EclipseTimeEquation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enter the times of eclipse you have measured with their cycle numbers (in the first column) here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Example values for TIC 278826996\n",
    "my_eclipse_times = \"\"\"\n",
    "1 1468.405 \n",
    "2 1487.571\n",
    "3 1506.740\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eclipses at times 1449.237 + 19.1675 x Cycle\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from io import StringIO\n",
    "data = np.loadtxt(StringIO(my_eclipse_times))\n",
    "cycle = data[:,0]\n",
    "eclipse_time = data[:,1]\n",
    "c = np.polyfit(cycle,eclipse_time,1)\n",
    "print('Eclipses at times {:0.3f} + {:0.4f} x Cycle'.format(c[1],c[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1187b12b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as p\n",
    "offset = eclipse_time - np.polyval(c,cycle)\n",
    "p.plot(cycle,offset,'bo')\n",
    "p.plot([cycle.min()-1,cycle.max()+1],[0,0])\n",
    "p.xlim([cycle.min()-1,cycle.max()+1])\n",
    "p.ylim(-max(abs(offset))-0.001,max(abs(offset))+0.001)\n",
    "p.xlabel('Cycle number')\n",
    "p.ylabel('Observed - Calculated time [days]')\n",
    "p.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}