Created
May 1, 2018 21:21
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pyastro2018 synphot/stsynphot lightning talk
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Synthetic Photometry with Astropy Models" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"This is a short demo for \"synphot\" package (http://synphot.readthedocs.io/en/latest/), which does synthetic photometry using Astropy models. This 5-minute demo is for pyastro2018 (May 2018, NYC) lightning talk series.\n", | |
"\n", | |
"*This package is implemented in Python and not related to the IRAF task ``synphot``.*" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from __future__ import division, print_function # Only if you use Python 2\n", | |
"\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x179e9128>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"from astropy import units as u\n", | |
"from synphot import SourceSpectrum, SpectralElement\n", | |
"\n", | |
"# Load a Vega spectrum.\n", | |
"# Data can be obtained from http://ssb.stsci.edu/cdbs/calspec/alpha_lyr_stis_008.fits\n", | |
"datafile = 'alpha_lyr_stis_008.fits'\n", | |
"vega = SourceSpectrum.from_file(datafile)\n", | |
"\n", | |
"# Normalize it to 22 STMAG in V-band.\n", | |
"v_band = SpectralElement.from_filter('johnson_v')\n", | |
"sp = vega.normalize(22 * u.STmag, v_band)\n", | |
"\n", | |
"# Visualize it.\n", | |
"sp.plot(left=1000, right=12000)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1a6fd240>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Zoom in on feature of interest in a different flux unit.\n", | |
"sp.plot(left=6400, right=6700, bottom=2e-18, top=3.75e-18, flux_unit='flam')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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v+ufA73ZZB0nS6CbujmZJ0mCGgiSpx1CQJPUYCpKkHkNBktST+XZbQJJNwC0DVu8F3DGH1dke2ObJYJsnQ5dtPqCqlgwrNO9CYTZJ1lXVynHXYy7Z5slgmyfD9tBmTx9JknoMBUlSz0ILhdXjrsAY2ObJYJsnw9jbvKCuKUiSts5CO1KQJG2FeREKSXZP8rEkX0tyY5JnJ3lrkmuSXJXkgiRPaMsmyd8kWd+uf3rfdl6R5Bvt6xWD9zheM7W3b90bk1SSvdr5ed9eGPg7Pi3Jd9rf8VVJju4r/5a2zTcl+c2+5Ue2y9YnOXk8rRnNoN9zkj9p23B9krf3lV+QbU7y0b7f8beSXNVXfqG2+WlJLmnbvC7JIW3Z8f89V9V2/6J5jvN/aqd3BHYHdu1b/1rg79vpo4FP0zzV7VnApe3yPYEN7c892uk9xt22UdvbTi+lGYr8FmCvhdLeWX7HpwFvnKHsCuBqYCdgOfBNmuHZF7XTT2y3cTWwYtxt28w2Pxf4LLBTu3zvhd7maev/Cjh1obcZuAA4ql12NPDFvumx/j1v90cKSXYFfoPm2QtU1b1V9YOq+mFfscfy0GM8jwU+WI1LgN2T7AP8JvCZqrqzqu4CPgMcOWcNGdGg9rar3wG8mYc/snRetxeGtnkmxwJnV9U9VXUzsB44pH2tr6oNVXUvcHZbdrszS5v/EDijqu5pl9/evmUht3lqfYCXAB9pFy3kNhewa1tsNx56KuXY/563+1Cg+TawCXh/kiuTvCfJYwGS/EWSW4GXAlPPadgXuLXv/RvbZYOWb29mbG+SY4DvVNXV08rP9/bCLL9j4MT2MPp9SfZoly3kNh8E/HqSS5NcmOSZbfmF3OYpvw58r6q+0c4v5Da/Djiz/fz6S+Atbfmxt3k+hMJi4OnAu6rqYOAnwMkAVXVKVS0F/gk4sS2fGbZRsyzf3szU3tOAU3go+PrN9/bC4N/xu4AnAU8DvktzagEWdpsX05weeBbwJuCc9hv0Qm7zlFU8dJQAC7vNfwic1H5+nUR7JMF20Ob5EAobgY1VdWk7/zGaf+R+HwZe3Fd+ad+6/WgOzQYt394Mau9y4Ook36Kp+1eTPJ75314Y0Oaq+l5VPVBVDwLvpjltMFV+Qba5Xf6J9vTBZcCDNOPhLOQ2k2Qx8DvAR6eVX6htfgXwiXbZuWxH/7e3+1Coqv8H3Jrk37SLng/ckOTAvmLHAF9rp9cAL2+v4j8LuLuqvktzgfaFSfZoT0O8sF22XRnQ3q9W1d5VtayqltH8B3l6W3Zetxdm/R3v01fsRcB17fQa4PgkOyVZDhwIXAZcDhyYZHmSHWme+b1mThqxmQa1GfgX4HkASQ6iuTB5Bwu7zQBHAF+rqo19b1nIbb4NeE677HnA1Cmz8f89d3H1elu/aE4frAOuofmj2QP4OM2HxDXAJ4F927IBzqLpnXAtsLJvO79Pc7FqPfCqcbdrc9o7bf23eKj30bxv7yy/4w+1bbqG5o9ln77yp7Rtvom2F0e7/Gjg6+26U8bdri1o847AP7b/t78KPG+ht7ld/gHgD2YovyDbDPwacAVNz6lLgWe0Zcf+9+wdzZKknu3+9JEkae4YCpKkHkNBktRjKEiSegwFSVKPoaBOJXlHktf1zZ+f5D1983+V5PXbeJ8/3pbba7f5tDx8lNbTkrxxhPclyefbMXCmlr0ozUi3T+mgnsuS/Idtvd2+7Z+Y5FVdbV/jZyioa18BDgVIsgPN3bn/tm/9ocBFY6jX5noaTd/4zXU0cHU9fADHVcCXaW662taWATOGQnvX8NZ6H82oxFqgDAV17SLaUKAJg+uAH7V3Zu4E/BJwZZKdk3wuyVeTXJvkWIAkb0vyR1Mba7+hv6GdflOSy9sB8/58pp3PVKb9Nn1jkneneWbBBUl+oV33zLbsxUnOTHJde9fs6cDvpRn//vfaza9I8sUkG5IM+qB8KfCvffXZGTgMeDV9oZDk8HZbU+Pu/1M75hFJjm6XfTnNWPvntcufk4eeQ3Blkl2AM2gG1LsqyUlJXpnk3CSfBC5oj1ym2nXtVFva/V+Y5JwkX09yRpKXJrmsLfckgKr6KfCttOP/awEa991+vhb+i+YO7P2B/wz8AfBWmm/QhwFfassspn1GBs3RxHqauzsPBi7s29YN7bZeSPM829B8uTkP+I22zI/bnzOWofk2fT/wtLbcOcDL2unrgEPb6TOA69rpVwJ/21eP02iOgnZq6/t94FEztP0WYJe++ZcB722nv0IzXAnA4cDdNGPa7ABcTHPX66NpRsdc3pb7CHBeO/1J4LB2euf23/DwqfV99d4I7NnOv5hm2OVFwOOAbwP7tO/7QTu9E/Ad4M/b9/wp8M6+bZ4CvGHc/698dfPySEFzYepo4VCaD7uL++a/0pYJ8N+TXEPzkJl9gcdV1ZXA3kmekOTfAXdV1bdpPvBfCFxJMxzEU2jGxuk3W5mbq2rqCV9XAMuS7E7zAT5Vpw8Padenqhnr/w7gdpoP2en2rKof9c2vohn/n/bnqr51l1XVxmoGALyKJryeAmyo5nkC8PBRRC8C/ro9Stm9qu4fUM/PVNWd7fSvAR+pZqDB7wEXAlPDc19eVd+t5lkO36R5EAw0wy0s69ve7cATBuxL89y2OMcoDTN1XeGpNN/EbwXeAPyQ5hw1NKdZltCMAXNfmtFgH92u+xhwHPB4HvpADfA/quofZtnvjGWSLAPu6Vv0APALzDw88Wymb2Omv6f7k+xQVQ8m+UWawc9+OUnRfFuvJG+eZXsD61RVZyT5FM1R1yVJjhhQ9Cd907O1sX//D/bNP8jD2/Zo4GezbEfzmEcKmgsXAb8N3Nl+Q72T5pGEz6Y5aoDm6VO3t4HwXOCAvvefTXP+/TiagIBmhMjfb8/Rk2TfJHtP2+8oZXqqeaLVj9rRKeHhF4J/BOyyOY1u3UTzoBXa+n+wqg6oZsTbpcDNNN/eB/ka8MQ2yACmrmeQ5ElVdW1VvY1mwLWnjFDPL9FcG1mUZAnN6bTLNrNNB/HQiLVaYAwFzYVrac67XzJt2d3tqRdoHpS0Msk6mqOGqaHQqarraT7ovlPNMMJU1QU0p3cuTnItTVg87MNwlDIzeDWwOsnFNN+q726Xf4HmwnL/heZRfIrmfD00p4r+edr6jzOgt1Dbhp8BfwT8nyRfBr7XV6fXtReMr6b55v5pmpE4709ydZKTZtjkP7dlrgY+D7y5muGdN8dhNKf4tAA5SqrUJ8nOVfXjdvpkmuG6/3QrtrcPzdHBC7a2Tm1vpLOAb1TVO7Z0e1sjycHA66vqP45j/+qeRwrSw/1WezRwHc0zg//b1mysPbJ5d/puXtsCr0lyFXA9zWm22a6jdG0v4L+Mcf/qmEcKkqQejxQkST2GgiSpx1CQJPUYCpKkHkNBktRjKEiSev4/pR6dLPg1IOYAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x17e05f60>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# This is identical to astropy.models.Box1D but with some extra properties.\n", | |
"from synphot.models import Box1D\n", | |
"\n", | |
"# A boxy bandpass around feature of interest.\n", | |
"bp = SpectralElement(Box1D, x_0=6563*u.AA, width=50*u.nm)\n", | |
"bp.plot()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1b7fee80>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"from synphot import Observation\n", | |
"\n", | |
"# A simulated observation of Vega through the boxy bandpass.\n", | |
"obs = Observation(sp, bp)\n", | |
"obs.plot()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/latex": [ | |
"$24.284528 \\; \\mathrm{\\frac{ct}{s}}$" | |
], | |
"text/plain": [ | |
"<Quantity 24.28452842 ct / s>" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# Predicted count rate for given telescope (HST) collecting area.\n", | |
"obs.countrate(area=45238.93416*(u.cm*u.cm))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Model: CompoundModel4\n", | |
"Inputs: ('x',)\n", | |
"Outputs: ('y',)\n", | |
"Model set size: 1\n", | |
"Expression: [0] - [1]\n", | |
"Components: \n", | |
" [0]: <Linear1D(slope=1., intercept=0.)>\n", | |
"\n", | |
" [1]: <Lorentz1D(amplitude=0., x_0=6560., fwhm=1.)>\n", | |
"Parameters:\n", | |
" slope_0 intercept_0 ... fwhm_1 \n", | |
" ----------------------- ---------------------- ... ------------------\n", | |
" -1.5390917855809249e-21 1.3461240425145207e-17 ... 27.931246779642834\n" | |
] | |
} | |
], | |
"source": [ | |
"from astropy.modeling import models, fitting\n", | |
"\n", | |
"# Build a composite model to fit observed feature of interest.\n", | |
"# Some reasonable initial guess is recommended.\n", | |
"bg = models.Linear1D()\n", | |
"ab = models.Lorentz1D(x_0=6560, amplitude=1e-18)\n", | |
"init_model = bg - ab\n", | |
"\n", | |
"# Astropy models and fitting do not support units yet,\n", | |
"# so for now, we only use unitless portion for fitting.\n", | |
"x = bp.waveset.value # Angstrom\n", | |
"y = obs(bp.waveset, flux_unit='flam').value # FLAM\n", | |
"\n", | |
"# Do the fitting.\n", | |
"fitter = fitting.LevMarLSQFitter()\n", | |
"fit_model = fitter(init_model, x, y)\n", | |
"y_fit = fit_model(x)\n", | |
"\n", | |
"# Components only list initial guess.\n", | |
"# Parameters are the actual fitted values (background + line).\n", | |
"print(fit_model)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"6564.498146523282\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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+xz+bfcrj63oSH+91KhNsdu1yru0xaZLz+zw1Fcpdksrz1y7iBplE2bXTkahWcP0YUN8FyBs3hmbNoGhRr+ObbLDZckKFCDHzpnL06o68vLYXw5qm8ejaGylQwOtgJtAlJ8OoUc6s4ytXOo9VqwYPPgj3HH6WslPeQKbsdrYXtWnjNHDWDp591rvg5oJYUQglcXEUXDyT/U068cTG3jzTqiCPLe0QOhPpffml1wmCzvz5cN99zhFDDRvCS/8+wE0R4yn9+N+dM6v+dcRZE+jVCzp3dg7vNEEtVH4dmDNiYym+dBr7arckeuVC7r67A6NHh8gBHfYLK9t+/RWGDIFx46BiRVj44kqar3kNRn4OSUnQoZ5TDEaM8Dqq8TObEC8UFSxI/PeLSHz0ed55B556MkRm0HvzTaeZTCUnw4svOgcBTZoEIx/4jS2XtqH54EbOzoQ774S1a52CYPIkKwqhKjaWp58RHumykWufvJJPXvrd60Tu+/xzp5kMLVgA9erBkCHK9Y1+Y+NGGPx8ccJPnYAXXnBWH954wzl81ORZVhRCmAg8+e/T1A9fR+XB1zNzkl3fMxT99hvccgu0agVVDy5jX62WfPhDIyqXPgH58jknkQ0Z4px9ZvI8KwohLrJRffSDj2jMco70vIsVy0NkU5IhORleesnZVLRs/C421OjJpN+bUnzfZuTRRyEy0uuIxgNWFAzRt9xA4sPPcFPqx8xqPYKtW71OZNy2cCHUr++cRnBj/R/ZElmDWj9NhyeegG3b4J57rCiEKCsKBoDY5x7laMcbaZ48jw7XprJnj9eJjBt274bevZ1poPToMSZPhtFfVyFs8APOjHXDhjmT+ZuQlWsT4vmLTYjnohMnWPZdflpfE06tWrBokXM+kskb3nsPBg6E5FOpTGr+Eu3WDEdWrIBKlbyOZnJBdifEszUF84eYGJpcFc7EN3/nhlWP8OpLqV4nMn7y8cfOtEPta+/iQL3WtP/qX0iLFlCwoNfRTICxomD+on3YHB7hPyQ//TwHDnidxo9GjnRaiJkzx7mExD/rfc1nWxsQs+lb59oS48fbhYfMX1hRMH/Vpw+HO/bi4aQnef/+b71O4z/TpzsthKxcCTfcALVqwfP1xiHFisGKFXDbbSFyGrvJKSsK5q9EKPzR6yTGlKDDp33Y+YOdvxCMfvwRunRIpnaRX5k5E/K/9SosX+5ct8CYTFhRMBkrWpSUUWOoqd+zuud/vE5jcmj3buh2TSJjj3ZhUVgLShc6AVFRtg/BZMmKgslUfJ/2fNHhXf6+/n7WrPE6jcmuI0fg5rZ7GburBa3T5pLvsYdtMkCTbVYUzHld80lfKFqURx5McU6BDWbR0Xn+GNtTp6Bvxz28/n0r6kRuQqZMgbvu8jqWCSJWFMx5FS4MTww+xjPzGrN1QJAfuTNzptPyqNRU6NMHblg6mGr5fiJ85gzo1MnrWCbIWFEwWfr7A3Hsja5A2feeIm3rdq/jmAyowv33O0eZHnn6NSIWfOXMcGdMDllRMFmKioLjz/+X0xrJ7z3udX4DBaOnn3ZaHjRi2HHKvPkojww6yb1Di0DTpl5HMkHKtaIgIlEiskJE1orIRhF5MoM++UXkMxHZKiLLRaSCW3nMxbnhH2UYdcnTXLJ2FsmTZ3gd58J89ZXT8ph33krm8qd78rAM55nOy7yOY4Kcm2sKSUBrVa0L1APai0iTc/rcCRxS1cuAl4HhLuYxFyEsDOq+fQ+bqcauoaO8jmN8pkxWwu/pT0dmkvbmKMLa2CYjc3FcKwrqSPT9GOlr52536Ap84Ls/HmgjYqdZBqprO0XyTJMZXLVnIkePep3GLF4MS3q+Ql/e4/TDjxMx4O9eRzJ5gKv7FEQkXETWAHuBuaq6/JwuZYBfAFQ1BTgCFMtgnH4iskpEVu3bt8/NyOY8RGDQa5XZfSAfrz6biFUG72zYADd3PspD+jxJnbqR79lhXkcyeYSrRUFVU1W1HlAWaCQitc/pktFawV/2YqrqaFVNUNWE+Ph4N6KabEpIgFu7H6fvCzVIHBJkv4iKFXNakPv5Z2jfHlJiCnJqwXLyf/qBs33PGD/IlU+Sqh4GFgDtz3lqF1AOQEQigELAwdzIZC7c48MLMJdryT/mTfjlF6/jZN+ECU4LYgcOQOdrT3PdwQ+Y9WUaZZpVtGsnG79y8+ijeBEp7LsfDbQFNp/TbSpwm+9+D2C+BttVf0JQ5cqw49bH0TTl0IPPeh0npAwaBLdtGcpbJ2+nzpH/eR3H5EFurimUBr4WkXXASpx9CtNF5CkR6eLrMwYoJiJbgQeAh13MY/zo3hGX8n7k34n7fAxsD5IT2h55xGlBavNm2PfxHAanvQD9+0OLFl5HMnlQhFsDq+o6oH4Gjz+e7v4poKdbGYx7SpSA4wP/TfLId/ntP59QfvRQryNl7ZtvvE5wUV4Zup8P9FZSqtci4uWXvY5j8ijXioLJ+/o9cQlt3ltL+PdVWKR2zRY3bd4MzScMpFjYQSI+m5PnJ/Yz3rFDFswFK1AAbn2mKouXCDPGn/Q6Tp72zDPwUf67OPn8q1CnjtdxTB5mRcFclDvvhH+UHk/zm0qTsmOX13HypM2blE8/hToDWxH3r7u9jmPyOCsK5qJERkLHxxOISUtkc7+XvI5zfmXLOi3I7Op6LyPCHmbIEK+TmFBgRcFctHb9KzC3eC8qzhvNiV8OeB0nc2PHOi2I7Bi/itZbRnFFvWTsvE2TG6womIsmAiVGPkQBPc6qvm94HSfvSEsjZcB97KUkNT8LsrPHTdCyomD8ouFttVlRsjM15r/Ogd8D9LKdgwY5LUjsHv4hlx1YzsKOwyleqaDXcUyIsKJg/Kbo6OG0YgHPjoj0OkrG1qxxWjBISSHymcdZFtaU1u/29jqNCSFWFIzfXNalJnV71eTddyEpyes0wW3z1gianZjL0r7vEF/SvqYm99inzfhV33a/8faRG/n2tSVeRwleqak8/TTsKlCNPs/X9DqNCTF2RrPxq6s7F+Ikc/jxDWDIVV7HCUqH+tzP9Z/upfy/PiM+3v5uM7nLPnHGr/IXLcDSmnfRYMcEkn8KsJPZqlZ1WiDbupW4caM5FFGCB4bY19PkPvvUGb/LN+gewkjj56GjvY7yZ6NHOy2AHRn0OEmaj339h9p5CcYTVhSM3zXrU5F54e0oMuU9SE31Ok7w2LyZuBnjGBX5D/oNK+11GhOirCgYv4uKgm+bDeT9tFtJSTzldZw/9OvntAB16LEXOUUUJ/o/YGsJxjO2o9m4our97enevT11V0GbNl6n8fnxR68TnNe/0oazL393/u/xEl5HMSHM1hSMK9q3h7joFDa/OB327vU6TsDbtAnGTCpK9UHtbS3BeMqKgnFFTAzc0WIb9868jrR33/c6TmDbvZuw5ldxVf5VNhOq8ZwVBeOaq+6oxiKu5tQb74Cq13EC1v5hr3HZ/mV0ua0IxYt7ncaEOisKxjUdOsDYyDuI2bUFli3zOg7Uq+e0QHLiBPk/eJvp4dfT95nKXqcx5vw7mkVkXWZPAaqqmV4XUETKAR8CpYA0YLSqvnpOn0LAWKC8L8tIVX0v+/FNIIuNhRPtb+DktLuJ+mgs0rSpt4FeecXb5Wdg9wtjKX36IL/fMsjWEkxAyOroozRAgU+AaUBOLsSbAgxW1W9FJA5YLSJzVfX7dH3uBb5X1etEJB74QUQ+VtXTOViOCWCdehVkyrSudJ73DbGqzsUXjEOV1Jde4buwBnR/uZnXaYwBsigKqlpPRKoDvXAKw/e+2zmqmpLFa3cDu333j4nIJqCMb4yz3YA4EREgFjiIU0xMHtG5M1TJ9xbLOxTiZa8LQm/fFNQBcvW179en8urRQVzZ4xLqx1uxNIEhy30KqrpZVYepagOctYUPgX/mZCEiUgGoDyw/56nXgRrAb8B6YKCqpmXw+n4iskpEVu3bty8nizYei4uDJh2KMH5iGGmpHu9s3rXLaQHi6ecj+LhAPzq91dnrKMaclWVREJEyIjJYRBYDvXEKwlvZXYCIxAITgEGqevScp9sBa4BLgHrA6yLyl0tMqepoVU1Q1YR4O4g76PToAY13jed0uUpw9NyPQGjaMncHxca9weB+x2xfggko5y0KIrIQZ+0gErgduA2YAeQTkaJZDS4ikTgF4WNVnZhBl77ARHVsBX4CqufoX2AC3nXXwZ7wMkTt3gETM/oYhJ6NA0fzKvcz8PYjXkcx5k+yWlO4FCgC9AfmAKt8bbXvNlO+/QRjgE2q+lIm3X4G2vj6lwSqAduzG94Eh0KFoFC7JuyMqIR++qnXcTz348ZkGm96jx8rd6RonbJexzHmT7La0VzhIsa+CugDrBeRMxfGfRTn8FNUdRTwNPC+iKzHOcz1IVXdfxHLNAGqR0/hky//xsNfvQAHDkCxYrkfwutDYn3WPTedHvxOvqGBOzmfCV2iOTzTVEQqAzcBvVS1tiupziMhIUFXrTrvSooJQIcOQfv41SxPTYAxY+COO7yO5JmlRTpS+fg6Sp7YARE2J6XJHSKyWlUTsuqXrTOaRaS0iAwSkRXARpw1jF4XmdGEkCJFoGjbBrxd6EH08kzPeczz9v6azOnDJ9h85Z1WEExAympH899FZD6wECgO3AXsVtUnVXV9bgQ0eUePnsKAIyP4LjzLP1bc0b270zz05dxIWrGAuJHDPM1hTGayWlN4AwgHblbVoaq6DueEM2NyrGtXCA9Tlr62Glavzv0ABw44zSuqLJh4kDJloH5Dm3bMBKasPpmXAOOAl0TkBxF5GufwVGNyrHhxaNUKbhjbDX3iCa/j5LrTC5YyelopBtefb7N9mIB13qKgqvtV9S1VbY5z6OgRYK+IbBKR53IloclTevQUxqX0QGfPgSOhdYz+nhfHkkwk1W9t5HUUYzKV7XVYVd2lqiNVtSFwPc60FcbkSLduMEF6EpZ8GqZP9zpO7klKoui8z5gWfj0tO8d6ncaYTF3Qhk1V/QGo5ecsJgSUKAFRLRqzL7wkOm1a7i68TRvPLhitM76kQNIhNif0ITrakwjGZMvFHBNnW0XNBeneM4zJC67j9vkziExLg7Bc2un62GO5s5wMHH1rLCcpSZnb2nqWwZjsuJhvox2FZC5It24wlGcZ0W9b7hUEj31abwS38QGdutq5CSawZXXltWlk/MtfAA/mKTB5QenSUO3qEoybAv9+JhcX3KGDcztzZi4u1PHR0sokNazMJZfk+qKNyZGs/mwZeYHPGXNePXrANwM/JbHlx8R+PS13rsh2MicXDvSfxKHPE7e0AdcMa+fJ8o3JiayKwk+q+nOuJDEhpXt3WD8wkdiFM2DDBrj8cq8juePgQWL+8zit+SdtrrOiYAJfVht0J5+5IyITXM5iQkiZMrAnwXfFsalTvQ3jpilTCEtNYUHxnjRo4HUYY7KWVVFIv05fyc0gJvS0urk0y2nEyS9y+dDUXJT2+Xh2SAXKXp9gZzGboJBVUdBM7htz0bp3h6l0IXrtcvj9d/cX2Lmz03LL4cMwby5faA+u62IVwQSHrPYp1BWRozhrDNG++/h+VlX9y/WUjcmu8uVhe+2uLPhlJS2PHIFSpdxd4JAh7o5/rp9+4kCB8kw92ZN7vTlnzpgcy+rKa+G5FcSEpoa31abVg5PZng8qeh3Gz7RefRoX2UKtZhAT43UaY7InNM4cMgHrzOUNZo/ZBamp7i6sZUun5YbTp/l+XQo/7RA6X2ebjkzwsKJgPFWxItx/2QwGPFsOVqzwOo7/TJ5MpaYluIwtubobw5iLZUXBeK5ir6akEsbhcbO8juI/U6dyKjmcwvUrUaaM12GMyT7XioKIlBORr33XXtgoIgMz6ddSRNb4+ix0K48JXJ36FGU5jTk5OY8UheRk0qbPYEpKZzpeZ7vlTHBxc00hBRisqjWAJsC9IlIzfQcRKQy8CXRR1VpATxfzmABVpQqsKdmekj+vhP37vY5z8ZYsIezIYabQheuu8zqMMTnjWlFQ1d2q+q3v/jFgE3DuivTNwMQzU2mo6l638pjAFt2tPWEoBz+b695C/vY3p7lt6lROh+Vnfclr7CxmE3RyZZ+CiFTAuVLb8nOeqgoUEZEFIrJaRG7N5PVVN3daAAAV9klEQVT9RGSViKzat2+fu2GNJ678R0Nu5z0mHm7t3kLuucdpLkvufhNDIv9Lq+tiQ2VmcJOHuP6RFZFYYAIwSFWPnvN0BNAQ6AS0Ax4TkarnjqGqo1U1QVUT4uPj3Y5sPFCtZjirat3Op/NLureQEyec5rJFpxrxWlI/23RkgpKrRUFEInEKwseqOjGDLruAWap6XFX3A4uAum5mMoGrS4sj1Fg0iqT1P7qzgI4dneam+fPZOGoRUVHQ1i6yZoKQm0cfCTAG2KSqL2XSbQpwtYhEiEgM0Bhn34MJQS2bnOL1lLv55b+TvI5ywXToUFpOG0zr1nYWswlObq4pXAX0AVr7DjldIyIdRWSAiAwAUNVNwCxgHbACeEdVN7iYyQSwJl1Lso7L4at5Xke5MHv2wLJlfJFkRx2Z4OXaBWNVdTF/nno7s34vAC+4lcMEj4IFYWOpttyw403nKmnR0V5HypnZsxFVptOZaXYWswlSdmyECSinm7clvyZxfO5Sr6Pk3KxZHIgsidStS9myXocx5sJYUTABpcKtzUkiHzumr/f/4Lff7jQ3qJK6bCUzk6+hcxf7Wpng5drmI2MuRKPWsZTLt5c+cYV40d+Du1UQAEQYN2wTA28/wizbn2CCmP1JYwJKdDTUvqoQX33lwuD797s6jcbULyPIV6oYDRu6tghjXGdFwQScro1289zajhz5ZIZ/B+7Rw2kuSLv9DipOeYVOnbCzmE1Qs4+vCTiNOxXnav7Hvg+/9DpK9hw9CmM/olDSHjsU1QQ9Kwom4DRsEsmS8BbELQuS8xXmzycsNYWvI9vZWcwm6FlRMAEnMhJ+rtaWkkd+hJ9/9jpOlnTWbBIllqjWV1KggNdpjLk4VhRMQMrf0fmT++AXbuxx9iNVkmfMZr62okPXfF6nMeaiWVEwAaneLbWYRmfW7Sjov0Hvvttp/nTqFFuLN2E8PejUyb9DG+MFO0/BBKTL6whtik2j8zFo6a9Bb7zRXyP9ITqafgU+4Xg9KF/e/8Mbk9tsTcEEpLAwaNUKls49jh495p9Bf/nFaX60b8Meli6F66/367DGeMaKgglYnRrtY8NvRdg/fIx/BuzTx2n+kpRE4YaVeEqH0rWr/4Y1xktWFEzAurJrPDuowIlpAbqzeelSIk+fYEd8I+rapaFMHmFFwQSsKlXgmwJtKbFpASQnex3nL5KnzyaZCIrc0ArJcpJ4Y4KDFQUTsETgaEIbolMSSVu+0us4f3F80myWcBXte8Z5HcUYv7GiYAJa8Z6tSEPY+0mAnd28Zw+Ff1rDoqh2NG/udRhj/McOSTUBrVmXotx53xhaFmzCbRc72ODB/ogEQGp0LAPiPiW25RVERvptWGM8Z0XBBLRy5WBJlb7s38jFFwU/zla3dG0B3jl2E5/19tuQxgQE23xkAt61LZIoNG8CKWs2XNxAP/zgtIuVlsb+Ya9ROWIn7dtf/HDGBBLXioKIlBORr0Vkk4hsFJGB5+l7hYikiog7k92boNa6RSpjTt3M3hc+uLiB+vd32kXSb7+j29f307/GIgr6cRYOYwKBm2sKKcBgVa0BNAHuFZGa53YSkXBgODDbxSwmiF3dLoalXEnY14FxvsK+sc5HNf6Waz1OYoz/uVYUVHW3qn7ru38M2ASUyaDrP4AJwF63spjgFh8PG0q2pdTu71y9nGZ2nZoym++oxzW9S3odxRi/y5V9CiJSAagPLD/n8TJAN2BUFq/vJyKrRGTVvn373IppAlhaqzYAnJ79tbdBjh7lkp1LWVeqHWUy+hPHmCDnelEQkVicNYFBqnr0nKdfAR5S1dTzjaGqo1U1QVUT4uPj3YpqAthlNyVwhIL8PmV51p1ddOCrNaBKWId2nuYwxi2uHpIqIpE4BeFjVZ2YQZcEYJw4cwQUBzqKSIqqTnYzlwk+V7eKoFbYJm6vWpqnL3SQoUMvOseEfc15kAMsvi/moscyJhC5VhTE+U0/Btikqi9l1EdVK6br/z4w3QqCyUjBglC20SV8NZ8LLwp+uIDylClQrGIhate/6KGMCUhubj66CugDtBaRNb7WUUQGiMgAF5dr8qj2zRIZsOw2Tn40/sIGWLPGaRfo+LptPDSzJfc1XW0T4Jk8y7U1BVVdDGT7q6Oqt7uVxeQNzdoVoObIORwck0yZPhdwSsugQc7tggUXtPwf/zuL5rqQ/B0LXdDrjQkGdkazCRpXXiUsCGtDwZVfgWruB5g1i+1hlWl442W5v2xjcokVBRM0oqPhl6ptiDuxFzZc5JQXOZRyPImqv85ne5V2RNiMYSYPs6Jggkp0Z+d8hcQpuXt284a3l1CAE8R0s8mOTN5mRcEElYQbyjOT9vy4M3+uLnfxN+HMk2uoM7BVri7XmNxmK8ImqCQkQNHYmfSJhDdz+uLnnrugZarCi6taUKtjC6aXuqAhjAkaVhRMUImMhBYt4Ot5qXAiCWJycBLZlVde0DI3Lk/k8I5kuj5a5IJeb0wwsc1HJuhce/VJFm0pxZHHR+bshUuXOi2Htg//gv0U5/paW3L8WmOCjRUFE3Sat4tmJ5dyanoOr9v86KNOy6GohbM4GFmS+KZ2KKrJ+6womKBTpw4sjWpDsS3L4PhxV5e1a0cKCYfmsrtOe+w0ZhMKrCiYoBMWBkeuaEtEWjK66H+uLmv56yspyiGK3myzoprQYEXBBKWS3ZtxivwcHj/X1eWcmjSTVMIoc+vFT6ZnTDCwomCCUssO0fyTl1lY3L3Leh85Ak/+3JdxncYixYu5thxjAokdkmqCUpUqMK3M3RzcAddn90WvvJKjZcyaBVtSKnLpwxWz7mxMHmFFwQQlEWjdSjk4bQlpiyCsebOsX1SvXo6WsX3UHAbE7aNp417YSrUJFfZJN0GrTVth+JH+HH/oyey9YN48p2VDcjJcsfglhsmThEfa18SEDvu0m6DVujXMoBMxKxfC0XMv/52BZ55xWjYs/vIozVPmk9i6y0WmNCa4WFEwQatcOVhTtjPhqckw179HIW0fNYd8JFP2bisKJrRYUTBBrXCHKzlEYdKmTPPbmKpQeNFUjkYWJar1hc2XZEywsqJgglrLthHMpANJC5b67Wpsa75TSp/Yyp5G12FX1DGhxj7xJqi1agW1eZmf7yrCw36ahmLyFOEZWcLvn5zyy3jGBBPX1hREpJyIfC0im0Rko4gMzKDPLSKyzteWikhdt/KYvCk+HkrVKcm8Rfmy7vz2207LwtTJaVx5lRBfPtoPCY0JLm5uPkoBBqtqDaAJcK+I1Dynz09AC1WtAzwNjHYxj8mjWreGEgu/4GT9ppCSknnHatWcdh47fzjF1HWXMrTUO35OaUxwcK0oqOpuVf3Wd/8YsAkoc06fpap6yPfjMqCsW3lM3nXPPRAZHUH0mmXs/ezrzDtOm+a081j/wizKsYvaHcv7OaUxwSFXdjSLSAWgPrD8PN3uBGZm8vp+IrJKRFbt27fP/wFNUKtSBQbN7sAxYll4z2fs2ZNJxxdfdNp5FJw6lgPh8ZTpbddiNqHJ9aIgIrHABGCQqmZ4hpGItMIpCg9l9LyqjlbVBFVNiI+Pdy+sCVr1m0Zxsl03rj36Bde1OcHBgzkf4/CWfTTZN5XvG/RxrvtpTAhytSiISCROQfhYVSdm0qcO8A7QVVUPuJnH5G0lHrmTQhyl9ubxdOgAx47l7PXbnhxLPpIpOLCvOwGNCQJuHn0kwBhgk6q+lEmf8sBEoI+q/uhWFhMimjeH/v3p+0xlVq+GLl3g5Mnsv3zs/nY8FTeCy3vVdi+jMQHOzfMUrgL6AOtFZI3vsUeB8gCqOgp4HCgGvOnUEFJUNcHFTCYvE4FRo7ga+LA89O4N3bvD5MmQL4sjVpOS4J0lNenVqyZhdkqnCWGuFQVVXQyc92wiVb0LuMutDCZE7dzJzZErSBzVk/794ZZb4NNPIeKjjzJ9yY57RlAjsSVduzbKxaDGBB47o9nkPc8+Cx99RL8dzUlMLMngwRAbC2PGlMt4LeDHH6ny7sN0i3yMNm2sKJjQZivKJu8ZMsTZHvTqqzzwAAwbBu+/Dx90/Awd99lfuuuTT5EkUWy55h6ionI/rjGBxIqCyXuqVoW//Q1efRV27WLYMHjgAag4+y1+euitP/dduxY+/YRXdCCtbirpTV5jAogVBZM3/ec/kJoKDz2ECIwcCaVLw88/O08BzvP33svJqCKMDHuITp08TWxMQLB9CiZvqlABHnoIfvsN0tKQsDCqVnHqwCOPOPsY7hugcM01PLXzbupcVpiiRb0ObYz3rCiYvGvYMM7uWU5NRQSqV4euTeGxfxwiLq4IzXoPY/gT8PJgT5MaEzBs85HJu84UhG3boEYN2LmTsMOHGF/+AXZGVGZ4383ceqvTpWtX72IaE0hsTcHkfTExTlGYOhWAiPXrib71DkpvLsf8pVCqFFSs6HFGYwKEFQWT95UuDVOmwI4dsHMnVK9OZMmSTDj8x1nPxhiHFQUTOhYscG5btACgcGGYPt27OMYEItunYELH++87zRiTKSsKxhhjzrKiYIwx5iwrCsYYY86yomCMMeYsO/rIhI4vv/Q6gTEBz4qCCR0xMV4nMCbg2eYjEzrefNNpxphMWVEwoePzz51mjMmUFQVjjDFnuVYURKSciHwtIptEZKOIDMygj4jIf0Vkq4isE5EGbuUxxhiTNTd3NKcAg1X1WxGJA1aLyFxV/T5dnw5AFV9rDLzluzXGGOMB19YUVHW3qn7ru38M2ASUOadbV+BDdSwDCotIabcyGWOMOb9cOSRVRCoA9YHl5zxVBvgl3c+7fI/tPuf1/YB+vh+TRGSDK0HdUxzY73WIHAi2vJCTzCLuJsm+YHufgy0vWOb0Ls1OJ9eLgojEAhOAQap69NynM3iJ/uUB1dHAaN94q1Q1we9BXRRsmYMtL1jm3BBsecEyXwhXjz4SkUicgvCxqk7MoMsuoFy6n8sCv7mZyRhjTObcPPpIgDHAJlV9KZNuU4FbfUchNQGOqOruTPoaY4xxmZubj64C+gDrRWSN77FHgfIAqjoK+BLoCGwFTgB9szHuaP9HdV2wZQ62vGCZc0Ow5QXLnGOi+pdN+MYYY0KUndFsjDHmLCsKxhhjzgqIoiAihUVkvIhs9k2L0VREnhCRX0Vkja91TNf/Ed/UGD+ISLt0j7f3PbZVRB4OlMwico2IrBaR9b7b1unGaeh7fKtvyg/XDqLP6fvse015EUkUkSHpHsuV9/kCPhd1ROQb37Qq60Ukyvd4QL7HIhIpIh/4sm0SkUfSjePpZ9n3+D98GTaKyIh0/T39/uUkbyB/9zLLnO413nz3VNXzBnwA3OW7nw8oDDwBDMmgb01gLZAfqAhsA8J9bRtQyTfGWqBmgGSuD1ziu18b+DXdcyuApjjnbMwEOgRC5nSvmQB8caZPbr7POXyPI4B1QF3fz8WA8EB+j4GbgXG++zHADqBCgHyWWwHzgPy+x0v4bj3//uUwbyB/9zLMnO41nnz3PF9TEJGCQHOcw1dR1dOqevg8L+mK80VKUtWfcI5cauRrW1V1u6qeBsb5+nqeWVW/U9Uz519sBKJEJL84U3oUVNVv1Plf/xC4PhAy+15zPbDdl/mMXHmfLyDvtcA6VV3r639AVVMD/D1WoICIRADRwGngKIHxWb4b+I+qJvke3+t7iaffv5zmDfDvXmbvsaffPc+LAk7V2we8JyLficg7IlLA99x94sye+q6IFPE9ltnUGJk9HgiZ0+sOfOf7IJTx5Qy4zL7nHgKePGec3Hqfc/oeVwVURGaLyLci8q90eQPyPQbGA8dxpnX5GRipqgcJjM9yVeBqEVkuIgtF5Apff6+/fznNm16gffcyzOz1dy8QikIE0AB4S1Xr43xJHsaZMbUyUA/nS/Oir39mU2Nka8oMP8lpZgBEpBYwHOh/5qEAzvwk8LKqJp4zTm5lzmneCKAZcIvvtpuItMnFvBeSuRGQClyCsylmsIhUCpDMEUARoAnwIPC5b5u719+/nOYFAva7l1lmT797gVAUdgG7VPXMZHnjgQaqukdVU1U1Dfg/nC/Qmf4ZTY2Rm1Nm5DQzIlIWmATcqqrb0o1TNkAzNwZGiMgOYBDwqIjcR+69zxfyuVioqvtV9QTOiZENCOz3+GZglqom+zYdLAESCIDPsu/xiepYAaThTNTm9fcvp3kD9rt3nsyefvc8Lwqq+jvwi4hU8z3UBvhe/jyFdjfgzMyoU4GbfNsFK+Jci2EFsBKoIiIVRSQfcJOvr+eZRaQwMAN4RFWXpBtnN3BMRJr4/kK4FZgSCJlV9WpVraCqFYBXgOdU9XVy6X2+gM/FbKCOiMT4ttG3AL4P5PcYZ5NRa3EUwPmLcTMB8FkGJgOtAUSkKs6Ozf14/P3Lad5A/u5lltnr754re9pz2nBWq1fhHD0yGWeV6iNgve+xqUDpdP3/jbMX/gfSHTGAM2XGj77n/h0omYGhOKuMa9K1M0dHJOD8ktgGvI7vLHOvM5/zuidId/RMbr3PF/C56I2zY24DMCLd4wH5HgOxOEeXbMT5JfFgAH2W8wFjfe/bt0DrdP09/f7lJG+Af/cyfY/TvS7Xv3s2zYUxxpizPN98ZIwxJnBYUTDGGHOWFQVjjDFnWVEwxhhzlhUFY4wxZ1lRMCFHRLqJiIpIdT+PW0FEbvbnmMbkNisKJhT1AhbjnPzjTxVwzlL+C98JdcYEPDtPwYQUEYnFOemqFTBVVauLSEuck4T240yvvBroraoqzrUPXvI99y1QSVU7i0gL4FXfsIozC+ZcoAbwE85UyYeATkAUUADnTNYRQAffa55R1c98y38S2INzktNEnJPdBuLMnnq9/jE9gzGusr9eTKi5Hme+oR9F5KCINPA9Xh+ohTOXzBLgKhFZBbwNNFfVn0Tk03TjDAHuVdUlvkJzCmeSsyGq2hlARG7Hma+/jqoeFJHuOL/06+LMcbNSRBb5xquLU1AO4kyZ/I6qNhKRgcA/cObAMcZ1tvnIhJpeOPPQ47vt5bu/QlV3qTNp3RqcTUHVge3qXDcAIH1RWAK8JCL3A4VVNSWT5c1VZzpscGZv/VSdyfH2AAuBM1M8r1TV3epM67wNmON7fL0vizG5wtYUTMgQkWI4E5DVFhHFuZKV4syompSuayrOdyPTyzOq6n9EZAbOXDTLRKRtJl2Pp49wnnjpl5+W7uc07HtqcpGtKZhQ0gP4UFUvVWcWynI42/+bZdJ/M1BJRCr4fr7xzBMiUllV16vqcJyJzqoDx4C48yx/EXCjiISLSDzOfogVF/MPMsbfrCiYUNILZ1799CaQyRFDqnoSuAeYJSKLcXYEH/E9PUhENojIWuAkzjV+1wEpIrJWRP6ZwZCTfH3WAvOBf6kzrbIxAcOOPjLmPEQkVlUTfXPuvwFsUdWXvc5ljFtsTcGY8/u7iKzBueZBIZyjkYzJs2xNwRhjzFm2pmCMMeYsKwrGGGPOsqJgjDHmLCsKxhhjzrKiYIwx5qz/ByDfmvP9r50QAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1c8af4e0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"# Plot the fitted model against observed data.\n", | |
"fig, ax = plt.subplots()\n", | |
"ax.plot(x, y, 'b')\n", | |
"ax.plot(x, y_fit, 'r--')\n", | |
"ax.set_xlim(6500, 6650)\n", | |
"ax.set_ylim(2e-18, 3.5e-18)\n", | |
"ax.set_xlabel('Angstrom')\n", | |
"ax.set_ylabel('FLAM')\n", | |
"\n", | |
"# Fitted center of the absorption line.\n", | |
"fitted_center = fit_model.x_0_1.value\n", | |
"ax.axvline(fitted_center, ls='--', color='r')\n", | |
"print(fitted_center)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"EW = 26.1135 Angstrom\n" | |
] | |
} | |
], | |
"source": [ | |
"import math\n", | |
"\n", | |
"# Area inside curve.\n", | |
"a_in_curve = (math.sqrt(2 * math.pi) * fit_model.amplitude_1 * fit_model.fwhm_1)\n", | |
"\n", | |
"# Approx. continuum level.\n", | |
"h_at_center = fit_model.slope_0 * fitted_center + fit_model.intercept_0\n", | |
"\n", | |
"# Equivalent width.\n", | |
"print('EW = {:.4f} Angstrom'.format(a_in_curve / h_at_center))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### stsynphot: HST specific add-on" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Documentation at http://stsynphot.readthedocs.io/en/latest/" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Need to download a bunch of data first\n", | |
"import os\n", | |
"\n", | |
"os.environ['PYSYN_CDBS'] = 'C:\\\\Users\\\\lim\\\\cdbs\\\\grp\\\\hst\\\\cdbs\\\\'" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"WARNING: Failed to load Vega spectrum from C:\\Users\\lim\\cdbs\\grp\\hst\\cdbs\\\\calspec\\alpha_lyr_stis_008.fits; Functionality involving Vega will be cripped: [Errno 2] No such file or directory: 'C:\\\\Users\\\\lim\\\\cdbs\\\\grp\\\\hst\\\\cdbs\\\\\\\\calspec\\\\alpha_lyr_stis_008.fits' [stsynphot.spectrum]\n" | |
] | |
} | |
], | |
"source": [ | |
"import stsynphot" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"C:\\Users\\lim\\AppData\\Local\\Continuum\\Anaconda\\envs\\py36\\lib\\site-packages\\stsynphot\\stio.py:238: FutureWarning: Conversion of the second argument of issubdtype from `str` to `str` is deprecated. In future, it will be treated as `np.str_ == np.dtype(str).type`.\n", | |
" if not np.issubdtype(data[key].dtype, val):\n", | |
"C:\\Users\\lim\\AppData\\Local\\Continuum\\Anaconda\\envs\\py36\\lib\\site-packages\\stsynphot\\stio.py:238: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n", | |
" if not np.issubdtype(data[key].dtype, val):\n" | |
] | |
} | |
], | |
"source": [ | |
"bp = stsynphot.band('acs,wfc1,f555w')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1df91cf8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"bp.plot(left=4300, right=6500)" | |
] | |
}, | |
{ | |
"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": "3.6.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 1 | |
} |
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