diff --git a/Lab_1/Lab_1.ipynb b/Lab_1/Lab_1.ipynb new file mode 100644 index 0000000..53f41fc --- /dev/null +++ b/Lab_1/Lab_1.ipynb @@ -0,0 +1,615 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#
PHYS 134L Spring 2024 Lab 1
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ds9 Fundamentals
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The program ds9 allows you to display astronomical images, measure many of their properties, and much more. Unfortunately, the\n", + "manual for ds9 is particularly terse and unhelpful. The only way to understand the program’s capabilities is to try things, or to consult a\n", + "guru (perhaps your own lab partner). Do the following few first steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download _object.fits_ and _object.cat_ from Gauchospace. You may right-click _object.fits_ (if you associated fits files with ds9) and open with ds9, or you my load ds9 by clicking on the icon pinned to the taskbar and then using the open function (you can do this with both the GUI button and from the \"File\" menu) to find the file _object.fits_.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Image Scaling\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have opened the image, About how many stars do you see?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Left click on “scale”, on “more. . . ” if necessary to make “zscale” visible, and on “zscale”.\n", + "Changing the scale parameters adjusts how the 2D image array in ds9 is display to the user,\n", + "but doens’t actually change the data itself. About how many stars now?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Right click inside the image window, and drag the cursor around. Describe the effect of moving it left-right? Describe the effect of moving the (right-clicked) cursor up-down?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Experiment with different scale options (there are many!) to get a feel for how this functionality works. Try different colormaps (in the ”color” menu). Describe the settings that\n", + "you find best display the image and why." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Header Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Astronomical images ordinarily come with “metadata” = (data about the data). In images in FITS format, the metadata appear in a ”header” that can be displayed separately from the data. In the ds9 window, click on “File/Display Fits Header. . . ” Answer the following questions, based on the header." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What are the x- and y- dimensions of the image (in pixels)?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What was the image exposure time (in s)?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What filter was used? (We will go into what this means later)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What was the name of the object being observed?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What was the right ascension object being observed?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Google the object name you found above, and find an image of it on the web. Match this image to your ds9 display of object.fits. Adjust the scaling so you get a recognizable picture of the object (which is a supernova) and its host galaxy. (Try clicking “scale”, then “min/max” and then various scalings. Then play with right-click-and-drag-the-cursor to get a good-looking image. This is as much art as science!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What are the x,y coordinates of the supernova?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is the signal in the central pixel of the supernova? (units of counts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is the signal in the central pixel of the host galaxy?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Type Your Answer Here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##Fits Files and Python
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While ds9 can be powerful, sometimes you'll want to have a more flexible and customizable way to interact with your data. To do this, we'll read the data into python. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os #This package allows you to interact with your operating system\n", + "import numpy as np #This is a standard \n", + "from astropy.io import fits #Astropy is a multi-purpose python package made by astronomers\n", + "import matplotlib.pyplot as plt #You've already used this in your bootcamp!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we type `import numpy as np`, this means that we are importing a package called `numpy`, and that in\n", + "the future we will refer to it as `np`. We can call functions, classes, and objects defined within\n", + "numpy using syntax like `np.sin(x)`. The first thing we will do here is to read in the same image file as earlier\n", + "and try to make a pretty plot in python. You will use lines similar to the following, first defining a string\n", + "containing the path to your image file, and then opening the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imagefile = \"object.fits\" #The file has automatically been downloaded to the appropriate location in your JupyterHub drive\n", + "image = fits.open(imagefile)[0].data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first line is to tell the program where to find the image file. Don’t worry at the moment why we have to use `[0].data` when we open\n", + "the file: this is due to how fits files are structured. Feel free to read up on the astropy documentation to learn a bit more. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will plot the image. Use the following at first:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(image, origin='lower')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates and displays a plot of the same image as before. It is inverted vertically relative to what you\n", + "saw in ds9 unless we force origin to be ’lower’. Try it leaving this argument out and see for yourself!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it is up to you to make the figure look better (leave the output of these commands as\n", + "they are; create a new cell to make your new figure). Try changing the size of the figure (e.g. with\n", + "`plt.figure(figsize=(10, 10)`) before `plt.imshow`), the color map, the minimum and maximum values\n", + "to show, and the scaling (you can plot any function of the values in image). **Read the documentation\n", + "pages.** Try square root scales, log scales, linear scales, other scales, and see what looks good! To plot with\n", + "some other scale, you may plot, e.g., `np.log(image)` or `np.exp(image)` instead of just `image`, or you could try using different [image normalizations](https://matplotlib.org/stable/tutorials/colors/colormapnorms.html). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also, it’s a good idea to read [this](https://matplotlib.org/users/colormaps.html) page on color maps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, you may\n", + "turn off the axes labeling pixel values with another line\n", + "```\n", + "plt.axis(’off’)\n", + "```\n", + "before\n", + "```\n", + "plt.show()\n", + "```\n", + "You may also save your figure to a png file using `plt.savefig` so that you can print it out and tape it to your door or wall. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Put the code for your nicer looking figure here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##Stellar Photometry
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this final section of Lab 1 we'll move on to a catalog of stellar photometry (i.e. how bright the stars are) based on your ``object.fits`` files that was extracted using a program called Source Extractor (sometimes known as SExtractor). Yes, the name is crass, and no, you should not choose a similar name when you write your own useful pieces of software. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The catalog of extracted stars is in a file called `object.cat`. We will visualize this catalog a few ways,\n", + "getting a little practice with python arrays along the way. The first step is to load in the catalog. You will\n", + "define the catalog filename in exactly the same way you did before. You will then read it into python with the syntax\n", + "```\n", + "catalog = np.loadtxt(catalogfile)\n", + "```\n", + "where `catalogfile` is a string giving the filename of the catalog. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Open the catalog in the text editor of your choice (notepad, wordpad, word, emacs, etc.) and\n", + "take a look: the first few lines show what each column represents. For now, focus on three columns:\n", + "x-position, y-position, and flags. Note that you can download files off the JupyterHub onto any computer you're using. \n", + "\n", + "\n", + "Set a new variable x equal to the array of x-positions from the\n", + "catalog, a new variable y equal to the array of y-positions, and flags equal to the flag entry.\n", + "When you do this, remember that the first array element is indexed with a zero. Play around to see what to do, use a search\n", + "engine, and don’t immediately ask for the answer. For example, what would the following lines give?\n", + "```\n", + "x = catalog[0, 0]\n", + "x = catalog[:, 0]\n", + "x = catalog[0]\n", + "x = catalog[0, :]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Your code to read in the file goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have never programmed before, this sort of indexing varies between programming languages but the\n", + "basic idea is very standard. Now plot the extracted stars atop your pretty image of `object.fits` using plt.plot(x,\n", + "y) (use a new cell). Use dots/markers rather than lines, and consult the [manual](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html) for guidance if you need it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Your code goes here. Overplot the catalog positions on top of the image of object.fits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, change the plot a bit: plot those stars that have flag == 0 in one color/marker style, and stars that have flag != 0 in a different color/marker style. Hint: the easiest way to do this will involve either `numpy.where` or boolean indexing. You might want to go back to the indexing tutorial.\n", + "Also, there is a very important difference between = and ==. The statement\n", + "```\n", + "x = 5\n", + "```\n", + "sets the variable x equal to the value 5. The statement\n", + "```\n", + "x == 5\n", + "```\n", + "will not change the value of x. It will evaluate to True if, indeed, x is the integer 5, and False otherwise. If\n", + "x is an array, this statement will return an array of True and False values (a boolean array). The statement\n", + "will return an error if x is not defined." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have extracted arrays like `x_flagged` and `y_flagged` and have plotted them atop\n", + "your image, comment on any differences that you notice between the flagged (flag != 0) and\n", + "unflagged (flag == 0) stars. As usual, this comment should appear in a markdown cell below\n", + "the figure. If you need insight or are curious, go search for what the flags represent in the\n", + "SExtractor manual and use this to help inform your comments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##Your code goes Here##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Your comments go here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The exact factor relating the peak intensity and total flux will vary from star to star. Part of the reason why is that the flux in any one pixel is subject to photon noise (also called shot noise or Poisson noise). If we have a lot of photons, the scatter in the count rate scales roughly as the square root of the number of counts (refereed to below as the \"square root rule\"). Remind yourself of why this is the case for Poisson statistics!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose the eight stars with fluxes between 1990 and 2010 as reported by SExtractor. If you haven’t already played with\n", + "boolean indexing and/or `np.where`, now is the time. You probably don’t want to do this by hand. Hint: You can combine logical expressions with the python and (&) and or operators." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using your newfound ability to select elements from arrays, find the x and y coordinates of\n", + "these eight stars. Then compute the standard deviation of the eight peak pixel values. You\n", + "may compute the peak pixel values either by hand in ds9 or directly in python. Either way, be explicit\n", + "about what you are doing in your jupyter notebook. When you compute the standard deviation, you\n", + "may also do that as you like, but again be clear. What would you expect for the standard deviation\n", + "according to the square root rule? Is this what you find? If your standard deviation was larger\n", + "or smaller than this expectation, try to come up with possible explanations, and write these\n", + "in a markdown cell in your jupyter notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Your reponse here*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have one, last, easy task for this lab. You will make histograms of the fluxes returned by\n", + "SExtractor. Use the function `plt.hist()` for this purpose. Make two separate histograms: one\n", + "of the fluxes, and one of the log of the fluxes (either base ten or natural log, your choice).\n", + "Comment on which of these looks more informative and why. As before, use your judgment and the\n", + "manual pages to make a figure with labeled axes that looks nice. We will work in logarithmic units of flux\n", + "called magnitudes for the rest of the course, and indeed, this is another column of the catalog file returned\n", + "by SExtractor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Your reponse here*" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90" + }, + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Lab_1/object.cat b/Lab_1/object.cat new file mode 100644 index 0000000..61566f9 --- /dev/null +++ b/Lab_1/object.cat @@ -0,0 +1,2792 @@ +# 1 NUMBER Running object number +# 2 FLUX_ISOCOR Corrected isophotal flux [count] +# 3 FLUXERR_ISOCOR RMS error for corrected isophotal flux [count] +# 4 MAG_ISOCOR Corrected isophotal magnitude [mag] +# 5 MAGERR_ISOCOR RMS error for corrected isophotal magnitude [mag] +# 6 X_IMAGE Object position along x [pixel] +# 7 Y_IMAGE Object position along y [pixel] +# 8 ELONGATION A_IMAGE/B_IMAGE +# 9 FWHM_IMAGE FWHM assuming a gaussian core [pixel] +# 10 FLAGS Extraction flags + 1 137552.5 375.8596 -12.8462 0.0030 1774.1411 29.3371 1.170 6.39 0 + 2 3778.12 139.0238 -8.9432 0.0400 1677.0443 26.5926 1.469 5.85 0 + 3 114508.3 342.0595 -12.6471 0.0032 965.4194 20.1197 1.257 6.89 0 + 4 9418.047 171.5427 -9.9349 0.0198 866.9758 18.0141 1.429 7.13 0 + 5 1627.632 125.121 -8.0289 0.0835 1124.3032 20.9448 1.795 6.91 0 + 6 1319.264 113.6128 -7.8008 0.0935 434.4773 18.4992 1.084 7.94 2 + 7 2030.389 124.8445 -8.2689 0.0668 1984.4579 17.3376 2.002 7.16 0 + 8 5062.194 225.1474 -9.2608 0.0483 938.4644 15.3867 1.440 14.93 0 + 9 10724.54 188.4139 -10.0759 0.0191 290.0507 15.3994 1.607 12.72 0 + 10 4545.719 148.9974 -9.1440 0.0356 1405.0404 14.8089 1.477 5.94 0 + 11 4954.849 151.4636 -9.2376 0.0332 768.9023 14.3789 1.626 7.43 0 + 12 957.6191 114.0246 -7.4530 0.1293 607.7924 13.7408 2.676 5.50 0 + 13 2889.278 130.7937 -8.6520 0.0492 1748.2845 9.2695 1.496 6.41 0 + 14 345.2233 74.71275 -6.3453 0.2350 1502.7939 12.0205 2.008 3.93 0 + 15 2077.587 138.4293 -8.2939 0.0724 818.1962 9.9015 1.574 8.62 0 + 16 2865.464 163.1265 -8.6430 0.0618 196.3600 9.5707 1.846 10.68 0 + 17 50141.69 241.343 -11.7505 0.0052 1213.2777 3.2669 1.302 4.71 8 + 18 24916.77 194.9609 -10.9912 0.0085 1636.6233 3.9326 1.103 6.29 8 + 19 3878.291 145.2896 -8.9716 0.0407 1813.5483 9.7783 1.397 5.53 0 + 20 1129.712 129.7442 -7.6324 0.1247 1608.3971 6.4095 2.206 7.24 0 + 21 5526.799 147.3997 -9.3562 0.0290 881.0013 3.2109 1.517 5.36 8 + 22 563.6392 86.95042 -6.8775 0.1675 1526.0643 3.8232 1.240 4.97 0 + 23 739.1181 107.0757 -7.1718 0.1573 267.1777 2.5335 1.695 6.06 8 + 24 656.4226 80.7977 -7.0430 0.1337 320.3735 2.0577 1.339 2.61 8 + 25 284.9618 58.80227 -6.1370 0.2241 1248.7662 1.4191 1.589 2.58 8 + 26 281.5588 63.36724 -6.1239 0.2444 1155.8119 1.6163 1.694 3.38 8 + 27 1560.654 123.3721 -7.9833 0.0858 1912.6394 25.8433 1.957 7.86 0 + 28 6427.81 151.6622 -9.5202 0.0256 1406.5195 31.4710 1.435 6.43 0 + 29 1324.01 105.7524 -7.8047 0.0867 436.3173 21.9837 1.097 3.83 2 + 30 3554.622 145.1283 -8.8770 0.0443 1688.8298 30.8943 1.520 6.36 0 + 31 20953.18 200.3363 -10.8031 0.0104 922.9108 51.0554 1.363 6.96 0 + 32 14805.95 196.0076 -10.4261 0.0144 811.4080 40.1660 1.290 7.44 0 + 33 2246.59 134.6042 -8.3788 0.0651 1137.1548 28.1798 1.461 6.74 2 + 34 2063.447 146.3676 -8.2865 0.0770 1131.9209 32.3787 1.482 6.72 2 + 35 957.9109 113.6607 -7.4533 0.1289 825.0031 25.5119 1.416 5.09 2 + 36 15831.52 189.3186 -10.4988 0.0130 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1984.3376 1.224 3.97 0 + 1515 1915.198 115.4292 -8.2055 0.0655 1697.6859 1996.9286 1.101 2.85 0 + 1516 281.5787 62.05323 -6.1240 0.2393 797.6011 2005.7883 1.636 2.61 0 + 1517 21293.26 199.786 -10.8206 0.0102 702.7600 1992.6661 1.137 3.19 0 + 1518 67967.19 282.9876 -12.0807 0.0045 324.7526 1994.1123 1.116 4.19 0 + 1519 563.0588 89.44023 -6.8764 0.1725 710.4706 2005.9694 1.643 4.89 0 + 1520 9401.253 150.8616 -9.9330 0.0174 1273.9502 1992.5692 1.063 2.57 0 + 1521 521.3619 92.57753 -6.7928 0.1928 228.7755 1933.5580 2.514 4.55 0 + 1522 3799.085 126.9267 -8.9492 0.0363 1431.0786 1984.8850 1.079 2.89 0 + 1523 2122.664 130.4954 -8.3172 0.0668 531.5143 1982.2041 1.320 5.03 0 + 1524 1300.405 100.7928 -7.7852 0.0842 1.6209 1984.6367 2.798 5.80 8 + 1525 640.2507 103.0957 -7.0159 0.1749 665.8204 1981.8790 2.383 5.42 0 + 1526 1535.877 106.1606 -7.9659 0.0751 1908.5895 1950.8309 1.234 3.09 0 + 1527 281.644 61.8609 -6.1243 0.2385 120.1764 1932.2001 4.192 3.83 0 + 1528 13329.57 385.1 -10.3120 0.0314 6.6094 1960.5469 1.420 23.65 10 + 1529 1919.512 115.2785 -8.2080 0.0652 1333.2896 1913.6111 1.105 3.04 0 + 1530 2223.68 111.153 -8.3677 0.0543 788.4324 1949.3401 1.171 3.28 0 + 1531 454.2151 75.19934 -6.6432 0.1798 1734.4872 1986.0994 1.182 2.39 0 + 1532 118470 334.0061 -12.6840 0.0031 1038.5160 1964.6919 1.188 3.14 0 + 1533 1269.672 109.9931 -7.7592 0.0941 733.2433 1973.7163 1.286 4.14 0 + 1534 1351.014 150.9612 -7.8266 0.1213 22.7217 1943.4227 2.918 4.99 2 + 1535 285.882 67.34128 -6.1405 0.2558 1185.6305 1976.8546 1.759 2.04 0 + 1536 282.1612 60.96872 -6.1262 0.2347 973.7169 1982.4281 1.641 3.43 0 + 1537 797.7531 98.44095 -7.2547 0.1340 1488.0435 1973.4447 1.028 3.97 0 + 1538 572.8265 82.14127 -6.8951 0.1557 831.5590 1986.2036 1.328 3.35 0 + 1539 1243.111 127.0167 -7.7363 0.1110 1426.1763 1974.5876 1.605 7.74 0 + 1540 686.05 77.24825 -7.0909 0.1223 1416.0302 1977.0275 1.405 4.80 0 + 1541 2645.013 113.6475 -8.5561 0.0467 1548.6514 1987.9552 1.022 3.24 0 + 1542 10316.63 154.1954 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114.9888 -8.2862 0.0605 1810.7025 1955.6143 1.240 3.39 0 + 1557 3033.216 114.5981 -8.7048 0.0410 1290.8418 1952.6379 1.074 2.86 0 + 1558 141929.6 350.0636 -12.8802 0.0027 1232.2367 1964.9811 1.155 2.94 0 + 1559 4110.579 118.7164 -9.0348 0.0314 1481.2338 1964.3864 1.056 3.06 0 + 1560 5894.888 137.3963 -9.4262 0.0253 1268.2007 1963.9597 1.134 2.80 0 + 1561 679.6685 92.87542 -7.0807 0.1484 543.6067 1947.8022 1.369 5.51 0 + 1562 1470.069 98.67666 -7.9183 0.0729 1830.9517 1955.6154 1.137 3.32 0 + 1563 2581.755 117.646 -8.5298 0.0495 977.6540 1954.8229 1.168 5.32 0 + 1564 35560.34 229.4164 -11.3774 0.0070 492.4230 1938.6083 1.137 3.87 2 + 1565 788.3066 104.0921 -7.2417 0.1434 199.9491 1928.2255 2.160 5.84 0 + 1566 2149.537 109.1681 -8.3309 0.0552 1558.8562 1955.2861 1.124 2.92 0 + 1567 1537.587 111.6244 -7.9671 0.0788 1593.2881 1954.0417 1.336 5.16 0 + 1568 3787.206 134.0106 -8.9458 0.0384 555.5133 1960.6836 1.208 3.82 0 + 1569 1085.897 94.16058 -7.5895 0.0942 1764.3867 1954.0182 1.254 3.27 0 + 1570 73083.94 295.4225 -12.1596 0.0044 770.4258 1946.7670 1.075 3.32 0 + 1571 2295.593 116.6329 -8.4022 0.0552 351.7437 1957.5563 1.395 4.34 0 + 1572 11564.99 161.8387 -10.1579 0.0152 1598.7728 1929.8546 1.127 2.77 0 + 1573 1238.776 130.4682 -7.7325 0.1144 53.3799 1953.8651 1.206 8.02 0 + 1574 87972.16 314.1297 -12.3609 0.0039 1658.0686 1901.3987 1.060 2.72 0 + 1575 4515.753 126.6225 -9.1368 0.0305 1350.4745 1939.9374 1.133 2.94 0 + 1576 2067.349 128.0872 -8.2885 0.0673 481.8680 1942.3082 1.479 5.74 2 + 1577 30423.53 221.8675 -11.2080 0.0079 613.0357 1938.4109 1.133 3.66 0 + 1578 639.7793 102.9833 -7.0151 0.1748 187.7330 1952.5347 3.162 5.25 0 + 1579 2158.808 105.5701 -8.3355 0.0531 1464.8096 1928.8109 1.201 3.02 0 + 1580 396.3846 77.56626 -6.4953 0.2125 1423.0258 1956.0360 1.691 3.88 0 + 1581 4993.943 133.778 -9.2461 0.0291 1847.9061 1923.5052 1.071 2.89 0 + 1582 622.905 97.24295 -6.9861 0.1695 780.6753 1939.6583 1.508 5.38 0 + 1583 1403.868 101.3688 -7.8683 0.0784 887.6447 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723.5266 -14.6536 0.0011 564.6719 1620.2086 1.095 3.66 4 + 2011 961.9066 110.9765 -7.4578 0.1253 1674.9750 1612.3958 1.457 4.73 0 + 2012 30198.13 222.3113 -11.2000 0.0080 337.4997 1615.2510 1.050 4.35 2 + 2013 732.1644 102.4114 -7.1615 0.1519 511.8529 1633.3948 1.753 5.66 0 + 2014 3567.779 115.2331 -8.8810 0.0351 1515.5894 1585.9281 1.122 2.68 0 + 2015 800.5344 97.58709 -7.2585 0.1324 967.4479 1600.0002 1.765 5.05 0 + 2016 9730.483 173.4309 -9.9703 0.0194 276.5149 1619.7380 1.193 4.84 0 + 2017 941.6088 99.20483 -7.4347 0.1144 1429.7678 1614.0763 1.082 3.33 0 + 2018 2012.508 108.5338 -8.2593 0.0586 1824.8342 1607.5989 1.123 2.78 0 + 2019 704.6321 108.9928 -7.1199 0.1680 1278.5597 1570.9979 1.548 5.54 0 + 2020 563.213 89.84138 -6.8767 0.1732 867.2634 1614.3021 1.267 4.17 0 + 2021 1841.493 113.5406 -8.1629 0.0670 1917.1847 1628.0177 1.192 3.97 0 + 2022 1226.695 98.74482 -7.7218 0.0874 1952.0560 1604.7056 1.106 3.38 0 + 2023 863.2462 99.62459 -7.3403 0.1253 1363.0630 1612.2159 1.216 4.97 0 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0.0197 1065.6836 2064.8008 1.101 3.06 0 + 2644 452.0283 82.18217 -6.6379 0.1974 1042.1830 2091.2512 1.489 4.59 0 + 2645 1242.122 104.5519 -7.7354 0.0914 2032.4028 1101.7217 1.197 4.39 0 + 2646 2522.232 115.5275 -8.5045 0.0497 1064.7255 2075.7419 1.070 3.48 0 + 2647 1021.675 112.9087 -7.5233 0.1200 717.9244 1131.3656 1.201 4.12 0 + 2648 951.2556 97.88872 -7.4457 0.1118 1859.6434 2079.4607 1.386 4.10 2 + 2649 628.0205 86.82516 -6.9949 0.1501 1374.5367 1121.6447 1.172 4.46 0 + 2650 2792.13 129.5872 -8.6148 0.0504 485.3602 1895.4178 1.298 4.84 0 + 2651 1584.803 109.2404 -7.9999 0.0749 998.9081 1104.2650 1.098 3.45 0 + 2652 4123.348 122.651 -9.0381 0.0323 1176.1320 2042.9088 1.063 3.00 0 + 2653 8655.515 141.3711 -9.8432 0.0177 1984.2468 2089.9795 1.040 2.95 2 + 2654 12127.23 159.9172 -10.2094 0.0143 1274.1337 2065.3269 1.104 2.74 0 + 2655 3393.371 111.8428 -8.8266 0.0358 1324.7980 1110.7543 1.088 2.87 0 + 2656 3735.105 142.1781 -8.9308 0.0413 223.9512 1106.0879 1.566 5.09 0 + 2657 972.3671 95.48934 -7.4696 0.1066 1446.5028 2094.0762 1.162 3.10 0 + 2658 1579.192 144.8056 -7.9961 0.0996 626.0229 1886.9546 1.196 4.39 2 + 2659 54573.25 257.045 -11.8424 0.0051 307.6335 2078.2358 1.162 4.33 0 + 2660 295.8306 55.06003 -6.1776 0.2021 1705.7684 1111.5359 1.643 2.87 0 + 2661 793.9148 110.2478 -7.2494 0.1508 282.5414 1896.9829 1.858 5.83 0 + 2662 1134.331 97.946 -7.6368 0.0938 978.5649 1098.4272 1.176 3.81 0 + 2663 1498.13 130.8478 -7.9389 0.0949 134.6006 1107.5923 2.243 8.17 0 + 2664 3095.213 133.0142 -8.7267 0.0467 255.3880 1103.8340 1.512 5.61 0 + 2665 1479.787 134.5884 -7.9255 0.0988 315.1943 1101.7867 1.301 5.66 0 + 2666 905.4805 117.0036 -7.3922 0.1403 1366.8889 2052.3701 1.355 3.95 0 + 2667 2270.37 113.6628 -8.3902 0.0544 1385.3306 1100.8087 1.026 2.62 0 + 2668 1780.598 102.5368 -8.1264 0.0625 1206.8024 2066.0291 1.180 3.01 0 + 2669 4050.735 140.6254 -9.0188 0.0377 461.2163 1102.4436 1.181 4.20 0 + 2670 454.2125 75.20078 -6.6431 0.1798 1355.8224 1105.1055 1.248 3.22 0 + 2671 5117.694 127.9976 -9.2727 0.0272 1711.7972 1105.6346 1.077 2.69 0 + 2672 719.6088 74.42878 -7.1427 0.1123 1976.1490 1876.1798 1.035 4.61 0 + 2673 1313.429 106.4722 -7.7960 0.0880 970.8096 2077.6008 1.181 3.32 0 + 2674 734.9188 97.4202 -7.1656 0.1440 737.4161 1094.3137 1.538 4.99 0 + 2675 3136.685 126.6172 -8.7412 0.0438 1250.8207 1102.3766 1.101 3.39 2 + 2676 3576.452 119.6254 -8.8836 0.0363 1853.7322 2083.1396 1.060 3.05 2 + 2677 394.11 74.68987 -6.4890 0.2058 900.7248 1090.5585 1.781 2.45 0 + 2678 53903.96 260.7173 -11.8291 0.0053 1243.0690 2077.8225 1.056 2.84 0 + 2679 478.491 68.71769 -6.6997 0.1560 624.0974 2086.1611 1.227 4.42 0 + 2680 3640.337 128.3576 -8.9029 0.0383 334.0568 2080.6025 1.295 4.96 0 + 2681 2643.426 119.7003 -8.5554 0.0492 1861.1586 2087.3889 1.114 3.06 0 + 2682 3361.111 101.172 -8.8162 0.0327 2014.6555 2041.0443 1.110 2.68 0 + 2683 901.079 110.9542 -7.3869 0.1337 99.9954 1097.1802 2.100 6.40 0 + 2684 405.5177 67.23006 -6.5200 0.1800 1116.6393 1921.5461 2.050 4.04 0 + 2685 128601.9 356.7502 -12.7731 0.0030 1236.0068 2024.1636 1.167 3.26 0 + 2686 1354.801 110.5159 -7.8297 0.0886 1419.4594 1095.7394 1.319 4.10 0 + 2687 2020.484 136.111 -8.2636 0.0732 710.3196 1099.7936 1.657 5.70 0 + 2688 2690.802 125.1723 -8.5747 0.0505 1587.3486 2021.3665 1.150 3.60 2 + 2689 1387.033 102.1687 -7.8552 0.0800 1606.9462 2026.8530 1.177 3.05 0 + 2690 694.882 88.08907 -7.1048 0.1377 371.1877 2017.9414 1.910 5.36 0 + 2691 490.7534 66.89112 -6.7272 0.1480 1158.2180 2031.1277 1.215 4.36 0 + 2692 7019.765 151.2442 -9.6158 0.0234 93.4491 2046.5492 1.265 6.14 0 + 2693 1261 96.87258 -7.7518 0.0834 699.2609 2028.0394 1.038 3.44 0 + 2694 1485.495 108.6134 -7.9297 0.0794 1039.7900 2029.7932 1.099 3.81 0 + 2695 511.1955 88.84022 -6.7715 0.1887 582.9534 2056.0283 1.051 3.74 0 + 2696 1435.363 100.0067 -7.8924 0.0757 1096.2303 2072.6096 1.107 3.55 0 + 2697 19116.81 228.3701 -10.7035 0.0130 21.0186 2067.8040 1.215 5.84 2 + 2698 2386.5 114.7398 -8.4444 0.0522 1492.5909 2047.3142 1.144 3.06 0 + 2699 2007.63 121.0782 -8.2567 0.0655 176.9869 2071.1746 1.489 5.01 0 + 2700 788.7974 103.2355 -7.2424 0.1421 471.7373 2090.4883 1.327 5.65 0 + 2701 2242.666 117.902 -8.3769 0.0571 884.5356 2075.2192 1.241 3.93 0 + 2702 6536.012 140.493 -9.5383 0.0233 537.2120 2067.5908 1.209 3.71 0 + 2703 453.0597 82.95452 -6.6404 0.1988 1148.7583 2029.7373 1.485 4.15 0 + 2704 2663.601 201.5717 -8.5637 0.0822 1471.7543 2065.3037 1.931 10.73 0 + 2705 8306.794 151.6 -9.7986 0.0198 1437.5862 2045.6119 1.025 2.80 0 + 2706 1152.95 96.68497 -7.6545 0.0911 948.6937 2048.7727 1.210 3.24 0 + 2707 1143.414 116.6887 -7.6455 0.1108 250.4668 1926.5549 1.495 7.04 0 + 2708 450.3626 79.3172 -6.6339 0.1913 1114.6044 2073.7961 1.663 4.32 0 + 2709 1788.392 115.6306 -8.1312 0.0702 321.7087 2063.5652 1.494 5.04 0 + 2710 1564.542 100.1694 -7.9860 0.0695 1615.9865 2050.3193 1.132 3.64 0 + 2711 559.1909 70.10897 -6.8689 0.1362 2002.9822 2009.1516 1.039 3.15 0 + 2712 575.2653 81.4085 -6.8997 0.1537 1170.0045 2046.6476 1.125 3.21 0 + 2713 2158.294 112.5569 -8.3353 0.0566 901.6337 1924.7853 1.174 3.33 0 + 2714 12757.76 160.227 -10.2644 0.0136 601.5705 1973.8696 1.507 4.80 0 + 2715 10004.7 159.9574 -10.0005 0.0174 1055.5801 1988.8503 1.080 2.79 0 + 2716 703.9883 86.2963 -7.1189 0.1331 1624.0564 2054.2910 1.204 3.42 0 + 2717 868.1783 98.57106 -7.3465 0.1233 204.2550 2066.4646 1.799 6.59 0 + 2718 4790.84 140.6111 -9.2010 0.0319 156.5295 2049.1831 1.461 5.18 0 + 2719 3516.793 122.4035 -8.8654 0.0378 1428.0309 2034.9392 1.148 2.74 0 + 2720 2982.631 129.0369 -8.6865 0.0470 1835.3202 2046.0171 1.199 3.24 0 + 2721 679.1122 93.1722 -7.0799 0.1490 1210.2627 2012.7628 1.098 3.23 0 + 2722 3668.948 118.9877 -8.9114 0.0352 1901.3955 2008.5603 1.058 3.13 0 + 2723 1781.832 115.9135 -8.1272 0.0706 646.7089 2031.8628 1.140 3.92 2 + 2724 281.6541 63.65999 -6.1243 0.2455 344.7359 2051.4219 1.699 3.43 0 + 2725 619.2491 93.13755 -6.9797 0.1633 702.9641 2012.9077 1.473 5.32 0 + 2726 5235.71 122.6545 -9.2974 0.0254 1838.5062 2018.6974 1.432 3.09 2 + 2727 12194.77 165.0234 -10.2154 0.0147 469.0217 2036.6527 1.129 4.00 0 + 2728 5714.993 134.6595 -9.3925 0.0256 1289.4628 2047.0675 1.072 2.67 0 + 2729 22533.38 203.5826 -10.8821 0.0098 642.5867 2039.9712 1.082 3.36 2 + 2730 3977.157 123.4801 -8.9989 0.0337 1105.8558 1925.5835 1.303 3.03 0 + 2731 10273.27 226.5268 -10.0293 0.0239 4.8302 2056.7898 1.203 7.95 8 + 2732 624.5624 88.17159 -6.9889 0.1533 786.2952 2048.1079 1.300 3.75 0 + 2733 796.7473 85.34554 -7.2533 0.1163 1111.8564 2036.7817 1.240 3.03 0 + 2734 2867.803 125.0189 -8.6439 0.0473 1176.0751 2025.6990 1.123 2.73 0 + 2735 1393.893 114.8213 -7.8606 0.0895 1700.1221 2034.0551 1.153 3.53 0 + 2736 39354.62 246.8536 -11.4875 0.0068 1834.0985 2012.2029 1.075 2.70 2 + 2737 1141.828 90.93485 -7.6440 0.0865 1292.8357 2016.6920 1.256 3.27 0 + 2738 680.2791 92.57291 -7.0817 0.1478 1534.9895 2035.1718 1.167 3.37 0 + 2739 702.8086 86.50707 -7.1171 0.1337 264.5731 2022.9968 1.222 3.58 0 + 2740 1930.362 110.6724 -8.2141 0.0623 1363.3539 2024.4656 1.159 3.92 0 + 2741 78413.04 295.4994 -12.2360 0.0041 1838.6178 2026.9249 1.075 2.68 2 + 2742 79975.76 300.8535 -12.2574 0.0041 1915.7416 2013.7467 1.068 2.79 0 + 2743 394.1216 73.73378 -6.4891 0.2032 1663.1554 2042.3103 1.501 3.36 0 + 2744 2727.301 124.4569 -8.5893 0.0496 339.8560 2011.2029 1.219 5.05 0 + 2745 17196.75 182.856 -10.5886 0.0115 1581.3053 2028.9185 1.020 2.69 2 + 2746 1170.681 102.6528 -7.6711 0.0952 1787.2922 2020.6738 1.086 3.15 0 + 2747 4027.512 136.2243 -9.0126 0.0367 121.4717 2052.9199 1.331 5.54 0 + 2748 1905.072 115.7902 -8.1998 0.0660 400.1123 2049.7349 1.276 4.48 0 + 2749 2963.157 107.6918 -8.6794 0.0395 1987.7723 2016.5015 1.039 2.88 0 + 2750 3329.695 129.3911 -8.8060 0.0422 1459.5966 2022.1084 1.271 2.55 0 + 2751 904.0641 93.27966 -7.3905 0.1121 845.6000 2008.1967 1.192 3.89 2 + 2752 282.1056 64.47244 -6.1260 0.2482 137.0956 2013.6449 1.823 3.39 0 + 2753 22126.85 199.6123 -10.8623 0.0098 1452.0442 2007.4897 1.035 2.61 0 + 2754 4325.652 129.5999 -9.0901 0.0325 1472.3702 2041.7649 1.118 2.56 0 + 2755 1378.556 126.5054 -7.8486 0.0997 992.7592 2013.7443 1.438 5.56 0 + 2756 1306.935 100.437 -7.7906 0.0835 1139.2271 2040.8754 1.171 3.31 0 + 2757 1942.153 123.8326 -8.2207 0.0692 1667.2969 2025.3760 1.217 4.95 0 + 2758 7085.335 149.6027 -9.6259 0.0229 626.4941 1991.3829 1.073 3.60 2 + 2759 506.6704 83.933 -6.7618 0.1799 837.3547 2027.5806 1.527 2.73 0 + 2760 2142.986 116.7097 -8.3275 0.0591 1747.6578 2020.6570 1.171 3.03 0 + 2761 914.5541 104.4176 -7.4030 0.1240 863.8208 2018.3585 1.567 6.41 0 + 2762 17198.44 189.6361 -10.5887 0.0120 348.2033 2021.5592 1.116 4.02 0 + 2763 507.5881 82.10741 -6.7638 0.1757 1758.9091 2017.3539 1.329 2.31 0 + 2764 2283.125 103.6551 -8.3963 0.0493 824.7443 2001.7623 1.146 3.28 0 + 2765 1305.445 94.83878 -7.7894 0.0789 554.7977 1989.1647 1.019 3.23 0 + 2766 3614.577 121.6057 -8.8951 0.0365 981.6976 1974.5747 1.149 3.16 0 + 2767 3281.467 117.2918 -8.7902 0.0388 1557.7844 2013.5884 1.038 2.50 0 + 2768 29304.27 215.1875 -11.1673 0.0080 557.7562 1928.7201 1.145 3.68 0 + 2769 547.8101 71.50213 -6.8466 0.1417 1953.0642 1921.0667 1.048 4.05 0 + 2770 283.6612 59.60298 -6.1320 0.2282 1469.2081 1920.9714 1.585 1.58 0 + 2771 13611.3 174.1741 -10.3347 0.0139 275.1719 1978.8250 1.131 4.53 0 + 2772 792.4929 85.80109 -7.2475 0.1176 675.0348 2007.2382 1.638 5.61 0 + 2773 2687.748 122.0386 -8.5735 0.0493 850.0427 2005.3823 1.200 3.39 2 + 2774 2554.499 111.9688 -8.5183 0.0476 655.3920 2000.6948 1.196 3.37 0 + 2775 1337.414 111.9358 -7.8157 0.0909 1622.4910 1994.7990 2.544 8.11 0 + 2776 932.7863 100.5878 -7.4245 0.1171 1185.9806 2000.6470 1.243 4.50 0 + 2777 1060.294 96.02937 -7.5636 0.0984 1867.2429 2010.8054 1.164 3.27 0 + 2778 542.6016 97.21107 -6.8362 0.1946 165.3388 1973.8866 1.395 4.46 0 + 2779 1752.521 162.1565 -8.1092 0.1005 82.3789 1998.2902 2.198 8.68 2 + 2780 765.918 89.28376 -7.2105 0.1266 1183.3757 1920.5032 1.159 2.81 0 + 2781 1052.618 96.65678 -7.5557 0.0997 1687.2463 1998.0814 1.345 3.21 0 + 2782 41872.16 252.6864 -11.5548 0.0066 635.0281 2002.3348 1.353 3.41 2 diff --git a/Lab_1/object.fits b/Lab_1/object.fits new file mode 100644 index 0000000..be39d34 --- /dev/null +++ b/Lab_1/object.fits @@ -0,0 +1,63499 @@ +SIMPLE = T / conforms to FITS standard BITPIX = -32 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 2100 NAXIS2 = 2100 EQUINOX = 2000.00000000 / Mean equinox MJD-OBS = 56365.29978605324 / [UTC days] Start date/time (Modified Julian DatRADECSYS= 'ICRS ' / Astrometric system CTYPE1 = 'RA---TAN' / WCS projection type for this axis CUNIT1 = 'deg ' / Axis unit CRVAL1 = 2.181411666670E+02 / World coordinate on this axis CRPIX1 = 1.050500000000E+03 / Reference pixel on this axis CD1_1 = -1.297222243415E-04 / Linear projection matrix CD1_2 = 0.000000000000E+00 / Linear projection matrix CTYPE2 = 'DEC--TAN' / WCS projection type for this axis CUNIT2 = 'deg ' / Axis unit CRVAL2 = -4.422438888890E+01 / World coordinate on this axis CRPIX2 = 1.050500000000E+03 / Reference pixel on this axis CD2_1 = 0.000000000000E+00 / Linear projection matrix CD2_2 = 1.297222243415E-04 / Linear projection matrix EXPTIME = 60.0 / [s] Exposure length GAIN = 1.782535332273E+00 / Maximum equivalent gain (e-/ADU) SATURATE= 4.541940455960E+04 / Saturation Level (ADU) COMMENT SOFTNAME= 'SWarp ' / The software that processed those data SOFTVERS= '2.19.1 ' / Version of the software SOFTDATE= '2012-03-15' / Release date of the software SOFTAUTH= 'Emmanuel BERTIN