Matplotlib vs. netcdftime

mpl_vs_nctime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import netCDF4 as nc4\n",
    "import netcdftime\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# An annual cycle\n",
    "month = np.arange(1, 13, 1)\n",
    "data = np.sin(np.arange(12) / 12.0 * 2 * np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7eff01dc8208>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wwO0ZKaUqFN4wlFk39eXYmULu+mA9xSWldpekKpF1/Cz3z99I97ZNefyKbl7d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff08827cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this works\n",
    "time_nc4 = np.array([nc4.datetime(2017, m, 1) for m in month])\n",
    "plt.plot(time_nc4, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'netcdftime._netcdftime.DatetimeProlepticGregorian'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-05cf49da1f46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# this doesn't\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtime_netcdftime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnetcdftime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2017\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmonth\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_netcdftime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3316\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3317\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3318\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3319\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3320\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1890\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1891\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1892\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1893\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1894\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1785\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1787\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1788\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1807\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1808\u001b[0m         \"\"\"\n\u001b[0;32m-> 1809\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1810\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1811\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    987\u001b[0m         \"\"\"\n\u001b[1;32m    988\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 989\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    990\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    674\u001b[0m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxconv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wrath/TM/software/miniconda3_20170204/envs/py3_std/lib/python3.5/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \"\"\"\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'netcdftime._netcdftime.DatetimeProlepticGregorian'"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff08accac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this doesn't\n",
    "time_netcdftime = np.array([netcdftime.datetime(2017, m, 1) for m in month])\n",
    "plt.plot(time_netcdftime, data)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "SSH wrath@taurus.geomar.de python_py3_std_/home/wrath/WORK/nb",
   "language": "",
   "name": "rik_ssh_wrath_taurus_geomar_de_python_py3_std_homewrathworknb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}