{ "cells": [ { "cell_type": "markdown", "id": "029327ef-9fbb-473e-ab4c-6dfae5663147", "metadata": {}, "source": [ "

ARCTIC data reduction from 10/23 trip" ] }, { "cell_type": "markdown", "id": "1fe6e89c-75d1-4116-a679-5424fc268c95", "metadata": {}, "source": [ "Start with usual setup" ] }, { "cell_type": "code", "execution_count": 2, "id": "05292d8d-4afb-4b0b-bbea-2fcb2aa27de7", "metadata": {}, "outputs": [], "source": [ "from pyvista import tv,imred,stars\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import importlib\n", "import matplotlib\n", "from astropy.table import Table, vstack\n", "%matplotlib qt" ] }, { "cell_type": "markdown", "id": "ea426e0e-6172-4d66-9830-babae0585b42", "metadata": {}, "source": [ "Instantiate a Reducer. Since the setup file is for 2x2, change the scale manually. (Note for bias subtraction to work correctly in 4x4, need to use overscan regions as specified in the headers; new version of Reducer allows for this.)" ] }, { "cell_type": "code", "execution_count": 3, "id": "4a019419-b03a-410b-a7bc-4c4a7f57d9d2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INSTRUMENT: ARCTIC config: quad\n", " will use format: /home/holtz/raw/apo/oct23/UT231023/arctic/*{:04d}.f*.fits*\n", " gain: [2.0] rn: [ 3.7]\n", " scale: 0.228 \n", " Biastype : 1\n", " Bias box: \n", " SC NC SR NR\n", " 1026 26 20 981 \n", " 1026 26 1050 979 \n", " 1052 24 20 981 \n", " 1052 24 1050 979 \n", " Trim box: \n", " SC NC SR NR\n", " 2 1024 0 1024 \n", " 2 1024 1026 1024 \n", " 1076 1024 0 1024 \n", " 1076 1024 1026 1024 \n", " Norm box: \n", " SC NC SR NR\n", " 800 201 800 201 \n", " SC NC SR NR\n", " 550 201 550 201 \n" ] } ], "source": [ "red=imred.Reducer('ARCTIC',conf='quad',dir='/home/holtz/raw/apo/oct23/UT231023/arctic')\n", "red.scale=0.44\n", "red.headerbox\n", "red.normbox[0].set(550,750,550,750)\n", "red.normbox[0].show()" ] }, { "cell_type": "markdown", "id": "2c03f4b7-2f82-407c-b562-d2421cdafd4c", "metadata": {}, "source": [ "Get the image log from headers. This is useful to have to search through to find images, e.g. through a particular filter, etc." ] }, { "cell_type": "code", "execution_count": 4, "id": "a0060d6f-3aef-4ee4-8be8-e67f7392f9ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " FILE DATE-OBS OBJNAME RA DEC FILTER EXPTIME\n", "----------------- -------------------------- --------- ----------- ----------- -------- -------\n", " cal.0001.fits 2023-10-22T23:58:11.679580 6:56:00.00 75:00:00.00 CUVR 0\n", " cal.0002.fits 2023-10-23T00:00:29.811612 10:00:00.00 83:00:00.00 CUVR 0\n", " cal.0003.fits 2023-10-23T00:03:56.789339 5:00:00.00 10:00:00.00 CUVR 0\n", " cal.0004.fits 2023-10-23T00:15:14.384060 6:56:00.00 60:00:00.00 SDSS r#1 1.5\n", " cal.0005.fits 2023-10-23T00:16:50.980140 6:56:00.00 60:00:00.00 SDSS r#1 1.5\n", " cal.0006.fits 2023-10-23T00:16:55.871686 6:56:00.00 60:00:00.00 SDSS r#1 1.5\n", " cal.0007.fits 2023-10-23T00:17:00.850195 6:56:00.00 60:00:00.00 SDSS r#1 1.5\n", " 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2023-10-23T04:31:02.160797 chart_145 22:41:34.00 1:11:30.00 SDSS i 1.0\n", "science.0210.fits 2023-10-23T04:31:12.150512 chart_145 22:41:34.00 1:11:30.00 SDSS r#1 0.5\n", "science.0211.fits 2023-10-23T04:31:16.147362 chart_145 22:41:34.00 1:11:30.00 SDSS r#1 0.5\n", "science.0212.fits 2023-10-23T04:31:20.079518 chart_145 22:41:34.00 1:11:30.00 SDSS r#1 0.5\n", "science.0213.fits 2023-10-23T04:31:29.638386 chart_145 22:41:34.00 1:11:30.00 SDSS z 1.0\n", "science.0214.fits 2023-10-23T04:31:34.076468 chart_145 22:41:34.00 1:11:30.00 SDSS z 1.0\n", "science.0215.fits 2023-10-23T04:31:38.568447 chart_145 22:41:34.00 1:11:30.00 SDSS z 1.0\n", "science.0216.fits 2023-10-23T04:38:45.409441 chart_18 2:57:34.00 0:18:00.00 SDSS z 1.0\n", "science.0217.fits 2023-10-23T04:40:30.136900 chart_18 2:57:34.00 0:18:00.00 SDSS u 10.0\n", "science.0218.fits 2023-10-23T04:40:43.517109 chart_18 2:57:34.00 0:18:00.00 SDSS u 10.0\n", "science.0219.fits 2023-10-23T04:40:56.937923 chart_18 2:57:34.00 0:18:00.00 SDSS u 10.0\n", "science.0220.fits 2023-10-23T04:41:16.207689 chart_18 2:57:34.00 0:18:00.00 SDSS g#2 1.0\n", "science.0221.fits 2023-10-23T04:41:20.599483 chart_18 2:57:34.00 0:18:00.00 SDSS g#2 1.0\n", "science.0222.fits 2023-10-23T04:41:25.051391 chart_18 2:57:34.00 0:18:00.00 SDSS g#2 1.0\n", "science.0223.fits 2023-10-23T04:41:35.268456 chart_18 2:57:34.00 0:18:00.00 SDSS i 2.0\n", "science.0224.fits 2023-10-23T04:41:40.821946 chart_18 2:57:34.00 0:18:00.00 SDSS i 2.0\n", "science.0225.fits 2023-10-23T04:41:46.288592 chart_18 2:57:34.00 0:18:00.00 SDSS i 2.0\n", "science.0226.fits 2023-10-23T04:41:57.538278 chart_18 2:57:34.00 0:18:00.00 SDSS r#1 1.0\n", "science.0227.fits 2023-10-23T04:42:01.961736 chart_18 2:57:34.00 0:18:00.00 SDSS r#1 1.0\n", "science.0228.fits 2023-10-23T04:42:06.346289 chart_18 2:57:34.00 0:18:00.00 SDSS r#1 1.0\n", "science.0229.fits 2023-10-23T04:42:16.788959 chart_18 2:57:34.00 0:18:00.00 SDSS z 2.0\n", "science.0230.fits 2023-10-23T04:42:22.271212 chart_18 2:57:34.00 0:18:00.00 SDSS z 2.0\n", "science.0231.fits 2023-10-23T04:42:27.728454 chart_18 2:57:34.00 0:18:00.00 SDSS z 2.0\n", "science.0232.fits 2023-10-23T04:52:35.308384 chart_129 20:42:50.00 0:16:00.00 SDSS z 1.0\n", "science.0233.fits 2023-10-23T04:54:37.975634 chart_129 20:42:52.00 0:15:30.00 SDSS z 1.0\n", "science.0234.fits 2023-10-23T04:55:41.749001 chart_129 20:42:52.00 0:15:30.00 SDSS u 10.0\n", "science.0235.fits 2023-10-23T04:55:55.179444 chart_129 20:42:52.00 0:15:30.00 SDSS u 10.0\n", "science.0236.fits 2023-10-23T04:56:08.695185 chart_129 20:42:52.00 0:15:30.00 SDSS u 10.0\n", "science.0237.fits 2023-10-23T04:56:27.868121 chart_129 20:42:52.00 0:15:30.00 SDSS g#2 1.0\n", "science.0238.fits 2023-10-23T04:56:32.478160 chart_129 20:42:52.00 0:15:30.00 SDSS g#2 1.0\n", "science.0239.fits 2023-10-23T04:56:36.887844 chart_129 20:42:52.00 0:15:30.00 SDSS g#2 1.0\n", "science.0240.fits 2023-10-23T04:56:47.157215 chart_129 20:42:52.00 0:15:30.00 SDSS i 2.0\n", "science.0241.fits 2023-10-23T04:56:52.579384 chart_129 20:42:52.00 0:15:30.00 SDSS i 2.0\n", "science.0242.fits 2023-10-23T04:56:57.995385 chart_129 20:42:52.00 0:15:30.00 SDSS i 2.0\n", "science.0243.fits 2023-10-23T04:57:09.238048 chart_129 20:42:52.00 0:15:30.00 SDSS r#1 1.0\n", "science.0244.fits 2023-10-23T04:57:13.787765 chart_129 20:42:52.00 0:15:30.00 SDSS r#1 1.0\n", "science.0245.fits 2023-10-23T04:57:18.238851 chart_129 20:42:52.00 0:15:30.00 SDSS r#1 1.0\n", "science.0246.fits 2023-10-23T04:57:28.606670 chart_129 20:42:52.00 0:15:30.00 SDSS z 2.0\n", "science.0247.fits 2023-10-23T04:57:34.056460 chart_129 20:42:52.00 0:15:30.00 SDSS z 2.0\n", "science.0248.fits 2023-10-23T04:57:39.479077 chart_129 20:42:52.00 0:15:30.00 SDSS z 2.0\n" ] } ], "source": [ "log=red.log()\n", "log.pprint_all()" ] }, { "cell_type": "markdown", "id": "13dd8c87-6f6a-4471-998d-b3a40403cda3", "metadata": {}, "source": [ "Get the filter names from the headers" ] }, { "cell_type": "code", "execution_count": 7, "id": "61a69883-6585-40df-bb7d-97e79ba64f36", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'CUVR', 'SDSS g#2', 'SDSS i', 'SDSS r#1', 'SDSS u', 'SDSS z'}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(log['FILTER'])" ] }, { "cell_type": "markdown", "id": "669c8416-c31b-4cce-90f7-fa9c8090f2b0", "metadata": {}, "source": [ "Specifiy the image numbers of the twilight flats in each filter. Set correspondence of simple filter names to header filter names." ] }, { "cell_type": "code", "execution_count": 8, "id": "0424895c-8870-4811-98e7-897a9dc02ed1", "metadata": {}, "outputs": [], "source": [ "twiframes=[]\n", "twiframes.append(range(34,41+1)) #u\n", "twiframes.append(range(65,70+1)) # g\n", "twiframes.append(range(71,76+1)) # r\n", "twiframes.append(range(77,86+1)) # i\n", "twiframes.append(range(42,64+1)) #z\n", "bias=np.arange(87,97+1)\n", "filts=['u','g','r','i','z']\n", "filtnames=['SDSS u','SDSS g#2','SDSS r#1','SDSS i','SDSS z']" ] }, { "cell_type": "markdown", "id": "df51ffc5-e79b-4ce8-9df2-9417f06964d3", "metadata": {}, "source": [ "Create the flats for each filter, and load into a flat dictionary. Inspect each componentm and modify twilight flat list as needed to remove bad frames." ] }, { "cell_type": "code", "execution_count": 9, "id": "c553b978-e32d-43b0-b6ee-8c4136674961", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "libGL error: No matching fbConfigs or visuals found\n", "libGL error: failed to load driver: swrast\n" ] } ], "source": [ "t=tv.TV()" ] }, { "cell_type": "code", "execution_count": 10, "id": "d11e58e0-d36c-403f-8e2e-69b01ff6a591", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/cal.0071.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/cal.0072.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/cal.0073.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/cal.0074.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/cal.0075.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/cal.0076.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " combining data with median....\n", " calculating uncertainty....\n", " See final image, use - key for S/N image.\n", " To continue, hit space in display window (p for debug) \n", " see image: cal.0071.fits divided by master\n", " To continue, hit space in display window (p for debug) \n", " see image: cal.0072.fits divided by master\n", " To continue, hit space in display window (p for debug) \n", " see image: cal.0073.fits divided by master\n", " To continue, hit space in display window (p for debug) \n", " see image: cal.0074.fits divided by master\n", " To continue, hit space in display window (p for debug) \n", " see image: cal.0075.fits divided by master\n", " To continue, hit space in display window (p for debug) \n", " see image: cal.0076.fits divided by master\n", " To continue, hit space in display window (p for debug) \n" ] } ], "source": [ "flat={}\n", "for filt,frames in zip(filts[2:3],twiframes[2:3]) :\n", " flat[filt] = red.mkflat(frames,display=t)" ] }, { "cell_type": "code", "execution_count": 8, "id": "0744547a-e7b7-4eb7-9b69-2ed1907a9694", "metadata": {}, "outputs": [], "source": [ "t.tv(flat['r'])" ] }, { "cell_type": "markdown", "id": "a2d8a0d1-a7cd-48d5-9060-2147b98eb2bb", "metadata": {}, "source": [ "Get the positions and magnitudes of standard stars" ] }, { "cell_type": "code", "execution_count": 11, "id": "e9e804cf-c679-40c0-b95e-baad4013df20", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BD+33 4737353.65054234.03950011.069.418.848.668.60155
PG2336+004B354.6594170.71288913.7512.6512.3112.2112.20156
SA 115 420355.6520001.09966712.4411.3511.0610.9810.98157
SA 115 516356.0640831.23680613.0810.9110.119.799.62158
" ], "text/plain": [ "\n", " NAME RA DEC u g r i z ID \n", " bytes11 float64 float64 float64 float64 float64 float64 float64 int64\n", "----------- ---------- ---------- ------- ------- ------- ------- ------- -----\n", " Hilt 31 7.046458 64.131056 12.58 11.58 11.00 10.70 10.55 1\n", " G 158-100 8.477500 -12.133028 16.30 15.20 14.69 14.47 14.38 2\n", " BD+71 0031 10.934750 72.178639 11.25 10.35 10.09 10.01 10.00 3\n", " SA 92 342 13.791250 0.720250 12.85 11.78 11.53 11.48 11.49 4\n", " SA 92 263 13.914208 0.605556 14.50 12.27 11.47 11.16 10.99 5\n", " SA 92 502 14.033875 1.073639 13.03 12.00 11.71 11.63 11.62 6\n", " SA 92 282 14.195250 0.641917 14.07 13.07 12.94 12.91 12.92 7\n", " SA 92 288 14.320833 0.613528 13.78 12.01 11.35 11.12 11.02 8\n", " SA 93 317 28.657208 0.716806 12.82 11.75 11.44 11.35 11.35 9\n", " SA 93 333 28.771750 0.761806 14.14 12.39 11.76 11.56 11.47 10\n", " SA 93 424 28.859792 0.945139 14.53 12.12 11.30 11.02 10.90 11\n", " Hilt 190 29.600292 61.895417 12.43 11.52 10.88 10.56 10.34 12\n", " LHS 0014 33.087458 3.575667 13.39 10.76 9.48 8.55 8.10 13\n", " Hilt 233 33.124875 59.901139 12.63 11.49 10.66 10.21 9.92 14\n", " ... ... ... ... ... ... ... ... ...\n", " BD+17 4708 332.880708 18.092806 10.56 9.64 9.35 9.25 9.23 144\n", " BD-11 5781 333.294500 -11.177333 11.86 9.90 9.18 8.94 8.81 145\n", " SA 114 531 340.153250 0.865444 13.84 12.42 11.88 11.69 11.61 146\n", " SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 11.50 147\n", " SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 11.88 148\n", " SA 114 548 340.403458 0.984917 15.40 12.25 11.13 10.69 10.44 149\n", " SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 12.41 150\n", " G 27-45 341.234583 -2.353556 12.90 11.79 11.28 11.09 11.01 151\n", " Ross 786 347.388917 0.717250 11.47 10.25 9.71 9.48 9.39 152\n", " GD 246 348.096125 10.784500 12.35 12.84 13.35 13.72 14.09 153\n", " BD+38 4955 348.411708 39.417389 12.35 11.32 10.80 10.58 10.48 154\n", " BD+33 4737 353.650542 34.039500 11.06 9.41 8.84 8.66 8.60 155\n", "PG2336+004B 354.659417 0.712889 13.75 12.65 12.31 12.21 12.20 156\n", " SA 115 420 355.652000 1.099667 12.44 11.35 11.06 10.98 10.98 157\n", " SA 115 516 356.064083 1.236806 13.08 10.91 10.11 9.79 9.62 158" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tab=Table.read('ugriz_smith.fits')\n", "#table formatting\n", "for col in ['u','g','r','i','z'] : tab[col].info.format='.2f'\n", "for col in ['RA','DEC'] : tab[col].info.format='.6f'\n", "tab\n" ] }, { "cell_type": "markdown", "id": "eca0bb8a-a4e4-4663-802d-c688d0ab4d40", "metadata": {}, "source": [ "Define a function to read/reduce a frame, solve for WCS, find standard stars on frame, recenter, and do photometry" ] }, { "cell_type": "code", "execution_count": 28, "id": "6daa5eed-d30c-40bb-a562-97858b00692a", "metadata": {}, "outputs": [], "source": [ "import os\n", "def dophot(im,tab_phot, display, inter=False) :\n", "\n", " # populate x and y columns with pixel locations in this image\n", " tab['x'],tab['y']=im.wcs.wcs_world2pix(tab['RA'],tab['DEC'],0)\n", "\n", " nrow,ncol=im.data.shape\n", " gd=np.where((tab['x']>0)&(tab['y']>0)&(tab['x'] ./tmp8kez8u84xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmp8kez8u84xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.4,1.18333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-17.fits, field objects 1-10).\n", " log-odds ratio 127.109 (1.59489e+55), 27 match, 0 conflict, 6 distractors, 525 index.\n", " RA,Dec = (340.162,1.30573), pixel scale 0.45726 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++-+++++++-+-+++-++++++++--+++(best)--++++\n", "Field 1: solved with index index-4201-17.fits.\n", "Field 1 solved: writing to file ./tmp8kez8u84xy.solved to indicate this.\n", "Field: tmp8kez8u84xy.fits\n", "Field center: (RA,Dec) = (340.162604, 1.305878) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:39.025, +01:18:21.162).\n", "Field size: 36.388 x 22.3635 arcminutes\n", "Field rotation angle: up is -0.0521489 degrees E of N\n", "Field parity: pos\n", "\n", " See plate solve stars\n", " To continue, hit space in display window (p for debug) \n" ] } ], "source": [ "im=red.reduce(114,flat=flat['r'],solve=True,display=t)" ] }, { "cell_type": "markdown", "id": "218e86df-9ccc-402a-b1f4-8ebf80f3ce17", "metadata": {}, "source": [ "Show the derived WCS header cards" ] }, { "cell_type": "code", "execution_count": 14, "id": "c50b29ec-6047-4b82-9db1-0b5333e9571b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WCSAXES = 2 / Number of coordinate axes \n", "CRPIX1 = 260.05594212 / Pixel coordinate of reference point \n", "CRPIX2 = 715.468698974 / Pixel coordinate of reference point \n", "PC1_1 = -0.000127366744027 / Coordinate transformation matrix element \n", "PC1_2 = 2.77366245184E-07 / Coordinate transformation matrix element \n", "PC2_1 = -5.0890778923E-07 / Coordinate transformation matrix element \n", "PC2_2 = 0.000127026810102 / Coordinate transformation matrix element \n", "CDELT1 = 1.0 / [deg] Coordinate increment at reference point \n", "CDELT2 = 1.0 / [deg] Coordinate increment at reference point \n", "CUNIT1 = 'deg' / Units of coordinate increment and value \n", "CUNIT2 = 'deg' / Units of coordinate increment and value \n", "CTYPE1 = 'RA---TAN-SIP' / TAN (gnomonic) projection + SIP distortions \n", "CTYPE2 = 'DEC--TAN-SIP' / TAN (gnomonic) projection + SIP distortions \n", "CRVAL1 = 340.433752743 / [deg] Coordinate value at reference point \n", "CRVAL2 = 1.20631338904 / [deg] Coordinate value at reference point \n", "LONPOLE = 180.0 / [deg] Native longitude of celestial pole \n", "LATPOLE = 1.20631338904 / [deg] Native latitude of celestial pole \n", "MJDREF = 0.0 / [d] MJD of fiducial time \n", "RADESYS = 'FK5' / Equatorial coordinate system \n", "EQUINOX = 2000.0 / [yr] Equinox of equatorial coordinates \n", "A_ORDER = 2 / SIP polynomial order, axis 0, detector to sky \n", "A_0_2 = -6.63338685338E-06 / SIP distortion coefficient \n", "A_1_1 = 2.79093006704E-06 / SIP distortion coefficient \n", "A_2_0 = -2.39567982696E-06 / SIP distortion coefficient \n", "B_ORDER = 2 / SIP polynomial order, axis 1, detector to sky \n", "B_0_2 = -5.30664887484E-06 / SIP distortion coefficient \n", "B_1_1 = -6.38523985819E-06 / SIP distortion coefficient \n", "B_2_0 = 4.67732261704E-06 / SIP distortion coefficient \n", "AP_ORDER= 2 / SIP polynomial order, axis 0, sky to detector \n", "AP_0_0 = -0.235536753643 / SIP distortion coefficient \n", "AP_0_1 = -0.000130857780066 / SIP distortion coefficient \n", "AP_0_2 = 6.9931793451E-06 / SIP distortion coefficient \n", "AP_1_0 = 0.000121295766968 / SIP distortion coefficient \n", "AP_1_1 = -3.05749948783E-06 / SIP distortion coefficient \n", "AP_2_0 = 2.47611058711E-06 / SIP distortion coefficient \n", "BP_ORDER= 2 / SIP polynomial order, axis 1, sky to detector \n", "BP_0_0 = -0.112489486079 / SIP distortion coefficient \n", "BP_0_1 = -0.000197539325936 / SIP distortion coefficient \n", "BP_0_2 = 5.59599593941E-06 / SIP distortion coefficient \n", "BP_1_0 = 0.000438152080978 / SIP distortion coefficient \n", "BP_1_1 = 6.55931263975E-06 / SIP distortion coefficient \n", "BP_2_0 = -4.99916589186E-06 / SIP distortion coefficient " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "im.wcs.to_header(relax=True)" ] }, { "cell_type": "markdown", "id": "613494f8-7cd0-4573-b5fc-5f5778db451d", "metadata": {}, "source": [ "Demo identification of standards and aperture photometry on one image" ] }, { "cell_type": "code", "execution_count": 29, "id": "674dee8e-e176-4bbe-a6d5-0b1255b965de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " NAME RA DEC u g r i z ID ... EXPTIME FILTER AIRMASS MJD aper10 aper10err sky skysig peak \n", "---------- ---------- -------- ----- ----- ----- ----- ----- --- ... ------- -------- ------- ------------ ------- --------- ----- ------ -------\n", "SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 11.50 147 ... 0.5 SDSS r#1 1.173 60240.148217 -13.352 0.002 30.54 4.61 50631.9\n", "SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 11.88 148 ... 0.5 SDSS r#1 1.173 60240.148217 -12.680 0.002 29.93 4.47 24872.5\n", "SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 12.41 150 ... 0.5 SDSS r#1 1.173 60240.148217 -12.961 0.002 30.28 4.56 31863.4\n", "Hit key near star center, \"q\" to quit\n" ] }, { "data": { "text/html": [ "
Table length=3\n", "
\n", "\n", "\n", "\n", "\n", "\n", "
col0NAMERADECugrizIDxyEXPTIMEFILTERAIRMASSMJDaper10aper10errskyskysigpeak
float64bytes11float64float64float64float64float64float64float64int64float64float64float64str8float64float64float64float64float64float64float64
--SA 114 654340.3589171.16963913.5212.1211.6711.5411.50147847.668169025426.8992511030.5SDSS r#11.17360240.148217-13.3520.00230.544.6150631.9
--SA 114 656340.3960831.18605615.0413.0812.3312.0311.88148555.724582639556.6765344110.5SDSS r#11.17360240.148217-12.6800.00229.934.4724872.5
--SA 114 750340.4362501.21005612.3611.8112.0212.2512.41150240.126243046744.6697637590.5SDSS r#11.17360240.148217-12.9610.00230.284.5631863.4
" ], "text/plain": [ "\n", " col0 NAME RA DEC u g r i ... FILTER AIRMASS MJD aper10 aper10err sky skysig peak \n", "float64 bytes11 float64 float64 float64 float64 float64 float64 ... str8 float64 float64 float64 float64 float64 float64 float64\n", "------- ---------- ---------- -------- ------- ------- ------- ------- ... -------- ------- ------------ ------- --------- ------- ------- -------\n", " -- SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 ... SDSS r#1 1.173 60240.148217 -13.352 0.002 30.54 4.61 50631.9\n", " -- SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 ... SDSS r#1 1.173 60240.148217 -12.680 0.002 29.93 4.47 24872.5\n", " -- SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 ... SDSS r#1 1.173 60240.148217 -12.961 0.002 30.28 4.56 31863.4" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tab_phot=[]\n", "dophot(im,tab_phot,t,inter=True) " ] }, { "cell_type": "markdown", "id": "b1530d9c-e214-45ab-af78-3d6ebf29c7a4", "metadata": {}, "source": [ "

Bias creation and subtraction" ] }, { "cell_type": "code", "execution_count": 15, "id": "b341d684-16ac-4f1c-af8b-b6565e729a9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0001.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0002.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0003.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0004.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0005.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0006.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0007.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0008.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0009.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0010.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0011.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0012.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0013.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0014.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0015.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " combining data with median....\n", " calculating uncertainty....\n", " See final image, use - key for S/N image.\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0001.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0002.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0003.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0004.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0005.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0006.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0007.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0008.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0009.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0010.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0011.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0012.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0013.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0014.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " see image: bias4x4.0015.fits minus master\n", " To continue, hit space in display window (p for debug) \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0047.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0048.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0049.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0050.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0051.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0052.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0053.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0054.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0055.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0056.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0057.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0058.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0059.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0060.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0061.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " combining data with median....\n", " calculating uncertainty....\n" ] } ], "source": [ "old_dir=red.dir\n", "red.dir='/home/holtz/raw/apo/oct23/UT231102'\n", "bias1=red.mkbias(range(1,16),display=t)\n", "bias2=red.mkbias(range(47,62))\n", "red.dir=old_dir" ] }, { "cell_type": "code", "execution_count": 18, "id": "46712e7e-46ab-4008-aaac-f47dfad3bfb5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0114.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " See bias box (solid outlines applied to dashed regions of the same color), and cross section. \n", " To continue, hit space in display window (p for debug) \n", " subtracting bias...\n", " flat fielding...\n", " See flat-fielded image and original with - (minus) key.\n", " To continue, hit space in display window (p for debug) \n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 47 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.400000 --dec 1.183333 --radius 3 tmptusrrvktxy.fits\n", "Reading input file 1 of 1: \"tmptusrrvktxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.HWrvr8 -i ./tmptusrrvktxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmptusrrvktxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmptusrrvktxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmptusrrvktxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.4,1.18333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-17.fits, field objects 1-10).\n", " log-odds ratio 123.125 (2.96977e+53), 27 match, 0 conflict, 9 distractors, 525 index.\n", " RA,Dec = (340.162,1.30573), pixel scale 0.457259 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++-+++++++-+-+++-++++++-+-+---+++(best)-------++++\n", "Field 1: solved with index index-4201-17.fits.\n", "Field 1 solved: writing to file ./tmptusrrvktxy.solved to indicate this.\n", "Field: tmptusrrvktxy.fits\n", "Field center: (RA,Dec) = (340.162747, 1.305888) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:39.059, +01:18:21.196).\n", "Field size: 36.3404 x 22.3644 arcminutes\n", "Field rotation angle: up is -0.0377698 degrees E of N\n", "Field parity: pos\n", "\n", " See plate solve stars\n", " To continue, hit space in display window (p for debug) \n" ] } ], "source": [ "im=red.reduce(114,bias=bias1,flat=flat['r'],solve=True,display=t) " ] }, { "cell_type": "markdown", "id": "c5926df1-69d6-4a1b-8736-67652662c5f3", "metadata": {}, "source": [ "Now run it for all of the frames of a given filter! Will take a little while with the plate solving. Note you could modify the for loop to do every nth frame for a quick test, etc...." ] }, { "cell_type": "code", "execution_count": 14, "id": "f3862d84-a177-4f77-8ddb-0fda66068f82", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "r SDSS r#1 [110 111 112 113 133 134 135 148 149 150 163 173 174 175 188 189 190 209\n", " 210 211 225 226 227 242 243 244]\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0111.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 44 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.400000 --dec 1.183333 --radius 3 tmphh2lyhwhxy.fits\n", "Reading input file 1 of 1: \"tmphh2lyhwhxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.cs3t5i -i ./tmphh2lyhwhxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmphh2lyhwhxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmphh2lyhwhxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmphh2lyhwhxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.4,1.18333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-17.fits, field objects 1-10).\n", " log-odds ratio 128.755 (8.26817e+55), 27 match, 1 conflict, 8 distractors, 525 index.\n", " RA,Dec = (340.162,1.30587), pixel scale 0.457404 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++-+++-+++++-+++--+-+-++++++++c-+(best)+---++++\n", "Field 1: solved with index index-4201-17.fits.\n", "Field 1 solved: writing to file ./tmphh2lyhwhxy.solved to indicate this.\n", "Field: tmphh2lyhwhxy.fits\n", "Field center: (RA,Dec) = (340.162604, 1.306238) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:39.025, +01:18:22.458).\n", "Field size: 36.4228 x 22.664 arcminutes\n", "Field rotation angle: up is -0.0455912 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0112.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 39 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.400000 --dec 1.183333 --radius 3 tmpc42t258_xy.fits\n", "Reading input file 1 of 1: \"tmpc42t258_xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.VuFH9d -i ./tmpc42t258_xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpc42t258_xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpc42t258_xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpc42t258_xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.4,1.18333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-17.fits, field objects 1-10).\n", " log-odds ratio 127.322 (1.97287e+55), 27 match, 0 conflict, 6 distractors, 525 index.\n", " RA,Dec = (340.162,1.30571), pixel scale 0.457308 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++-+++++++-+-++++++++++-++--++(best)--++++\n", "Field 1: solved with index index-4201-17.fits.\n", "Field 1 solved: writing to file ./tmpc42t258_xy.solved to indicate this.\n", "Field: tmpc42t258_xy.fits\n", "Field center: (RA,Dec) = (340.163667, 1.306636) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:39.280, +01:18:23.890).\n", "Field size: 36.1696 x 22.6348 arcminutes\n", "Field rotation angle: up is -0.0416897 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0113.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 36 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.400000 --dec 1.183333 --radius 3 tmpco_of_1xxy.fits\n", "Reading input file 1 of 1: \"tmpco_of_1xxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.x4wBKv -i ./tmpco_of_1xxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpco_of_1xxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpco_of_1xxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpco_of_1xxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.4,1.18333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-17.fits, field objects 1-10).\n", " log-odds ratio 113.67 (2.32523e+49), 24 match, 0 conflict, 6 distractors, 525 index.\n", " RA,Dec = (340.162,1.30552), pixel scale 0.457107 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++-+++++++++-+++-++++-+--++(best)--++++\n", "Field 1: solved with index index-4201-17.fits.\n", "Field 1 solved: writing to file ./tmpco_of_1xxy.solved to indicate this.\n", "Field: tmpco_of_1xxy.fits\n", "Field center: (RA,Dec) = (340.162590, 1.306403) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:39.022, +01:18:23.052).\n", "Field size: 36.3471 x 22.6073 arcminutes\n", "Field rotation angle: up is -0.0469782 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0114.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 39 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.400000 --dec 1.183333 --radius 3 tmp9gnppa0cxy.fits\n", "Reading input file 1 of 1: \"tmp9gnppa0cxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.1Qaacr -i ./tmp9gnppa0cxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmp9gnppa0cxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmp9gnppa0cxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmp9gnppa0cxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.4,1.18333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-17.fits, field objects 1-10).\n", " log-odds ratio 127.109 (1.59489e+55), 27 match, 0 conflict, 6 distractors, 525 index.\n", " RA,Dec = (340.162,1.30573), pixel scale 0.45726 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++-+++++++-+-+++-++++++++--+++(best)--++++\n", "Field 1: solved with index index-4201-17.fits.\n", "Field 1 solved: writing to file ./tmp9gnppa0cxy.solved to indicate this.\n", "Field: tmp9gnppa0cxy.fits\n", "Field center: (RA,Dec) = (340.162604, 1.305878) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:39.025, +01:18:21.162).\n", "Field size: 36.388 x 22.3635 arcminutes\n", "Field rotation angle: up is -0.0521489 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0134.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 15 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmp459tl9b9xy.fits\n", "Reading input file 1 of 1: \"tmp459tl9b9xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.QPsuLn -i ./tmp459tl9b9xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmp459tl9b9xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmp459tl9b9xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmp459tl9b9xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 48.333 (9.78916e+20), 8 match, 0 conflict, 3 distractors, 231 index.\n", " RA,Dec = (44.149,0.428997), pixel scale 0.459206 arcsec/pix.\n", " Hit/miss: Hit/miss: ++-++-+-+++(best)++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmp459tl9b9xy.solved to indicate this.\n", "Field: tmp459tl9b9xy.fits\n", "Field center: (RA,Dec) = (44.146204, 0.431511) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:35.089, +00:25:53.439).\n", "Field size: 37.193 x 23.2226 arcminutes\n", "Field rotation angle: up is -0.0809638 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0135.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 11 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmp3sx6at96xy.fits\n", "Reading input file 1 of 1: \"tmp3sx6at96xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.PIJzuB -i ./tmp3sx6at96xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmp3sx6at96xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmp3sx6at96xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmp3sx6at96xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 45.9567 (9.09405e+19), 7 match, 0 conflict, 0 distractors, 231 index.\n", " RA,Dec = (44.1489,0.428773), pixel scale 0.459163 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++++(best)++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmp3sx6at96xy.solved to indicate this.\n", "Field: tmp3sx6at96xy.fits\n", "Field center: (RA,Dec) = (44.145112, 0.430810) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:34.827, +00:25:50.916).\n", "Field size: 37.3877 x 23.1326 arcminutes\n", "Field rotation angle: up is -0.0848196 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0136.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 14 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmpskj036bmxy.fits\n", "Reading input file 1 of 1: \"tmpskj036bmxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.CroMTA -i ./tmpskj036bmxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpskj036bmxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpskj036bmxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpskj036bmxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 42.7379 (3.63775e+18), 7 match, 0 conflict, 2 distractors, 231 index.\n", " RA,Dec = (44.1485,0.42913), pixel scale 0.46002 arcsec/pix.\n", " Hit/miss: Hit/miss: +-++++-++(best)-++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmpskj036bmxy.solved to indicate this.\n", "Field: tmpskj036bmxy.fits\n", "Field center: (RA,Dec) = (44.146379, 0.430054) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:35.131, +00:25:48.196).\n", "Field size: 37.188 x 23.3642 arcminutes\n", "Field rotation angle: up is -0.0382071 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0149.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 18 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmpfpp7ohumxy.fits\n", "Reading input file 1 of 1: \"tmpfpp7ohumxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.jZy2GZ -i ./tmpfpp7ohumxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpfpp7ohumxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpfpp7ohumxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpfpp7ohumxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 62.262 (1.09658e+27), 10 match, 0 conflict, 3 distractors, 231 index.\n", " RA,Dec = (44.1486,0.428899), pixel scale 0.459664 arcsec/pix.\n", " Hit/miss: Hit/miss: ++-++-+-+++++(best)-++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmpfpp7ohumxy.solved to indicate this.\n", "Field: tmpfpp7ohumxy.fits\n", "Field center: (RA,Dec) = (44.147153, 0.431708) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:35.317, +00:25:54.148).\n", "Field size: 37.0108 x 23.2358 arcminutes\n", "Field rotation angle: up is -0.140772 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0150.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 21 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmpcmn4prr3xy.fits\n", "Reading input file 1 of 1: \"tmpcmn4prr3xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.9TYxGv -i ./tmpcmn4prr3xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpcmn4prr3xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpcmn4prr3xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpcmn4prr3xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 73.5583 (8.83043e+31), 12 match, 0 conflict, 4 distractors, 231 index.\n", " RA,Dec = (44.149,0.429014), pixel scale 0.45907 arcsec/pix.\n", " Hit/miss: Hit/miss: ++-++-++++-+++-+(best)-++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmpcmn4prr3xy.solved to indicate this.\n", "Field: tmpcmn4prr3xy.fits\n", "Field center: (RA,Dec) = (44.147256, 0.432174) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:35.341, +00:25:55.826).\n", "Field size: 36.9113 x 23.2612 arcminutes\n", "Field rotation angle: up is -0.125308 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0151.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 20 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmpnlk6qmiwxy.fits\n", "Reading input file 1 of 1: \"tmpnlk6qmiwxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.s5a5Dn -i ./tmpnlk6qmiwxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpnlk6qmiwxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpnlk6qmiwxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpnlk6qmiwxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 67.8264 (2.86168e+29), 11 match, 0 conflict, 4 distractors, 231 index.\n", " RA,Dec = (44.1487,0.428895), pixel scale 0.459509 arcsec/pix.\n", " Hit/miss: Hit/miss: ++-++-+-++-++++(best)-++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmpnlk6qmiwxy.solved to indicate this.\n", "Field: tmpnlk6qmiwxy.fits\n", "Field center: (RA,Dec) = (44.149612, 0.429788) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:35.907, +00:25:47.235).\n", "Field size: 36.3927 x 23.034 arcminutes\n", "Field rotation angle: up is -0.119872 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0164.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 167 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.720833 --dec 0.254167 --radius 3 tmph0ze00klxy.fits\n", "Reading input file 1 of 1: \"tmph0ze00klxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.QOPzmz -i ./tmph0ze00klxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmph0ze00klxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmph0ze00klxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmph0ze00klxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.721,0.254167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 140.359 (9.05824e+60), 33 match, 1 conflict, 26 distractors, 537 index.\n", " RA,Dec = (310.48,0.383619), pixel scale 0.457497 arcsec/pix.\n", " Hit/miss: Hit/miss: ++++++++++-++++++++++--++-++--++-----+++---c------++---+---+(best)-------+-------+-----------------+------\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmph0ze00klxy.solved to indicate this.\n", "Field: tmph0ze00klxy.fits\n", "Field center: (RA,Dec) = (310.480093, 0.379665) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:55.222, +00:22:46.796).\n", "Field size: 36.5791 x 22.8843 arcminutes\n", "Field rotation angle: up is -0.0586758 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0174.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 135 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.720833 --dec 0.254167 --radius 3 tmpetbm6kp9xy.fits\n", "Reading input file 1 of 1: \"tmpetbm6kp9xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.9xSCaf -i ./tmpetbm6kp9xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpetbm6kp9xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpetbm6kp9xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpetbm6kp9xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.721,0.254167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 141.247 (2.20196e+61), 32 match, 2 conflict, 20 distractors, 537 index.\n", " RA,Dec = (310.48,0.383409), pixel scale 0.457099 arcsec/pix.\n", " Hit/miss: Hit/miss: +++c+++++++++++++++++++--+------++--+c-++----+---+--++(best)-------+-----------+--c----------------------+\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmpetbm6kp9xy.solved to indicate this.\n", "Field: tmpetbm6kp9xy.fits\n", "Field center: (RA,Dec) = (310.479593, 0.379965) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:55.102, +00:22:47.874).\n", "Field size: 36.7119 x 22.8728 arcminutes\n", "Field rotation angle: up is -0.0766993 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0175.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 132 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.720833 --dec 0.254167 --radius 3 tmpiu6ne_75xy.fits\n", "Reading input file 1 of 1: \"tmpiu6ne_75xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.I7W8ZU -i ./tmpiu6ne_75xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpiu6ne_75xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpiu6ne_75xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpiu6ne_75xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.721,0.254167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 142.029 (4.81357e+61), 32 match, 1 conflict, 21 distractors, 536 index.\n", " RA,Dec = (310.481,0.383489), pixel scale 0.45696 arcsec/pix.\n", " Hit/miss: Hit/miss: ++++++++++++++++++++-++-++--+----+-++c-------+--++---+(best)-------+-----------------------+-----+--------\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmpiu6ne_75xy.solved to indicate this.\n", "Field: tmpiu6ne_75xy.fits\n", "Field center: (RA,Dec) = (310.480912, 0.379433) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:55.419, +00:22:45.960).\n", "Field size: 36.3924 x 22.7167 arcminutes\n", "Field rotation angle: up is -0.0839242 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0176.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 122 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.720833 --dec 0.254167 --radius 3 tmprrbousx9xy.fits\n", "Reading input file 1 of 1: \"tmprrbousx9xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.sDXRF3 -i ./tmprrbousx9xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmprrbousx9xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmprrbousx9xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmprrbousx9xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.721,0.254167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 137.793 (6.96049e+59), 33 match, 1 conflict, 29 distractors, 538 index.\n", " RA,Dec = (310.48,0.383238), pixel scale 0.457235 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++++++++++++++++++-++--+--+---++---+---c--+----+---+--+----+(best)-----------+-------------------------\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmprrbousx9xy.solved to indicate this.\n", "Field: tmprrbousx9xy.fits\n", "Field center: (RA,Dec) = (310.478475, 0.380768) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:54.834, +00:22:50.766).\n", "Field size: 36.9359 x 23.0499 arcminutes\n", "Field rotation angle: up is -0.061548 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0189.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 102 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.720833 --dec 0.254167 --radius 3 tmpad9ey_mexy.fits\n", "Reading input file 1 of 1: \"tmpad9ey_mexy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.Xcmvat -i ./tmpad9ey_mexy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpad9ey_mexy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpad9ey_mexy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpad9ey_mexy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.721,0.254167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4201-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4201-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4201-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4200-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4200-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4200-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4209.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4208.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 11-20).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 11-20).\n", " log-odds ratio 108.508 (1.33119e+47), 33 match, 19 conflict, 37 distractors, 538 index.\n", " RA,Dec = (310.48,0.382531), pixel scale 0.456944 arcsec/pix.\n", " Hit/miss: Hit/miss: -c++++c+-c-++c+++-++-+++ccc++c+cc++c-++-c-+--+---c---c+----+--+---c--+----++---c+--cc++-c(best)-c---+---++\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmpad9ey_mexy.solved to indicate this.\n", "Field: tmpad9ey_mexy.fits\n", "Field center: (RA,Dec) = (310.475347, 0.381218) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:54.083, +00:22:52.385).\n", "Field size: 37.9898 x 22.9352 arcminutes\n", "Field rotation angle: up is -0.115592 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0190.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 106 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.720833 --dec 0.254167 --radius 3 tmpl0uitgl0xy.fits\n", "Reading input file 1 of 1: \"tmpl0uitgl0xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.GhqIlc -i ./tmpl0uitgl0xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpl0uitgl0xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpl0uitgl0xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpl0uitgl0xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.721,0.254167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 111.782 (3.51686e+48), 29 match, 18 conflict, 23 distractors, 538 index.\n", " RA,Dec = (310.48,0.384248), pixel scale 0.457761 arcsec/pix.\n", " Hit/miss: Hit/miss: -++++c+c+-cc-++cc+c+ccc+-+c+c++++++c-c+-c+-c++------++--c+--+----+-+-c(best)--+----++-c--c-----c------c--c\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmpl0uitgl0xy.solved to indicate this.\n", "Field: tmpl0uitgl0xy.fits\n", "Field center: (RA,Dec) = (310.479240, 0.382398) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:55.018, +00:22:56.634).\n", "Field size: 36.8132 x 23.066 arcminutes\n", "Field rotation angle: up is -0.120201 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0191.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 81 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.720833 --dec 0.254167 --radius 3 tmpt_wqbit3xy.fits\n", "Reading input file 1 of 1: \"tmpt_wqbit3xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.2xFX5d -i ./tmpt_wqbit3xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpt_wqbit3xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpt_wqbit3xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpt_wqbit3xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.721,0.254167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 126.432 (8.10833e+54), 32 match, 5 conflict, 28 distractors, 538 index.\n", " RA,Dec = (310.48,0.383289), pixel scale 0.457473 arcsec/pix.\n", " Hit/miss: Hit/miss: ++c+++++++++++++++++-++-++---c---c---c--++--c+--+----+-+---+--+-+(best)c-----------++++\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmpt_wqbit3xy.solved to indicate this.\n", "Field: tmpt_wqbit3xy.fits\n", "Field center: (RA,Dec) = (310.479031, 0.380244) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:54.967, +00:22:48.880).\n", "Field size: 36.9052 x 22.5223 arcminutes\n", "Field rotation angle: up is -0.114157 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0210.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 16 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.391667 --dec 1.191667 --radius 3 tmpmy75a2coxy.fits\n", "Reading input file 1 of 1: \"tmpmy75a2coxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.Q8A50B -i ./tmpmy75a2coxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpmy75a2coxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpmy75a2coxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpmy75a2coxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.392,1.19167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", " log-odds ratio 54.9859 (7.58683e+23), 11 match, 0 conflict, 1 distractors, 472 index.\n", " RA,Dec = (340.149,1.3215), pixel scale 0.460506 arcsec/pix.\n", " Hit/miss: Hit/miss: ++-+++++++++(best)++f+\n", "Field 1: solved with index index-4202-17.fits.\n", "Field 1 solved: writing to file ./tmpmy75a2coxy.solved to indicate this.\n", "Field: tmpmy75a2coxy.fits\n", "Field center: (RA,Dec) = (340.143475, 1.323396) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:34.434, +01:19:24.226).\n", "Field size: 38.1749 x 23.2013 arcminutes\n", "Field rotation angle: up is -0.136572 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0211.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 16 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.391667 --dec 1.191667 --radius 3 tmphh9gje62xy.fits\n", "Reading input file 1 of 1: \"tmphh9gje62xy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.pOKlz6 -i ./tmphh9gje62xy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmphh9gje62xy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmphh9gje62xy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmphh9gje62xy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.392,1.19167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-17.fits, field objects 1-10).\n", " log-odds ratio 56.8301 (4.7972e+24), 11 match, 0 conflict, 1 distractors, 470 index.\n", " RA,Dec = (340.15,1.32117), pixel scale 0.460005 arcsec/pix.\n", " Hit/miss: Hit/miss: +-++++++++++(best)+++f\n", "Field 1: solved with index index-4202-17.fits.\n", "Field 1 solved: writing to file ./tmphh9gje62xy.solved to indicate this.\n", "Field: tmphh9gje62xy.fits\n", "Field center: (RA,Dec) = (340.153298, 1.315791) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:36.792, +01:18:56.848).\n", "Field size: 36.2777 x 22.3251 arcminutes\n", "Field rotation angle: up is -0.0745088 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0212.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 18 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 340.391667 --dec 1.191667 --radius 3 tmpuc5cwrwlxy.fits\n", "Reading input file 1 of 1: \"tmpuc5cwrwlxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.rVOxvf -i ./tmpuc5cwrwlxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpuc5cwrwlxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpuc5cwrwlxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpuc5cwrwlxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (340.392,1.19167)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-17.fits, field objects 1-10).\n", " log-odds ratio 41.9725 (1.69209e+18), 8 match, 0 conflict, 2 distractors, 262 index.\n", " RA,Dec = (340.149,1.3217), pixel scale 0.461418 arcsec/pix.\n", " Hit/miss: Hit/miss: ++++-++-++(best)----++++\n", "Field 1: solved with index index-4203-17.fits.\n", "Field 1 solved: writing to file ./tmpuc5cwrwlxy.solved to indicate this.\n", "Field: tmpuc5cwrwlxy.fits\n", "Field center: (RA,Dec) = (340.145905, 1.322260) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (22:40:35.017, +01:19:20.136).\n", "Field size: 37.3939 x 23.157 arcminutes\n", "Field rotation angle: up is -0.283622 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0226.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 19 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmpjt3mwlrzxy.fits\n", "Reading input file 1 of 1: \"tmpjt3mwlrzxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.YL8qqX -i ./tmpjt3mwlrzxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpjt3mwlrzxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpjt3mwlrzxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpjt3mwlrzxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 60.9017 (2.81359e+26), 10 match, 0 conflict, 4 distractors, 231 index.\n", " RA,Dec = (44.1501,0.428567), pixel scale 0.459055 arcsec/pix.\n", " Hit/miss: Hit/miss: -+-++-+++++-++(best)-++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmpjt3mwlrzxy.solved to indicate this.\n", "Field: tmpjt3mwlrzxy.fits\n", "Field center: (RA,Dec) = (44.149131, 0.430404) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:35.791, +00:25:49.454).\n", "Field size: 36.814 x 23.1582 arcminutes\n", "Field rotation angle: up is -0.132692 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0227.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 16 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmpl0eyo92lxy.fits\n", "Reading input file 1 of 1: \"tmpl0eyo92lxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.P2sEw9 -i ./tmpl0eyo92lxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpl0eyo92lxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpl0eyo92lxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpl0eyo92lxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 36.575 (7.66186e+15), 10 match, 0 conflict, 2 distractors, 233 index.\n", " RA,Dec = (44.1468,0.429953), pixel scale 0.464002 arcsec/pix.\n", " Hit/miss: Hit/miss: ++++++-+++-+(best)++f+\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmpl0eyo92lxy.solved to indicate this.\n", "Field: tmpl0eyo92lxy.fits\n", "Field center: (RA,Dec) = (44.149195, 0.431379) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:35.807, +00:25:52.963).\n", "Field size: 36.7822 x 23.1796 arcminutes\n", "Field rotation angle: up is -0.1057 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0228.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 17 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 44.391667 --dec 0.300000 --radius 3 tmpkhmtaf7rxy.fits\n", "Reading input file 1 of 1: \"tmpkhmtaf7rxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.LkxBj0 -i ./tmpkhmtaf7rxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpkhmtaf7rxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpkhmtaf7rxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpkhmtaf7rxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (44.3917,0.3)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-08.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-05.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-04.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-21.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-18.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-00.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-35.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-21.fits, field objects 1-10).\n", " log-odds ratio 62.414 (1.27659e+27), 10 match, 0 conflict, 2 distractors, 230 index.\n", " RA,Dec = (44.1507,0.428671), pixel scale 0.458271 arcsec/pix.\n", " Hit/miss: Hit/miss: ++-++-++++++(best)-++++\n", "Field 1: solved with index index-4203-18.fits.\n", "Field 1 solved: writing to file ./tmpkhmtaf7rxy.solved to indicate this.\n", "Field: tmpkhmtaf7rxy.fits\n", "Field center: (RA,Dec) = (44.145136, 0.431036) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (02:56:34.833, +00:25:51.728).\n", "Field size: 37.8873 x 23.2591 arcminutes\n", "Field rotation angle: up is -0.0893152 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0243.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 85 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.716667 --dec 0.258333 --radius 3 tmpk43dnv3hxy.fits\n", "Reading input file 1 of 1: \"tmpk43dnv3hxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.Y52dTf -i ./tmpk43dnv3hxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmpk43dnv3hxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmpk43dnv3hxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmpk43dnv3hxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.717,0.258333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 144.202 (4.22725e+62), 30 match, 0 conflict, 11 distractors, 533 index.\n", " RA,Dec = (310.476,0.387405), pixel scale 0.458305 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++++++++-++++++++++++++-------+-+++--+(best)----c---+-------+---+--+-----+----------++++\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmpk43dnv3hxy.solved to indicate this.\n", "Field: tmpk43dnv3hxy.fits\n", "Field center: (RA,Dec) = (310.478464, 0.381027) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:54.831, +00:22:51.696).\n", "Field size: 35.9507 x 22.4221 arcminutes\n", "Field rotation angle: up is -0.080831 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0244.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 79 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.716667 --dec 0.258333 --radius 3 tmp0cmccqylxy.fits\n", "Reading input file 1 of 1: \"tmp0cmccqylxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.GgqC7Q -i ./tmp0cmccqylxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmp0cmccqylxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmp0cmccqylxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmp0cmccqylxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.717,0.258333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 142.543 (8.05014e+61), 34 match, 2 conflict, 27 distractors, 534 index.\n", " RA,Dec = (310.476,0.38751), pixel scale 0.458492 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++++++-++-++++++++-+++++--++------++-----+-c--c+--++---+---+(best)--------+---++++\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmp0cmccqylxy.solved to indicate this.\n", "Field: tmp0cmccqylxy.fits\n", "Field center: (RA,Dec) = (310.478599, 0.382176) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:54.864, +00:22:55.832).\n", "Field size: 35.8893 x 22.4842 arcminutes\n", "Field rotation angle: up is -0.0586185 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n", " Reading file: /home/holtz/raw/apo/oct23/UT231023/arctic/science.0245.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " flat fielding...\n", "INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [astropy.nddata.ccddata]\n", " plate solving with local astrometry.net....\n", "found 95 objects \n", "/usr/local/astrometry/bin/solve-field --scale-units arcsecperpix --scale-low 0.396000 --scale-high 0.484000 -X xcentroid -Y ycentroid -w 4800 -e 3000 --overwrite --ra 310.716667 --dec 0.258333 --radius 3 tmp8u1zib6gxy.fits\n", "Reading input file 1 of 1: \"tmp8u1zib6gxy.fits\"...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n", "/usr/local/astrometry/bin/plotxy: error while loading shared libraries: libnetpbm.so.11: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "solve-field.c:327:plot_source_overlay Plotting command failed\n", " solve-field.c:133:run_command Command was: \"/usr/local/astrometry/bin/plotxy -I /tmp/tmp.ppm.klmN2d -i ./tmp8u1zib6gxy.axy -X xcentroid -Y ycentroid -C red -w 2 -N 50 -x 1 -y 1 -P | /usr/local/astrometry/bin/plotxy -i ./tmp8u1zib6gxy.axy -X xcentroid -Y ycentroid -I - -w 2 -r 3 -C red -n 50 -N 200 -x 1 -y 1 > ./tmp8u1zib6gxy-objs.png\"\n", "\n", " solve-field.c:132:run_command Command exited with exit status 127\n", "Solving...\n", "Reading file \"./tmp8u1zib6gxy.axy\"...\n", "Only searching for solutions within 3 degrees of RA,Dec (310.717,0.258333)\n", "Field 1 did not solve (index index-4209.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4208.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4207-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4206-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-11.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-07.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4205-03.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4204-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-47.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-30.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4203-12.fits, field objects 1-10).\n", "Field 1 did not solve (index index-4202-47.fits, field objects 1-10).\n", " log-odds ratio 144.008 (3.48239e+62), 33 match, 2 conflict, 23 distractors, 533 index.\n", " RA,Dec = (310.476,0.387437), pixel scale 0.458234 arcsec/pix.\n", " Hit/miss: Hit/miss: +++++++++++-++++++++++++++--++------++---c-+----c+---+---+(best)-------+-------------------+-----++++\n", "Field 1: solved with index index-4202-30.fits.\n", "Field 1 solved: writing to file ./tmp8u1zib6gxy.solved to indicate this.\n", "Field: tmp8u1zib6gxy.fits\n", "Field center: (RA,Dec) = (310.477990, 0.381799) deg.\n", "Field center: (RA H:M:S, Dec D:M:S) = (20:41:54.718, +00:22:54.478).\n", "Field size: 36.0952 x 22.4827 arcminutes\n", "Field rotation angle: up is -0.0609711 degrees E of N\n", "Field parity: pos\n", "\n", "appending uncertainty\n", "appending bitmask\n" ] } ], "source": [ "tab_phot=[] # initialize final photometry table, the table for each image will be appended to this\n", "matplotlib.use('Agg') # this will turn off display\n", "t=tv.TV()\n", "for filt,filtname in zip(filts[2:3],filtnames[2:3]) :\n", " frames=np.where((log['FILTER'] == filtname) & (np.char.find(log['FILE'],b'science') >=0) )[0]\n", " print(filt,filtname,frames)\n", " for frame in frames :\n", " try :\n", " file = log['FILE'][frame]\n", " im=red.reduce(log['FILE'][frame],flat=flat[filt],solve=True)\n", " try: os.mkdir(red.dir+'/red')\n", " except : pass\n", " im.write(red.dir+'/red/{:s}'.format(file))\n", "\n", " tab_phot = dophot(im,tab_phot,t)\n", " except : \n", " continue" ] }, { "cell_type": "code", "execution_count": 15, "id": "43241255-e5d2-4d21-9376-2405ce1c0731", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'SDSS r#1'}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(tab_phot['FILTER'])" ] }, { "cell_type": "code", "execution_count": 17, "id": "22e45dec-025f-4c38-bb46-69ca4c1a5f68", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=59\n", "

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col0NAMERADECugrizIDxyEXPTIMEFILTERAIRMASSMJDaper10aper10errskyskysigpeak
float64bytes11float64float64float64float64float64float64float64int64float64float64float64str8float64float64float64float64float64float64float64
--SA 114 654340.3589171.16963913.5212.1211.6711.5411.50147847.555671574427.8102672911.0SDSS r#11.17360240.147547-14.1070.00162.455.8463232.1
--SA 114 656340.3960831.18605615.0413.0812.3312.0311.88148555.794741948557.178654321.0SDSS r#11.17360240.147547-13.4350.00260.465.8830045.2
--SA 114 750340.4362501.21005612.3611.8112.0212.2512.41150240.259390037744.9138817691.0SDSS r#11.17360240.147547-13.7180.00160.585.8938673.9
--SA 114 654340.3589171.16963913.5212.1211.6711.5411.50147847.468443123427.2145363340.5SDSS r#11.17360240.148125-13.3440.00231.804.4424781.4
--SA 114 656340.3960831.18605615.0413.0812.3312.0311.88148555.593289012556.6593162220.5SDSS r#11.17360240.148125-12.6800.00230.524.5416458.9
--SA 114 750340.4362501.21005612.3611.8112.0212.2512.41150239.942375078744.5143580290.5SDSS r#11.17360240.148125-12.9590.00230.544.3526065.5
--SA 114 654340.3589171.16963913.5212.1211.6711.5411.50147847.795698395427.8516761560.5SDSS r#11.17360240.148171-13.3500.00231.464.6729874.0
--SA 114 656340.3960831.18605615.0413.0812.3312.0311.88148555.795654532557.2878068890.5SDSS r#11.17360240.148171-12.6770.00230.714.4924562.9
--SA 114 750340.4362501.21005612.3611.8112.0212.2512.41150240.241355576745.1392713550.5SDSS r#11.17360240.148171-12.9640.00230.654.2731064.7
--SA 114 654340.3589171.16963913.5212.1211.6711.5411.50147847.668102905426.8993895990.5SDSS r#11.17360240.148217-13.3520.00231.534.4850631.9
--SA 114 656340.3960831.18605615.0413.0812.3312.0311.88148555.724438976556.676044350.5SDSS r#11.17360240.148217-12.6800.00230.024.3824871.5
--SA 114 750340.4362501.21005612.3611.8112.0212.2512.41150240.125992352744.6702281690.5SDSS r#11.17360240.148217-12.9600.00230.674.5031863.4
--SA 94 24244.3385000.31080612.9711.8211.7111.7411.7916917.89060958571.9266657120.5SDSS r#12.48960240.159860-13.1290.00234.324.488552.7
--SA 94 25144.4457500.26741714.5511.7810.8010.4310.221774.5617039935231.3112647590.5SDSS r#12.48960240.159860-13.9910.00135.884.8513070.8
...............................................................
--SA 114 654340.3589171.16963913.5212.1211.6711.5411.50147763.709492582310.1726447070.5SDSS r#11.20060240.188427-13.3340.00240.644.9312358.4
--SA 114 656340.3960831.18605615.0413.0812.3312.0311.88148471.370116622439.3206737390.5SDSS r#11.20060240.188427-12.6800.00234.834.7210568.7
--SA 114 750340.4362501.21005612.3611.8112.0212.2512.41150156.408372242627.3645031470.5SDSS r#11.20060240.188427-12.9490.00236.124.588629.5
--SA 94 24244.3385000.31080612.9711.8211.7111.7411.7916926.09862586574.7283934961.0SDSS r#11.79360240.195805-13.9700.00153.825.6415910.6
--SA 94 25144.4457500.26741714.5511.7810.8010.4310.221783.1838008699233.0221194131.0SDSS r#11.79360240.195805-14.8580.00154.795.8531577.1
--SA 94 24244.3385000.31080612.9711.8211.7111.7411.7916926.817730007573.6886117571.0SDSS r#11.79260240.195856-13.9710.00154.295.8216378.2
--SA 94 25144.4457500.26741714.5511.7810.8010.4310.221783.7168608158232.7456471121.0SDSS r#11.79260240.195856-14.8850.00155.005.6329787.4
--SA 94 24244.3385000.31080612.9711.8211.7111.7411.7916926.513247708574.5063834841.0SDSS r#11.79160240.195907-13.9630.00154.005.6513957.6
--SA 94 25144.4457500.26741714.5511.7810.8010.4310.221782.697681507232.7255985721.0SDSS r#11.79160240.195907-14.8640.00154.465.6132220.2
--SA 112 805310.6947500.26900013.2712.0912.1712.3112.40132676.919698033572.6120968971.0SDSS r#11.78760240.206357-13.5070.002104.057.007703.4
--SA 112 822310.7287500.25052814.3212.0311.2310.9410.79133408.720035858426.0133159841.0SDSS r#11.78760240.206357-14.4770.001104.307.5615365.8
--SA 112 805310.6947500.26900013.2712.0912.1712.3112.40132677.837558108573.2074548661.0SDSS r#11.78860240.206410-13.5170.002104.457.348020.9
--SA 112 822310.7287500.25052814.3212.0311.2310.9410.79133409.632840829426.4306243541.0SDSS r#11.78860240.206410-14.4630.001105.477.4217844.2
--SA 112 805310.6947500.26900013.2712.0912.1712.3112.40132677.628866687572.7703297591.0SDSS r#11.78860240.206461-13.5120.002104.147.349925.5
--SA 112 822310.7287500.25052814.3212.0311.2310.9410.79133409.799880484425.9697361231.0SDSS r#11.78860240.206461-14.4790.001104.057.5122931.5
" ], "text/plain": [ "\n", " col0 NAME RA DEC u g r i ... FILTER AIRMASS MJD aper10 aper10err sky skysig peak \n", "float64 bytes11 float64 float64 float64 float64 float64 float64 ... str8 float64 float64 float64 float64 float64 float64 float64\n", "------- ---------- ---------- -------- ------- ------- ------- ------- ... -------- ------- ------------ ------- --------- ------- ------- -------\n", " -- SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 ... SDSS r#1 1.173 60240.147547 -14.107 0.001 62.45 5.84 63232.1\n", " -- SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 ... SDSS r#1 1.173 60240.147547 -13.435 0.002 60.46 5.88 30045.2\n", " -- SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 ... SDSS r#1 1.173 60240.147547 -13.718 0.001 60.58 5.89 38673.9\n", " -- SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 ... SDSS r#1 1.173 60240.148125 -13.344 0.002 31.80 4.44 24781.4\n", " -- SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 ... SDSS r#1 1.173 60240.148125 -12.680 0.002 30.52 4.54 16458.9\n", " -- SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 ... SDSS r#1 1.173 60240.148125 -12.959 0.002 30.54 4.35 26065.5\n", " -- SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 ... SDSS r#1 1.173 60240.148171 -13.350 0.002 31.46 4.67 29874.0\n", " -- SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 ... SDSS r#1 1.173 60240.148171 -12.677 0.002 30.71 4.49 24562.9\n", " -- SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 ... SDSS r#1 1.173 60240.148171 -12.964 0.002 30.65 4.27 31064.7\n", " -- SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 ... SDSS r#1 1.173 60240.148217 -13.352 0.002 31.53 4.48 50631.9\n", " -- SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 ... SDSS r#1 1.173 60240.148217 -12.680 0.002 30.02 4.38 24871.5\n", " -- SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 ... SDSS r#1 1.173 60240.148217 -12.960 0.002 30.67 4.50 31863.4\n", " -- SA 94 242 44.338500 0.310806 12.97 11.82 11.71 11.74 ... SDSS r#1 2.489 60240.159860 -13.129 0.002 34.32 4.48 8552.7\n", " -- SA 94 251 44.445750 0.267417 14.55 11.78 10.80 10.43 ... SDSS r#1 2.489 60240.159860 -13.991 0.001 35.88 4.85 13070.8\n", " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", " -- SA 114 654 340.358917 1.169639 13.52 12.12 11.67 11.54 ... SDSS r#1 1.200 60240.188427 -13.334 0.002 40.64 4.93 12358.4\n", " -- SA 114 656 340.396083 1.186056 15.04 13.08 12.33 12.03 ... SDSS r#1 1.200 60240.188427 -12.680 0.002 34.83 4.72 10568.7\n", " -- SA 114 750 340.436250 1.210056 12.36 11.81 12.02 12.25 ... SDSS r#1 1.200 60240.188427 -12.949 0.002 36.12 4.58 8629.5\n", " -- SA 94 242 44.338500 0.310806 12.97 11.82 11.71 11.74 ... SDSS r#1 1.793 60240.195805 -13.970 0.001 53.82 5.64 15910.6\n", " -- SA 94 251 44.445750 0.267417 14.55 11.78 10.80 10.43 ... SDSS r#1 1.793 60240.195805 -14.858 0.001 54.79 5.85 31577.1\n", " -- SA 94 242 44.338500 0.310806 12.97 11.82 11.71 11.74 ... SDSS r#1 1.792 60240.195856 -13.971 0.001 54.29 5.82 16378.2\n", " -- SA 94 251 44.445750 0.267417 14.55 11.78 10.80 10.43 ... SDSS r#1 1.792 60240.195856 -14.885 0.001 55.00 5.63 29787.4\n", " -- SA 94 242 44.338500 0.310806 12.97 11.82 11.71 11.74 ... SDSS r#1 1.791 60240.195907 -13.963 0.001 54.00 5.65 13957.6\n", " -- SA 94 251 44.445750 0.267417 14.55 11.78 10.80 10.43 ... SDSS r#1 1.791 60240.195907 -14.864 0.001 54.46 5.61 32220.2\n", " -- SA 112 805 310.694750 0.269000 13.27 12.09 12.17 12.31 ... SDSS r#1 1.787 60240.206357 -13.507 0.002 104.05 7.00 7703.4\n", " -- SA 112 822 310.728750 0.250528 14.32 12.03 11.23 10.94 ... SDSS r#1 1.787 60240.206357 -14.477 0.001 104.30 7.56 15365.8\n", " -- SA 112 805 310.694750 0.269000 13.27 12.09 12.17 12.31 ... SDSS r#1 1.788 60240.206410 -13.517 0.002 104.45 7.34 8020.9\n", " -- SA 112 822 310.728750 0.250528 14.32 12.03 11.23 10.94 ... SDSS r#1 1.788 60240.206410 -14.463 0.001 105.47 7.42 17844.2\n", " -- SA 112 805 310.694750 0.269000 13.27 12.09 12.17 12.31 ... SDSS r#1 1.788 60240.206461 -13.512 0.002 104.14 7.34 9925.5\n", " -- SA 112 822 310.728750 0.250528 14.32 12.03 11.23 10.94 ... SDSS r#1 1.788 60240.206461 -14.479 0.001 104.05 7.51 22931.5" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tab_phot" ] }, { "cell_type": "markdown", "id": "8bc4235e-5ddf-45cb-90a0-d77570b9ce8c", "metadata": {}, "source": [ "Plot photometry vs airmass, color, and MJD. Do linear photometric transformation and plot residuals" ] }, { "cell_type": "code", "execution_count": 19, "id": "fcc5f259-4525-44c3-9265-2f762f348b2f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_16175/1853721690.py:28: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n", "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n", " fit,resid,rank,s = np.linalg.lstsq(design,rhs.value.data)\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "matplotlib.use('QT5Agg')\n", "%matplotlib inline\n", "\n", "for filt,filtname in zip(filts[2:3],filtnames[2:3]) :\n", " # get all table entries in this filter\n", " gd = np.where(tab_phot['FILTER'] == filtname)[0]\n", "\n", " # load up instrument magnitudes, uncertainties, airmass, color, mjd, and standard mag from table\n", " instmag = tab_phot['aper10'][gd]+2.5*np.log10(tab_phot['EXPTIME'][gd])\n", " instmag_err = tab_phot['aper10err'][gd]\n", " airmass = tab_phot['AIRMASS'][gd]\n", " mjd = tab_phot['MJD'][gd]\n", " color = tab_phot['g']-tab_phot['r']\n", " stan = tab_phot[filt][gd]\n", "\n", " # plot stan-mag vs airmass, color, and MJD\n", " fig,ax=plots.multi(2,2,figsize=(12,8))\n", " plots.plotc(ax[0,0],airmass,stan-instmag,instmag,yerr=instmag_err,\n", " cmap='viridis',xt='Airmass',yt='stan-instmag',colorbar=True,zt='instmag',size=40)\n", " plots.plotc(ax[0,1],color,stan-instmag,airmass,yerr=instmag_err,\n", " cmap='viridis',xt='g-r',yt='stan-instmag',colorbar=True,zt='Airmass',size=40)\n", " plots.plotc(ax[1,0],mjd,stan-instmag,airmass,yerr=instmag_err,\n", " cmap='viridis',zr=[1,2.5],xt='MJD',yt='stan-instmag',colorbar=True,zt='Airmass',size=40)\n", "\n", " # set up and do least squares fit\n", " design = np.array([airmass,color,np.ones(len(gd))]).T\n", " rhs = stan-instmag\n", " fit,resid,rank,s = np.linalg.lstsq(design,rhs.value.data)\n", "\n", " # use fit to calculate transformed mags, and plot stan-transformed vs MJD\n", " trans = instmag + fit[0]*airmass + fit[1]*color + fit[2]\n", " plots.plotc(ax[1,1],mjd,stan-trans, airmass,yerr=instmag_err,cmap='viridis', zr=[1,2.5],\n", " xt='MJD',yt='stan-transformed',zt='Airmass',size=40,colorbar=True)\n", " ax[1,1].text(0.1,0.9,'rms: {:.3f}'.format((stan-trans).std()),transform=ax[1,1].transAxes)\n", " \n", " fig.suptitle(filt)\n", " fig.tight_layout()\n", "\n", " # 3D plot of stan-instmag vs airmass and color, with best fit plane\n", " ax=plt.figure().add_subplot(projection='3d')\n", " ax.scatter(airmass,color,stan-instmag,marker='o',c=stan-trans,cmap='viridis')\n", " yy,xx=np.mgrid[-0.5:1.5:0.1,1:3:0.1]\n", " ax.plot_surface(xx,yy,fit[0]*xx+fit[1]*yy+fit[2],alpha=0.2)\n", " " ] }, { "cell_type": "markdown", "id": "5f840367-d633-41b2-9758-5b805644a0df", "metadata": {}, "source": [ "

Biases investigation

" ] }, { "cell_type": "code", "execution_count": 20, "id": "2675842a-a805-4761-943f-0860755b7e7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0001.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0002.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0003.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0004.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0005.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0006.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0007.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0008.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0009.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0010.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0011.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0012.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0013.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0014.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " Reading file: /home/holtz/raw/apo/oct23/UT231102/bias4x4.0015.fits\n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " subtracting overscan vector \n", " combining data with median....\n", " calculating uncertainty....\n" ] }, { "ename": "ValueError", "evalue": "The Axes must have been created in the present figure", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[20], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m red\u001b[38;5;241m.\u001b[39mdir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/holtz/raw/apo/oct23/UT231102\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m bias1\u001b[38;5;241m=\u001b[39m\u001b[43mred\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmkbias\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m bias2\u001b[38;5;241m=\u001b[39mred\u001b[38;5;241m.\u001b[39mmkbias(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m47\u001b[39m,\u001b[38;5;241m62\u001b[39m))\n\u001b[1;32m 4\u001b[0m red\u001b[38;5;241m.\u001b[39mdir\n", "File \u001b[0;32m/home/holtz/git/pyvista/python/pyvista/imred.py:1292\u001b[0m, in \u001b[0;36mReducer.mkbias\u001b[0;34m(self, ims, display, scat, type, sigreject, trim)\u001b[0m\n\u001b[1;32m 1288\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmkbias\u001b[39m(\u001b[38;5;28mself\u001b[39m,ims,display\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,scat\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m'\u001b[39m,sigreject\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m 1289\u001b[0m trim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m) :\n\u001b[1;32m 1290\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\" Driver for superbias combination (no superbias subtraction no normalization)\u001b[39;00m\n\u001b[1;32m 1291\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1292\u001b[0m bias\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mims\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisplay\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdiv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mscat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscat\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43msigreject\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msigreject\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i,f \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(bias) :\n\u001b[1;32m 1295\u001b[0m bias[i]\u001b[38;5;241m.\u001b[39mheader[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOBJECT\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCombined bias\u001b[39m\u001b[38;5;124m'\u001b[39m\n", "File \u001b[0;32m/home/holtz/git/pyvista/python/pyvista/imred.py:1250\u001b[0m, in \u001b[0;36mReducer.combine\u001b[0;34m(self, ims, normalize, display, div, return_list, type, sigreject, **kwargs)\u001b[0m\n\u001b[1;32m 1248\u001b[0m comb\u001b[38;5;241m.\u001b[39mheader[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOBJECT\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCombined frame\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1249\u001b[0m display\u001b[38;5;241m.\u001b[39mclear()\n\u001b[0;32m-> 1250\u001b[0m \u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcomb\u001b[49m\u001b[43m,\u001b[49m\u001b[43msn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1251\u001b[0m display\u001b[38;5;241m.\u001b[39mtv(comb)\n\u001b[1;32m 1252\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m comb\u001b[38;5;241m.\u001b[39mmask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m :\n", "File \u001b[0;32m/home/holtz/git/pyvista/python/pyvista/tv.py:510\u001b[0m, in \u001b[0;36mTV.tv\u001b[0;34m(self, img, min, max, same, cmap, sn, object)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;66;03m# set figure and axes\u001b[39;00m\n\u001b[1;32m 509\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfig\u001b[38;5;241m.\u001b[39mnumber)\n\u001b[0;32m--> 510\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 511\u001b[0m \u001b[38;5;66;03m#self.clear()\u001b[39;00m\n\u001b[1;32m 512\u001b[0m \n\u001b[1;32m 513\u001b[0m \u001b[38;5;66;03m# make last image not visible so we don't see anything \u001b[39;00m\n\u001b[1;32m 514\u001b[0m \u001b[38;5;66;03m# if new image is smaller\u001b[39;00m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxlist[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \n", "File \u001b[0;32m/home/local/Anaconda3-2020.02/envs/pyvista/lib/python3.9/site-packages/matplotlib/pyplot.py:1225\u001b[0m, in \u001b[0;36maxes\u001b[0;34m(arg, **kwargs)\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig\u001b[38;5;241m.\u001b[39madd_axes(pos, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1224\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_axes\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/home/local/Anaconda3-2020.02/envs/pyvista/lib/python3.9/site-packages/matplotlib/figure.py:639\u001b[0m, in \u001b[0;36mFigureBase.add_axes\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 637\u001b[0m key \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39m_projection_init\n\u001b[1;32m 638\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mget_figure() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 639\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 640\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe Axes must have been created in the present figure\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 642\u001b[0m rect, \u001b[38;5;241m*\u001b[39mextra_args \u001b[38;5;241m=\u001b[39m args\n", "\u001b[0;31mValueError\u001b[0m: The Axes must have been created in the present figure" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "red.dir='/home/holtz/raw/apo/oct23/UT231102'\n", "bias1=red.mkbias(range(1,16),display=t)\n", "bias2=red.mkbias(range(47,62))\n", "red.dir" ] }, { "cell_type": "code", "execution_count": 58, "id": "8eff0771-655c-413a-b230-16d983413f33", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "tv() got an unexpected keyword argument 'display'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[58], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mqt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbias1\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m t\u001b[38;5;241m.\u001b[39mtv(bias2)\n", "\u001b[0;31mTypeError\u001b[0m: tv() got an unexpected keyword argument 'display'" ] } ], "source": [ "%matplotlib qt\n", "t.tv(bias1)\n", "t.tv(bias2)" ] }, { "cell_type": "code", "execution_count": null, "id": "c1cfd5ab-4b42-4665-957c-01383fb379e7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }