From da91ecba17189a5104bd8a71fe354d6c3131deb7 Mon Sep 17 00:00:00 2001 From: danhuber Date: Mon, 20 Feb 2017 13:15:41 -1000 Subject: [PATCH] updated example --- grid/example.ipynb | 178 ++++++++++++++++++++++++++++++++++----------- grid/single.py | 16 ++-- 2 files changed, 142 insertions(+), 52 deletions(-) diff --git a/grid/example.ipynb b/grid/example.ipynb index a8c09c1..f00971f 100644 --- a/grid/example.ipynb +++ b/grid/example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -20,9 +20,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -74,26 +74,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ "# add any combiantion of observables\n", "# Teff, logg, FeH + uncertainties\n", - "x.addspec([5065.,-99.0,-0.1],[120.,0.0,0.2]) \n", + "x.addspec([5801.,-99.0,-0.07],[80.,0.0,0.1])\n", "# numax & Dnu + uncertainties\n", - "x.addseismo([231.,16.5],[10.,0.5])\n", - "# photometry \n", - "x.addjhk([6.025,5.578,5.496],[0.019,0.038,0.018])\n", - "# parallax\n", - "x.addplx(8.9536/1e3,0.7/1e3)" + "x.addseismo([1240.,63.5],[70.,1.5])\n", + "# 2MASS photometry \n", + "x.addjhk([10.369,10.07,10.025],[0.022,0.018,0.019])\n", + "# Sloan photometry\n", + "x.addgriz([11.776,11.354,11.238,11.178],[0.02,0.02,0.02,0.02])" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -102,23 +102,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "teff 555904\n", - "dnu 13897\n", - "numax 8438\n", - "feh 6373\n", - "number of models used within non-phot obsconstraints: 6373\n", - "number of models incl reddening: 828490\n", - "number of models after phot constraints: 828198\n", + "teff 182165\n", + "dnu 7483\n", + "numax 7279\n", + "feh 4602\n", + "number of models used within non-phot obsconstraints: 4602\n", + "number of models incl reddening: 598260\n", + "number of models after phot constraints: 598260\n", "----\n", - "teff 5035.37364818 81.120639045 81.120639045\n", - "logg 3.27740127438 0.016 0.016\n", - "feh -0.25 0.15 0.15\n", - "rad 4.55448438278 0.184337950611 0.184337950611\n", - "mass 1.42923048888 0.148268842169 0.148268842169\n", - "rho 0.0151510278714 0.00071404594506 0.000748388148029\n", - "lum 11.9209489039 1.45457575907 1.29639200986\n", - "age 2.5 1.0 0.75\n", - "avs -0.0275 0.29 0.19\n" + "teff 5847.91388296 75.4675416274 75.4675416274\n", + "logg 4.01789973159 0.012 0.0135\n", + "feh -0.05 0.1 0.1\n", + "rad 1.67234242702 0.0415016962858 0.0373515266572\n", + "mass 1.0636632814 0.0562220380412 0.0511109436738\n", + "rho 0.22555418561 0.0095448733239 0.00915735749469\n", + "lum 2.9515382176 0.247713494219 0.228533391887\n", + "age 7.5 1.0 1.0\n", + "avs 0.0725 0.08 0.08\n", + "dis 371.949892658 9.90515739598 9.90515739598\n" ] } ], @@ -129,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -137,18 +138,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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S5R6JjCSFv4yoRMJOMn7+89Zz/uIXyz0iGTsWZs60FwCpHKq5ZMS0tcFnPgMT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\niIiIiIiI/H8gRIOX/OznqwAAAABJRU5ErkJggg==\n", 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3deAueuO2EBQImVFaaqvVPfGEBQJYIMybF2xdkjoFgmS1pUvtQJ8oEFz9+sGe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\n64nqnIX9e4NdGHwIafp9Zio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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -171,13 +172,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.5 1.0 0.75\n" + "7.5 1.0 1.0\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 14, @@ -186,9 +187,9 @@ }, { "data": { - "image/png": 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U5+WLVPUD3Ae8Shp/h22EfEs3WIVBDDNddAjwNvCk1Woy92vgfmc/bP/t+2La\ne/+F+af2ILvlZOx9oA9m6vFfMGs8hUUD0OjslwP7XMcSN0LmK3ftYP4ORIDvAQ873+ez5Pr7A9cC\nL6Rzso1n8bR0g1VYPALcivltusRyLZmYhPkFlWhzhG0kfwjzL6cbMX+G1mJ+0R60WVQGpmNGvTOA\nnsBK4DXCU39CHeY+mYTumPWqwqIPpm32OWYplhq75WTsGeAn6b7YRsj/NyYg38dc+HvXQg3ZmIi5\ncHMZcNJyLZkai/kXyBLgImA4JjjD8mjtauByZ/+oszU2//K80w7Y7ewfwLT7wjTISQwKTmCCcSjm\nF+3NmL58PnMPaGYBv8XSulqtlKi/DHM9J7E+WAXwBjChuRNthPwsYDamJ3YYM7oMk/GYfy4tcr6P\nA9fYKycj/+zafwnzhzwsAQ/mWs5YzC+pEqAK82coLJ7ELMl9C2aW1kzC05eHM9urP8IEeyPmIuyf\nbRWVpkTtJZhZTe2A7zvH3gKesFNW2hL1H8YM0BJ20kLAi4iIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\niIiExv8H1HqJychBu3gAAAAASUVORK5CYII=\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -203,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -212,24 +213,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.42923048888 0.148268842169 0.148268842169\n" + "1.0636632814 0.0562220380412 0.0511109436738\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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ikS59RlAAmI/V6pcC5wILfceWAdMB71Zdk7F+gkexTt7ngb3AbmAfUAscj5WBLgYeTvZN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\nEOzE/lfgUWLXBH4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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -242,6 +243,95 @@ "plt.plot(paras.masspx,paras.masspy)" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# delete numax & Dnu constraint\n", + "x.addseismo([-99.,-99.],[70.,1.5])\n", + "# add parallax with a 3% uncertainty\n", + "x.addplx(1./372.,1./372.*0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "teff 182165\n", + "feh 58680\n", + "number of models used within non-phot obsconstraints: 58680\n", + "number of models incl reddening: 7628400\n", + "number of models after phot constraints: 1100754\n", + "----\n", + "teff 5848.99794064 75.8674310669 75.8674310669\n", + "logg 4.01457260117 0.03 0.03\n", + "feh -0.05 0.1 0.1\n", + "rad 1.67489117621 0.0506814245653 0.0506814245653\n", + "mass 1.05815894365 0.0513964837403 0.0420516685148\n", + "rho 0.225334118051 0.0240259763259 0.0217110608625\n", + "lum 2.94823896376 0.232753802539 0.215723165698\n", + "age 7.5 1.0 1.0\n", + "avs 0.0725 0.08 0.08\n" + ] + } + ], + "source": [ + "# re-run classification\n", + "paras=classify(input=x,model=model,dustmodel=0.,doplot=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.5 1.0 1.0\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# print age median +/- 1 sigma, plot posterior\n", + "print paras.age,paras.ageep,paras.ageem\n", + "plt.plot(paras.agepx,paras.agepy)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/grid/single.py b/grid/single.py index 41c919a..c4c40fa 100644 --- a/grid/single.py +++ b/grid/single.py @@ -1,4 +1,4 @@ -%matplotlib inline +#%matplotlib inline import numpy as np import matplotlib.pyplot as plt from classify_grid import * @@ -23,10 +23,10 @@ x=obsdata() - x.addspec([5065.,-99.0,-0.1],[120.,0.0,0.2]) - x.addseismo([231.,16.5],[10.,0.5]) - - x.addjhk([6.025,5.578,5.496],[0.019,0.038,0.018]) - x.addplx(8.9536/1e3,0.7/1e3) - - paras=classify(input=x,model=model,dustmodel=0.,doplot=1) + x.addspec([5801.,-99.0,-0.07],[80.,0.0,0.1]) + x.addseismo([1240.,63.5],[70.,1.5]) + x.addjhk([10.369,10.07,10.025],[0.022,0.018,0.019]) + x.addgriz([11.776,11.354,11.238,11.178],[0.02,0.02,0.02,0.02]) + + #x.addplx(2.71/1e3,0.08/1e3) + paras=classify(input=x,model=model,dustmodel=0.,doplot=1) \ No newline at end of file