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Spectroscopy Workshop Lightning Talk: synphot using Astropy Models
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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 a new \"synphot\" package (http://synphot.readthedocs.io/en/latest/), which does synthetic photometry using Astropy models. This 5-minute demo is for Spectroscopy Workshop (Apr 2017, STScI) 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": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"from __future__ import division, print_function\n", | |
"\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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T/Pz6nOMoK1Oe+mIVAP/voj5J3VnGmFiw5XJMUnllxvo6P2dj/GAXEX55znFc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\nRK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x9f9ef60>" | |
] | |
}, | |
"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 ftp://ftp.stsci.edu/cdbs/calspec/\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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JTmLzFvBX4HyceznuFZGPq+o/jvYGVV3HydOyj1TmHeCKCcZgTNgaGllVWdvK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\nMX6xxGGMMcYvljiMMcb4xRKHMcYYv/x/6YfJNKDs8SUAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x486e390>" | |
] | |
}, | |
"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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TsLxdPhC4HtgeWAp8F1jQbrsSeD4Q4MvAUaMe27DjbZf3oZmK/HvA7vNlvFP8js8Afn+S\nvvN5zEcA/wBs37bvMd/HPGH7+4B3zfcxAxeP10xz+4CvzZYxz/ojhSQ7Ay8GPgZQVQ9U1b9U1b0D\n3Z4EjF8cWQacV1X3V9WtNPdqOCTJXjRBckU1P+FPAL82YwMZ0qbG225+P/AHPDJWmOPjhWnHPJn5\nPObfA86sqvvb9jvbh8znMY9vD/AbwKfbpvk85gJ2arvtDNzeLo98zLM+FGjScgPwF0muTfLRJE8C\nSPLHSdYCxwHvavsvBtYOPH5d27a4XZ7YPttMOt4ky4D1VXX9hP5zfbwwxe8YeGOa04TnJtm1bZvP\nYz4AeFGSbyS5JMlz2/7zeczjXgT8oKq+067P5zGfCpzVvn79CfCOtv/IxzwXQmEh8BzgI1X1bOAn\nwOkAVfXOqtoH+BRwyuhK3KomG+8ZwH/hkeCbbzb1O/4I8FTgIOAOmlML88WmxrwQ2I3mNMHbgL+e\nzefLN9Mm/y+3juWRo4T5YlNj/j3gtPb16zTaI4nZYC6EwjpgXVV9o13/HM0PedCngFe3y+tpzr2P\n27ttW98uT2yfbTY13qXA9Uluo6n9miR7MvfHC5sYc1X9oKoeqqqHgXOA8Ytx83bMbfv51bgSeJhm\nPpz5PGaSLAR+HfjMQP/5PObfAc5v2z7LLPq3PetDoar+H7A2ydPbpl8Gvplk/4Fuy4Bvt8sXAMck\n2T7JUmB/4MqqugO4N8nz27+8jge+MDOjGN4mxntNVe1RVUuqagnNP7TntH3n9Hhhyt/xXgPdXgXc\n2C7P2zEDf0tzsZkkB9BcmLyL+T1mgJcB366qwVMk83nMtwMvadteCoyfMhv9mPu4er21v2hOH6wC\nVtP8p9kV+DzNi8Rq4IvA4oH+76S5an8zA1fogbH2Md8FPkz74b3Z9jXZeCdsv4323UfzYbxT/I4/\nCdzQtl0A7LUNjHk74P+0Y7gGeOl8H3Pb/nHgpEn6z8sxAy8ErqZ5p9E3gINny5j9RLMkqTPrTx9J\nkmaOoSBJ6hgKkqSOoSBJ6hgKkqSOoaBeJXl/klMH1i9K8tGB9fclectWfs4fb839tfs8KI+epfWM\nJL8/xOOS5KtJdhpo+7U0M90+o4c6lyR57dbe78D+T0nyu33tX6NnKKhvlwIvAEjyOJpP5/7iwPYX\nAJeNoK7NdRDNbJab62jg+nr0BI7HAl9vv29tS4BJQ6H91PBjdS7wxq2wH81ShoL6dhkwfj+IX6T5\n8M19SXZNsj3wCzRTduyQ5CtJrmnnjF8GkOTMJCeP72zwL/Qkb0tyVTth3rsne/LJ+rR/TX8ryTlp\n7llwcZKfa7c9N4/cp+OsJDcm2Q74I+A32/bfbHd/YJKvJbklyZs2Mf7jGPjkaZIdaD649HrgmIH2\nw9t9jc+7/6nxOY+SHN22XZ1mHv0L2/aX5JH7EFybZEfgTJoJ9a5LclqS1yW5IMlXga+0Ry7j47ph\nfCzt81+S5AvteM5MclySK9t+TwOoqp8Ct2Vg/n/NM6P+tJ9f8/8LuBXYF/hPwEnAe2j+gj4M+Oe2\nz0Lae2TQHE2soZk3/tnAJQP7+ibN3DCvoLmfbWj+uLkQeHHb58ft90n70Pw1vRE4qO3318Bvtcs3\nAoe2y2cCN7bLrwM+PFDHGTSBt31b7w+Bx08y9u8BOw6sHwd8rF2+jPaTrMDhwD00c9o8DricJjye\nQDNr5tK236eBC9vlLwKHtcs7tD/Dw8e3D9S9DtitXX818PfAAuDJwPeBvdrH/Uu7vD3NvDrvbh/z\nZuADA/t8J/DWUf+78qufL48UNBMuozlN9AKaF7vLB9YvbfsE+B9JVtPcZGYx8OSquhbYI8lTkjwL\n+FFVraV5wX8FcC3NdBDPoJknZtBUfW6tqvE7fF0NLEmyC80L+OVt+19NM64vVTPv/V3AnTQvshPt\nVlX3DawfC5zXLp/Ho08hXVlV66qZAPA6mvB6BnBLNXPrw6NnEb0U+NP2KGWXqtq4iTr/vqrubpdf\nCHy6mokGfwBcAoxPz31VVd1Rzb0cvktzIxhophpZMrC/O4GnoHlpa5xjlKYzfl3hmTR/ia8F3grc\nC/xF2+c4YBHNX84PppkN9gntts8CrwH25JFZNAP8z6r631M876R9kiwB7h9oegj4uS0Y18R9TPb/\naWOSx1XVw0l2o5n87JlJiuav9Uryts3YX6eqzkzyJZqjrkszcOvGCX4yxFgmPv/DA+sPT6jlCcC/\nDrlPzTEeKWgmXAb8KnB3+xfq3TS3JDyURy4y7wzc2QbCEcB+A4//DM3599fQBAQ0tyX93fYcPUkW\nJ9ljwvMO06dTzR2x7kvyvLbpmIHN9wE7bs6gWzfT3BOCtv5PVtV+1cx4uw/NqbUXTff4NsgAxq9n\nkORpVXVDVb0XuIrmqGK6Ov+Z5trIgiSLaE6nXbmZYzqAR2as1TxjKGgm3EBz3v2KCW33tKdeoLkn\nxliSG2imBR6fCp2quonmhW59NVMIU1UX05zeubx9zOeY8GI4TJ9JvB44J83N459Ec54f4B9pLiwP\nXmgexpdoztdDc6robyZs/zxTvAupqv4V+M/A3yW5muZFf7ymU9sLxquBB2nu27saeCjJ9UlOm2SX\nf9P2uR74KvAH1UzvvDkOo7kuoXnIWVKlAUl2qKoft8un00zX/ebHsL+9gE9U1csfa03tu5HOBr5T\nVe/f0v09FkmeDbylqn57FM+v/nmkID3ar7RHAzfSnNb5749lZ+2RzTkZ+PDaFviP7ZHLTTSn2aa6\njtK33YE/HOHzq2ceKUiSOh4pSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqfP/AdFdbFDeqxMjAAAA\nAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x71d6470>" | |
] | |
}, | |
"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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RRJCFwD2hHkK918d918KQfh1Q6+7TgbuAb3Tq+zfd/UxgPnCgp/6KiJyqfAC+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\nFKxERCTtKViJiEja+//e7CciAc6pgQAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0xa1185c0>" | |
] | |
}, | |
"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": [ | |
"$24.28453 \\; \\mathrm{\\frac{ct}{s}}$" | |
], | |
"text/plain": [ | |
"<Quantity 24.2845301459964 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=1.0, intercept=0.0)>\n", | |
"\n", | |
" [1]: <Lorentz1D(amplitude=1e-18, x_0=6560.0, fwhm=1.0)>\n", | |
"Parameters:\n", | |
" slope_0 intercept_0 ... x_0_1 fwhm_1 \n", | |
" ------------------ ----------------- ... ------------- -------------\n", | |
" -1.53909178558e-21 1.34612404251e-17 ... 6564.49814652 27.9312467796\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": [ | |
"6564.49814652\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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PQcPC6VdxKV+3HUN0tCsRjCmQokyyY2dFzTkZeHUIszJ6kzlvfokZC2nzZvji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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0xb1e0eb8>" | |
] | |
}, | |
"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))" | |
] | |
} | |
], | |
"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": "3.5.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 1 | |
} |
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