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@pllim
Last active September 27, 2018 18:54
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synphot issue 159 (3)
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import math\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from astropy import units as u\n",
"\n",
"import stsynphot\n",
"from synphot import SourceSpectrum, Observation, models, units\n",
"from stsynphot import band"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AREA: 45238.9342 cm2\n"
]
}
],
"source": [
"# Telescope area (HST)\n",
"a = stsynphot.conf.area * units.AREA\n",
"print('AREA: {:.4f}'.format(a))\n",
"\n",
"# Define a bandpass to investigate\n",
"bp = band('acs,wfc1,f555w')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PHOTFLAM: 1.9671e-19 FLAM\n",
"PHOTPLAM: 5360.9383 Angstrom\n"
]
}
],
"source": [
"# Calculations we are doing in the pipeline\n",
"photflam = bp.unit_response(a)\n",
"photplam = bp.pivot()\n",
"print('PHOTFLAM: {:.4e}'.format(photflam))\n",
"print('PHOTPLAM: {:.4f}'.format(photplam))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"STMAG: 25.6654\n",
"ABMAG: 25.7112\n"
]
}
],
"source": [
"# Calculations from ACS Zeropoints website\n",
"# http://www.stsci.edu/hst/acs/analysis/zeropoints/#_keywords\n",
"zpst = -2.5 * math.log10(photflam.value) - 21.1\n",
"zpab = -2.5 * math.log10(photflam.value) - 5 * math.log10(photplam.value) - 2.408\n",
"print('STMAG: {:.4f}'.format(zpst))\n",
"print('ABMAG: {:.4f}'.format(zpab))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we attempt to calculate using the `effstim()` of an observation. However, one has to be careful to use a source that is flat in the wavelength space for *LAM* calculations and flat in the frequency space for *NU* calculations."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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fkdQq6SOp+YmSfiHpMUkd/XL9KLknmtPabFPJHn47LZd+bGqr8t6TeZX3V0ia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\nZmaFOJCYmVkhDiRmZlaIA4mZmRXy/wFhaLSxL+dAvQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define a source that is flat in wavelength space.\n",
"sp_w_flat = SourceSpectrum(models.ConstFlux1D, amplitude=1*units.FLAM)\n",
"\n",
"# Normalize it to have 1 count in the desired bandpass.\n",
"sp_w = sp_w_flat.normalize(1 * u.ct, band=bp, area=a)\n",
"\n",
"# Plot to make sure it is still flat across wavelength space.\n",
"sp_w.plot(bp.binset, flux_unit='flam')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new STMAG: 25.6654 mag(ST)\n",
"prev STMAG: 25.6654\n"
]
}
],
"source": [
"# Create an observation from the source.\n",
"obs_w = Observation(sp_w, bp, binset=bp.binset)\n",
"\n",
"# Re-calculate STMAG\n",
"zpst2 = obs_w.effstim('stmag')\n",
"\n",
"print('new STMAG: {:.4f}'.format(zpst2))\n",
"print('prev STMAG: {:.4f}'.format(zpst))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new ABMAG: 25.7113 mag(AB)\n",
"prev ABMAG: 25.7112\n"
]
},
{
"data": {
"image/png": 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IeFrZU4Z3TXVXkT0Q8D/x/C9hAX8fEd+u8b1V2yh7f8xTuaJngN2o/ujvWvqv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\nAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Repeat in frequency space.\n",
"sp_nu_flat = SourceSpectrum(models.ConstFlux1D, amplitude=1*units.FNU)\n",
"sp_nu = sp_nu_flat.normalize(1 * u.ct, band=bp, area=a)\n",
"sp_nu.plot(bp.binset, flux_unit='fnu')\n",
"obs_nu = Observation(sp_nu, bp, binset=bp.binset)\n",
"zpab2 = obs_nu.effstim('abmag')\n",
"print('new ABMAG: {:.4f}'.format(zpab2))\n",
"print('prev ABMAG: {:.4f}'.format(zpab))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, take note that answers don't agree if you calculate ABMAG from source that is flat in wavelength space (and thus, not flat in frequency space). However, for this case, STMAG seems unaffected. If you could explain to me why, it would be greatly appreciated!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"25.6916 mag(AB) =/= 25.7113 mag(AB)\n",
"25.6654 mag(ST) = 25.6654 mag(ST) ?\n"
]
}
],
"source": [
"print('{:.4f} =/= {:.4f}'.format(obs_w.effstim('abmag'), zpab2))\n",
"print('{:.4f} = {:.4f} ?'.format(obs_nu.effstim('stmag'), zpst2))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f0938b9fcc0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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DuWFiP/z3r4QDq6HvhKOlHMAaGgoIhYFntzhWWXUDox79DIApA+P5103jj+vfRbVN6+G3\nryfUwz8fWN1Zslcebs83kD71uJN9UWUdT3y6jXOH9+Ld209jRLLVKxcRLhzZh9tP709aXCg3vf4d\nTy3cRn1jy7LIlXWN3P+Otd7thaf04cELOk4E543ozec/n8ZNUzLYWlDJQ4XTASiwHx3Hf/7LHcz+\n27eU1TTwf7dM4OYpGfh/ci+8eh58+mt45Xz47DdWmYa89dbQ0BuXtyrZHBUawHNXjWZon0iWbi/i\nm50nWUdIKSdzxbTMq2hnOEf1EI31ULgVBp5z3G+dt3Iv9U127jt3SJszWkID/Xnvrsn89r2NPPfF\ndp7/cgf94kL5QVYqV47ry6PvbyL3UDVv3TaRceldX/7wrukDGNI7gjvegPfsk7B/6s+TEfsZGJDP\nzsXv8Wn0IjLSMwgoLYO+syFtCgRFQuY1VnJf+RKMuhL8Ao/uNGcpZExrcZxLRiVxzrBenPHEYh59\nfxPz75hESKDfcf87Ke/y2GOP8fbbb7d4bfbs2W4vmezUIR0RCQX2ARnGmLLO2uuQjofKWw9/mwyX\nvQyndH3OcEOTnSmPf8nAXuFdGu74alsh3+0uIXtPCct3lRx5/a7pA/jFOYNPKPQXF+/k5WW7SYgI\nIi1/IX8NeI4m/PALicQvIAgufs5abau56hJoqj96cbqhBv480JqOetlLbR7nyy0F3Pj6d1w3oR+P\nnsDMI3XU5s2bGTKk7Q6CLzPGsGXLlpMa0nFqD98YUw2cRHF05RH8gmD0tZA85rje9tnGfPLKa/nd\nzK4lwGmDEpg2KAGA7/ce4tON+YzuG83ZQ0+8aNntp/fntqkZVDc08c2GBHI3FdKrYjOBP3wbwhPa\nftOxC6kHhMCY66Bou1Wf39a6B3/GkESuGJvKW9n7+MXZg4kK1ZuyTlRwcDDFxcXExcVp0ncwxlBc\nXExw8Indt3KYLmKunGJXYSV3zf2e8toGFt97Bn62Hv4ft7Heug/Br/1EvvFAGRc+t4yfnz2In545\n0IXBeZeGhgZyc3OdNhe9pwoODiYlJYWAgJY/gx7Tw1deomw/RCZ1+c5Vu91w1UvLKayo48+Xj+r5\nyR6s2v9gLee44m9w1iNHX3MYnhTF9CGJPLVwG8WVdSzaWsAtUzL44cQ018fbgwUEBJCenu7uMLyS\nVstUHWtqhL+MhYX/0+W3bC+oJL+8jj/OGsllY1OcGJwbHFgNy1+ALx5pc/PfrxvLrDHJvP7tHvaV\n1PDa1zl6Y5byGJrwVccKN0NjzXFVnlzuuHN2Yn8vvHwz/FIY80NrWcXyg602B/jZ+N3MEUwblMCZ\nQxLZVVTF6r26/KLyDJrwVcf2r7a+J3X9zr/lu4pJjg4hNTbUSUG52eSfg70RFvwYDrS+jT400J/X\nbxzHs1eNJiTAj3dWaR195Rk04auOHVwDwVEQm9Gl5oUVdXy9o4gJGV7Yuz8sNh2m/AJ2LYYdC9tt\nFh7kz/mn9OaDtQeobdDVspT7acJXHSvcBglDunzB9r531lLfZOfGyWnOjcvdpj8ED5fC1Ps6bHb5\nmBQq6hq5cs5ynv9yhyZ+5Vaa8FXHTrsLJt3Tpaa1DU0s217E9aelMTwpysmBeYDDvwRL91mVRNsw\nsX8cN01Op7q+kT9/upUXF+90YYBKtaQJX3Vs8Hkw5IIuNV2/v4xGuyGrX9dLIPR479wIz4yAL3/f\n5mYR4TcXDeOzn03j1LQYPt2Y5+IAlTpKE75qX0W+VTStofMbYOob7azecwiAzNRoZ0fmOc79Aww8\nF759Hmo6no1z3og+bMmrYG9xtYuCU6olTfiqfds/hVfPh4oDHTYrq27gjCcW84ePt5AcHUJCROeL\niHiNiF7WeH5DFax+vcOm5wyzSkTM+26vKyJTqhVN+Kp9hVutOjrR/dptYozh1wvWs7/UWlUqNTbE\nVdF5jj4jrWqby1+E+qp2m6XGhjIjM4kXFu/khcU7sNv1hizlWlpaQbWvaDvED2yzWNhhb2fn8uG6\ng9x37mB6RQYzMsUHLta2ZfpDsHGBVVytA3++fBSNTYY/fbKV0uoGft1JfX+lupMmfNW+oq0d3nBV\nVFnH7z7cxLj0WG6f1h+bN9TMOVF9J1hfnQj0t/HXq0cTOd+ffyzdxYzMJN+Y0aQ8gg7pqLY11FqF\nwuLbr0P/1MJtVNc38ftLR/h2sj+sqRG2fWatH9ABEeGB84ciInyyQWftKNdxasIXkWgReUdEtojI\nZhGZ6MzjqW5k84cbP4XMq9rcvC2/gnkr93LthH4MSIxos43PsTfCf26GhQ+3Wg7xWFEhAQztE8Eq\nx8wmpVzB2T38Z4FPjDFDgFHAZicfT3UXP3/oOx5i0trc/IePNhMW5M/dWvf9qIBgOPM3sPMLWPpE\np83H9o1hzb5SGps6/uWgVHdxWsIXkUhgKvAygDGm3hijZQN7itxsWPd2mz3VZduL+HJrIXdNH0BM\nWGAbb/Zhp94Mw2fBl4/Bty902HRMvxiq65sY8ODHfLWt0EUBKl/mzB5+BlAIvCoi34vIP0QkzInH\nU91p3Zvw4c/brKHzwuIdJEeHcP1paa6Py9OJwNmPgC3AKqHcwayd8elx+Duuffzr2z2uilD5MGcm\nfH9gDPCiMWY0UAU8cGwjEblVRLJFJLuwUHs5HqNkl1UV8piEf6C0hm93FTM7K4Ug//ana/q06L5w\n1yq4/Wso3w+Vbf9c944KZt1vz+GWKel8ta2AQ1X1Lg5U+RpnJvxcINcYs8Lx/B2sXwAtGGPmGGOy\njDFZCQntLCqtXK9kN8S0XmZuwZr9GAOXjk52Q1A9SEw/qK+GZ0bCqtfabRYa6M/M0ck0NBn+u2a/\n6+JTPslpCd8YkwfsE5HD8/rOBDY563iqGzU1Quleq4ffjDGG+av3M7ZfDP3idHSuU+EJkDoeNr7b\nYbNhfSIZlxbL059vJ79cF+5WzuPsWTp3AW+IyDogE2i7pKDyLOW5YG9o1cPfeKCc7QWV2rs/HiMu\ng4JNUND+BDUR4Y+XnUJNfRN/WbTdhcEpX+PUhG+MWeMYrhlpjJlpjNFJxz1BVF/42UYYNqPFy++s\nyiXQz8ZFI/u4KbAeaPhMqx7RvGusmU/tyEgIZ+boJN7OzqW4ss6FASpfonfaqtZsNohKgZCjZY5r\nG5p4d3Uu543oTXSoTsXssvBEuOYt8AvssCYRwK1TM6hrtPOXRTtcFJzyNZrwVWub3rPquzfzyYY8\nymsbufLUVDcF1YNlnA53fNvpQvADEiO44bQ0Xvsmhy+3FrgkNOVbNOGr1ta/DdmvtHhp4aZ8+kQF\ne/fi5M4kAkU74PNHoLH9IZsHzh/CwMRwHv7vRuoadf1b1b004avWDrWekrlmXylj+8VokbSTUbIT\nlj0Fu5e02yQ4wI+HLx7O3pJqrpqznLKaBhcGqLydJnzVkjFQktNiSmZRZR37S2sYleJDSxc6Q/o0\nCAy3VsZqZ9FzgMkD43ly9ihW7y3lPZ2br7qRJnzVUnUJ1Fe06OGvy7VKII3ypbVqnSEgGCbdDZvf\nhzX/12HTy8amkB4fxsLNOpavuo8mfNVSeS6IX4sqmWv2lmITGJ4U6b64vMWUX1jLIX5wD+xd3mHT\ns4YmsnxnMZV1jS4KTnk7TfiqpT6j4KF8GHDWkZdW7y1lcO9IwoJ0gbSTZvODH/wLpv0SEoZ02PSs\nob2ob7LzyrLdLgpOeTtN+Ko1vwDwt+baN9kNa/aVMqavDud0m5AYmHpvi/sc2jIuPZZLRiXx1MJt\nrN6r9yyqk6cJX7W0Yo41ddBhe0EFlXWNjOkb48agvNSaua2mvzYnIjx26QgC/ISP1x90YWDKW2nC\nVy1t+wR2fXnk6eo91gXbMf004Xe7ze/BN3/psElEcAAT+8ezcFM+poOZPUp1hSZ81VL5fog8Whxt\nXW4p0aEBpMWFujEoL5U22Vp3oKzjqZdnD+tFTnE1OwoqXRSY8laa8NVRxkBZrlVHx2FzXgVDe0ci\nbax8pU5S2hTr+9PDYP/qdpudPbQXNoE3Vux1UWDKW2nCV0fVlkF95ZGEb7cbtuVVMKRPhJsD81K9\nRsApV1iPD3zfbrPeUcH84NRU3lixh73F1S4KTnkjTfjqqJoSiEiCKKtA2t6SamoamhjSWxO+U9hs\ncNlL8JtiOPWmDpvefeYgmuyGN7O1l69OnCZ8dVRsBvxis1XDHdiSVwHA4N56w5VT+Tnub+igqFrv\nqGCmDkpg/ur92O168VadGKcmfBHJEZH1IrJGRNpf/UF5pC155YjAoF7h7g7F+70xG/45s8MaO7PG\npHCgrJZvdha7MDDlTVzRwz/DGJNpjMlywbHUyVj5krUykyPpbM2roF9sKKGBeoet0w06D/Z+A1s/\nbrfJOcN6kRARxLNfbNMpmuqE6JCOOio3Gw6utWq3Yw3pDNHhHNcY80OrftHXz7TbJDjAj5+eOZDv\ncg6xfFeJ62JTXsPZCd8An4nIKhG5ta0GInKriGSLSHZhYaGTw1EdajYHv7ahiT3FVTqc4yp+ATD+\nx7BvRYdTNGePTSE00I8P1x9wYXDKWzg74U8yxowBzgfuFJGpxzYwxsxxLHSelZCQ4ORwVIfKciHK\nSvg5xVXYDfRP1ITvMpnXQFAUfPdyu02CA/w4Y3Ain2zIp0kv3qrj5NSEb4w54PheAMwHxjnzeOok\n2O1WD98xB39nQRUAAzThu05wJFz3Llz4RIfNzj+lN0WVdazcrcM66vg4LeGLSJiIRBx+DJwDbHDW\n8dRJaqiySiMnDAU4cht/RrwmfJdKyYKAEGiobXea5vQhiUSFBPD6NzmujU31eM7s4fcClonIWmAl\n8KEx5hMnHk+djKAIuPlzyLwKgJ2FlSRHhxAS6OfmwHxQdQn89VT4+JdtTtMMDfTnhxP78emmPPYU\nV7khQNVTOS3hG2N2GWNGOb6GG2Mec9axVPfbWVipwznuEhoLw2fAqldh+2dtNvnBqakYA1/oEojq\nOOi0TGVZ9Tq8OBnqKrDbDTsLK+mfoAnfbab/j3UBd/N7bW5OiQklIz6Mpdt1ZpvqOk34ylK0DYq3\nQ2A4B8pqqG2w0z8xzN1R+S7/QBh0jnUjlr2pzSZTBsazfFcJdY1tb1fqWJrwleXwHHwRdhZa48La\nw3ezwRdAzSEoaXtN2ykDE6hpaGKF3oSlukgTvrKU7T8yB39XoTVDRxO+mw06F67/AOIHtLl58sB4\nYsMC+ee3OS4NS/VcmvCVpSz3SFnknYWVRAb7Ex8e6OagfFxgGKRNsh7b7a02Bwf4cd2Efny+uYCc\nIp2tozqnCV9ZU//6TYTU8QDsKqwiIyFcV7nyFAvuhHlXtbnp8rHWjXJfbtXZOqpzmvCVVSzt8ldg\n7PUAOkPH04TGwI4vrBXJjpEaG0p6fBhLtulsHdU5Tfiqxc09lXWN5JfX6QwdTzL0ErA3wOYP2tw8\nVWfrqC7ShK9g/TvweBocyjlywVZLKniQ5CxIHGaVTm5jiqbO1lFdpQlfQdlea/pfWAK7HRf/MhK0\nh+8xbDaYep91r0QbN2JNHhhPRLA/87/f74bgVE/S4VJGIrKuvU2AMcaM7P6QlMuV5UJILASGsaf4\nACLQNzbU3VGp5obNhAuKIW1Kq03BAX7MyEzi7excHpkxnMjgADcEqHqCztaus2MtYvJ/wPtAjdMj\nUq5Xug+irSmZe4qr6R0ZTHCAFk3zKDYbjLul3c2zx6by7+V7+WDtQa4e39eFgamepMMhHWNMJnAV\nEI6V9B8DhgP7jTF7nB+ecolmc/D3FFdp796TrXurzQVSRqZEMSAxnP+u0WEd1b5Ox/CNMVuMMQ87\nVq56H/gn8DOnR6ZcZ+hFMPh8APaUVNMvThO+x9rxOXz6IFS2nHcvIlwyKokVu0vYdKDcTcEpT9dp\nwheRZBH5hYgsA67FSvYvOj0y5TrTH4LR11Jd30hhRR394vSCrceaci801sDaea02XT2+L70ig7j9\njVVU1ze6ITjl6TpM+CLyFVavPgC4Abge+BAIFJFYp0ennK+xDuqrAWv8HtAevidLGARJY2D92602\nxYcH8fQVmewpruY/q3VoR7XWWQ+/HxAD3AZ8BmQ7vlY5vndKRPxE5HsRafuuEeVeOxfB7/vAge+P\nJvxY7eF7tJFXQN46KNzaatPE/nGMTInita93Y9dFztUxOrtom2aMSXd8ZTT7SjfGZHTxGHcDm08+\nVOUUZbnW94gk9pZYc/D7ag/FwYyYAAAdPklEQVTfsw2fBb1HQn1lq00iwg2npbGzsIqvdxa5ITjl\nyY77xisR6S8iD4pIpwuSi0gKcCHwjxMJTrlAWS74BUJYAjnF1cSEBhAVovO4PVpEL/jxUkge2+bm\nC0f2ITo0gDe/2+fiwJSn61LCF5E+InKPiKwENmLN32+7fF9LzwD3Y83nb2/ft4pItohkFxZqASiX\nK8u1Fj6x2dhbXE1fvWDbc9SUQuneVi8H+fsxMzOZzzbmU1hR54bAlKfq7KLtLSKyCPgKiAduBg4a\nYx4xxqzv5L0XAQXGmFUdtTPGzDHGZBljshISEo4zfHXSynIhyiqxu6ekin46B79nMAb+NgU++02b\nm6+d0A8R+PG/V1FaXe/i4JSn6qyH/zzgB1xtjHnIGLMO687brpgEXCIiOcA8YLqI/PuEI1XOMeY6\nGHM99Y129h+qIU3H73sGEeh/unXRvbF1Qh+QGM7TP8hkzb5SznrqK7JztLCa6jzhJ2El66dEZKuI\n/C/WFM1OGWN+ZYxJMcakAVcCi4wx155UtKr7jb4WRs5mf2kNdoMO6fQkg86DunLIWdLm5gtO6cP7\nP5lMZHAAV/9jBS8t2eXiAJWn6WyWTpEx5kVjzFTgTKAMKBCRzSLye5dEqJynoQaKdkBjPXuKrRk6\nOge/B+k/HcISYHn790EOS4rkndtPY1RKFI99tJmCiloXBqg8TZdn6Rhjco0xTxhjxgIzgdHH8d7F\nxpiLTiRA5UQH18Jfx0LOEvaW6E1XPU5ACEy43Sq3ULyz3WaxYYE8cskIABZv0YkRvuyE6uEbY7Zi\nFVFTPdnhOfiRKeQUVRMa6EdCeJB7Y1LHJ+sm+On3ENe/w2ZD+0TQJyqYRVt07VtfdjILoOgK1z3d\n4YQflczeEqtKpi5c3sOERENs5/dAighnDElk6fZCXQrRh51Mwtf7tnu6slwIjoagCHKKq7Usck9V\nsBneuv7oL/B2nDkkkar6Jlbu1hk7vqqzFa/ep+3ELkCcUyJSrlO2D6JSaLIb9hZXc+aQRHdHpE6E\n+MGmBZA6Dibe2W6z0/rHE+Rv44vNBUwZqPe8+KLOVrx64gS3qZ5gwu1QV0leeS31TXYti9xTJQyy\nFjpf9RpMuMOao9+GkEA/Tusfx6ItBTx88TAdvvNBnSX83caY1vduK++QcToAe3ZYRbb0pqseLOtH\n8N87Yc83kDap3WanD07ky60byT1UQ6oO4fmczsbwFxx+ICL/cXIsypUaamDXYqguIcdRFjktXnv4\nPdbwWRAUBdmvdNhsaJ9IAHYWtq60qbxfZwm/+d98XS2HrHqCou3wzxmwewl7iqsI9LfROzLY3VGp\nExUYCpN+Cr1HdNgs3fFLfXdRlSuiUh6msyEd085j1dOVOUrnRvdl9yqraJrNpmO6PdrUezttEh8e\nSESQvyZ8H9VZwh8lIuVYPf0Qx2Mcz40xJtKp0SnnKT2a8PcUb9ILtt6iqRHWvQnRqZA+tdVmESE9\nIUwTvo/qMOEbY/xcFYhysbJ94B+CPTiWPSVVTBkY7+6IVHdZ/EeISm4z4YM1rJOdc8jFQSlPcDI3\nXqmerHQPRKdSUFlPbYOdfnrB1jv4+cPEO2Dvt7DvuzabpMeHcaCshtoGvePW12jC91VnPAgXPUOO\no0pmug7peI/R10FQJKz8e5ub0+PDMIYji9Yr36EJ31clDoW0SeQUaVlkrxMUDplXw8YFbS6B2D8h\nHIDdRTo109dowvdFDbXw/RtwaA85xdUE+AlJ0SHujkp1pwm3Q8IQ636LYxy+32KXXrj1OZrwfVHp\nHvjvHbBvJXuKq0iNDcVPp2R6l5g0uG0JJAwGux3KDxzZFB7kT2JEELsLNeH7GqclfBEJFpGVIrJW\nRDaKyCPOOpY6TkemZKaSU1xNmo7feyeb47/3okfh79Ng74ojm9LjdWqmL3JmD78OmG6MGQVkAueJ\nyAQnHk91VekeAExUCnuKq3T83tuNuhr8AuHfs6DOGrfP0Ln4PslpCd9YDl8VCnB86d26nqBsH9j8\nKSSG6vqmI7fbKy+VMAhm/R3qK2HbJ4DVwy+uqqespsHNwSlXcuoYvoj4icgaoABYaIxZ0UabW0Uk\nW0SyCwt1vU2XKN0HkcnklNQB6F22vqDvaRDRBza8C3DkIn1emS5q7kucmvCNMU3GmEwgBRgnIq0q\nOxlj5hhjsowxWQkJuiiDS5z/OFz91pE5+FoW2QfYbDD8UqgqALudxAirUF5BhSZ8X9JZLZ1uYYwp\nFZHFwHnABlccU3UgLB7C4tnz/Rb8bUKyTsn0DWc/Cn4BAPSKtBarzy+vc2dEysWcOUsnQUSiHY9D\ngLOALc46nuqixnr46k+Qt56c4mpSYkLw99PZuT7BkexprNcevo9y5v/0PsCXIrIO+A5rDP8DJx5P\ndUX5fvjyMTi41jFDR8fvfcq6t+DPAwhpqiAi2J8C7eH7FKcN6Rhj1gGjnbV/dYIcdfBNVAo5RdVk\n9Yt1c0DKpULjoK4M8taTGBGkPXwfo3/L+xpHbZVDgb2prGvUOfi+ppdj3kT+RnpFBusYvo/RhO9r\nSvcBQk59NIDeZetrwhOtXn7+Bu3h+yBN+L6mPBcierP7UCOgVTJ9jgj0Gg4Fm4708I3R+yF9hUum\nZSoPcvFzUFNKztdF2ARSYjTh+5xTZkNlAQkSRH2jnfKaRqJCA9wdlXIBTfi+xuYHYXHsKtxDamwo\ngf76R57PGfNDABLXWhU08ytqNeH7CP3f7kvsTfD+3bB7CbuKqsjQGjq+q7acpECrVr5OzfQdmvB9\nSUUerHoNe+E2dhdVkuFY+Uj5mIYaeLwf/Xe/AUB+uV649RWa8H2JYw5+SUBvahvsZCRoD98nBYRA\nTDoRZdsAKKrUHr6v0ITvSxwLn+xpsm62yojXHr7P6jUM/8JNhAb6UVihCd9XaML3JWXWTVdbqq05\n+P21h++7eo2Akl0kh0Gh9vB9hiZ8X1JXCeG92HbITniQPwkRQe6OSLlL4lDAkBmSrz18H6IJ35ec\n9TD8fAu7iqpIjw9DRBcu91nJWXDhU0hEkiZ8H6IJ39fYbOwqrNILtr4uKhlOvYmgmD560daHaML3\nFcbA3KupW/su+0tr9IKtgoItDLblcqi6gfpGu7ujUS6gCd9XVJfA1g85lJcDoD18Be/8iGm5fwOg\nuEp7+b5AE76vKN8PwH67Y0qmJnwVlUp0/UEAHcf3Ec5c4jBVRL4Ukc0islFE7nbWsVQXlFt1U3bW\nRgGQrmUVVHRfQqqtnwtN+L7BmcXTGoFfGGNWi0gEsEpEFhpjNjnxmKo9jh7+pqpwkqJshAZq3Tyf\nF52Kf305EVRrwvcRTuvhG2MOGmNWOx5XAJuBZGcdT3VCbBDdj7WHArWGjrJE9wUgWYp0po6PcMkY\nvoikYa1vu6KNbbeKSLaIZBcWFroiHN+U9SPM3WvZUVSr4/fK0vc0uPotyoJ6aw/fRzg94YtIOPAf\n4B5jTPmx240xc4wxWcaYrISEBGeH49MKK+uoqGvU8XtliegFg84lNCJGyyv4CKcO5IpIAFayf8MY\n864zj6U6MfdqakOHAmN0SEcdteMLTgvKYUvFUHdHolzAmbN0BHgZ2GyMecpZx1FdYAzsWkz1oXwA\nXfhEHfXRfVxa974O6fgIZw7pTAKuA6aLyBrH1wVOPJ5qT105NFSxrymGIH8bydEh7o5IeYqoFBLt\nhRRV1rs7EuUCThvSMcYsA7Q6lycos6Zk7qiNJD0+DJtNPxblEJVK9P5NVNY1Ul3fqNN1vZzeaesL\nynIB2FgZrjN0VEtRKYTVFxFAI0UV2sv3dprwfYGfP/aUcXxXFqUzdFRLUSkIht5STGGlrm3r7TTh\n+4L+09k7cwH59ijS4jThq2YGn8+Oyz4lz8TphVsfoAnfR+wpqQagnyZ81VxYPJFpmTTgrwnfB+gV\nGl/w2kX0buoDzCQtLtTd0ShPYgxxm99goq2YwsqB7o5GOZn28H1B3nrKaxsJCfDTdWxVSyL4ffUH\nZget1B6+D9CE7+1qy6C2lN2NcfSLC9V1bFVrUSmk+hVrwvcBmvC93aE9AGyutRK+Uq1EpZBEkdbT\n8QGa8L1dqZXwvy/XGTqqHVGpxDcVUlSu0zK9nSZ8bxcaT/Wgmexqiqev9vBVW6JSCDI11FcWYbcb\nd0ejnEgTvrfrN5E1456knDDt4au2jb6ON89cRmFTGAU6ju/VNOF7u7oKdhZVAZCmd9mqtgRHkpjQ\nCxD2l1a7OxrlRJrwvZkx8MxI+q/6HeFB/iRFBbs7IuWJGusYue05JtvWk3uoxt3RKCfShO/Nqgqh\npoTtdbEM7BWuUzJV22wBxK6dw2TbevaXasL3ZprwvVnhFgCWVyYyKDHCzcEoj2WzIVHJpPmXsF97\n+F5NE743K7ASfnZ1Lwb20mUNVQeiUujrV6I9fC/nzCUOXxGRAhHZ4KxjqE4UbqYxMJJCohnUS3v4\nqgNRqfShUHv4Xs6ZPfzXgPOcuH/VmUHnsTrjx4Bowlcdi0knxNRSUFqOMToX31s5LeEbY5YAJc7a\nv+qCQefyH/+LiQ4NoFekFk1THZh0N/8+fSll9TZKqxvcHY1yEreP4YvIrSKSLSLZhYWF7g7He9SW\nQd561u8tYlRKtM7QUR3zDyQ5xroTW6dmei+3J3xjzBxjTJYxJishIcHd4XiPXV/B3yYTULSBzNRo\nd0ejPJ0xnLbhYWb7LWZXUaW7o1FO4vaEr5wkbx1G/NhiTyWzryZ81QkRIvOXM9W2nh0FmvC9lSZ8\nb5W3nkMhadQRSGaKJnzVOYkfyJCAfLbna8L3Vs6cljkX+BYYLCK5InKTs46l2nBwHdts6aTFhRIT\nFujuaFRPED+IvuYAO/LL3B2JchKnrWlrjLnKWftWnSg/ABUH+Np2DqMHx7g7GtVTxA0gyNRSW7Kf\n+kY7gf46AOBt9BP1RiGx7L/o//hP9RjGp8e6OxrVUyQOoyI8gwhTQU5xlbujUU6gCd8bBQSzuGkE\nB4hnfEacu6NRPUW/iey7+is2m35sPlju7miUE2jC90bZr7B/07ckRgSRpqtcqeMwsFc4wQE21u7T\ncXxvpAnf29RVYD68l4R9nzI+I05vuFLHJeC7v/N54H2s3XfI3aEoJ9CE7212L0FME5/VDmXyAB3O\nUcfJP4iUpn2UHthBQ5Pd3dGobqYJ39tsX0i9XyjZ9sGcMSTR3dGoniY5C4CR9s2s2qO9fG+jCd+b\nGAPbF7LKL5NhqfEkRuiShuo49RqBPSSOqf4b+HJLgbujUd1ME743ObQbe20Zb1eO5Czt3asTYbNh\n6386p/tvZNHmfHdHo7qZJnxvEpvBnHGf8KF9AjMyk90djeqphs9iT+pM9hYeYl9JtbujUd1IE763\naGrE3tTEvO8LyUzvTV+djqlO1NCLiL3kMeoIZMH3+90djepGmvC9xdq5VD+TRVXxAa4e39fd0age\nrm90IHck72TBdzuw23UFLG+hCd8bNDVivnmOghoIienDhaf0cXdEqqfL/Y77i3/DxIpP+XhDnruj\nUd1EE743WPUqUrSNx6tncPsZA/D3049VnaS+EzCpE7k78D3+8tk6GnVOvlfQzNDTle7Dvuh3fCen\nsL/3dK7ISnV3RMobiCBn/oYEU8zs0lf5+5Jd7o5IdQNN+D1c4zfPU1tXz0ONN/Ln2Zn42bSUguom\naZMwp97CTf4fs+HzN1i4Sadp9nSa8Huw/PJafrjvYi6te5jbZ53L0D6R7g5JeRk5+1EaMs4iKqEP\nd7yxipeX7aZJL+L2WGKM8z48ETkPeBbwA/5hjPljR+2zsrJMdna20+LxFiZ/E4Vv/ZQrim/lYGME\nf5h1CrPGpLg7LOXFymoa+MVbazhl+wtUBCYiiUOJSR3GiIHpZKXFEBrotLWUVCdEZJUxJqsrbZ32\nKYmIH/A8cDaQC3wnIu8ZYzY565hex26nzm4oq6qlfvPH1Oxdg61gA32KV2DsgYyJb+Kuq6aSHh/m\n7kiVl4sKCeClKwZQ+9y3hNQchDwgD/JXRvO0/WLWJl/DhH5hnGLfQkhEDOGRsUTGxBEdE09IcAgB\nfqKTCTyAM38tjwN2GGN2AYjIPGAG0G7Crz+4kX2PDG3x2o2hz9GAP9fXz+OcxsWA9ReJAI34cU3I\n8wDcUf8apzd9g3C4iaFSwrg+6BmMMfyy4UUm2lcdea8BCiWOGwOsPzp+1/gkY+yHQ7OOkUMyt/n/\nL2B4tukxhpmdCObI9k0M4E7bgwC8an+IDHJbbP+O4dzD/RhgPj8nicLDmwD4kizuNz8FYJH8mCgq\nOTwCLxgWmKncX38zgp3tQbdgw7Db9GapfxblUx7giemnYdMxe+UiEhJDyH2boDQHirZTl7cZs3sN\nI2wDWVnWxPwlq/l50N2t3vebhhv4V9M5DLbl8lbAIzSJDTt+NOKPHeE5/+tZbDuNoWYHv2t40nGw\no+9/MuA2vvMfTWbTJn5Z/9cjrxtHoz8F/5SN/kMZ17iaO+r+0Wr7YyH3scsvnckN3/Cjujdaxxf6\nIAdsSZxV/yVX1b/Tavv9oY9SbIvjwvpPmFX/fqvtPw37E1USxmV1C7igYWGr7beGPUuT+HNN3Tym\nNyxtsa0Bf34c/iwAN9W+zsTGlS22V0o494Q9DsCdtXMY3bi2xfYiiW91vI44M+EnA/uaPc8Fxh/b\nSERuBW4FGNQngoLwwRjEkZSF4X2isYs/AeVpHKwaARz9IO3iR1ZSLALYDg1iX01d8z1TbwthYpJV\nIripZAS7awIdx7T2Ue0Xxem9ExGB2qIsttclHPk5MwgVAfGc07sXAOUFE9lan3Y4aAxCWWASFyUm\nIQKFeWdQ01h85JcJYqM6qC+zEqyhlr1551PYWIGRo+fWGJLBlfF9EWDr/ln4m7ojsSPgHzaUe5MG\nERUayDe1bxLbdxhpyb3pH6R/Pis3sdkgNgNiMwgadC69p1q9uBlAfc1oSnb1pbKshJqKQ9RVHaKx\nqpTRUeNICBtEUHUk23IvhKYGjL0Rm2lEjKFvTDpTw+KJr6shryDT0Sc63DMypMQkY0JiSartTV7x\ncMfLR3tOfWITsAdFEleTSMGhQQCOjpclOS6GgMAIYqoTKC7LaHVKqfExRAREEFmVSHF56+39EmOI\n94sgrLIXxRWtt/dPjKLOFkJIeW+Kq1pvH9QrErv4EVCWRHF1y+1N4s/gXhEA+JUmUVLTcnutLfTI\ndg4lU1Jb0WJ7lV90q+N1xGlj+CIyGzjXGHOz4/l1wDhjzF3tvUfH8JVS6vgczxi+MwfVcoHmk8JT\ngANOPJ5SSqkOODPhfwcMFJF0EQkErgTec+LxlFJKdcBpg8HGmEYR+QnwKda0zFeMMRuddTyllFId\nc+rVP2PMR8BHzjyGUkqprtGJsUop5SM04SullI/QhK+UUj5CE75SSvkIpxZPO14iUgjs6abdxQNF\n3bQvd/KG89Bz8Ax6Dp6jO8+jnzEmoSsNPSrhdycRye7q3WeezBvOQ8/BM+g5eA53nYcO6SillI/Q\nhK+UUj7CmxP+HHcH0E284Tz0HDyDnoPncMt5eO0YvlJKqZa8uYevlFKqGU34SinlI3pcwhcRPxH5\nXkQ+cDxPF5EVIrJdRN50lGJGRIIcz3c4tqc128evHK9vFZFzPeAcXhOR3SKyxvGV6XhdROQ5R6zr\nRGRMs31c7zjn7SJyvRvOIUdE1jvizXa8FisiCx0xLRSRGE8+j3bO4bcisr/ZZ3FBs/Zt/tyIyHmO\n13aIyAMuPodoEXlHRLaIyGYRmdjTPocOzqPHfBYiMrhZnGtEpFxE7vG4z8IY06O+gJ8D/wd84Hj+\nFnCl4/HfgNsdj+8A/uZ4fCXwpuPxMGAtEASkAzsBPzefw2vA5W20uwD4GGuFzwnACsfrscAux/cY\nx+MYF59DDhB/zGt/Ah5wPH4AeNyTz6Odc/gtcG8bbdv8uXF87QQygEBHm2EuPIfXgZsdjwOB6J72\nOXRwHj3qs2gWnx/WMu/9PO2z6FE9fBFJAS4E/uF4LsB04PDKw68DMx2PZzie49h+pqP9DGCeMabO\nGLMb2IG14LpLHHsOnZgB/NNYlgPRItIHOBdYaIwpMcYcAhYC5zkt6K5r/m9+7GfRk86jLe393IwD\ndhhjdhlj6oF5jrZOJyKRwFTgZQBjTL0xppQe9jl0cB7t8bjP4hhnAjuNMXvwsM+iRyV84BngfsDu\neB4HlBpjGh3Pc7EWT4dmi6g7tpc52re1uHoyrnPsORz2mONPu6dFJMjxWnuxuvscwFpl+jMRWSXW\nQvQAvYwxBwEc3xMdr3vqebR1DgA/cXwWrxz+ExzPPIcMoBB4Vawhwn+ISBg973No7zyg53wWzV0J\nzHU89qjPosckfBG5CCgwxqxq/nIbTU0n2zp6j1O1cw4AvwKGAKdi/Sn3y8NvaWM3bj2HZiYZY8YA\n5wN3isjUDtp66nm0dQ4vAv2BTOAg8KSjrSeegz8wBnjRGDMaqMIaNmiPJ54DtH8ePemzAECsa4iX\nAG931rSN15x+Dj0m4QOTgEtEJAfrT7XpWL3laBE5vHJX84XSjyyi7tgeBZTg3sXVW52DiPzbGHPQ\n8addHfAqR4eY2ovV7QvEG2MOOL4XAPOxYs53/FmK43uBo7lHnkdb52CMyTfGNBlj7MBLePZnkQvk\nGmNWOJ6/g5U4e9TnQDvn0cM+i8POB1YbY/Idzz3rs3D1BY3u+AJO5+gFz7dpedH2DsfjO2l50fYt\nx+PhtLzgswsXX7Rt4xz6OL4L1i+xPzqeX0jLCzsrzdELO7uxLurEOB7HujD2MCCi2eNvsMYZ/0zL\nC1R/8tTz6OAc+jRr8zOsseJ2f26weqe7HK8dvlA43IWfxVJgsOPxbx2fQY/5HDo5jx71WThimwf8\nqNlzj/osXPYP0c3/qKdzNFlmACuxLty8DQQ5Xg92PN/h2J7R7P0PYl3N3wqc7wHnsAhYD2wA/g2E\nO14X4HlHrOuBrGbvv9Fxbjua/4C5KPYMx3+mtcBG4EHH63HAF8B2x/dYTz2PDs7hX44Y1wHvHZN0\n2vy5wZpxsc2x7UEXfxaZQLYj3gWOJNFjPodOzqOnfRahQDEQ1ew1j/ostLSCUkr5iJ40hq+UUuok\naMJXSikfoQlfKaV8hCZ8pZTyEZrwlVLKR2jCV15FRC4VESMiQ7p5v2kicnV37lMpV9OEr7zNVcAy\nrJvtulMa0GbCb3ant1IeTefhK68hIuFYN+KcAbxnjBkiIqdj3blZBIwAVgHXGmOMo776U45tq7Fu\nzrtIRKYBzzp2a7AqOS4EhmLd+fg6cAjrbslgrDt1z8QqhXu+4z2/M8a86Tj+I0A+1s1F72LdaHM3\nEALMNMbsdNI/iVItaM9EeZOZwCfGmG0iUtJsUYnRWLfjHwC+BiaJteDJ34GpxpjdIjK32X7uBe40\nxnzt+CVSi3Vb/L3GmIsAROQGYCIw0hhTIiKXYSX0UUA88J2ILHHsbxTWL4sSrFv//2GMGScidwN3\nAfc45V9DqWPokI7yJldh1TLB8f0qx+OVxphcYxXhWoM1PDME2GWseupwtJwtWL8UnhKRnwLR5mj5\n7WMtNMaUOB5PBuYaq9hXPvAVVvVTgO+MVSCvDutW+s8cr693xKKUS2gPX3kFEYnDqqA6QkQMVjEt\nA3wE1DVr2oT1c99WGVoAjDF/FJEPseqyLBeRs9ppWtU8hA7Ca358e7PndvT/oHIh7eErb3E51gpC\n/YwxacaYVKzx9snttN8CZMjRtY5/cHiDiPQ3xqw3xjyOVdBrCFABRHRw/CXAD8RarzgBa9x/5cmc\nkFLdTRO+8hZXYdW0b+4/tDOzxhhTg7Xu8ScisgzromqZY/M9IrJBRNYCNVhlbNcBjSKyVkR+1sYu\n5zvarMWqfnq/MSbvJM9JqW6ls3SUzxKRcGNMpWOt4+eB7caYp90dl1LOoj185ctuEZE1WPXwo7Bm\n7SjltbSHr5RSPkJ7+Eop5SM04SullI/QhK+UUj5CE75SSvkITfhKKeUj/h8lH8q7g8ecLAAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the observations in wavelength space\n",
"w = bp.binset\n",
"plt.plot(w, obs_w(w, flux_unit='flam'), label='obs_w')\n",
"plt.plot(w, obs_nu(w, flux_unit='flam'), ls='--', label='obs_nu')\n",
"plt.xlabel(w.unit)\n",
"plt.ylabel('FLAM')\n",
"plt.xlim(3800, 7200)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f09386e4a90>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qm+iJqgImITqCS0dlMG/tfg6XVZ24wqzJ8N5PAh9MtSlaAELcwRI3ucVufjVt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\nAS0AIjINOGSMWdnUag08FxRtVf3M/z7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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the observations in frequency space\n",
"nu = w.to(u.Hz, u.spectral())\n",
"plt.plot(nu, obs_w(nu, flux_unit='fnu'), label='obs_w')\n",
"plt.plot(nu, obs_nu(nu, flux_unit='fnu'), ls='--', label='obs_nu')\n",
"plt.xlabel(nu.unit)\n",
"plt.ylabel('FNU')\n",
"plt.xlim(4e14, 7.5e14)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trying Eq. 7 from Fukugita et al. (1996) below."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ABMAG from Fukugita: 25.7113 mag(AB) (= 25.7113 mag(AB))\n"
]
}
],
"source": [
"flux_nu = sp_nu(nu, flux_unit=units.FNU)\n",
"thru_nu = bp(nu)\n",
"log_nu = np.log10(nu.value)\n",
"numerator = np.trapz(flux_nu * thru_nu, x=log_nu)\n",
"denominator = np.trapz(thru_nu, x=log_nu)\n",
"photfnu_fukugita = numerator / denominator\n",
"m = photfnu_fukugita.to(u.ABmag)\n",
"print('ABMAG from Fukugita: {:.4f} (= {:.4f})'.format(m, zpab2))"
]
}
],
"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": 2
}
@pllim
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pllim commented Sep 27, 2018

Don't comment here. I won't get notified due to GitHub bug.

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