{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Full Frame Images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning Goals\n", "\n", "In this tutorial we will learn the following, \n", " - What a TESS Full Frame Image (FFI) is. \n", " - How to obtain an FFI from the [MAST archive](https://archive.stsci.edu/tess/) via *Lightkurve*.\n", " - How to cut out data around an object of interest in an FFI.\n", " - How to plot the cut FFI data.\n", " - How to access the metadata, and understand the files properties and units.\n", "\n", "We will also show the user where they can find more details about TESS FFIs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is a Full Frame Image?\n", "The *TESS* telescope observes stars for long periods of time, just under a month per sector. Each sector is covered by 4 cameras.\n", "\n", "A single FFI is the full set of all science and collateral pixels across all CCDs of a given camera. FFIs were taken every 30 minutes during science operations in the primary mission, and every 10 min in the extended. \n", "\n", "FFI data is provided in three types: uncalibrated, calibrated, and uncertainty. \n", "\n", "Uncalibrated FFI data is provided in one file with two Header/Data Units (HDUs): a primary header and the CCD image header and data. \n", "\n", "The calibrated image and its uncertainty are provided in a separate file with several HDUs: a primary header, the CCD calibrated image header and data, the CCD uncertainty image header and data, and the cosmic ray corrections binary table header and data. \n", "\n", "Cosmic Ray Mitigated (CRM) FFIs are the same as FFIs except they will are collected with the on-board cosmic ray mitigation enabled.\n", "\n", "Sometimes an object of interest isn't in a TPF (see the [TPF tutorial](Target-Pixel-Files.html)) but is in an FFI and as such the user may wish to create a cut out of the object in this FFI and work with this object only.\n", "\n", "In this tutorial we'll cover the basics of working with FFIs. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports\n", "This tutorial requires that you import *Lightkurve* only." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline \n", "import lightkurve as lk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining Terms\n", "\n", "- Target Pixel File (TPF): A file containing the original CCD pixel observations from which light curves are extracted. \n", "\n", "- Full Frame Image (FFI): A file containing the full set of all science and collateral pixels across all CCDs of a given camera.\n", "\n", "- Cadence: The rate at which TESS photometric observations are stored. \n", "\n", "- Sector: One of TESS's 27 (to date) observing periods, approximately ~27 days in duration. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading Data\n", "The *TESS* FFIs are stored on the [Mikulksi Archive for Space Telescopes](https://archive.stsci.edu/tess/) (MAST) archive.\n", "\n", "First, let's create a TPF for an object of interest, let's choose Gaia object DR25290850609994130560. This object was observed in the *TESS* FFI data only. We'll use the [`search_tesscut`](https://docs.lightkurve.org/api/lightkurve.search.search_tesscut.html) function to download a cut out of the target in a chosen sector. You can determine which sectors the target was observed in using the [MAST TESS portal](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html).\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SearchResult containing 5 data products.\n", "\n", " # observation author target_name productFilename distance\n", "--- -------------- ------ --------------------------- --------------- --------\n", " 0 TESS Sector 4 MAST Gaia DR25290850609994130560 TESSCut 0.0\n", " 1 TESS Sector 7 MAST Gaia DR25290850609994130560 TESSCut 0.0\n", " 2 TESS Sector 8 MAST Gaia DR25290850609994130560 TESSCut 0.0\n", " 3 TESS Sector 9 MAST Gaia DR25290850609994130560 TESSCut 0.0\n", " 4 TESS Sector 10 MAST Gaia DR25290850609994130560 TESSCut 0.0\n" ] } ], "source": [ "search_result = lk.search_tesscut('Gaia DR25290850609994130560')\n", "print(search_result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that this object is detected in Sectors 4, 7, 8, 9, and 10. You can download data from just one sector and specify the cutout_size in number of TESS pixels on a side as an argument to `.download()`. The default is a meager 5 × 5 square. Let’s go with 10 pixels square." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TessTargetPixelFile(TICID: Gaia DR25290850609994130560)\n" ] } ], "source": [ "search_result_s4 = lk.search_tesscut('Gaia DR25290850609994130560', sector=4)\n", "tpfs_s4 = search_result_s4.download(cutout_size=10)\n", "print(tpfs_s4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above code has created a variable named `tpfs_s4` which is a Python object of type `TessTargetPixelFile`\n", "This can then be treated and examined the same way as in the previous [Target Pixel File tutorial](Target-Pixel-Files.html), for example lets plot the object." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "tpfs_s4.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great we now see our object of interest and the surrounding region. As indicated in the previous [Target Pixel File tutorial](Target-Pixel-Files.html), we can examine the header of this file via, " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "XTENSION= 'BINTABLE' / binary table extension \n", "BITPIX = 8 / array data type \n", "NAXIS = 2 / number of array dimensions \n", "NAXIS1 = 2066 / length of dimension 1 \n", "NAXIS2 = 1060 / length of dimension 2 \n", "PCOUNT = 0 / number of group parameters \n", "GCOUNT = 1 / number of groups \n", "TFIELDS = 12 / number of table fields \n", "TTYPE1 = 'TIME ' / column name \n", "TFORM1 = 'D ' / column format \n", "TUNIT1 = 'BJD - 2457000, days' / unit \n", "TDISP1 = 'D14.7 ' / display format \n", "TTYPE2 = 'TIMECORR' / column name \n", "TFORM2 = 'E ' / column format \n", "TUNIT2 = 'd ' / unit \n", "TDISP2 = 'E14.7 ' / display format \n", "TTYPE3 = 'CADENCENO' / column name \n", "TFORM3 = 'J ' / column format \n", "TDISP3 = 'I10 ' / display format \n", "TTYPE4 = 'RAW_CNTS' / column name \n", "TFORM4 = '100J ' / column format \n", "TUNIT4 = 'count ' / unit \n", "TNULL4 = -1 / null value \n", "TDISP4 = 'I8 ' / display format \n", "TDIM4 = '(10, 10)' / multi-dimensional array spec \n", "WCAX4 = 2 / number of WCS axes \n", "1CTYP4 = 'RA---TAN' / right ascension coordinate type \n", "2CTYP4 = 'DEC--TAN' / declination coordinate type \n", "1CRPX4 = 5.4970243878873 / [pixel] reference pixel along image axis 1 \n", "2CRPX4 = 5.5084913439498 / [pixel] reference pixel along image axis 2 \n", "1CRVL4 = 120.49774824319 / [deg] right ascension at reference pixel \n", "2CRVL4 = -60.35838348711 / [deg] declination at reference pixel \n", "1CUNI4 = 'deg ' / physical unit in column dimension \n", "2CUNI4 = 'deg ' / physical unit in row dimension \n", "1CDLT4 = 1.0 / [deg] pixel scale in RA dimension \n", "2CDLT4 = 1.0 / [deg] pixel scale in DEC dimension \n", "11PC4 = 0.00082008272000062 / Coordinate transformation matrix element \n", "12PC4 = -0.0052652158454813 / Coordinate transformation matrix element \n", "21PC4 = 0.0052951771787336 / Coordinate transformation matrix element \n", "22PC4 = 0.0012306942427273 / Coordinate transformation matrix element \n", "WCSN4P = 'PHYSICAL' / table column WCS name \n", "WCAX4P = 2 / table column physical WCS dimensions \n", "1CTY4P = 'RAWX ' / table column physical WCS axis 1 type, CCD col \n", "2CTY4P = 'RAWY ' / table column physical WCS axis 2 type, CCD row \n", "1CUN4P = 'PIXEL ' / table column physical WCS axis 1 unit \n", "2CUN4P = 'PIXEL ' / table column physical WCS axis 2 unit \n", "1CRV4P = 1948 / table column physical WCS ax 1 ref value \n", "2CRV4P = 380 / table column physical WCS ax 2 ref value \n", "1CDL4P = 1.0 / table column physical WCS a1 step \n", "2CDL4P = 1.0 / table column physical WCS a2 step \n", "1CRP4P = 1 / table column physical WCS a1 reference \n", "2CRP4P = 1 / table column physical WCS a2 reference \n", "TTYPE5 = 'FLUX ' / column name \n", "TFORM5 = '100E ' / column format \n", "TUNIT5 = 'e-/s ' / unit \n", "TDISP5 = 'E14.7 ' / display format \n", "TDIM5 = '(10, 10)' / multi-dimensional array spec \n", "WCAX5 = 2 / number of WCS axes \n", "1CTYP5 = 'RA---TAN' / right ascension coordinate type \n", "2CTYP5 = 'DEC--TAN' / declination coordinate type \n", "1CRPX5 = 5.4970243878873 / [pixel] reference pixel along image axis 1 \n", "2CRPX5 = 5.5084913439498 / [pixel] reference pixel along image axis 2 \n", "1CRVL5 = 120.49774824319 / [deg] right ascension at reference pixel \n", "2CRVL5 = -60.35838348711 / [deg] declination at reference pixel \n", "1CUNI5 = 'deg ' / physical unit in column dimension \n", "2CUNI5 = 'deg ' / physical unit in row dimension \n", "1CDLT5 = 1.0 / [deg] pixel scale in RA dimension \n", "2CDLT5 = 1.0 / [deg] pixel scale in DEC dimension \n", "11PC5 = 0.00082008272000062 / Coordinate transformation matrix element \n", "12PC5 = -0.0052652158454813 / Coordinate transformation matrix element \n", "21PC5 = 0.0052951771787336 / Coordinate transformation matrix element \n", "22PC5 = 0.0012306942427273 / Coordinate transformation matrix element \n", "WCSN5P = 'PHYSICAL' / table column WCS name \n", "WCAX5P = 2 / table column physical WCS dimensions \n", "1CTY5P = 'RAWX ' / table column physical WCS axis 1 type, CCD col \n", "2CTY5P = 'RAWY ' / table column physical WCS axis 2 type, CCD row \n", "1CUN5P = 'PIXEL ' / table column physical WCS axis 1 unit \n", "2CUN5P = 'PIXEL ' / table column physical WCS axis 2 unit \n", "1CRV5P = 1948 / table column physical WCS ax 1 ref value \n", "2CRV5P = 380 / table column physical WCS ax 2 ref value \n", "1CDL5P = 1.0 / table column physical WCS a1 step \n", "2CDL5P = 1.0 / table column physical WCS a2 step \n", "1CRP5P = 1 / table column physical WCS a1 reference \n", "2CRP5P = 1 / table column physical WCS a2 reference \n", "TTYPE6 = 'FLUX_ERR' / column name \n", "TFORM6 = '100E ' / column format \n", "TUNIT6 = 'e-/s ' / unit \n", "TDISP6 = 'E14.7 ' / display format \n", "TDIM6 = '(10, 10)' / multi-dimensional array spec \n", "WCAX6 = 2 / number of WCS axes \n", "1CTYP6 = 'RA---TAN' / right ascension coordinate type \n", "2CTYP6 = 'DEC--TAN' / declination coordinate type \n", "1CRPX6 = 5.4970243878873 / [pixel] reference pixel along image axis 1 \n", "2CRPX6 = 5.5084913439498 / [pixel] reference pixel along image axis 2 \n", "1CRVL6 = 120.49774824319 / [deg] right ascension at reference pixel \n", "2CRVL6 = -60.35838348711 / [deg] declination at reference pixel \n", "1CUNI6 = 'deg ' / physical unit in column dimension \n", "2CUNI6 = 'deg ' / physical unit in row dimension \n", "1CDLT6 = 1.0 / [deg] pixel scale in RA dimension \n", "2CDLT6 = 1.0 / [deg] pixel scale in DEC dimension \n", "11PC6 = 0.00082008272000062 / Coordinate transformation matrix element \n", "12PC6 = -0.0052652158454813 / Coordinate transformation matrix element \n", "21PC6 = 0.0052951771787336 / Coordinate transformation matrix element \n", "22PC6 = 0.0012306942427273 / Coordinate transformation matrix element \n", "WCSN6P = 'PHYSICAL' / table column WCS name \n", "WCAX6P = 2 / table column physical WCS dimensions \n", "1CTY6P = 'RAWX ' / table column physical WCS axis 1 type, CCD col \n", "2CTY6P = 'RAWY ' / table column physical WCS axis 2 type, CCD row \n", "1CUN6P = 'PIXEL ' / table column physical WCS axis 1 unit \n", "2CUN6P = 'PIXEL ' / table column physical WCS axis 2 unit \n", "1CRV6P = 1948 / table column physical WCS ax 1 ref value \n", "2CRV6P = 380 / table column physical WCS ax 2 ref value \n", "1CDL6P = 1.0 / table column physical WCS a1 step \n", "2CDL6P = 1.0 / table column physical WCS a2 step \n", "1CRP6P = 1 / table column physical WCS a1 reference \n", "2CRP6P = 1 / table column physical WCS a2 reference \n", "TTYPE7 = 'FLUX_BKG' / column name \n", "TFORM7 = '100E ' / column format \n", "TUNIT7 = 'e-/s ' / unit \n", "TDISP7 = 'E14.7 ' / display format \n", "TDIM7 = '(10, 10)' / multi-dimensional array spec \n", "WCAX7 = 2 / number of WCS axes \n", "1CTYP7 = 'RA---TAN' / right ascension coordinate type \n", "2CTYP7 = 'DEC--TAN' / declination coordinate type \n", "1CRPX7 = 5.4970243878873 / [pixel] reference pixel along image axis 1 \n", "2CRPX7 = 5.5084913439498 / [pixel] reference pixel along image axis 2 \n", "1CRVL7 = 120.49774824319 / [deg] right ascension at reference pixel \n", "2CRVL7 = -60.35838348711 / [deg] declination at reference pixel \n", "1CUNI7 = 'deg ' / physical unit in column dimension \n", "2CUNI7 = 'deg ' / physical unit in row dimension \n", "1CDLT7 = 1.0 / [deg] pixel scale in RA dimension \n", "2CDLT7 = 1.0 / [deg] pixel scale in DEC dimension \n", "11PC7 = 0.00082008272000062 / Coordinate transformation matrix element \n", "12PC7 = -0.0052652158454813 / Coordinate transformation matrix element \n", "21PC7 = 0.0052951771787336 / Coordinate transformation matrix element \n", "22PC7 = 0.0012306942427273 / Coordinate transformation matrix element \n", "WCSN7P = 'PHYSICAL' / table column WCS name \n", "WCAX7P = 2 / table column physical WCS dimensions \n", "1CTY7P = 'RAWX ' / table column physical WCS axis 1 type, CCD col \n", "2CTY7P = 'RAWY ' / table column physical WCS axis 2 type, CCD row \n", "1CUN7P = 'PIXEL ' / table column physical WCS axis 1 unit \n", "2CUN7P = 'PIXEL ' / table column physical WCS axis 2 unit \n", "1CRV7P = 1948 / table column physical WCS ax 1 ref value \n", "2CRV7P = 380 / table column physical WCS ax 2 ref value \n", "1CDL7P = 1.0 / table column physical WCS a1 step \n", "2CDL7P = 1.0 / table column physical WCS a2 step \n", "1CRP7P = 1 / table column physical WCS a1 reference \n", "2CRP7P = 1 / table column physical WCS a2 reference \n", "TTYPE8 = 'FLUX_BKG_ERR' / column name \n", "TFORM8 = '100E ' / column format \n", "TUNIT8 = 'e-/s ' / unit \n", "TDISP8 = 'E14.7 ' / display format \n", "TDIM8 = '(10, 10)' / multi-dimensional array spec \n", "WCAX8 = 2 / number of WCS axes \n", "1CTYP8 = 'RA---TAN' / right ascension coordinate type \n", "2CTYP8 = 'DEC--TAN' / declination coordinate type \n", "1CRPX8 = 5.4970243878873 / [pixel] reference pixel along image axis 1 \n", "2CRPX8 = 5.5084913439498 / [pixel] reference pixel along image axis 2 \n", "1CRVL8 = 120.49774824319 / [deg] right ascension at reference pixel \n", "2CRVL8 = -60.35838348711 / [deg] declination at reference pixel \n", "1CUNI8 = 'deg ' / physical unit in column dimension \n", "2CUNI8 = 'deg ' / physical unit in row dimension \n", "1CDLT8 = 1.0 / [deg] pixel scale in RA dimension \n", "2CDLT8 = 1.0 / [deg] pixel scale in DEC dimension \n", "11PC8 = 0.00082008272000062 / Coordinate transformation matrix element \n", "12PC8 = -0.0052652158454813 / Coordinate transformation matrix element \n", "21PC8 = 0.0052951771787336 / Coordinate transformation matrix element \n", "22PC8 = 0.0012306942427273 / Coordinate transformation matrix element \n", "WCSN8P = 'PHYSICAL' / table column WCS name \n", "WCAX8P = 2 / table column physical WCS dimensions \n", "1CTY8P = 'RAWX ' / table column physical WCS axis 1 type, CCD col \n", "2CTY8P = 'RAWY ' / table column physical WCS axis 2 type, CCD row \n", "1CUN8P = 'PIXEL ' / table column physical WCS axis 1 unit \n", "2CUN8P = 'PIXEL ' / table column physical WCS axis 2 unit \n", "1CRV8P = 1948 / table column physical WCS ax 1 ref value \n", "2CRV8P = 380 / table column physical WCS ax 2 ref value \n", "1CDL8P = 1.0 / table column physical WCS a1 step \n", "2CDL8P = 1.0 / table column physical WCS a2 step \n", "1CRP8P = 1 / table column physical WCS a1 reference \n", "2CRP8P = 1 / table column physical WCS a2 reference \n", "TTYPE9 = 'QUALITY ' / column name \n", "TFORM9 = 'J ' / column format \n", "TDISP9 = 'B16.16 ' / display format \n", "TTYPE10 = 'POS_CORR1' / column name \n", "TFORM10 = 'E ' / column format \n", "TUNIT10 = 'pixel ' / unit \n", "TDISP10 = 'E14.7 ' / display format \n", "TTYPE11 = 'POS_CORR2' / column name \n", "TFORM11 = 'E ' / column format \n", "TUNIT11 = 'pixel ' / unit \n", "TDISP11 = 'E14.7 ' / display format \n", "TTYPE12 = 'FFI_FILE' / column name \n", "TFORM12 = '38A ' / column format \n", "TUNIT12 = 'pixel ' / unit \n", "EXTNAME = 'PIXELS ' \n", "INHERIT = T \n", "BACKAPP = 0.0 / background is subtracted \n", "CDPP0_5 = '' / RMS CDPP on 0.5-hr time scales \n", "CDPP1_0 = '' / RMS CDPP on 1.0-hr time scales \n", "CDPP2_0 = '' / RMS CDPP on 2.0-hr time scales \n", "CROWDSAP= '' / Ratio of target flux to total flux in op. ap. \n", "DEADAPP = 1.0 / deadtime applied \n", "DEADC = 0.7919999957084656 / deadtime correction \n", "EXPOSURE= 0.01649999618530273 / [d] time on source \n", "FLFRCSAP= '' / Frac. of target flux w/in the op. aperture \n", "FRAMETIM= 2.0 / [s] frame time [INT_TIME + READTIME] \n", "FXDOFF = 3355400 / compression fixed offset \n", "GAINA = 5.239999771118164 / [electrons/count] CCD output A gain \n", "GAINB = 5.119999885559082 / [electrons/count] CCD output B gain \n", "GAINC = 5.159999847412109 / [electrons/count] CCD output C gain \n", "GAIND = 5.159999847412109 / [electrons/count] CCD output D gain \n", "INT_TIME= 1.980000019073486 / [s] photon accumulation time per frame \n", "LIVETIME= 0.01649999618530273 / [d] TELAPSE multiplied by DEADC \n", "MEANBLCA= 6689 / [count] FSW mean black level CCD output A \n", "MEANBLCB= 6826 / [count] FSW mean black level CCD output B \n", "MEANBLCC= 6751 / [count] FSW mean black level CCD output C \n", "MEANBLCD= 6503 / [count] FSW mean black level CCD output D \n", "NREADOUT= 720 / number of read per cadence \n", "NUM_FRM = 900 / number of frames per time stamp \n", "READNOIA= 10.27040004730225 / [electrons] read noise CCD output A \n", "READNOIB= 7.424000263214111 / [electrons] read noise CCD output B \n", "READNOIC= 7.327199459075928 / [electrons] read noise CCD output C \n", "READNOID= 9.391200065612793 / [electrons] read noise CCD output D \n", "READTIME= 0.01999999955296516 / [s] readout time per frame \n", "TIERRELA= 1.16000001071370E-05 / [d] relative time error \n", "TIMEDEL = 0.02083333395421505 / [d] time resolution of data \n", "TIMEPIXR= 0.5 / bin time beginning=0 middle=0.5 end=1 \n", "TMOFST11= '' / (s) readout delay for camera 1 and ccd 1 \n", "VIGNAPP = 1.0 / vignetting or collimator correction applied \n", "WCS_FFI = 'tess2018307065940-s0004-4-4-0124-s_ffic.fits' / FFI used for cutout W\n", "EXTVER = 1 / extension version number (not format version) \n", "SIMDATA = F / file is based on simulated data \n", "ORIGIN = 'STScI/MAST' / institution responsible for creating this file \n", "DATE = '2019-01-24' / file creation date. \n", "TSTART = 1410.917241951712 / observation start time in TJD \n", "TSTOP = 1436.833902272608 / observation stop time in TJD \n", "DATE-OBS= '2018-10-19T09:59:40.521Z' / TSTART as UTC calendar date \n", "DATE-END= '2018-11-14T07:59:39.972Z' / TSTOP as UTC calendar date \n", "CREATOR = 'astrocut' / software used to produce this file \n", "PROCVER = '0.7 ' / software version \n", "FILEVER = '1.0 ' / file format version \n", "TIMVERSN= 'OGIP/93-003' / OGIP memo number for file format \n", "TELESCOP= 'TESS ' / telescope \n", "INSTRUME= 'TESS Photometer' / detector type \n", "DATA_REL= 5 / data release version number \n", "ASTATE = T / archive state F indicates single orbit processi\n", "SCCONFIG= 124 / spacecraft configuration ID \n", "RADESYS = 'ICRS ' / reference frame of celestial coordinates \n", "EQUINOX = 2000.0 / equinox of celestial coordinate system \n", "CRMITEN = T / spacecraft cosmic ray mitigation enabled \n", "CRBLKSZ = 10 / [exposures] s/c cosmic ray mitigation block siz\n", "CRSPOC = F / SPOC cosmic ray cleaning enabled \n", "SECTOR = 4 / Observing sector \n", "CAMERA = 4 / Camera number \n", "CCD = 4 / CCD chip number \n", "RA_OBJ = 120.49008353007 / [deg] right ascension \n", "DEC_OBJ = -60.3546627100264 / [deg] declination \n", "TIMEREF = 'SOLARSYSTEM' / barycentric correction applied to times \n", "TASSIGN = 'SPACECRAFT' / where time is assigned \n", "TIMESYS = 'TDB ' / time system is Barycentric Dynamical Time (TDB)\n", "BJDREFI = 2457000 / integer part of BTJD reference date \n", "BJDREFF = 0.0 / fraction of the day in BTJD reference date \n", "TIMEUNIT= 'd ' / time unit for TIME, TSTART and TSTOP \n", "TELAPSE = 25.91666032089597 / [d] TSTOP - TSTART \n", "OBJECT = '' / string version of target id \n", "TCID = 0 / unique tess target identifier \n", "PXTABLE = 0 / pixel table id \n", "PMRA = 0.0 / [mas/yr] RA proper motion \n", "PMDEC = 0.0 / [mas/yr] Dec proper motion \n", "PMTOTAL = 0.0 / [mas/yr] total proper motion \n", "TESSMAG = 0.0 / [mag] TESS magnitude \n", "TEFF = 0.0 / [K] Effective temperature \n", "LOGG = 0.0 / [cm/s2] log10 surface gravity \n", "MH = 0.0 / [log10([M/H])] metallicity \n", "RADIUS = 0.0 / [solar radii] stellar radius \n", "TICVER = 0 / TICVER \n", "TICID = '' / unique tess target identifier \n", "CHECKSUM= 'f752h751f751f751' / HDU checksum updated 2020-08-26T14:10:38 \n", "DATASUM = '3805688324' / data unit checksum updated 2020-08-26T14:10:38 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tpfs_s4.hdu[1].header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also examine specific things like the flux or time via, " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$[[[129.99297,~128.63951,~132.87094,~\\dots,~139.99612,~137.95688,~142.58392],~\n", " [138.64349,~153.85022,~161.18605,~\\dots,~132.13853,~131.88116,~136.26653],~\n", " [147.42535,~185.44145,~202.03874,~\\dots,~131.27419,~129.55354,~132.28387],~\n", " \\dots,~\n", " [148.45624,~168.75594,~169.70703,~\\dots,~193.291,~262.43341,~225.49281],~\n", " [143.34943,~148.85297,~140.99071,~\\dots,~632.60272,~1230.0093,~481.08664],~\n", " [138.4722,~136.3645,~133.1722,~\\dots,~582.95276,~1186.0709,~478.35709]],~\n", "\n", " [[129.82346,~128.34328,~132.64188,~\\dots,~139.19876,~137.08186,~142.32404],~\n", " [139.79184,~152.97951,~161.24356,~\\dots,~132.91368,~131.2858,~136.80386],~\n", " [147.51363,~185.70856,~202.45325,~\\dots,~131.01465,~129.47301,~132.81329],~\n", " \\dots,~\n", " [147.99286,~167.25301,~168.89616,~\\dots,~194.54517,~264.9231,~225.16179],~\n", " [143.70619,~149.3633,~140.49521,~\\dots,~632.60107,~1227.5686,~482.08145],~\n", " [138.03108,~136.32384,~133.2,~\\dots,~582.40741,~1182.1221,~479.85046]],~\n", "\n", " [[129.21542,~129.74716,~132.78502,~\\dots,~138.40413,~137.88484,~143.00275],~\n", " [139.7187,~155.0994,~160.00632,~\\dots,~132.12894,~131.79468,~136.66957],~\n", " [147.73445,~183.78162,~200.47708,~\\dots,~130.74011,~129.2097,~132.41162],~\n", " \\dots,~\n", " [147.66319,~166.57088,~168.81226,~\\dots,~215.93103,~281.56219,~224.84123],~\n", " [142.83057,~148.42052,~140.91992,~\\dots,~642.88855,~1205.141,~467.6749],~\n", " [137.69144,~135.93571,~133.34496,~\\dots,~573.22418,~1151.689,~483.14612]],~\n", "\n", " \\dots,~\n", "\n", " [[792.39258,~789.263,~795.30682,~\\dots,~809.82086,~811.76941,~815.80994],~\n", " [803.67145,~809.58545,~835.54602,~\\dots,~800.33563,~802.27563,~808.07153],~\n", " [803.70325,~832.35767,~866.13525,~\\dots,~802.09814,~800.79468,~804.69025],~\n", " \\dots,~\n", " [799.46198,~814.78491,~819.1286,~\\dots,~869.16278,~1009.9353,~943.88312],~\n", " [794.48761,~799.0069,~792.25012,~\\dots,~1211.3436,~2031.7046,~1349.0428],~\n", " [790.42419,~785.95496,~787.04718,~\\dots,~1130.4243,~1644.5442,~1246.1456]],~\n", "\n", " [[813.54431,~809.97974,~817.47009,~\\dots,~830.65527,~830.61475,~836.75928],~\n", " [826.25146,~831.77899,~856.91522,~\\dots,~821.61914,~823.5957,~828.23602],~\n", " [825.62567,~854.96924,~889.86981,~\\dots,~822.89606,~820.79828,~824.80457],~\n", " \\dots,~\n", " [821.97601,~836.54681,~844.15063,~\\dots,~892.07336,~1033.9146,~965.64771],~\n", " [816.31024,~823.79694,~816.89545,~\\dots,~1233.7531,~2053.4348,~1371.8173],~\n", " [813.43365,~809.15466,~809.1488,~\\dots,~1154.3142,~1664.0698,~1269.6807]],~\n", "\n", " [[843.42645,~838.84576,~847.90161,~\\dots,~861.24701,~861.25537,~867.8017],~\n", " [855.00861,~862.23065,~888.0213,~\\dots,~849.95538,~852.78198,~859.7309],~\n", " [856.35095,~883.28583,~919.44934,~\\dots,~852.11145,~851.24445,~854.23767],~\n", " \\dots,~\n", " [849.98053,~867.28394,~872.92499,~\\dots,~923.02142,~1061.4458,~994.54596],~\n", " [847.56006,~852.9693,~846.55609,~\\dots,~1264.6205,~2082.7625,~1403.7192],~\n", " [843.9043,~838.604,~839.48645,~\\dots,~1181.7628,~1696.9923,~1301.2625]]] \\; \\mathrm{\\frac{e^{-}}{s}}$" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tpfs_s4.flux" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": 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