{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the csv files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "GBM = pd.read_csv(\"gbm_data.csv\")\n",
    "OPT = pd.read_csv(\"optical_data.csv\")\n",
    "LAT = pd.read_csv(\"lat_data.csv\")\n",
    "XRT = pd.read_csv(\"xrt_data.csv\")\n",
    "BAT = pd.read_csv(\"bat_data.csv\")\n",
    "VHE = pd.read_csv(\"vhe_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ls = \"-\"\n",
    "color_GBM = \"tab:green\"\n",
    "color_OPT = \"tab:purple\"\n",
    "color_LAT = \"tab:blue\"\n",
    "color_Swift = \"tab:grey\"\n",
    "color_VHE = \"tab:red\"\n",
    "\n",
    "gbmkwargs     = { 'fmt': '', 'color': color_GBM , 'linestyle':ls, 'alpha':1.0 }\n",
    "opticalkwargs = { 'fmt':'.', 'color': color_OPT , 'linestyle': '', 'markersize' : 7}\n",
    "latkwargs     = { 'fmt': '.', 'color': color_LAT , 'linestyle':'', 'alpha': 1.0, 'markersize' : 7 }\n",
    "swiftkwargs   = { 'fmt': '', 'color': color_Swift , 'linestyle':'', 'alpha':1.0, 'linewidth':2.0 }\n",
    "batkwargs     = { 'fmt': '', 'color': color_Swift , 'linestyle':'', 'alpha':1.0, 'linewidth':1.0 }\n",
    "vhekwargs     = { 'fmt': '', 'color': color_VHE , 'linestyle':'', 'markersize': 8, 'zorder' : 50}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 518.74x351.496 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18.3/2.54,12.4/2.54))\n",
    "\n",
    "ax1 = fig.add_axes((.15,.3,.8,.5))\n",
    "\n",
    "ax1.errorbar(x = GBM[\"time\"], y = GBM[\"Eflux\"], xerr = [ GBM[\"time\"] - GBM[\"time_d\"], GBM[\"time_u\"]-GBM[\"time\"] ], yerr = GBM[\"Err_Eflux\"], **gbmkwargs)\n",
    "ax1.errorbar(x = OPT[\"Time\"], y = OPT[\"EFlux\"], xerr = [ OPT[\"Time_err\"], OPT[\"Time_err\"]], **opticalkwargs)\n",
    "ax1.errorbar(x = LAT[\"Time\"], y = LAT['En_Flux'] , xerr = [LAT[\"Err_Time\"],LAT[\"Err_Time\"]], yerr = [LAT[\"Err_En_Flux\" ],LAT[\"Err_En_Flux\" ]] ,zorder = 30, **latkwargs)\n",
    "ax1.errorbar(x = XRT[\"Time\"], y = XRT[\"EFlux\"], xerr = XRT[\"Time_err\"], yerr = XRT[\"EFlux_err\"],  zorder = 20, **swiftkwargs)\n",
    "ax1.errorbar(x = BAT[\"Time\"], y = BAT[\"EFlux\"], xerr = BAT[\"Time_err\"], yerr = BAT[\"EFlux_err\"],  **batkwargs)\n",
    "ax1.errorbar(x = VHE[\"Time\"], y = VHE[\"EFlux\"], xerr = VHE[\"Time_err\"], yerr = VHE[\"EFlux_err\"], uplims = VHE[\"is_ul\"], **vhekwargs)\n",
    "\n",
    "ax2 = fig.add_axes((.15,.15,.8,.15))\n",
    "\n",
    "ax2.errorbar(x = LAT['Time'] , y = -1.*LAT['Index'] , xerr = [LAT[\"Err_Time\"],LAT[\"Err_Time\"]], yerr = [LAT[\"Index_d\"],LAT[\"Index_u\"]],zorder = 30, **latkwargs)\n",
    "ax2.errorbar(x = XRT[\"Time\"], y = XRT[\"Index\"], xerr = XRT[\"Time_err\"], yerr = XRT[\"Index_err\"],  zorder = 20, **swiftkwargs)\n",
    "ax2.errorbar(x = BAT[\"Time\"], y = BAT[\"Index\"], xerr = BAT[\"Time_err\"], yerr = BAT[\"Index_err\"],  **batkwargs)\n",
    "ax2.errorbar(x = VHE[\"Time\"], y = VHE[\"Index\"], xerr = VHE[\"Time_err\"], yerr = VHE[\"Index_err\"], uplims = VHE[\"is_ul\"], **vhekwargs)\n",
    "\n",
    "\n",
    "#estetica\n",
    "ax2.tick_params('both',which = 'both',direction = 'in',bottom = True, top =True,right = True, left = True, grid_linestyle = '--')\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_xlim(0.8,4e6)\n",
    "ax2.set_ylim(-1.8,5.8)\n",
    "ax2.grid()\n",
    "ax2.set_ylabel(\"Photon\\nindex\", labelpad = 10, fontsize = 7)\n",
    "ax2.set_xlabel(\"Time since GBM trigger (s)\", fontsize = 7)\n",
    "\n",
    "ax1.set_ylabel(r'Energy flux (erg cm$^{-2}$ s$^{-1}$)', fontsize = 7)\n",
    "ax1.set_xlim(0.8,4e6)\n",
    "ax1.set_ylim(2e-14,8e-3)\n",
    "ax1.tick_params('both',which = 'both',direction = 'in',bottom = True, top =True, right = True, left = True,grid_linestyle = '--', grid_alpha = 0.5)\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_yscale('log')\n",
    "ax1.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}