n", "\n", "But now, \n", "\n", "astropy.io.fits\n", "---------------" ] }, { "cell_type": "markdown", "metadata": { "id": "strong-estonia", "slideshow": { "slide_type": "slide" } }, "source": [ "First step, import the (sub) module.\n", "-----------------" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "studied-emperor", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from astropy.io import fits" ] }, { "cell_type": "markdown", "metadata": { "id": "tired-document", "slideshow": { "slide_type": "-" } }, "source": [ "If you are using old code that uses PyFits you can say, \n", "\n", " import astropy.io.fits as pyfits\n", "\n", "or whatever alias you use for PyFITS and most of PyFITS based code should work fine." ] }, { "cell_type": "markdown", "metadata": { "id": "center-drain", "slideshow": { "slide_type": "slide" } }, "source": [ "Next step, open a FITS file. The method used for this creates a hdulist object. HDU = Header Data Unit" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "hawaiian-destiny", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "hdulist = fits.open(\"example.fits\") # example.fits is a sample image on my computer." ] }, { "cell_type": "markdown", "metadata": { "id": "final-party", "slideshow": { "slide_type": "slide" } }, "source": [ "Next, check up some basic information about the FITS file." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "eight-butter", "outputId": "d1b4cfdc-cddd-4ca4-f5af-1c77c0065cf6", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: example.fits\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 71 (512, 512) int16 \n" ] } ], "source": [ "hdulist.info()" ] }, { "cell_type": "markdown", "metadata": { "id": "subsequent-orchestra", "slideshow": { "slide_type": "-" } }, "source": [ "As you can see, this is a single extension FITS file." ] }, { "cell_type": "markdown", "metadata": { "id": "enormous-museum", "slideshow": { "slide_type": "slide" } }, "source": [ "Accessing the FITS header\n", "-------------------" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "acoustic-bishop", "outputId": "494dfd35-e670-421b-88a1-0ff7ffee8fbc", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "SIMPLE = T / Fits standard \n", "BITPIX = 16 / Bits per pixel \n", "NAXIS = 2 / Number of axes \n", "NAXIS1 = 512 / Axis length \n", "NAXIS2 = 512 / Axis length \n", "EXTEND = F / File may contain extensions \n", "ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator \n", "DATE = '2017-02-17T04:36:31' / Date FITS file was generated \n", "IRAF-TLM= '2017-02-17T04:36:31' / Time of last modification \n", "OBJECT = 'm51 B 600s' / Name of the object observed \n", "IRAF-MAX= 1.993600E4 / DATA MAX \n", "IRAF-MIN= -1.000000E0 / DATA MIN \n", "CCDPICNO= 53 / ORIGINAL CCD PICTURE NUMBER \n", "ITIME = 600 / REQUESTED INTEGRATION TIME (SECS) \n", "TTIME = 600 / TOTAL ELAPSED TIME (SECS) \n", "OTIME = 600 / ACTUAL INTEGRATION TIME (SECS) \n", "DATA-TYP= 'OBJECT (0)' / OBJECT,DARK,BIAS,ETC. \n", "DATE-OBS= '05/04/87' / DATE DD/MM/YY \n", "RA = '13:29:24.00' / RIGHT ASCENSION \n", "DEC = '47:15:34.00' / DECLINATION \n", "EPOCH = 0.00 / EPOCH OF RA AND DEC \n", "ZD = '22:14:00.00' / ZENITH DISTANCE \n", "UT = ' 9:27:27.00' / UNIVERSAL TIME \n", "ST = '14:53:42.00' / SIDEREAL TIME \n", "CAM-ID = 1 / CAMERA HEAD ID \n", "CAM-TEMP= -106.22 / CAMERA TEMPERATURE, DEG C \n", "DEW-TEMP= -180.95 / DEWAR TEMPRATURE, DEG C \n", "F1POS = 2 / FILTER BOLT I POSITION \n", "F2POS = 0 / FILTER BOLT II POSITION \n", "TVFILT = 0 / TV FILTER \n", "CMP-LAMP= 0 / COMPARISON LAMP \n", "TILT-POS= 0 / TILT POSITION \n", "BIAS-PIX= 0 / \n", "BI-FLAG = 0 / BIAS SUBTRACT FLAG \n", "BP-FLAG = 0 / BAD PIXEL FLAG \n", "CR-FLAG = 0 / BAD PIXEL FLAG \n", "DK-FLAG = 0 / DARK SUBTRACT FLAG \n", "FR-FLAG = 0 / FRINGE FLAG \n", "FR-SCALE= 0.00 / FRINGE SCALING PARAMETER \n", "TRIM = 'Apr 22 14:11 Trim image section is [3:510,3:510]' \n", "BT-FLAG = 'Apr 22 14:11 Overscan correction strip is [515:544,3:510]' \n", "FF-FLAG = 'Apr 22 14:11 Flat field image is Flat1.imh with scale=183.9447' \n", "CCDPROC = 'Apr 22 14:11 CCD processing done' \n", "AIRMASS = 1.08015632629395 / AIRMASS \n", "HISTORY 'KPNO-IRAF' \n", "HISTORY '24-04-87' \n", "HISTORY 'KPNO-IRAF' / \n", "HISTORY '08-04-92' / \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[0].header" ] }, { "cell_type": "markdown", "metadata": { "id": "mobile-sustainability", "slideshow": { "slide_type": "slide" } }, "source": [ "Specific stuff within header.\n", "-------------------------" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "valuable-victoria", "outputId": "7043279c-37d2-4d85-ff5e-6417096957e7", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "512" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[0].header[\"NAXIS1\"] # by header keyword" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "individual-consent", "outputId": "04730e9a-5471-4523-8486-5f21b9cb2b98", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[0].header[1] # or by header number." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "sapphire-workplace", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "all_keys = hdulist[0].header.keys() # get a list of all keys." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "twenty-poster", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "all_values = hdulist[0].header.values()" ] }, { "cell_type": "markdown", "metadata": { "id": "relative-retreat", "slideshow": { "slide_type": "-" } }, "source": [ "You can also change the header values as if it were a dictionary." ] }, { "cell_type": "markdown", "metadata": { "id": "vietnamese-dispatch", "slideshow": { "slide_type": "slide" } }, "source": [ "Now, the data\n", "---------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "rural-stevens", "outputId": "bac529fb-6c7f-48cb-85bd-c89afeb2b808", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "identified-michael", "outputId": "72b21a1c-1d24-415c-e58a-e62f3b013905", "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.set_cmap('inferno_r')\n", "plt.imshow( np.log10(hdulist[0].data+abs(np.min(hdulist[0].data))+0.01),origin='lower')\n", "# Flat is better than nested - Zen of Python. We violated that and made the code difficult to read.\n", "# Don't do this!\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": { "id": "incomplete-excuse", "slideshow": { "slide_type": "slide" } }, "source": [ "Axis Conventions\n", "----------------\n", "\n", "If you load a FITS image in Python, in FORTRAN/C or in ds9, the image viewer, what does I(X,Y) can give you different results!!! Also remember the zero origin versus one origin problem.\n", "\n", "There is a difference in whether the following code moves along horizontal axis first or vertical axis first.\n", "\n", " for x in range(header[\"NAXIS1\"]):\n", " for y in range(header[\"NAXIS2\"]):\n", " \n", " \n", "**This gets really complicated when you have datacubes which produce 3-D arrays. Jupyter notebook to imshow the image. Also load image in ds9. Do a bit of fiddling around and write your loops! In most cases when you work with the whole image, you don't have to worry. While writing out a fits file, the y,x are reinverted automatically**" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "338\n" ] } ], "source": [ "im = hdulist[0].data\n", "print (im[461,68]) # this corresponds to 69,462 in the ds9 image. Display the image and verify it.\n", "hdulist.close()" ] }, { "cell_type": "markdown", "metadata": { "id": "rational-aging", "slideshow": { "slide_type": "slide" } }, "source": [ "Writing FITS files\n", "-------------------\n", "\n", "If you have a HDUlist object, you simply say,\n", "\n", " hdulist.writeto(\"outputfilename.fits\")\n", " \n", "If you want to make a file from scratch, create a dictionary of headers and the data array.\n", "\n", " primaryhdu = fits.PrimaryHDU(data, header)\n", " primaryhdu.writeto(\"something.fits\")\n", " \n" ] }, { "cell_type": "markdown", "metadata": { "id": "occupied-executive", "slideshow": { "slide_type": "slide" } }, "source": [ "World Coordinate Systems\n", "========================\n", "\n", "Few years ago, \n", "\n", " import pywcs \n", " \n", "In the era of Astropy, \n", "\n", " from astropy import wcs\n", " \n", "Funtionally, they are more or less the same." ] }, { "cell_type": "markdown", "metadata": { "id": "english-congo", "slideshow": { "slide_type": "slide" } }, "source": [ "Create a WCS object.\n", "--------------------" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "realistic-wilson", "outputId": "c7efbf9a-d5b8-4fc0-e271-6ef5efdd6614", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from astropy import wcs\n", "w = wcs.WCS(\"1105_160859.fits\") # cutout image from the 1.4 GHz VLA FIRST sky survey " ] }, { "cell_type": "markdown", "metadata": { "id": "willing-dragon", "slideshow": { "slide_type": "-" } }, "source": [ "While the above is allowed, taking into account that FITS files can have multiple extensions, you should," ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "particular-mother", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WCS Keywords\n", "\n", "Number of WCS axes: 2\n", "CTYPE : 'RA---SIN' 'DEC--SIN' \n", "CRVAL : 34.3492317 -3.9826033 \n", "CRPIX : 18.3727199548739 18.44725428547099 \n", "CD1_1 CD1_2 : -0.0005555555555556 0.0 \n", "CD2_1 CD2_2 : 0.0 0.0005555555555556 \n", "NAXIS : 35 35\n" ] } ], "source": [ "hdulist = fits.open(\"1105_160859.fits\")\n", "w = wcs.WCS(hdulist[0].header)\n", "print (w)\n", "hdulist.close()" ] }, { "cell_type": "markdown", "metadata": { "id": "august-algeria", "slideshow": { "slide_type": "-" } }, "source": [ "It's the WCS object which has methods to perform any coordinate transformations." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "round-grave", "outputId": "f3d5eebb-d0e4-44ed-a70e-f7948c353231", "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from astropy.wcs import utils\n", "print (utils.pixel_to_skycoord(5,25,w,origin=1)) # Functional form" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "matched-header", "outputId": "6ad003dc-cce5-49ad-a515-7faf87fafece", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "[array(34.35667894), array(-3.97896285)]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.wcs_pix2world(5, 25, 1) # OOP form" ] }, { "cell_type": "markdown", "metadata": { "id": "undefined-detroit", "slideshow": { "slide_type": "-" } }, "source": [ "* Which pixel? (5, 25) or (6, 26). It's (5,25), the third argument 1 assures you that.\n", "* Difference between wcs_pix2world and all_pix2world - the latter takes into account some higher order transformations / corrections into account.\n", "* Output? (RA, DEC) in degrees." ] }, { "cell_type": "markdown", "metadata": { "id": "reduced-details", "slideshow": { "slide_type": "slide" } }, "source": [ "To do a reverse transformation." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "id": "sudden-leader", "outputId": "01c34cb5-272b-4844-f4e2-3b0d1657c2dd", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "[array(16.99309841), array(41.13319346)]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.wcs_world2pix(34.35,-3.97, 1)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "sufficient-wayne", "outputId": "28cbbe92-94a1-442c-b38e-4fb73db8aeb4", "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "array([[34.35890669, -3.99229616],\n", " [34.35890647, -3.97340727],\n", " [34.33997206, -3.97340728],\n", " [34.33997185, -3.99229617]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.calc_footprint() # The four corners of an image." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 50255.000000 from DATE-OBS'. [astropy.wcs.wcs]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from astropy.wcs import WCS\n", "from astropy.visualization import ZScaleInterval\n", "from astropy.coordinates import SkyCoord\n", "hdulist = fits.open('h_n4603_f555_mosaic.fits')\n", "wcs = WCS(hdulist[0].header)\n", "interval = ZScaleInterval()\n", "vmin,vmax = interval.get_limits(hdulist[0].data)\n", "ax = plt.subplot(111, projection=wcs)\n", "ax.imshow(hdulist[0].data, cmap='gray_r', vmin=vmin, vmax=vmax, interpolation=None, origin='lower')\n", "ax.set_xlabel(\"Right Ascension\"); ax.set_ylabel(\"Declination\")\n", "ax.coords.grid(color='black', alpha=0.5, linestyle='solid')\n", "ax.plot_coord(SkyCoord(\"12h40m54s\",\"-40d58m0s\", frame=\"fk5\"), \"ro\")\n", "hdulist.close()\n" ] } ], "metadata": { "celltoolbar": "Slideshow", "colab": { "name": "lect8.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 1 }