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Initial commit of notebook that identifies keywords that are skipped and exist in multiple headers.
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‎notebooks/keywords_in_multiple_headers.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### This notebook identifies keywords that are in the purposely skipped list and also exist in multiple headers (e.g. PRIMARY and SCI, SCI and DQ, etc.)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import glob\n",
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"import os\n",
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"\n",
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"from astropy.io import fits\n",
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"\n",
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"from jwql.utils.utils import get_config"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"filesystem = get_config()['filesystem']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"filenames = glob.glob(os.path.join(filesystem, '*/*.fits'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"non_unique_keys = []\n",
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"for filename in filenames:\n",
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" \n",
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" header_keys = []\n",
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" hdulist = fits.open(filename, mode='readonly')\n",
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" for hdu in hdulist:\n",
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" keys = list(set(hdu.header))\n",
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" for key in keys:\n",
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" if key in header_keys:\n",
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" non_unique_keys.append(key)\n",
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" header_keys.append(key)\n",
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" \n",
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"non_unique_keys = set(non_unique_keys)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'',\n",
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" 'BITPIX',\n",
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" 'BSCALE',\n",
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" 'BUNIT',\n",
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" 'BZERO',\n",
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" 'CDELT1',\n",
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" 'CDELT2',\n",
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" 'CDELT3',\n",
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" 'CRPIX1',\n",
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" 'CRPIX2',\n",
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" 'CRPIX3',\n",
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" 'CRVAL1',\n",
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" 'CRVAL2',\n",
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" 'CRVAL3',\n",
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" 'CTYPE1',\n",
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" 'CTYPE2',\n",
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" 'CTYPE3',\n",
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" 'CUNIT1',\n",
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" 'CUNIT2',\n",
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" 'CUNIT3',\n",
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" 'DEC_REF',\n",
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" 'DEC_V1',\n",
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" 'EXTNAME',\n",
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" 'EXTVER',\n",
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" 'GCOUNT',\n",
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" 'NAXIS',\n",
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" 'NAXIS1',\n",
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" 'NAXIS2',\n",
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" 'NAXIS3',\n",
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" 'NAXIS4',\n",
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" 'PA_APER',\n",
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" 'PA_V3',\n",
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" 'PC1_1',\n",
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" 'PC1_2',\n",
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" 'PC1_3',\n",
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" 'PC2_1',\n",
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" 'PC2_2',\n",
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" 'PC2_3',\n",
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" 'PC3_3',\n",
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" 'PCOUNT',\n",
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" 'PHOTMJSR',\n",
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" 'PHOTUJA2',\n",
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" 'RADESYS',\n",
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" 'RA_REF',\n",
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" 'RA_V1',\n",
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" 'ROLL_REF',\n",
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" 'SHUTSTA',\n",
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" 'SLITID',\n",
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" 'SLIT_DEC',\n",
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" 'SLIT_RA',\n",
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" 'SLTNAME',\n",
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" 'SLTSIZE1',\n",
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" 'SLTSIZE2',\n",
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" 'SLTSTRT1',\n",
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" 'SLTSTRT2',\n",
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" 'SOURCEID',\n",
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" 'SPORDER',\n",
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" 'SRCXPOS',\n",
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" 'SRCYPOS',\n",
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" 'S_REGION',\n",
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" 'TFIELDS',\n",
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" 'TFORM1',\n",
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" 'TFORM10',\n",
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" 'TFORM11',\n",
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" 'TFORM12',\n",
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" 'TFORM13',\n",
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" 'TFORM2',\n",
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" 'TFORM3',\n",
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" 'TFORM4',\n",
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" 'TFORM5',\n",
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" 'TFORM6',\n",
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" 'TFORM7',\n",
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" 'TFORM8',\n",
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" 'TFORM9',\n",
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" 'TTYPE1',\n",
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" 'TTYPE10',\n",
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" 'TTYPE11',\n",
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" 'TTYPE12',\n",
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" 'TTYPE13',\n",
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" 'TTYPE2',\n",
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" 'TTYPE3',\n",
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" 'TTYPE4',\n",
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" 'TTYPE5',\n",
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" 'TTYPE6',\n",
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" 'TTYPE7',\n",
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" 'TTYPE8',\n",
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" 'TTYPE9',\n",
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" 'TUNIT1',\n",
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" 'TUNIT2',\n",
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" 'TUNIT3',\n",
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" 'TUNIT5',\n",
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" 'TUNIT6',\n",
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" 'TUNIT7',\n",
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" 'TUNIT8',\n",
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" 'V2_REF',\n",
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" 'V3I_YANG',\n",
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" 'V3_REF',\n",
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" 'VA_SCALE',\n",
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" 'VPARITY',\n",
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" 'WAVEND',\n",
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" 'WAVSTART',\n",
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" 'WCSAXES',\n",
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" 'XTENSION'}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"non_unique_keys"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### These are the keywords that exist in multiple headers and are skipped:\n",
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"- NAXIS\n",
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"- NAXIS1\n",
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"- NAXIS2\n",
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"- NAXIS3\n",
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"- NAXIS4\n",
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"- EXTNAME\n",
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"- BUNIT\n",
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"- BSCALE\n",
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"- BZERO\n",
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"- XTENSION"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda env:astroconda3]",
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"language": "python",
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"name": "conda-env-astroconda3-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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