plottingpapers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from astropy import units as u\n",
    "from astropy.visualization import quantity_support\n",
    "quantity_support()\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lynch1 = [10, 65]*u.mJy\n",
    "lynch2 =[6*60, 6*60]*u.s\n",
    "anderson1 = [10.5, 10.5]*u.Jy\n",
    "anderson2 = [13, 4*60]*u.s\n",
    "davis1 = [5.91, 5.91, 5.13, 5.13]*u.Jy\n",
    "davis2 = [30, 4.5*60, 4.5*60, 30]*u.s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Tricks with plotting astropy quantities\n",
    "# https://docs.astropy.org/en/stable/visualization/matplotlib_integration.html#plotting-quantities\n",
    "plt.loglog(lynch2, lynch1, label='lynch')\n",
    "plt.plot(anderson2, anderson1, 'k', label='anderson')\n",
    "plt.plot(davis2, davis1, 'r', label='davis')\n",
    "#plt.ylabel('Jy')\n",
    "#plt.xlabel('time (seconds)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_limit(data):\n",
    "    if data['Detection'] == 'Y':\n",
    "        linestyle = '-'\n",
    "    else:\n",
    "        linestyle = '--'\n",
    "    plt.loglog([data['T min'], data['T max']], [data['Flux limit'], data['Flux limit']],\n",
    "            label=data['Reference'], linestyle=linestyle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "limits = pd.read_excel('RadioVariableData.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120a85e50>"
      ]
     },
     "execution_count": 51,
     "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": [
    "for i in np.arange(len(limits)):\n",
    "    data = limits.iloc[i]\n",
    "    plot_limit(data)\n",
    "plt.xlabel('Seconds')\n",
    "plt.ylabel('Jy')\n",
    "plt.legend()  "
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}