plot_3FGL_mask.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from NPTFit import create_mask as cm\n",
    "from p3FGL import plot_3FGL\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/smsharma/p3FGL.py:50: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
      "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  convert_numeric=True)\n"
     ]
    }
   ],
   "source": [
    "p = plot_3FGL()\n",
    "\n",
    "mask_IG = cm.make_mask_total(band_mask = True, band_mask_range = 2,\n",
    "                              mask_ring = True, inner = 0, outer = 30,\n",
    "                              )\n",
    "\n",
    "x_counts, y_counts, error_L, error_H, x_errors_L, x_errors_H = \\\n",
    "                p.return_counts(flux_min = 5e-11,\n",
    "                                flux_max = 1e-7,\n",
    "                                flux_bins = 12,\n",
    "                                mask=mask_IG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$F$  [counts cm$^{-2}$ s$^{-1}$]')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot F^2*dN/dF:\n",
    "\n",
    "plt.errorbar(x_counts,x_counts**2*y_counts,xerr=[x_errors_L,x_errors_H],\n",
    "             yerr=x_counts**2*np.array([error_L,error_H]), fmt='o', \n",
    "             color='black', label='3FGL PS')\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "plt.xlim([1e-10,1e-8])\n",
    "plt.ylim([2e-13,1e-10])\n",
    "\n",
    "plt.ylabel('$F^2 dN/dF$ [counts cm$^{-2}$s$^{-1}$deg$^{-2}$]', fontsize=18)\n",
    "plt.xlabel('$F$  [counts cm$^{-2}$ s$^{-1}$]', fontsize=18)\n"
   ]
  },
  {
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}