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Commit 6eb1cba1 authored by Hubert Degaudenzi's avatar Hubert Degaudenzi
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Print the binary table

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Euclid/StrongLensing/MER Tile.ipynb
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......@@ -7,6 +7,16 @@
"# MER Tile"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from astropy.io import fits\n",
"from astropy.table import Table"
]
},
{
"cell_type": "code",
"execution_count": 2,
......@@ -25,16 +35,14 @@
}
],
"source": [
"from astropy.io import fits\n",
"\n",
"psf_url = \"https://degauden.isdc.unige.ch/euclid/data/SC8/MER/BksMosaic/EUC_MER_CATALOG-PSF-VIS_TILE40012-84CD68_20201024T024024.620456Z_00.00.fits\"\n",
"psf_data = fits.open(psf_url)\n",
"psf_data.info()"
"psf = fits.open(psf_url)\n",
"psf.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 3,
"metadata": {},
"outputs": [
{
......@@ -57,13 +65,224 @@
"EXTNAME = 'Grid points' / The grid PSF grid points in pixel units "
]
},
"execution_count": 7,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psf[2].header"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ColDefs(\n",
" name = 'x'; format = 'D'\n",
" name = 'y'; format = 'D'\n",
" name = 'FWHM'; format = 'D'\n",
")\n"
]
}
],
"source": [
"print(psf[2].columns)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<i>Table length=27582</i>\n",
"<table id=\"table140366731245328\" class=\"table-striped table-bordered table-condensed\">\n",
"<thead><tr><th>x</th><th>y</th><th>FWHM</th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>19030.99999999915</td><td>3602.0000000003974</td><td>0.22777076976917968</td></tr>\n",
"<tr><td>19191.999999998578</td><td>3537.000000000703</td><td>0.19992005047236766</td></tr>\n",
"<tr><td>19154.999999999192</td><td>3588.0000000002847</td><td>0.19564731253539935</td></tr>\n",
"<tr><td>18990.999999998865</td><td>3618.000000000653</td><td>0.2312260170851113</td></tr>\n",
"<tr><td>18844.99999999882</td><td>3641.0000000007794</td><td>0.2380589760064491</td></tr>\n",
"<tr><td>19155.999999999054</td><td>3661.000000000592</td><td>0.2178144465638079</td></tr>\n",
"<tr><td>19071.999999998774</td><td>3675.000000000583</td><td>0.23340589987048632</td></tr>\n",
"<tr><td>18900.99999999895</td><td>3691.000000000404</td><td>0.24099261740050598</td></tr>\n",
"<tr><td>18939.99999999915</td><td>3764.000000000504</td><td>0.18444364541538527</td></tr>\n",
"<tr><td>18899.99999999908</td><td>3828.0000000004165</td><td>0.23490122347127276</td></tr>\n",
"<tr><td>...</td><td>...</td><td>...</td></tr>\n",
"<tr><td>10243.9999999999</td><td>19197.9999999992</td><td>0.23665478091114026</td></tr>\n",
"<tr><td>7972.000000000273</td><td>19197.99999999859</td><td>0.2155999042779836</td></tr>\n",
"<tr><td>6916.000000000286</td><td>19197.99999999914</td><td>0.22466571158812737</td></tr>\n",
"<tr><td>6376.000000000371</td><td>19196.999999999185</td><td>0.2100148458869164</td></tr>\n",
"<tr><td>5993.000000000435</td><td>19196.999999999127</td><td>0.22244956809696664</td></tr>\n",
"<tr><td>2599.0000000007385</td><td>19197.999999999192</td><td>0.22511893862490548</td></tr>\n",
"<tr><td>177.00000000084947</td><td>19196.999999999338</td><td>0.23721171819205056</td></tr>\n",
"<tr><td>1457.000000000905</td><td>19196.999999999374</td><td>0.2221351482415968</td></tr>\n",
"<tr><td>7434.000000000276</td><td>19197.999999998814</td><td>0.22400002593106028</td></tr>\n",
"<tr><td>17699.999999998996</td><td>19196.999999999156</td><td>0.2159121058004628</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table length=27582>\n",
" x y FWHM \n",
" float64 float64 float64 \n",
"------------------ ------------------ -------------------\n",
" 19030.99999999915 3602.0000000003974 0.22777076976917968\n",
"19191.999999998578 3537.000000000703 0.19992005047236766\n",
"19154.999999999192 3588.0000000002847 0.19564731253539935\n",
"18990.999999998865 3618.000000000653 0.2312260170851113\n",
" 18844.99999999882 3641.0000000007794 0.2380589760064491\n",
"19155.999999999054 3661.000000000592 0.2178144465638079\n",
"19071.999999998774 3675.000000000583 0.23340589987048632\n",
" 18900.99999999895 3691.000000000404 0.24099261740050598\n",
" 18939.99999999915 3764.000000000504 0.18444364541538527\n",
" 18899.99999999908 3828.0000000004165 0.23490122347127276\n",
" ... ... ...\n",
" 10243.9999999999 19197.9999999992 0.23665478091114026\n",
" 7972.000000000273 19197.99999999859 0.2155999042779836\n",
" 6916.000000000286 19197.99999999914 0.22466571158812737\n",
" 6376.000000000371 19196.999999999185 0.2100148458869164\n",
" 5993.000000000435 19196.999999999127 0.22244956809696664\n",
"2599.0000000007385 19197.999999999192 0.22511893862490548\n",
"177.00000000084947 19196.999999999338 0.23721171819205056\n",
" 1457.000000000905 19196.999999999374 0.2221351482415968\n",
" 7434.000000000276 19197.999999998814 0.22400002593106028\n",
"17699.999999998996 19196.999999999156 0.2159121058004628"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table = Table(psf[2].data)\n",
"table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"XTENSION= 'IMAGE ' / Image extension \n",
"BITPIX = -32 / array data type \n",
"NAXIS = 2 / number of array dimensions \n",
"NAXIS1 = 19200 \n",
"NAXIS2 = 19200 \n",
"PCOUNT = 0 / number of parameters \n",
"GCOUNT = 1 / number of groups \n",
"EQUINOX = 2000.00000000 / Mean equinox \n",
"MJD-OBS = 5.912900000000E+04 / Modified Julian date at start \n",
"RADESYS = 'ICRS ' / Astrometric system \n",
"CTYPE1 = 'RA---TAN' / WCS projection type for this axis \n",
"CUNIT1 = 'deg ' / Axis unit \n",
"CRVAL1 = 1.086283392430E+01 / World coordinate on this axis \n",
"CRPIX1 = 9.600500000000E+03 / Reference pixel on this axis \n",
"CD1_1 = -2.777777777778E-05 / Linear projection matrix \n",
"CD1_2 = 0.000000000000E+00 / Linear projection matrix \n",
"CTYPE2 = 'DEC--TAN' / WCS projection type for this axis \n",
"CUNIT2 = 'deg ' / Axis unit \n",
"CRVAL2 = -1.880000000000E+01 / World coordinate on this axis \n",
"CRPIX2 = 9.600500000000E+03 / Reference pixel on this axis \n",
"CD2_1 = 0.000000000000E+00 / Linear projection matrix \n",
"CD2_2 = 2.777777777778E-05 / Linear projection matrix \n",
"EXPTIME = 4.520000000000E+03 / Maximum equivalent exposure time (s) \n",
"GAIN = 0.000000000000E+00 / Maximum equivalent gain (e-/ADU) \n",
"SATURATE= 4.935311753096E+04 / Saturation Level (ADU) \n",
"COMMENT \n",
"SOFTNAME= 'SWarp ' / The software that processed those data \n",
"SOFTVERS= '2.38.1 ' / Version of the software \n",
"SOFTDATE= '2015-01-29' / Release date of the software \n",
"SOFTAUTH= '2010-2012 IAP/CNRS/UPMC' / Maintainer of the software \n",
"SOFTINST= 'IAP http://www.iap.fr' / Institute \n",
"COMMENT \n",
"AUTHOR = 'euclid01' / Who ran the software \n",
"ORIGIN = 'eucgu100' / Where it was done \n",
"DATE = '2020-10-24T01:01:04' / When it was started (GMT) \n",
"COMBINET= 'MEDIAN ' / COMBINE_TYPE config parameter for SWarp \n",
"COMMENT \n",
"COMMENT Propagated FITS keywords \n",
"COMMENT \n",
"COMMENT Axis-dependent config parameters \n",
"RESAMPT1= 'BILINEAR' / RESAMPLING_TYPE config parameter \n",
"CENTERT1= 'MANUAL ' / CENTER_TYPE config parameter \n",
"PSCALET1= 'MANUAL ' / PIXELSCALE_TYPE config parameter \n",
"RESAMPT2= 'BILINEAR' / RESAMPLING_TYPE config parameter \n",
"CENTERT2= 'MANUAL ' / CENTER_TYPE config parameter \n",
"PSCALET2= 'MANUAL ' / PIXELSCALE_TYPE config parameter \n",
"STMPSIZE= 25 / The PSF stamp size in pixel units \n",
"FWHM = 0.2272222576135336 / The central PSF stamp FWHM in arcsec \n",
"EXTNAME = 'Grid image' / The grid PSF image "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psf_data[2].header"
"image_data = psf[1]\n",
"image_data.shape\n",
"image_data.header"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Image data of dtype object cannot be converted to float",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-647a1169e9a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m 2728\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2729\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2730\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 2731\u001b[0m \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2732\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1446\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1449\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m 5521\u001b[0m resample=resample, **kwargs)\n\u001b[1;32m 5522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5523\u001b[0;31m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5524\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5525\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m 701\u001b[0m not np.can_cast(self._A.dtype, float, \"same_kind\")):\n\u001b[1;32m 702\u001b[0m raise TypeError(\"Image data of dtype {} cannot be converted to \"\n\u001b[0;32m--> 703\u001b[0;31m \"float\".format(self._A.dtype))\n\u001b[0m\u001b[1;32m 704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Image data of dtype object cannot be converted to float"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"plt.figure()\n",
"plt.imshow(image_data, cmap='gray')\n",
"plt.colorbar()"
]
},
{
......
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