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meredith-durbin/Download, align, and find overlaps.ipynb

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Download, align, and find overlaps.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Programmatically downloading, aligning, and checking for PHATTER overlaps for prop 15932\n",
"\n",
"Cribbed heavily from [this notebook](https://github.com/spacetelescope/gaia_alignment/blob/master/Gaia_alignment.ipynb).\n",
"\n",
"### Some relevant paths\n",
"All aligned M33 exposures (plus a bunch of tweakreg outputs): `/astro/store/gradscratch/tmp/mdurbin/m33data/realign/`\n",
"\n",
"Or, tarball of just the fits files: `/astro/store/gradscratch/tmp/mdurbin/m33data/m33_realigned.tar.gz`\n",
"\n",
"GAIA DR2 reference catalog (coordinates only): `/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat`\n",
"\n",
"CSV of primary and science headers for all M33 exposures: `/astro/store/gradscratch/tmp/mdurbin/m33data/header_info.csv`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downloading the data\n",
"\n",
"All 15932 data is proprietary right now; see the astroquery docs on logging in to MAST [here](https://astroquery.readthedocs.io/en/latest/mast/mast.html#accessing-proprietary-data).\n",
"In the meantime I'm downloading some random archival IR exposures that happens to be in the M33 survey footprint as an example."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/icph76igq_flt.fits to ./mastDownload/HST/icph76igq/icph76igq_flt.fits ... [Done]\n",
"Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/icph76ihq_flt.fits to ./mastDownload/HST/icph76ihq/icph76ihq_flt.fits ... [Done]\n",
"Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/icph76ijq_flt.fits to ./mastDownload/HST/icph76ijq/icph76ijq_flt.fits ... [Done]\n",
"Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/icph76ikq_flt.fits to ./mastDownload/HST/icph76ikq/icph76ikq_flt.fits ... [Done]\n"
]
},
{
"data": {
"text/html": [
"<i>Table length=4</i>\n",
"<table id=\"table140110020861712\" class=\"table-striped table-bordered table-condensed\">\n",
"<thead><tr><th>Local Path</th><th>Status</th><th>Message</th><th>URL</th></tr></thead>\n",
"<thead><tr><th>str47</th><th>str8</th><th>object</th><th>object</th></tr></thead>\n",
"<tr><td>./mastDownload/HST/icph76igq/icph76igq_flt.fits</td><td>COMPLETE</td><td>None</td><td>None</td></tr>\n",
"<tr><td>./mastDownload/HST/icph76ihq/icph76ihq_flt.fits</td><td>COMPLETE</td><td>None</td><td>None</td></tr>\n",
"<tr><td>./mastDownload/HST/icph76ijq/icph76ijq_flt.fits</td><td>COMPLETE</td><td>None</td><td>None</td></tr>\n",
"<tr><td>./mastDownload/HST/icph76ikq/icph76ikq_flt.fits</td><td>COMPLETE</td><td>None</td><td>None</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table length=4>\n",
" Local Path Status Message URL \n",
" str47 str8 object object\n",
"----------------------------------------------- -------- ------- ------\n",
"./mastDownload/HST/icph76igq/icph76igq_flt.fits COMPLETE None None\n",
"./mastDownload/HST/icph76ihq/icph76ihq_flt.fits COMPLETE None None\n",
"./mastDownload/HST/icph76ijq/icph76ijq_flt.fits COMPLETE None None\n",
"./mastDownload/HST/icph76ikq/icph76ikq_flt.fits COMPLETE None None"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from astroquery.mast import Observations\n",
"\n",
"obs = Observations.query_criteria(obs_id='icph76*q', instrument_name='WFC3/IR') #proposal_id=15932\n",
"prod = Observations.filter_products(Observations.get_product_list(obs),\n",
" productSubGroupDescription='FLT', # ['FLT', 'FLC'] if including parallels\n",
" extension='fits')\n",
"table = Observations.download_products(prod)\n",
"table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Align images to GAIA DR2 catalog with TweakReg\n",
"\n",
"[Tweakreg docs](https://drizzlepac.readthedocs.io/en/deployment/tweakreg.html)\n",
"\n",
"The alignment function here is set to perform a dry run that doesn't update the FLT headers with the aligned WCS solution, so that the results can be inspected before the headers are updated."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The following task in the stsci.skypac package can be run with TEAL:\n",
" skymatch \n",
"The following tasks in the drizzlepac package can be run with TEAL:\n",
" astrodrizzle config_testbed imagefindpars mapreg \n",
" photeq pixreplace pixtopix pixtosky \n",
" refimagefindpars resetbits runastrodriz skytopix \n",
" tweakback tweakreg updatenpol\n"
]
}
],
"source": [
"import glob\n",
"\n",
"from drizzlepac import tweakreg\n",
"from stsci.tools import teal\n",
"from stwcs.updatewcs import updatewcs\n",
"\n",
"refcat = '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"visit_prefix = 'icph76'\n",
"wcsname = 'GAIA2'\n",
"\n",
"def align(visit_prefix, detector='ir', update_wcs=True, updatehdr=False, wcsname=wcsname, refcat=refcat):\n",
" teal.unlearn('tweakreg')\n",
" teal.unlearn('imagefindpars')\n",
" \n",
" if detector in ['acs', 'uvis']:\n",
" input_images = glob.glob(f'./mastDownload/HST/{visit_prefix}??q/{visit_prefix}??q_flc.fits')\n",
" threshold = 250.\n",
" cw = 3.5\n",
" elif detector == 'ir':\n",
" input_images = glob.glob(f'./mastDownload/HST/{visit_prefix}??q/{visit_prefix}??q_flt.fits')\n",
" threshold = 25.\n",
" cw = 2.5\n",
" else:\n",
" raise Exception('Accepted detector arg values are \"ir\", \"acs\", and \"uvis\"')\n",
" \n",
" outname = visit_prefix + '_' + detector\n",
" \n",
" print(f'Aligning visit {visit_prefix} {detector} images to GAIA DR2')\n",
" \n",
" # this step shouldn't be strictly necessary but it can be useful for consistency\n",
" # it prints out a whole bunch of annoying nonsense\n",
" if update_wcs:\n",
" for img in input_images:\n",
" updatewcs(img)\n",
" \n",
" tweakreg.TweakReg(input_images, # Pass input images\n",
" updatehdr = updatehdr, # update header with new WCS solution?\n",
" # Detection parameters, threshold varies for different data\n",
" imagefindcfg = {'threshold' : threshold,\n",
" 'conv_width' : cw},\n",
" separation = 0., # Allow for very small shifts\n",
" refcat = refcat, # Use user supplied catalog (Gaia)\n",
" clean = True, # Get rid of intermediate files\n",
" interactive = False, # interactivity doesn't work well in notebook\n",
" see2dplot = True, # show 2d histogram of match offsets\n",
" use2dhist = False, # there's an absolutely ridiculous bug here in recent versions\n",
" shiftfile = True, # Save out shift file (so we can look at shifts later)\n",
" wcsname = wcsname, # Give our WCS a new name\n",
" reusename = True, # Use the same name if run multiple times\n",
" runfile = f'{outname}.log',\n",
" outshifts = f'shifts_{outname}.txt',\n",
" outwcs = f'shifts_{outname}_wcs.txt',\n",
" fitgeometry = 'general') # Use the 6 parameter fit\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aligning visit icph76 ir images to GAIA DR2\n",
"AstrometryDB service available...\n",
"- IDCTAB: Distortion model from row 3 for chip 1 : F125W\n",
"- IDCTAB: Distortion model from row 3 for chip 1 : F125W\n",
"Updating astrometry for icph76igq\n",
"Accessing AstrometryDB service :\n",
"\thttps://mast.stsci.edu/portal/astrometryDB/observation/read/icph76igq\n",
"AstrometryDB service call succeeded\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i\" for observation \"icph76igq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS\" for observation \"icph76igq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-GSC240\" for observation \"icph76igq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-GSC240\" for observation \"icph76igq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-FIT_IMG_GAIADR1\" for observation \"icph76igq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-HSC30\" for observation \"icph76igq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-HSC30\" for observation \"icph76igq\"\n",
"Updating icph76igq with:\n",
"Replacing primary WCS with\n",
"\tHeaderlet with WCSNAME=IDC_w3m18525i-HSC30\n",
"AstrometryDB service available...\n",
"- IDCTAB: Distortion model from row 1 for chip 1 : F105W\n",
"- IDCTAB: Distortion model from row 1 for chip 1 : F105W\n",
"Updating astrometry for icph76ihq\n",
"Accessing AstrometryDB service :\n",
"\thttps://mast.stsci.edu/portal/astrometryDB/observation/read/icph76ihq\n",
"AstrometryDB service call succeeded\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i\" for observation \"icph76ihq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS\" for observation \"icph76ihq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-GSC240\" for observation \"icph76ihq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-GSC240\" for observation \"icph76ihq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-HSC30\" for observation \"icph76ihq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-HSC30\" for observation \"icph76ihq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-FIT_IMG_GAIADR2\" for observation \"icph76ihq\"\n",
"Updating icph76ihq with:\n",
"Replacing primary WCS with\n",
"\tHeaderlet with WCSNAME=IDC_w3m18525i-FIT_IMG_GAIADR2\n",
"AstrometryDB service available...\n",
"- IDCTAB: Distortion model from row 4 for chip 1 : F140W\n",
"- IDCTAB: Distortion model from row 4 for chip 1 : F140W\n",
"Updating astrometry for icph76ijq\n",
"Accessing AstrometryDB service :\n",
"\thttps://mast.stsci.edu/portal/astrometryDB/observation/read/icph76ijq\n",
"AstrometryDB service call succeeded\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i\" for observation \"icph76ijq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS\" for observation \"icph76ijq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-GSC240\" for observation \"icph76ijq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-GSC240\" for observation \"icph76ijq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-FIT_IMG_GAIADR1\" for observation \"icph76ijq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-HSC30\" for observation \"icph76ijq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-HSC30\" for observation \"icph76ijq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-FIT_IMG_GAIADR2\" for observation \"icph76ijq\"\n",
"Updating icph76ijq with:\n",
"Replacing primary WCS with\n",
"\tHeaderlet with WCSNAME=IDC_w3m18525i-FIT_IMG_GAIADR2\n",
"AstrometryDB service available...\n",
"- IDCTAB: Distortion model from row 5 for chip 1 : F160W\n",
"- IDCTAB: Distortion model from row 5 for chip 1 : F160W\n",
"Updating astrometry for icph76ikq\n",
"Accessing AstrometryDB service :\n",
"\thttps://mast.stsci.edu/portal/astrometryDB/observation/read/icph76ikq\n",
"AstrometryDB service call succeeded\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i\" for observation \"icph76ikq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS\" for observation \"icph76ikq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-GSC240\" for observation \"icph76ikq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-GSC240\" for observation \"icph76ikq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-FIT_IMG_GAIADR2\" for observation \"icph76ikq\"\n",
"Retrieving astrometrically-updated WCS \"OPUS-HSC30\" for observation \"icph76ikq\"\n",
"Retrieving astrometrically-updated WCS \"IDC_w3m18525i-HSC30\" for observation \"icph76ikq\"\n",
"Updating icph76ikq with:\n",
"Replacing primary WCS with\n",
"\tHeaderlet with WCSNAME=IDC_w3m18525i-HSC30\n",
"Setting up logfile : icph76_ir.log\n",
"TweakReg Version 1.4.7(18-April-2018) started at: 10:34:57.100 (25/09/2020) \n",
"\n",
"Version Information\n",
"--------------------\n",
"Python Version [GCC 7.3.0]\n",
"3.7.7 (default, Mar 26 2020, 15:48:22) \n",
"numpy Version -> 1.18.5 \n",
"astropy Version -> 4.0.1.post1 \n",
"stwcs Version -> 1.5.3 \n",
"\n",
"Finding shifts for: \n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits\n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits\n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits\n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits', EXT=('SCI', 1) started at: 10:34:57.488 (25/09/2020)\n",
" Found 951 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits': 951\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits', EXT=('SCI', 1) started at: 10:34:58.334 (25/09/2020)\n",
" Found 831 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits': 831\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits', EXT=('SCI', 1) started at: 10:34:59.064 (25/09/2020)\n",
" Found 1247 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits': 1247\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits', EXT=('SCI', 1) started at: 10:34:59.76 (25/09/2020)\n",
" Found 1033 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits': 1033\n",
"\n",
"\n",
"===============================================================\n",
"Performing alignment in the projection plane defined by the WCS\n",
"derived from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits'\n",
"===============================================================\n",
"\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 154 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits : \n",
"XSH: -0.0148 YSH: 0.0542 PROPER ROT: 0.002895462428 \n",
"<ROT>: 180.0028955 SKEW: 359.9883823 ROT_X: 0.008704308685 ROT_Y: 359.9970866\n",
"<SCALE>: 1.000205673 SCALE_X: 1.00029638 SCALE_Y: 1.000114995\n",
"FIT XRMS: 0.061 FIT YRMS: 0.046 \n",
"FIT RMSE: 0.076 FIT MAE: 0.068 \n",
"\n",
"RMS_RA: 2.9e-06 (deg) RMS_DEC: 9.7e-07 (deg)\n",
"\n",
"Final solution based on 145 objects.\n",
"wrote XY data to: icph76igq_flt_catalog_fit.match\n",
"Total # points: 145\n",
"# of points after clipping: 145\n",
"Total # points: 145\n",
"# of points after clipping: 145\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 174 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits : \n",
"XSH: 0.0112 YSH: 0.0034 PROPER ROT: 0.002287630439 \n",
"<ROT>: 0.002287630439 SKEW: 0.0006736649431 ROT_X: 0.001950797968 ROT_Y: 0.002624462911\n",
"<SCALE>: 1.000022952 SCALE_X: 1.000031132 SCALE_Y: 1.000014771\n",
"FIT XRMS: 0.058 FIT YRMS: 0.063 \n",
"FIT RMSE: 0.086 FIT MAE: 0.076 \n",
"\n",
"RMS_RA: 3.5e-06 (deg) RMS_DEC: 5.6e-07 (deg)\n",
"\n",
"Final solution based on 162 objects.\n",
"wrote XY data to: icph76ihq_flt_catalog_fit.match\n",
"Total # points: 162\n",
"# of points after clipping: 162\n",
"Total # points: 162\n",
"# of points after clipping: 162\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 157 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits : \n",
"XSH: -0.0223 YSH: 0.0216 PROPER ROT: 0.0001534135023 \n",
"<ROT>: 180.0001534 SKEW: 359.9823499 ROT_X: 0.008978469063 ROT_Y: 359.9913284\n",
"<SCALE>: 1.000160604 SCALE_X: 1.000273502 SCALE_Y: 1.000047765\n",
"FIT XRMS: 0.06 FIT YRMS: 0.051 \n",
"FIT RMSE: 0.079 FIT MAE: 0.069 \n",
"\n",
"RMS_RA: 3.1e-06 (deg) RMS_DEC: 8.5e-07 (deg)\n",
"\n",
"Final solution based on 141 objects.\n",
"wrote XY data to: icph76ijq_flt_catalog_fit.match\n",
"Total # points: 141\n",
"# of points after clipping: 141\n",
"Total # points: 141\n",
"# of points after clipping: 141\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 144 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits : \n",
"XSH: 0.0476 YSH: 0.0194 PROPER ROT: 0.002054239227 \n",
"<ROT>: 180.0020542 SKEW: 359.9824575 ROT_X: 0.01082550789 ROT_Y: 359.993283\n",
"<SCALE>: 1.000251536 SCALE_X: 1.000358481 SCALE_Y: 1.00014465\n",
"FIT XRMS: 0.052 FIT YRMS: 0.046 \n",
"FIT RMSE: 0.07 FIT MAE: 0.06 \n",
"\n",
"RMS_RA: 2.8e-06 (deg) RMS_DEC: 7e-07 (deg)\n",
"\n",
"Final solution based on 129 objects.\n",
"wrote XY data to: icph76ikq_flt_catalog_fit.match\n",
"Total # points: 129\n",
"# of points after clipping: 129\n",
"Total # points: 129\n",
"# of points after clipping: 129\n",
"Writing out shiftfile : shifts_icph76_ir.txt\n",
"Trailer file written to: icph76_ir.log\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"align(visit_prefix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### See alignment results\n",
"\n",
"The tweakreg results are summarized in the shift file, with the following columns:\n",
"`filename, xshift, yshift, rotation, scale, xrms, yrms`. Shifts and RMS are in pixels, rotation is in degrees."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# frame: output\r\n",
"# refimage: shifts_icph76_ir_wcs.txt[wcs]\r\n",
"# form: delta\r\n",
"# units: pixels\r\n",
"/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits -0.014829 0.054177 0.002895 1.000206 0.060675 0.045970\r\n",
"/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits 0.011220 0.003434 0.002288 1.000023 0.057992 0.063478\r\n",
"/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits -0.022326 0.021632 0.000153 1.000161 0.059904 0.051016\r\n",
"/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits 0.047598 0.019386 0.002054 1.000252 0.051980 0.046368\r\n"
]
}
],
"source": [
"!cat shifts_icph76_ir.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ideally we would be able to update the header WCS solutions from the shift file alone, but last I checked the function to do so is broken (in an incredibly trivial way). Just in case they ever fix it, here's code to use it.\n",
"\n",
"```\n",
"from drizzlepac.updatehdr import update_from_shiftfile\n",
"update_from_shiftfile('shifts_icph76_ir.txt', wcsname=wcsname, force=True)\n",
"```\n",
"\n",
"Instead of doing this, we simply rerun the alignment and tell it to update the headers."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aligning visit icph76 ir images to GAIA DR2\n",
"Setting up logfile : icph76_ir.log\n",
"TweakReg Version 1.4.7(18-April-2018) started at: 10:36:20.801 (25/09/2020) \n",
"\n",
"Version Information\n",
"--------------------\n",
"Python Version [GCC 7.3.0]\n",
"3.7.7 (default, Mar 26 2020, 15:48:22) \n",
"numpy Version -> 1.18.5 \n",
"astropy Version -> 4.0.1.post1 \n",
"stwcs Version -> 1.5.3 \n",
"\n",
"Finding shifts for: \n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits\n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits\n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits\n",
" /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits', EXT=('SCI', 1) started at: 10:36:21.250 (25/09/2020)\n",
" Found 951 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits': 951\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits', EXT=('SCI', 1) started at: 10:36:22.543 (25/09/2020)\n",
" Found 831 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits': 831\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits', EXT=('SCI', 1) started at: 10:36:23.880 (25/09/2020)\n",
" Found 1247 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits': 1247\n",
"\n",
"=== Source finding for image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits':\n",
" # Source finding for '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits', EXT=('SCI', 1) started at: 10:36:25.609 (25/09/2020)\n",
" Found 1033 objects.\n",
"=== FINAL number of objects in image '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits': 1033\n",
"\n",
"\n",
"===============================================================\n",
"Performing alignment in the projection plane defined by the WCS\n",
"derived from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits'\n",
"===============================================================\n",
"\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 154 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits : \n",
"XSH: -0.0148 YSH: 0.0542 PROPER ROT: 0.002895462428 \n",
"<ROT>: 180.0028955 SKEW: 359.9883823 ROT_X: 0.008704308685 ROT_Y: 359.9970866\n",
"<SCALE>: 1.000205673 SCALE_X: 1.00029638 SCALE_Y: 1.000114995\n",
"FIT XRMS: 0.061 FIT YRMS: 0.046 \n",
"FIT RMSE: 0.076 FIT MAE: 0.068 \n",
"\n",
"RMS_RA: 2.9e-06 (deg) RMS_DEC: 9.7e-07 (deg)\n",
"\n",
"Final solution based on 145 objects.\n",
"wrote XY data to: icph76igq_flt_catalog_fit.match\n",
"Total # points: 145\n",
"# of points after clipping: 145\n",
"Total # points: 145\n",
"# of points after clipping: 145\n",
"\n",
"....Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits...\n",
"\n",
"\n",
"Processing /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits['SCI',1]\n",
"\n",
"Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76igq/icph76igq_flt.fits[1]\n",
"WCS Keywords\n",
"\n",
"CD_11 CD_12: -1.9739726999631192e-05 -2.8656881626300452e-05\n",
"CD_21 CD_22: -3.201450721121106e-05 1.753719295674039e-05\n",
"CRVAL : 23.48478352938284 30.57479166615844\n",
"CRPIX : 507.0 507.0\n",
"NAXIS : 1014 1014\n",
"Plate Scale : 0.13543972225878664\n",
"ORIENTAT : -58.53455846214367\n",
"WCSNAME : GAIA2\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 174 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits : \n",
"XSH: 0.0112 YSH: 0.0034 PROPER ROT: 0.002287630439 \n",
"<ROT>: 0.002287630439 SKEW: 0.0006736649431 ROT_X: 0.001950797968 ROT_Y: 0.002624462911\n",
"<SCALE>: 1.000022952 SCALE_X: 1.000031132 SCALE_Y: 1.000014771\n",
"FIT XRMS: 0.058 FIT YRMS: 0.063 \n",
"FIT RMSE: 0.086 FIT MAE: 0.076 \n",
"\n",
"RMS_RA: 3.5e-06 (deg) RMS_DEC: 5.6e-07 (deg)\n",
"\n",
"Final solution based on 162 objects.\n",
"wrote XY data to: icph76ihq_flt_catalog_fit.match\n",
"Total # points: 162\n",
"# of points after clipping: 162\n",
"Total # points: 162\n",
"# of points after clipping: 162\n",
"\n",
"....Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits...\n",
"\n",
"\n",
"Processing /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits['SCI',1]\n",
"\n",
"Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ihq/icph76ihq_flt.fits[1]\n",
"WCS Keywords\n",
"\n",
"CD_11 CD_12: -1.9743019272800987e-05 -2.866221412709491e-05\n",
"CD_21 CD_22: -3.202166005276388e-05 1.754258382421193e-05\n",
"CRVAL : 23.48479077260615 30.57479062484044\n",
"CRPIX : 507.0 507.0\n",
"NAXIS : 1014 1014\n",
"Plate Scale : 0.13543186323740256\n",
"ORIENTAT : -58.53146441806858\n",
"WCSNAME : GAIA2\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 157 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits : \n",
"XSH: -0.0223 YSH: 0.0216 PROPER ROT: 0.0001534135023 \n",
"<ROT>: 180.0001534 SKEW: 359.9823499 ROT_X: 0.008978469063 ROT_Y: 359.9913284\n",
"<SCALE>: 1.000160604 SCALE_X: 1.000273502 SCALE_Y: 1.000047765\n",
"FIT XRMS: 0.06 FIT YRMS: 0.051 \n",
"FIT RMSE: 0.079 FIT MAE: 0.069 \n",
"\n",
"RMS_RA: 3.1e-06 (deg) RMS_DEC: 8.5e-07 (deg)\n",
"\n",
"Final solution based on 141 objects.\n",
"wrote XY data to: icph76ijq_flt_catalog_fit.match\n",
"Total # points: 141\n",
"# of points after clipping: 141\n",
"Total # points: 141\n",
"# of points after clipping: 141\n",
"\n",
"....Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits...\n",
"\n",
"\n",
"Processing /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits['SCI',1]\n",
"\n",
"Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ijq/icph76ijq_flt.fits[1]\n",
"WCS Keywords\n",
"\n",
"CD_11 CD_12: -1.9741028374903545e-05 -2.8656530500216457e-05\n",
"CD_21 CD_22: -3.201473073728997e-05 1.7536920015998646e-05\n",
"CRVAL : 23.48478678932032 30.57478913433168\n",
"CRPIX : 507.0 507.0\n",
"NAXIS : 1014 1014\n",
"Plate Scale : 0.1354398098369349\n",
"ORIENTAT : -58.534642920160394\n",
"WCSNAME : GAIA2\n",
"\n",
"====================\n",
"Performing fit for: /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits\n",
"\n",
"Matching sources from '/astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits' with sources from reference catalog '/astro/store/gradscratch/tmp/mdurbin/m33data/realign/gaia.cat'\n",
"Found 144 matches for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits...\n",
"Computed general fit for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits : \n",
"XSH: 0.0476 YSH: 0.0194 PROPER ROT: 0.002054239227 \n",
"<ROT>: 180.0020542 SKEW: 359.9824575 ROT_X: 0.01082550789 ROT_Y: 359.993283\n",
"<SCALE>: 1.000251536 SCALE_X: 1.000358481 SCALE_Y: 1.00014465\n",
"FIT XRMS: 0.052 FIT YRMS: 0.046 \n",
"FIT RMSE: 0.07 FIT MAE: 0.06 \n",
"\n",
"RMS_RA: 2.8e-06 (deg) RMS_DEC: 7e-07 (deg)\n",
"\n",
"Final solution based on 129 objects.\n",
"wrote XY data to: icph76ikq_flt_catalog_fit.match\n",
"Total # points: 129\n",
"# of points after clipping: 129\n",
"Total # points: 129\n",
"# of points after clipping: 129\n",
"\n",
"....Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits...\n",
"\n",
"\n",
"Processing /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits['SCI',1]\n",
"\n",
"Updating header for /astro/store/gradscratch/tmp/mdurbin/phat_ir/mastDownload/HST/icph76ikq/icph76ikq_flt.fits[1]\n",
"WCS Keywords\n",
"\n",
"CD_11 CD_12: -1.974008598931012e-05 -2.865607924318792e-05\n",
"CD_21 CD_22: -3.2013275426866095e-05 1.7536400675157067e-05\n",
"CRVAL : 23.484786146854706 30.574786233078388\n",
"CRPIX : 507.0 507.0\n",
"NAXIS : 1014 1014\n",
"Plate Scale : 0.1354450732903612\n",
"ORIENTAT : -58.5349966714952\n",
"WCSNAME : GAIA2\n",
"Writing out shiftfile : shifts_icph76_ir.txt\n",
"Trailer file written to: icph76_ir.log\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"align(visit_prefix, update_wcs=False, updatehdr=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check for overlap with existing PHATTER exposures\n",
"\n",
"I didn't include the code to make the CSV of headers but I can send that along too if it'd be useful."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from astropy.io import fits\n",
"from astropy.wcs import WCS\n",
"from scipy.spatial import ConvexHull\n",
"from matplotlib.path import Path\n",
"from matplotlib.patches import Polygon"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# read in table of M33 headers\n",
"header_csv = '/astro/store/gradscratch/tmp/mdurbin/m33data/header_info.csv'\n",
"df = pd.read_csv(header_csv, index_col=0)\n",
"# add column with Path object for each chip footprint\n",
"df = df.assign(MPLPATH = np.nan)\n",
"for i in df.index:\n",
" h = ConvexHull(np.c_[df.loc[i].filter(regex='^RA_CHIP[1-2]_[0-3]$').dropna().values.reshape(-1),\n",
" df.loc[i].filter(regex='^DEC_CHIP[1-2]_[0-3]$').dropna().values.reshape(-1)])\n",
" df.loc[i, 'MPLPATH'] = Path(h.points[h.vertices])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['idb645b9q_flc', 'idb638a5q_flc', 'jdb654vwq_flc', 'idb645brq_flt', 'jdb643bhq_flc', 'idb640krq_flc', 'jdb641geq_flc', 'idb639boq_flt', 'jdb643ahq_flc', 'idb639b2q_flc', 'idb639bcq_flc', 'jdb636pkq_flc', 'jdb654vnq_flc', 'jdb654vzq_flc', 'jdb642f5q_flc', 'jdb643alq_flc', 'jdb637w0q_flc', 'idb640l3q_flt', 'idb640l7q_flt', 'idb646lsq_flc', 'jdb636pdq_flc', 'jdb643brq_flc', 'jdb643boq_flc', 'jdb641g7q_flc', 'jdb637w7q_flc', 'idb644a2q_flc', 'jdb643aeq_flc', 'jdb636ouq_flc', 'jdb647h9q_flc', 'jdb654vhq_flc', 'idb638anq_flt', 'jdb636p6q_flc', 'idb638auq_flt', 'idb640koq_flc', 'idb646m0q_flt', 'idb645bhq_flt', 'idb638axq_flt', 'jdb641fyq_flc', 'jdb642fkq_flc', 'jdb648qeq_flc', 'jdb648pmq_flc', 'idb645b5q_flc', 'jdb648q8q_flc', 'jdb647grq_flc', 'jdb647g7q_flc', 'jdb647h2q_flc', 'idb639b8q_flc', 'jdb647guq_flc', 'jdb648qbq_flc', 'jdb648ppq_flc', 'jdb641gkq_flc', 'jdb636ooq_flc', 'jdb647gaq_flc', 'jdb637wdq_flc', 'jdb637vrq_flc', 'jdb641fsq_flc', 'jdb643apq_flc', 'jdb654w6q_flc', 'jdb636oyq_flc', 'jdb641fvq_flc', 'jdb642fhq_flc', 'jdb642fnq_flc', 'jdb637w3q_flc', 'idb646liq_flc', 'idb638afq_flc', 'idb646miq_flt', 'jdb654vrq_flc', 'idb638arq_flt', 'jdb648q1q_flc', 'idb645b2q_flc', 'jdb647gyq_flc', 'jdb636p3q_flc', 'jdb642f1q_flc', 'jdb643beq_flc', 'jdb637vvq_flc', 'jdb647ghq_flc', 'jdb648psq_flc', 'jdb636paq_flc', 'jdb642faq_flc', 'idb645boq_flt', 'idb646maq_flt', 'idb644acq_flc', 'jdb642eyq_flc', 'jdb648pwq_flc', 'jdb641gaq_flc', 'idb640laq_flt', 'idb640ldq_flt', 'jdb654w9q_flc', 'jdb642fdq_flc', 'jdb654w3q_flc', 'idb639brq_flt', 'jdb643blq_flc', 'jdb636orq_flc', 'jdb648q4q_flc', 'idb638a8q_flc', 'idb646m7q_flt', 'idb645azq_flc', 'idb640l0q_flt', 'idb639b5q_flc', 'jdb637voq_flc', 'idb646lxq_flt', 'idb639buq_flt', 'idb644a8q_flc', 'idb639bkq_flt', 'idb646loq_flc', 'jdb641ghq_flc', 'idb646llq_flc', 'idb645beq_flt', 'idb638abq_flc', 'idb638akq_flt', 'idb644a5q_flc', 'jdb654vkq_flc', 'jdb637vlq_flc', 'jdb637waq_flc', 'jdb642evq_flc', 'jdb641g2q_flc', 'idb640kvq_flc', 'idb639bhq_flt', 'jdb647gdq_flc', 'idb640klq_flc', 'idb645blq_flt']\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Get the footprint of one of the newly aligned files\n",
"fitsfile = table['Local Path'][0]\n",
"# astropy is really weird about WCS of multi-ext files\n",
"with fits.open(fitsfile) as f:\n",
" w = WCS(f[1].header, fobj=f) # need 1st and 4th headers for UVIS and ACS\n",
"ftp = w.calc_footprint()\n",
"\n",
"p = Path(np.r_[ftp, ftp[:1]], closed=True)\n",
"is_overlap = df['MPLPATH'].apply(lambda x: p.intersects_path(x))\n",
"\n",
"# Check footprints of overlapping fields\n",
"fig, ax = plt.subplots(1, figsize=(6, 6))\n",
"ax.set_aspect('equal')\n",
"for i, r in df[is_overlap].iterrows():\n",
" ax.add_patch(Polygon(r.MPLPATH.vertices, alpha=0.05))\n",
"ax.plot(p.vertices[:,0], p.vertices[:,1], color='k')\n",
"ax.invert_xaxis()\n",
"\n",
"# names of overlapping files\n",
"print(df[is_overlap].index.tolist())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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