rotation_period_literature_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "HEADER = None\n",
    "REFERENCE = []\n",
    "PERIOD = []\n",
    "UNCERTAINTY = []\n",
    "SOURCE = []\n",
    "START = []\n",
    "END = []\n",
    "\n",
    "reader = csv.reader(open('Estimates of the rotation period of Venus.csv'))\n",
    "skip = True\n",
    "for row in reader:\n",
    "    if not HEADER:\n",
    "        HEADER = row\n",
    "        continue\n",
    "    if skip:\n",
    "        skip = False\n",
    "        continue\n",
    "    reference, period, uncertainty, source, start, end = row[:6]\n",
    "    REFERENCE.append(reference)\n",
    "    PERIOD.append(float(period))\n",
    "    UNCERTAINTY.append(float(uncertainty))\n",
    "    SOURCE.append(source)\n",
    "    START.append(float(start))\n",
    "    END.append(float(end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'year of observation data')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as pl\n",
    "from matplotlib.collections import PatchCollection\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "# Create figure and axes\n",
    "fig, ax = pl.subplots(1)\n",
    "\n",
    "# p_a = np.array(PERIOD) - 243\n",
    "# u_a = np.array(UNCERTAINTY)\n",
    "# s_a = np.array(START)\n",
    "# e_a = np.array(END)\n",
    "# d_a = e_a - s_a\n",
    "# fc = []\n",
    "# for source in SOURCE:\n",
    "#     if 'Earth' in source or 'Goldstone' in source: fc.append('r')\n",
    "#     else: fc.append('b')\n",
    "# boxes = [Rectangle((s, p - u), e - s, 2 * u) for p, u, s, e in zip(p_a, u_a, s_a, e_a)]\n",
    "# patches = PatchCollection(boxes, facecolor=fc, alpha=0.5, edgecolor=None)\n",
    "# ax.add_collection(patches)\n",
    "# artists = ax.errorbar(s_a + d_a / 2, p_a, xerr=d_a / 2, yerr=u_a, fmt='none', ecolor=fc)\n",
    "# ax.set_title('Estimates of the rotation period of Venus')\n",
    "# ax.set_ylabel('rotation period (days + 243)')\n",
    "# ax.set_xlabel('year of observation data')\n",
    "\n",
    "PERIOD = [\n",
    "    243.021, # 1988 to 2001\n",
    "    243.0203, # 1988 to 2012\n",
    "    243.0209, # 1988 to 2015\n",
    "    243.0208, # 1988 to 2017\n",
    "    243.0207, # 1988 to 2020\n",
    "]\n",
    "UNCERTAINTY = [\n",
    "    0.001,\n",
    "    0.0006,\n",
    "    0.0003,\n",
    "    0.0003,\n",
    "    0.0003,\n",
    "]\n",
    "START = [\n",
    "    1988,\n",
    "    1988,\n",
    "    1988,\n",
    "    1988,\n",
    "    1988,\n",
    "]\n",
    "END = [\n",
    "    2001,\n",
    "    2012,\n",
    "    2015,\n",
    "    2017,\n",
    "    2020,\n",
    "]\n",
    "\n",
    "p_a = np.array(PERIOD) - 243\n",
    "u_a = np.array(UNCERTAINTY)\n",
    "s_a = np.array(START)\n",
    "e_a = np.array(END)\n",
    "d_a = e_a - s_a\n",
    "boxes = [Rectangle((s, p - u), e - s, 2 * u) for p, u, s, e in zip(p_a, u_a, s_a, e_a)]\n",
    "patches = PatchCollection(boxes, facecolor='g', alpha=0.3, edgecolor=None)\n",
    "ax.add_collection(patches)\n",
    "artists = ax.errorbar(s_a + d_a / 2, p_a, xerr=d_a / 2, yerr=u_a, fmt='none', ecolor='g')\n",
    "\n",
    "ax.set_title('Estimates of the rotation period of Venus')\n",
    "ax.set_ylabel('rotation period (days + 243)')\n",
    "ax.set_xlabel('year of observation data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}