adonath/fermi_spectrum_plot.ipynb

## fermi_spectrum_plot.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.units import Quantity\n",
    "from astropy.table import QTable\n",
    "from astropy.modeling.models import PowerLaw1D\n",
    "from gammapy.catalog import get_source_by_name\n",
    "from gammapy.spectrum import energy_bin_centers_log_spacing, compute_differential_flux_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "catalog = QTable.read('../../Downloads/gll_psc_v16.fit')\n",
    "source = get_source_by_name('3FGL J0000.1+6545 ', catalog) # For some reason there's whitespace after the name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$1.0053431 \\times 10^{-12} \\; \\mathrm{\\frac{ph}{MeV\\,s\\,cm^{2}}}$"
      ],
      "text/plain": [
       "<Quantity 1.005343052730423e-12 ph / (cm2 MeV s)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source['Flux_Density']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$1159.0813 \\; \\mathrm{MeV}$"
      ],
      "text/plain": [
       "<Quantity 1159.081298828125 MeV>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source['Pivot_Energy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_model = PowerLaw1D(amplitude=source['Flux_Density'].value,\n",
    "                      x_0=source['Pivot_Energy'].value,\n",
    "                      alpha=source['Spectral_Index'])\n",
    "x_model = Quantity(np.logspace(-2, 2, 100), 'GeV').to('MeV').value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xbins = Quantity([0.03, 0.1, 0.3, 1, 3, 10, 100], 'GeV').to('MeV')\n",
    "x = energy_bin_centers_log_spacing(xbins).value\n",
    "\n",
    "flux_columns = ['Flux30_100', 'Flux100_300', 'Flux300_1000',\n",
    "                'Flux1000_3000', 'Flux3000_10000', 'Flux10000_100000']\n",
    "\n",
    "y = np.array([source[_].value for _ in flux_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f56ed570910>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "MomIiLYlmURERNuSTCIiom3/H9Gt4O0fpTfAAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56ed7a00d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter(x, y / x)\n",
    "plot(x_model, y_model(x_model))\n",
    "ylim(1E-17, 1E-8)\n",
    "loglog()\n",
    "xlabel('Energy (MeV)')\n",
    "ylabel('Flux Density (ph / (MeV cm^2 s))')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Populating the interactive namespace from numpy and matplotlib\n"
	]
	}
	],
	"source": [
	"%pylab inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"from astropy.units import Quantity\n",
	"from astropy.table import QTable\n",
	"from astropy.modeling.models import PowerLaw1D\n",
	"from gammapy.catalog import get_source_by_name\n",
	"from gammapy.spectrum import energy_bin_centers_log_spacing, compute_differential_flux_points"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"catalog = QTable.read('../../Downloads/gll_psc_v16.fit')\n",
	"source = get_source_by_name('3FGL J0000.1+6545 ', catalog) # For some reason there's whitespace after the name"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/latex": [
	"$1.0053431 \\times 10^{-12} \\; \\mathrm{\\frac{ph}{MeV\\,s\\,cm^{2}}}$"
	],
	"text/plain": [
	"<Quantity 1.005343052730423e-12 ph / (cm2 MeV s)>"
	]
	},
	"execution_count": 5,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"source['Flux_Density']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/latex": [
	"$1159.0813 \\; \\mathrm{MeV}$"
	],
	"text/plain": [
	"<Quantity 1159.081298828125 MeV>"
	]
	},
	"execution_count": 6,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"source['Pivot_Energy']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"y_model = PowerLaw1D(amplitude=source['Flux_Density'].value,\n",
	" x_0=source['Pivot_Energy'].value,\n",
	" alpha=source['Spectral_Index'])\n",
	"x_model = Quantity(np.logspace(-2, 2, 100), 'GeV').to('MeV').value"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"xbins = Quantity([0.03, 0.1, 0.3, 1, 3, 10, 100], 'GeV').to('MeV')\n",
	"x = energy_bin_centers_log_spacing(xbins).value\n",
	"\n",
	"flux_columns = ['Flux30_100', 'Flux100_300', 'Flux300_1000',\n",
	" 'Flux1000_3000', 'Flux3000_10000', 'Flux10000_100000']\n",
	"\n",
	"y = np.array([source[_].value for _ in flux_columns])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.text.Text at 0x7f56ed570910>"
	]
	},
	"execution_count": 12,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
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	],
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f56ed7a00d0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"scatter(x, y / x)\n",
	"plot(x_model, y_model(x_model))\n",
	"ylim(1E-17, 1E-8)\n",
	"loglog()\n",
	"xlabel('Energy (MeV)')\n",
	"ylabel('Flux Density (ph / (MeV cm^2 s))')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.6"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}