{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f=nc.Dataset('/data/jpetrie/MEOPAR/SalishSea/results/d9f69a38-160e-11e6-9b98-0025909a8461/SS5x5_1h_20041019_20041023_ptrc_T.nc')\n",
    "fkeys=f.variables.keys()\n",
    "for ik in fkeys:\n",
    "    match = re.search(r'depth.',ik)\n",
    "    if match:\n",
    "        zkey=match.group(0)\n",
    "z=f.variables[zkey][:]\n",
    "t=f.variables['time_counter'][:]\n",
    "tz,zt=np.meshgrid((t-t[0])/3600/24,-z[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jpetrie/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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s92MkKfBqeOsxUPTgAACALtciXYHQIOAAAIAubNUAAAAsJwxbNUQCAQcAAHTp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\nn/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c82f5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c85bd908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9TlRuGAAAcSzqP+Jt1kbGCPy1O1EYPv74Y73xxhs6c+aMbrvtNmVnZ+unP/2p\npCvdrz/66CM5nc7rul9v2rTJ7H795JNPhnTOkydPhhsuAAC2crNONVYyrm3wfhOBv1gbR7AGlMjE\nAokMAGCwiEkiE+QpA3HydcyTfQEAQC+bVS2RyAAAgF4R7H69ZcsWHTx4UCkpKVq/fr0kaceOHdq5\nc6dSUlIkSY8++uiAnidHIgMAAHpFsNdSQUGB5s6dq6qqqj7rCwsLVVhYGJFzkMgAAIBeEUxkJk2a\npLa2tuvWR7J5LokMAADoFYU2Mu+//7527dql8ePH6/HHH5fL5er/RTcxoEEjAQBAgrF40Mg5c+ao\nqqpK69atk9vt1rZt2wYULiUyAAAgLOE8dXj48OHm/OzZs7V27doBxUAiAwAAwhLMU4cDgUCfNjGd\nnZ1yu6+MBr9//36NGTPmZi8NCg/EAwAgTsXkgXhGcPsFkz1UVFToyJEjOnv2rFJSUlRUVKSmpiYd\nO3ZMhmEoLS1NzzzzjJnYhBUviQwAAPEpNolMcK19A4FkiyMJDlVLAADgKsG25CWRAQAAcSfY/tdf\nszSKYJHIAACAq1wIcr/h/e8SBSQyAADgKvYaNZJEBgAAXCWCYxREAYkMAAC4CiUyAADAtiiRAQAA\ntkWJDAAAsK1gey3FBxIZAABwFaqWAACAbVG1BAAAbIsSGQAAYFuUyAAAANuiRAYAANgWJTIAAMC2\n6H4NAABsixIZAABgW4Oojcy+ffu0Y8cOtbS06NVXX9W4ceMkSZ999pnefvttdXd3y+l0atGiRbrz\nzjslSZ9//rk2b96sy5cva9q0aSouLh7wfwIAAERKZEtkGhsb9eabbyoQCKigoEDz58+P6PGTBvLi\nsWPH6oUXXtDkyZP7rB8+fLiWL1+udevWacmSJaqqqjK3bd26VT/84Q9VUVGhL774Qo2NjQMJAQAA\nRJQ/yKl/PT09+vWvf62VK1dqw4YN2r17t06cOBHRaAeUyGRmZiojI+O69dnZ2XK73ZKkMWPG6PLl\ny/L7/ers7NSFCxeUk5MjSXrwwQd14MCBgYQAAAAi6nKQU/+am5uVkZGhtLQ0OZ1OzZw5M+Lf+wNK\nZIKxb98+feMb35DT6ZTP51Nqaqq5LTU1VT6fz+oQAABA0CJXInPt977H44n4936/bWTKysrU1dVl\nLgcCARmGoQULFigvL++Wrz1+/Ljefvtt/exnPxt4pAAAIAoSrPv1qlWrwjpwR0eH1q9fr6VLlyo9\nPV3SlUyyHqi2AAAE0ElEQVSso6Ojzz4ej+emx2hqalJTU5O5XFRUpMzMzLDiAQDAjqqrq815r9cr\nr9dr6fkCgdVB7Xfp0iXV1taayzeKzePxqL293Vz2+Xy3/N4PhyXdr8+fP6/XXntNixYt0sSJE831\nbrdbLpdLzc3NGj9+vHbt2qW5c+fe9DjXXpTq6moVFRVZETL+H9fYelzj6OA6W49rbL14vsZDhgzp\nN7acnBy1traqra1NI0aM0O7du/X8889HNI4BJTIff/yx3njjDZ05c0avvfaasrOz9dOf/lTvv/++\nTp06pd/97nf693//dxmGoZUrV2r48OF66qmntGnTJrP79dSpUyP1fwEAAHEkKSlJTz31lNasWaNA\nIKBvfetbysrKiug5BpTITJ8+XdOnT79u/cMPP6yHH374hq8ZN26cNmzYMJDTAgAAm5g6daoqKios\nO77lvZYiyep6QXCNo4FrHB1cZ+txja3HNe6fEQgEArEOAgAAIBy2KpEBAAC4GokMAACwLVuMfm31\ngFOQtmzZooMHDyolJUXr16+PdTgJqaOjQ1VVVerq6pJhGJo9e7a++93vxjqshHL58mWtXr1afr9f\n3d3dmjFjhn7wgx/EOqyE1NPToxUrVsjj8WjZsmWxDichPffcc3K5XDIMQw6HQ6+++mqsQ4pLcZ/I\n/HXAqRdffFEjRozQihUrdO+992r06NGxDi2hFBQUaO7cuX0G+ERkORwOPfHEE8rOztZXX32lZcuW\nKTc3l/dyBCUnJ2v16tUaOnSoenp6tGrVKk2bNs0c3w2R895772n06NG6cMFeT4G1E8MwtHr1at1+\n++2xDiWuxX3VUjQGnII0adIk3XbbbbEOI6G53W5lZ2dLkoYNG6bRo0cz1pgFhg4dKulK6Ux3d3eM\no0lMHR0dOnTokGbPnh3rUBJaIBAQ/XH6F/clMjcacKq5uTmGEQEDd/r0af3v//6vJkyYEOtQEk5P\nT4+WL1+uU6dOac6cOZTGWGDbtm167LHHdP78+ViHktAMw9CaNWuUlJSk2bNn69vf/nasQ4pLcZ/I\nAInmq6++0i9+8QsVFxdr2LBhsQ4n4SQlJen111/X+fPntW7dOrW0tET8SaKD2V/b0mVnZ6upqYkS\nAwuVlZVpxIgROnPmjMrKypSVlaVJkybFOqy4E/eJTDQGnAKipbu7Wxs2bNCDDz6oe++9N9bhJDSX\nyyWv16vGxkYSmQg6evSoGhoadOjQIV26dEkXLlxQVVWVli5dGuvQEs6IESMkScOHD9f06dPV3NxM\nInMDcd9G5uoBp/x+v3bv3q28vLxYh5WQqI+13pYtW5SVlUVvJYucOXPGrO64dOmSDh8+rMzMzBhH\nlVgWLlyoLVu2qKqqSj/+8Y915513ksRY4OLFi/rqq68kXSnF/eyzzzRmzJgYRxWf4r5EJhoDTkGq\nqKjQkSNHdPbsWS1evFhFRUUqKCiIdVgJ5ejRo/rP//xPjR07Vj/5yU9kGIYeffRRBk6NoM7OTm3a\ntEk9PT0KBALKz8/X3XffHeuwgJB1dXVp3bp1MgxD3d3d+pu/+Rvl5ubGOqy4xBAFAADAtuK+agkA\nAOBmSGQAAIBtkcgAAADbIpEBAAC2RSIDAABsi0QGAADYFokMAACwLRIZAABgW/8HEiavu+qo24UA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c817e438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7PXv2RFVVFaqrq+Hz+VBaWors7OwmY2pqapCfn4/JkyejW7cwHYJW/q/GtXzn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\nExHFEZY+EVEcYekTEcWR/w81pgoZnssRxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c8271b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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49a9/rXnz5tnbBw8e1Je//GVVVlbK7/eroaFB0sgLBY8VAQcAAAyJ8UrGoVBI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\ncBwCDgAAcBwCDgAAcBwCDgAAcJz/BwlUfoeK8LznAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c80ca160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c812a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6dr7Gw4YNG7BtBQUFOnnypL7++mulp6dr3759uv/++/ssP6hQ8tFHH2nXrl1q\nbW3Vk08+qby8PD366KN6++23derUKb322mv64x//KMMwtHr1aqWlpWnJkiXatm2buSR4ypQpg2kC\nAACwqYSEBC1ZskRr165VMBjUj370I+Xk5PRZflChZPr06Zo+ffoPXr/99tt1++239/qZ/Px8bdy4\ncTDVAgCAIWLKlCnasmVLSGUtX30TSVaPo4FrHA1c4+jgOluPa2y9S+0aG8FgMBjrRgAAAAypnhIA\nABC/CCUAAMAWhsRdgsO5mQ8uTk1NjT755BONGDFCGzZsiHVz4pLH41F1dbVaWlpkGIbmzJmjW265\nJdbNiiudnZ2qqKiQz+eT3+/XzJkzdccdd8S6WXEpEAho1apVcjqdWrlyZaybE5fuvfdepaamyjAM\nORwOrVu3LtZNspztQ8n3N/N5/PHHlZ6erlWrVmnatGkaO3ZsrJsWV0pKSnTzzTd3u3kiIsvhcGjR\nokXKy8tTe3u7Vq5cqcLCQv4vR1BSUpIqKiqUnJysQCCg8vJyTZ061bzfFiLnzTff1NixY3XuXOx2\n/4x3hmGooqJCw4cPj3VTosb2wzfh3swHF2fixIm6/PLLY92MuDZy5Ejl5eVJklJSUjR27Fju/WSB\n5ORkSd/1mvj9/hi3Jj55PB4dOnRIc+bMiXVT4lowGNSlthbF9j0lvd3Mp6mpKYYtAgbv9OnTOnbs\nmCZMmBDrpsSdQCCgRx55RKdOndJNN91EL4kFnn/+eS1cuFBtbW2xbkpcMwxDa9euVUJCgubMmaMb\nb7wx1k2ynO1DCRBv2tvbtWnTJpWVlSklJSXWzYk7CQkJeuqpp9TW1qaqqio1Nzf3u4MkwvP93LO8\nvDy53e5L7i/5aKqsrFR6erpaW1tVWVmpnJwcTZw4MdbNspTtQ0m4N/MB7Mzv92vjxo2aPXu2pk2b\nFuvmxLXU1FS5XC41NDQQSiLo6NGjqq+v16FDh9TR0aFz586purpa9913X6ybFnfS09MlSWlpaZo+\nfbqampriPpTYfk7JhTfz8fl82rdvn4qKimLdrLh0KY5fRltNTY1ycnJYdWOR1tZWc0iho6NDjY2N\n3Fk8wubPn6+amhpVV1frgQce0NVXX00gscD58+fV3t4u6bve1cOHD2vcuHExbpX1bN9TEu7NfHBx\ntmzZoiNHjujbb7/VsmXLVFpaqpKSklg3K64cPXpU77//vnJzc/Xwww/LMAzdeeed3JQygr755htt\n27ZNgUBSWe0VAAAAZklEQVRAwWBQxcXFuuaaa2LdLCBsLS0tqqqqkmEY8vv9uv7661VYWBjrZlmO\nbeYBAIAt2H74BgAAXBoIJQAAwBYIJQAAwBYIJQAAwBYIJQAAwBYIJQAAwBYIJQAAwBYIJQAAwBb+\nH8InStjgAzFkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c7fd02e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g1lyzkxABYOmTQfLsaejf3we4ZkL7q+9BZM2cete0jQMxwwm9nQu0SB0sfRqT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c8045358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/AQCApECfGgAAkBToUwMAAJJCmPvUtLW1qampSaZpqqysTBUVFcOOHz58WD/8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c86e70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qagghUFRUhKlTp8Lj8WD58uWw2+0oKSmJRXbqJykl5MsvQr76W2j/+G2OD8Qa\nH25DChLyYhfyLdbQ0GB1hDCqnhb2JZNsOw2jcgPQ4of2Tysh9FTLM8WCSpmMF58D7KOhzb5LqVzn\nMZM5KmZKT0/v976cgTyCyKN1XY+onDQN4t7vcLE5qwjeWkrqYTEYAaSUkK9ug9z+X9DmfxMi70ar\nI41sXJuIFMRiMMzJj1u6Lgud/hjadx+HGJ9mdSTSbEBnp9UpiMKwGAxj8r0aGD//KcSMAoiv3M3n\nFKuCA8ikIP7rMAzJjnbI/9oC6fVAu/dBiCuusToSXYgzkElBLAbDjDy4F8ZzT0HkXA3t+xsgLhlt\ndST6NMEzA1IPi8EwITvaIV/8OaRnF7Svl0Bce73Vkag3PDMgBbEYDANy724Yv3wa4vJroD36FETi\nGKsj0cVw1VJSEIvBECZPncSZZ38C4/A+aN9YCnH1FKsjkRmaxuUoSDksBkOQNIKQO1+G/O3zGFUw\nG9r8EoiEBKtjkVka5xmQelgMhhhZtx/G8z8D7HZoD/4Il1zpRECxKfEUAS8TkYJYDIYI2eKD/NXP\nIQ+8B3HnQojrb+Iqo0OVjY+9JPWwGChOnu2AfGUr5KvbIG76ErR/3QRhv8TqWDQQmg2QLAakFhYD\nRclgEPLtP0D+9nmInKugfW8txKWXWR2LBoHQNBgcQCbFsBgoRkoJvPsOjBd/AYxxQPvmQxATr7Q6\nFg0mjfMMSD0sBoqQUgL7a2Fs/SVw7iy0OxYCk/I4LjAccW0iUhCLgcWklMCB92Bsex441QLxd/Mh\n8m6E0DSro1G02Hg3EamHxcAiUkrA64Hxuyqg9RTEba6uO4RsNqujUbTxMhEpyFQxqK2tRWVlJaSU\nKCgowNy5c7u12bJlC2pra5GQkIClS5ciKysLPp8PGzduxKlTpyCEwKxZszB79uxB/xBDiTSCgOcd\nGP/z30DnOYhb74SY9gUWgZGEM5BJQRGLgWEYqKiowCOPPILk5GSUlpZi2rRpyMjICLXxeDw4fvw4\nNmzYgMOHD2Pz5s147LHHYLPZcM899yArKwsdHR1YuXIlrr322rB9Rwp59izk269CVv8GGDMW2hwX\nMOl6Xg4aiXhrKSkoYjGoq6tDWloaUlO7Hpyen5+PmpqasH/Qa2pqMHPmTABAbm4u2tra0NLSgqSk\nJCQlJQEA7HY7MjIy4Pf7R1QxkCd9kK9vh3zjFSD7CmgLVwA5V3FgeCTjmQEpKGIx8Pv9SElJCW3r\nuo66uroZWaA8AAAMEElEQVSIbfx+f6gQAMCJEydw9OhR5ObmDkZupUkpgff3Q/7hd5DePRA3zIS2\ncg3EZelWRyMVcG0iUlBMBpA7Ojqwdu1aLFy4EHa7PRaHtITsaIPctRPyjy8DZzsgCm7rerbAaC4p\nTRfg8wxIQRGLga7raG5uDm37/X7out6tjc/nC237fL5Qm2AwiLKyMtx0002YNm1ar8fxer3wer2h\nbZfLBYfDYf6TxEB8fHy3TFJKBOv249xr29H5zk7EOacg/usliPvb62IyHtBTJqsx08UFxjjQBgmH\nw6FUrvOYyRwVMwFAVVVV6Gun0wmn02lqv4jFICcnB42NjWhqakJycjLcbjdWrFgR1iYvLw87duzA\njBkzcOjQISQmJoYuEZWXlyMzMzPiXUQ9hW5VbDVOh8MRyiRb/JDv7IR861Ug0AmRXwTx6FMwklLQ\nAQBnzsQ8kyqY6eJkRweMQACtra1K5TqPmcxRNZPL5erXvhGLgaZpKC4uxurVqyGlRGFhITIzM1Fd\nXQ0hBIqKijB16lR4PB4sX74cdrsdS5YsAQAcOHAAb7zxBiZMmICHHnoIQgjMmzcPkydP7ldYq8mO\ndhi7XoN8Zydw5CDElOnQ5v8TcLmTA8JkHgeQSUFCSimtDtGbhoYGqyNAdp4D/ncPZM0bkN49wMSr\nIG6YCTH5BogE68c/VP3rhJl6JxuPwXjqh7A99u9K5TqPmcxRMVN6ev9vUuEM5B7Ic2cBrwdyz1uQ\n7+0G/uZzEHk3Yuy938IZwclhNEAcQCYFsRh8Qp45Dbm3BtLzDrC/FpgwEeK6fGh3LIRI6hoM1xwO\nQLG/BGgIssUBnZ1WpyAKM2KLgZQSaPwI8r3dkO/VAB+8D1zxtxBTPg/x9SUQjrFWR6ThamwScPpj\nyEDA6iREISOqGMiONuDAe5BeD+TePwPSgLjmOmhf+nvgqkkQ8XyoPEWfiIvrKggtPiA52eo4RACG\neTGQgQDwl8OQ+9+FPPAucPQIkH05hHMKtOX/AqRP4F1AZI2UVMDXBHwux+okRACGWTGQgQDwwfuQ\nh/4X8uBeoG4/cOlnIK6eDO3WO4FcpxJ3ABEJfTyk74TVMYhChnQxkB3tQP0hyLr9kIe9QP0h4NLL\nIHKd0L7wJaD4WxBjeO2fFJSSCvibrE5BFDJkioGUEjjxf5D1B4EjByHfPwg0ftR122fO1dBm3d61\nGmiietPDibpJGQ8crYvcjihGlC4GsnYX5F/qIOsPd/3iJCRAfO4KIPtyaDd8sev2z1GjrI5J1GdC\nT4Wx5y2rYxCFKF0MjJ0vQ3w2B1rBbCArN3S/P9GQd34AmUgRShcD24pHrY5AFB0p4wF/ExReDYZG\nGD5zkcgCIsEOJCRAftxidRQiACwGRNbRU2E0H7c6BREAFgMi6+jjYTSxGJAaWAyILCJSeGZA6mAx\nILJKyngYzY1WpyACwGJAZJmuMwMuSUFqYDEgsoqeCsnLRKQIFgMiq6RwAJnUYWrSWW1tLSorKyGl\nREFBAebOndutzZYtW1BbW4uEhAQsXboUWVlZpvclGpHGjIUMdEJ2tEHYR1udhka4iGcGhmGgoqIC\nq1atQllZGdxuN44dOxbWxuPx4Pjx49iwYQPuu+8+bN682fS+RCOVEAJaynguS0FKiFgM6urqkJaW\nhtTUVMTFxSE/Px81NTVhbWpqajBz5kwAQG5uLtra2tDS0mJqX6KRTLt0PJeyJiVELAZ+vx8pKSmh\nbV3X4ff7TbUxsy/RSKZdehkfckNKUHqhOqLhTkv9DOTvtyG4989WRwk5HReHYCBgdYwwIyWTSP0M\ntK/dO6jvaVbEYqDrOpqbm0Pbfr8fuq53a+Pz+ULbPp8Puq4jEAhE3Pc8r9cLr9cb2na5XEhPTzf/\nSWLE4VDv4TnMZI6KmfCNkq7/iAZJVVVV6Gun0wmn02lqv4iXiXJyctDY2IimpiYEAgG43W7k5eWF\ntcnLy8POnTsBAIcOHUJiYiKSkpJM7XthaJfLFfrvwg+kCmYyh5nMUzEXM5mjaqYL/x01WwgAE2cG\nmqahuLgYq1evhpQShYWFyMzMRHV1NYQQKCoqwtSpU+HxeLB8+XLY7XaUlJRcdF8iIlKLqTGDyZMn\nY/369WHfu/nmm8O2i4uLTe9LRERqsT366KOPWh2iN+PHj7c6QjfMZA4zmadiLmYyZzhlEpLP3SMi\nGvG4NhEREbEYEBGRxZPOBrIAnlWZ9u3bh8cffxyXXXYZAOD666/HHXfcEdVM5eXl2LNnD8aNG4cn\nn3yyxzax7qdImazoJ5/Ph40bN+LUqVMQQmDWrFmYPXt2t3ax7Cszmazoq87OTnz/+99HIBBAMBjE\n9OnTcdddd3VrF8u+MpPJir4CutZZKy0tha7rWLlyZbfXY/37FylTv/pJWiQYDMply5bJEydOyM7O\nTvnggw/Kjz76KKzNnj175I9+9CMppZSHDh2SDz/8sOWZvF6v/Ld/+7eo5vi0/fv3y/r6evntb3+7\nx9dj3U9mMlnRTydPnpT19fVSSinb29vl/fffb/nPlJlMVvSVlFJ2dHRIKbt+7h9++GF5+PDhsNet\n+LmKlMmqvtq2bZtcv359j8e2op8iZepPP1l2mWggC+BZmQkAZIzH3K+88kokJib2+nqs+8lMJiD2\n/ZSUlBT6i8xutyMjI6PbWlix7iszmYDY9xUAJCQkAOj6izwYDHZ73Yqfq0iZgNj3lc/ng8fjwaxZ\ns3p83Yp+ipQJ6Hs/WXaZqKdF7Orq6iK28fv9SEpKsiwTABw+fBjf+c53oOs6FixYYPlEulj3k1lW\n9tOJEydw9OhR5Obmhn3fyr7qLRNgTV8ZhoHvfve7OH78OL785S8jJycn7HUr+ipSJiD2ffXcc89h\nwYIFaGtr6/F1K/opUiag7/3EAeQ+ys7OxqZNm/DEE0/glltuwRNPPGF1JCVZ2U8dHR1Yu3YtFi5c\nCLvdHrPjXszFMlnVV5qm4fHHH0d5eTkOHz6Mjz76KCbHHUimWPfV+XGxrKwsSCktOYPrT6b+9JNl\nxWAgC+BZmclut4dOZadMmYJAIIDTp09HLZMZse4nM6zqp2AwiLKyMtx0002YNm1at9et6KtImaz+\nmRo9ejScTidqa2vDvm/lz1VvmWLdVwcOHMDu3buxbNkyrF+/Hl6vFxs3bgxrE+t+MpOpP/1kWTEY\nyAJ4Vma68Frg+UtIY8aMiVqm8y72V0ms+8lMJqv6qby8HJmZmT3eRQRY01eRMlnRVx9//HHoEsO5\nc+ewd+/ebqsEx7qvzGSKdV/Nnz8f5eXl2LhxIx544AFcc801WLZsWVibWPeTmUz96SfLxgwGsgCe\nlZl27dqF6upq2Gw2xMfH44EHHohqJgBYv3499u3bh9bWVpSUlMDlciEQCFjWT2YyWdFPBw4cwBtv\nvIEJEybgoYceghAC8+bNQ1NTk2V9ZSaTFX3V0tKCn/70pzAMA1JKzJgxA1OnTrX0989MJiv6qidW\n9pOZTP3pJy5HQUREHEAmIiIWAyIiAosBERGBxYCIiMBiQEQUM+Xl5bj33nvx4IMPRmy7f/9+rFy5\nEvPmzcM777zT7fX29naUlJRgy5Ytg5KNxYCIKEYKCgqwatUqU21TU1OxdOlS3HjjjT2+/sILL+Cq\nq64atGyWLmFNRDSSXHnllWhqagr73vHjx1FRUYHW1lbEx8fjm9/8JtLT03HppZcCAIQQ3d7nyJEj\nOHXqFCZPnoz3339/ULLxzICIyEI/+9nPsHjxYvz4xz/GggUL8Mwzz1y0vZQSv/jFL7BgwYJBXSuJ\nZwZERBbp6OjAwYMHsW7dutA/7L0t3X3ejh07MGXKlEFf/4jFgIjIIlJKJCYmYs2aNab3OXToEA4e\nPIhXXnkF7e3tCAaDsNvtmD9//oCysBgQEcXQhQs8XnLJJRg/fjx27dqF6dOnAwCOHj2Kz372s932\nOe/+++8Pff3666/jyJEjAy4EANcmIiKKmQsXeBw3bhxcLheuueYabN68GSdPnoRhGJgxYwbuuOMO\nvP/++3jyySdx5swZjBo1CklJSSgrKwt7v/PFYPHixQPOxmJARES8m4iIiFgMiIgILAZERAQWAyIi\nAosBERGBxYCIiMBiQEREYDEgIiIA/x+L2VS1JqyRGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c88864a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AmKappKQkuzzYjOf/+Z//0dSpU+3yCy+8oHnz5gV8Ryd6520BAIDQ6wq8aihn\nZv3pT3/SH/7wB/3Lv/yLJOn999/XZZddpoyMDLW0tAT0oF6SGgAAcFYQA4UHm5nVe8azaZrnnfG8\nf/9+Pffcc3r44Yc1duxYSdLevXvV2NiopqYmnT59WidPnlR1dbUWL17cb3skNQAA4KwQzn7KzMxU\na2ur2tralJiYqPr6ei1ZsqRHnfb2dlVVVWnx4sX65je/aW+//fbbdfvtt0uS9uzZo1deeWXAhEYi\nqQEAAOcKYVLjcDhUWlqqyspKWZalgoICeTwe1dXVyTAMFRYW6re//a1OnDihmpoaWZYlp9Opxx9/\n/ILaM6woW03SMFZHOoQIiOInIQEhM/h0TiAWHDp0l/0+NTV12Ns3fhNYPetH4Y3jQtBTAwAAzgrh\nlO7hRlIDAADOiuKbAyQ1AADgrCCmdI80JDUAAOAsemoAAEBMIKkBAAAxgaQGAADEhBCu0j3cSGoA\nAMBZTOkGAAAxgdlPAAAgJjCmBgAAxATG1AAAgJjAmBoAABATuP0EAABiAkkNQi+Kf6oAANErxGNq\nmpubVVtbK8uylJ+fr6Kioh77Dx8+rPXr1+vjjz/Wbbfdpjlz5tj7Ojs79eyzz+rAgQMyDEP33HOP\nJk+e3G9bJDUAAOCsU6E7ld/vV01NjcrLy5WYmKiysjLl5uYqLS3NrjN27Fj94z/+o3bt2tXn+M2b\nN2vatGn66U9/Kp/Pp1OnBg7OEbrQAQBA1OsO8BUAr9erlJQUJScny+VyKS8vTw0NDT3qjBs3ThMn\nTpTT6eyxvbOzU3v37lV+fr4kyel0avTo0QO2R08NAAA4K4S3n0zTVFJSkl12u93yer0BHXvkyBFd\neumlWr9+vfbv36+JEydq/vz5iouL6/cYkhoAAHBWEFO6t2zZYr/PyspSVlZWyMLw+/36+OOPVVpa\nqkmTJqm2tlbbtm1TcXFxv8eQ1AAAgLOCmKcyUIIhfdUz097ebpdN05Tb7Q7o3G63W0lJSZo0aZIk\naebMmdq2bduAxzCmBgAAnBXCMTWZmZlqbW1VW1uburu7VV9fr5ycnH7rW5Zlvx8/frySkpJ0+PBh\nSdLu3bvl8XgGbI+eGgAAcFYIx9Q4HA6VlpaqsrJSlmWpoKBAHo9HdXV1MgxDhYWFOnr0qMrKynTy\n5EkZhqHXXntNa9asUUJCgubPn69f/OIX6u7u1uWXX65FixYN2J5hnZsWBendd9/Vyy+/rIMHD+rx\nxx/XxIni63+GAAAL90lEQVQT7X1bt27V9u3b5XQ6VVJSouzsbEnSvn37tH79ep05c0bTpk1TSUlJ\nUG0axuoLDTfK8JwaoKeTkQ4AGBaHDt1lv09NTR329o3ZgdWz3gpvHBdiSLef0tPTdf/99+vqq6/u\nsf3gwYPauXOn1qxZo7KyMm3atMnuUtq0aZPuvvturV27Vp999pmam5uHEgIAAAilEN5+Gm5Duv3U\nXwbZ2NioWbNmyel0asKECUpJSZHX61VycrJOnjypzMxMSdLs2bPV0NCgqVOnDiUMABeBD/WzHuUX\nz3l/Sa+6l/YqnzsscVyvfZcPUFeSLh9zTjtJvXZOCKLc+7/L9F7l1P73WVf0LB929wzkgM5WOKyU\nfvdJ0mfnNHS4V1C96/Yuf9559mqdOPiNnkG19iyq/Zz3R3vtOzFI+dxfmF299vW+NTLQTJ3ev3gH\n+kUczHkHayfY/X0cDvaA0GKV7p5M09SUKVPsstvtlmmacjqdPearJyUlyTTNcIQAAAAuRCyv0l1R\nUaFjx47ZZcuyZBiG5s6dO+AIZgAAEIVG6K2lQAya1KxYsSLok/ael97R0SG32y23262Ojo4+2/vT\n0tKilpYWuzzYfHgAAGJNOB9wd16xnNRciJycHK1bt05z5syRaZpqbW1VZmamDMPQ6NGj5fV6NWnS\nJL311lu68cYb+z3PsHx5AACMYMP+B30Uj6kZ0pTuXbt2afPmzTp+/LjGjBmjjIwMPfzww5K+mtL9\nv//7v3K5XH2mdD/zzDP2lO758+cH1ebXD+EBACDWRWRKd+9B7P2wPg1vHBdiSElNJJDUAAAuFhFJ\nagJs0hqBv455ojAAADgrim8/kdQAAICzYnlKNwAAuIgw+wkAAMQEkhoAABATGFMDAABiAj01AAAA\nfTU3N6u2tlaWZSk/P19FRUV96jz//PNqbm5WfHy87r33XmVkZEiSXn31VW3fvl2GYSg9PV2LFi2S\ny9V/6uII14cAAAAXN7/fr5qaGi1fvlxVVVWqr6/XoUOHetRpamrS559/rnXr1mnhwoXauHGjpK8W\nx3799df15JNP6umnn5bP51N9ff2A7ZHUAACAsPB6vUpJSVFycrJcLpfy8vLU0NDQo05DQ4O++93v\nSpImT56szs5OHT16VNJXSVFXV5d8Pp9OnTqlxMTEAdvj9hMAADhHoCOFRw1awzRNJSUl2WW32y2v\n1ztoHdM0NXHiRM2ZM0eLFi1SfHy8rrnmGl1zzTUDtkdSAwAAzhHoSOFRYV1B/Msvv1RjY6PWr1+v\n0aNHq6qqSjt27ND111/f7zEkNQAA4ByB9tRcMugK4m63W+3t7XbZNE253e4+dTo6OuxyR0eH3G63\ndu/erQkTJmjs2LGSpBkzZujPf/7zgEkNY2oAAMA5Tgb4GlxmZqZaW1vV1tam7u5u1dfXKycnp0ed\nnJwcvfnmm5KkDz/8UGPGjNH48eP1jW98Qx999JFOnz4ty7K0e/dupaWlDdgeq3QDADBCRWSVbuNg\nQPUsyxNQvebmZm3evFmWZamgoEBFRUWqq6uTYRgqLCyUJNXU1Ki5uVkJCQm65557NHHiREnSyy+/\nrHfeeUdOp1MZGRm6++67B5zSTVIDAMAIFZmk5pOA6llWRljjuBCMqQEAAOeI3nUSSGoAAMA5oned\nBJIaAABwDnpqAABATAhsZtNIRFIDAADOwe0nAAAQE7j9BAAAYgI9NQAAICbQUwMAAGICPTUAACAm\n0FMDAABiAlO6AQBATKCnBgAAxISLdEzNu+++q5dfflkHDx7U448/bi8V/sEHH+jFF1+Uz+eTy+XS\nHXfcoW9/+9uSpH379mn9+vU6c+aMpk2bppKSkiF/CAAAECqh7alpbm5WbW2tLMtSfn6+ioqK+tR5\n/vnn1dzcrPj4eN17773KyMgI+NhzOYYSaHp6uu6//35dffXVPbaPGzdODz30kFatWqVFixapurra\n3rdp0ybdfffdWrt2rT777DM1NzcPJQQAABBS3QG+Buf3+1VTU6Ply5erqqpK9fX1OnToUI86TU1N\n+vzzz7Vu3TotXLhQGzduDPjY3oaU1KSmpiolJaXP9oyMDI0fP16SdMUVV+jMmTPq7u7W0aNHdfLk\nSWVmZkqSZs+erYaGhqGEAAAAQupMgK/Beb1epaSkKDk5WS6XS3l5eX1+7zc0NOi73/2uJGny5Mnq\n7OzU0aNHAzq2tyElNYF499139a1vfUsul0umaSopKcnel5SUJNM0wx0CAAAIWOh6anr/3ne73X1+\n7/dXJ5Bjext0TE1FRYWOHTtmly3LkmEYmjt3rnJycgY89sCBA3rxxRf1yCOPDNYMAAAYEWJ4SveK\nFSsu6MQdHR16+umntXjxYk2YMEHSV1lWR0dHjzput7vfc7S0tKilpcUuFxcXKzU19YLiAQAgGm3Z\nssV+n5WVpaysrLC2Z1krA6p3+vRpbdu2zS6fLza326329na7bJpmn9/7/eUG3d3dgx7bW1huP3V2\nduqJJ57QHXfcoSlTptjbx48fr9GjR8vr9cqyLL311lvKzc3t9zxZWVkqLi62X+d+sQgPrnH4cY2H\nB9c5/LjG4bdly5YevwfDndAEIy4ubtDYMjMz1draqra2NnV3d6u+vr7PXZ6cnBy9+eabkqQPP/xQ\nY8aM0fjx4wM6trchTenetWuXNm/erOPHj+uJJ55QRkaGHn74Yb3++uv6/PPP9R//8R/67W9/K8Mw\ntHz5co0bN06lpaV65pln7CndU6dOHUoIAABghHI4HCotLVVlZaUsy1JBQYE8Ho/q6upkGIYKCwt1\n7bXXqqmpSf/0T/+khIQE3XPPPQMeO5AhJTXTp0/X9OnT+2y/9dZbdeutt573mIkTJ6qqqmoozQIA\ngCgxdepUrV27tse2733vez3KpaWlAR87kLDPfgqlkdTtFqu4xuHHNR4eXOfw4xqHH9c4OIZlWVak\ngwAAABiqqOqpAQAA6A9JDQAAiAlRsUp3sAtaIXgbNmzQ+++/r8suu0xPP/10pMOJSR0dHaqurtax\nY8dkGIZuuOEG3XTTTZEOK6acOXNGK1euVHd3t3w+n2bOnKkf/vCHkQ4rJvn9fpWVlcntdmvZsmWR\nDicm3XvvvRo9erQMw5DT6dTjjz8e6ZBGvBGf1Hy9oFV5ebkSExNVVlam3NxcpaWlRTq0mJKfn68b\nb7yxx+KjCC2n06k777xTGRkZ6urq0rJly5Sdnc3PcgiNGjVKK1euVHx8vPx+v1asWKFp06bZ680h\ndF577TWlpaXp5MnoffrsSGcYhlauXKmxY8dGOpSoMeJvP13IglYI3pVXXqkxY8ZEOoyYNn78eGVk\nZEiSEhISlJaWxtpnYRAfHy/pq14bn88X4WhiU0dHh5qamnTDDTdEOpSYZlmWmMsTnBHfU3O+Ba28\nXm8EIwKG7siRI9q/f78mT54c6VBijt/v10MPPaTPP/9cf/u3f0svTRi88MILmjdvnjo7OyMdSkwz\nDEOVlZVyOBy64YYbVFhYGOmQRrwRn9QAsaarq0urV69WSUmJEhISIh1OzHE4HHrqqafU2dmpVatW\n6eDBg4M+hRSB+3rsXUZGhlpaWuhJCKOKigolJibq+PHjqqiokMfj0ZVXXhnpsEa0EZ/UBLIYFhAt\nfD6fqqqqNHv27AHXPcPQjR49WllZWWpubiapCaG9e/eqsbFRTU1NOn36tE6ePKnq6motXrw40qHF\nnMTEREnSuHHjNH36dHm9XpKaQYz4MTUXsqAVLgz3b8Nvw4YN8ng8zHoKk+PHj9u3RE6fPq3du3cr\nNTU1wlHFlttvv10bNmxQdXW17rvvPn37298moQmDU6dOqaurS9JXvbsffPCBrrjiighHNfKN+J6a\nC1nQCsFbu3at9uzZoy+++EL33HOPiouLlZ+fH+mwYsrevXv19ttvKz09XQ8++KAMw9Btt93Goq4h\ndPToUT3zzDPy+/2yLEuzZs3StddeG+mwgKAdO3ZMq1atkmEY8vl8+qu/+itlZ2dHOqwRj2USAABA\nTBjxt58AAAACQVIDAABiAkkNAACICSQ1AAAgJpDUAACAmEBSAwAAYgJJDQAAiAkkNQAAICb8Pzhe\nMftmJUa7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c8a13be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BMYh5N4T3wJxNRIpiMaBRyfjDcxA33haeGURn42wiUhSLAY06suY9oO5DiMxrw39wwZEB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\nAP8fscg+ltlMbZEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c82c28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c8b09ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NGzciNTV1+raMaBbTs69AwNMHLd5B6LIX9UgiGAyisbERVVVVqKmpQVtbG7q6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\nLpYEERGNiyVBRETjYkkQEdG4/j8hwSIqyaW1oQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c83934e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y2KgJEyZox44dmjdv3k2PE5JvGAAAESzkf8TbbIzMgJ5+/cc//lFvvPGGOjo6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\nBBkAAGBbBBkAAGBb/x+5kDfKHGYrrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c96d3198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c885ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gD4vVyAQVSklJidra2rzrHo9HhmFo/vz5qqio0E9+8pOggqurq1NdXZ13PT8/P6jyAACw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\nyNYMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c936bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c868ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A/6B/GLA/47rtR+cPOvmJgbs9j/Rv10y4f8BzH2ngDzdo4P9HJ9X/h9ofznx7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\nV1vNwaWiOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c92cca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pLi7m2Wefdf2erVu3smXLFn7xi1/Q1dVFLBajsLCQefPmDSmLylxEZBBOf0Dd\niBEjKC8v580332TmzJkANDc3U1tbe8Z7Trnvvvv6P/7Vr37Fzp07h1zkoGeziIi4dvoD6kaNGkVj\nYyOXXXYZ3/72tzly5AiO4zBr1ixuvfVWduzYwXPPPcfx48fJy8ujpKSE559/fsD+TpX53XffPeRs\nKnMRkSygq1lERLKAylxEJAuozEVEsoDKXEQkC6jMRUSygMpcRCQLqMxFRLKAylxEJAv8fyq/3dee\nHaIvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c85b0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RysnJYcUSAAADRGeQyWCgPIjOlKElMzG0BAAYzMI9tHTlUnCdC1HDB0Z8GCiBCgAADABX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c896b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UpAUALJiEh4D8fr/y8vJmzOvu7tbGjRvldrslSXfeeWd8mW3bCxwRAJAKCfcA5nLx4kX1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c87b8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ik in fkeys:\n",
    "    if np.size(f.variables[ik].shape)==4:\n",
    "        fig, axs = plt.subplots(1,1,figsize=(10,4))\n",
    "        mesh=plt.pcolormesh(tz[:3600*24*1,:],zt[:3600*24*1,:],f.variables[ik][:3600*24*1,:,2,2].T)\n",
    "        plt.colorbar()\n",
    "        plt.title(ik)\n",
    "        \n",
    "        df = pd.DataFrame(f.variables[ik][:,0,2,2], index= pd.to_timedelta(t - min(t), \"s\"), columns = [ik])\n",
    "        plt.figure()\n",
    "        plt.plot(df.index, df[ik], '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xcUD3AvEdHR29yrhcLhISErzbNpsNp9MJwBdffMGJEyf45S9/ybhx4/jXf/1X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\nQikAiIhEKAUAEZEI9f8BW+6z6BxRqiQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c8a905f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FYeTIkdBoNLjjjjtQUlLSpYwgCGhqap+uoLm5GYGBgQ4TOMAkTkQkzYMTYJlM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c89c3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c8999160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FYeTIkdBoNLjjjjtQUlLSpYwgCGhqap+uoLm5GYGBgQ4TOMAkTkQkzYMTYJlM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c888bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xcUD3AvEdHR29yrhcLhISErzbNpsNp9MJwBdffMGJEyf45S9/ybhx4/jXf/1X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\nQikAiIhEKAUAEZEI9f8BW+6z6BxRqiQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c87d6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n in range(5):\n",
    "    ik = \"PHY2\"\n",
    "    fig, axs = plt.subplots()\n",
    "    mesh=plt.pcolormesh(tz[:3600*24*1,:],zt[:3600*24*1,:],f.variables[ik][:3600*24*1,:,n,2].T)\n",
    "    plt.colorbar()\n",
    "    plt.title(ik)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe9c8b2d898>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mMxwOx5D3C3jSOyUlBY2NjWhubkZ3dzdqa2uRnp7ep016ejoOHDgAAKivr0d0\ndHSfU01Syn7XH85fswCAgwcP4pprrhly+LHm/KNE9Reehfj6HdAeXQuRMMXoWKQKzm4iBQUcSWia\nhtzcXBQWFkJKiblz5yIpKQnV1dUQQiA7OxtpaWlwOp1YtmwZTCZTn9NGxcXFOHLkCNra2pCfnw+H\nw4HMzEzs2LEDx44dgxACNpsNeXl5o3qgRpLd3ZAHfg/521cgbs6E9vTzEBOvvOsvFABvpiMFCTkG\npxidOnXK6Ah9XGpoKY+4oL/yEhATC+17D4Rs5KDicBdQM5cqmfSdFcCkWGjzvqNMpgsxU/BUzJWQ\nkDCs/XgL7yiRpz/s/aE/cQzagn8FbvpHPgiILo0jCVIQi8QIkx0fQ/6uCvLPf4T4eg5E3mMQEyKM\njkVjAZflIAWxSIwQ2dMD+cZeyFdfhvjyTGhPlULExBodi8aSMI33SZByWCQuk5QSXf/3L9B/8TwQ\nGwftoacgpnzR6Fg0FmlhQHeX0SmI+mCRuAzyg3rov6rE2Y42aI77gRtm8LoDDR8fX0oKYpEYBtl0\nCnL3DsiGtyG+vRDmeTlo7+gwOhaNdWFhfOgQKYdFYgikzwv5u1cgD/03RPYd0O57ECLSBBEWZnQ0\nGg84u4kUxCIRBPlxG+SeX0O+vhciIwvaT8sgrpoUeEeioeDsJlIQi8QlyM4OyD++CrnvVYibboa2\nphjCajM6Fo1XXJaDFMQiMQD5ySeQNa9B7v01xD9Mh7ZiPcTnh3e3IlHQuFQ4KYhF4gLy3CeQr++B\n3PNr4LrroT2yFiKRC/BRiITxmgSph0UCn44cXt8DuXcXcG1q770O11xrdCy60mic3UTquaKLhOw8\nC3lgD2T1buC6qdAeXM0b4cg4vE+CFHRFFgnZ0Q65/7XeC9Jfmgbt4acgkjhyIINxdhMp6IoqEvKM\nD/KP/9U7lXVaOrTHfgYRP/4fdkRjgwjToHMkQYq5IoqEbGmC/MMuyIOvQ8y8BdqqIgjb1UbHIuqL\ns5tIQeO6SMi/vQe5dxek2wlx6zegPb2VK7OSusJ4uonUM+6KhJQScB+G/ofdwIcnILK/De37i/m4\nUFIfRxKkoHFTJGTXOcj/eR2y+jcAAPGNHIiv3AoRPsHgZERB4uwmUlBQRcLlcqGyshJSSmRmZiIn\nJ6dfm4qKCrhcLkRGRmLx4sW49tre2UJlZWU4fPgwYmJisHHjRn/79vZ2bNmyBc3NzZg8eTIKCgoQ\nFTX0v/Z7EwfIAAAPA0lEQVTlGR9kze8hD/weuOZaaAvuB/5hOpfsprGHN9ORgrRADXRdR3l5OVat\nWoWioiLU1tbi5MmTfdo4nU40NTWhpKQEeXl5eOmll/yvZWZmYtWqVf3ed/fu3Zg2bRqKi4tht9ux\na9euIQWXf3sPesUW6E/mAz4PtEcKEfbwTyDsN7FA0NikabwmQcoJWCQaGhoQHx8Pm82G8PBwZGRk\noK6urk+buro6zJ49GwCQmpqKjo4O+Hw+AMDUqVMRHR3d730PHTrk32fOnDn93vNSetYth166FohP\ngrb236DdsxQigctn0BjHaxKkoICnm7xeL+Li4vzbVqsVDQ0NAdt4vV5YLJZB37e1tdX/usViQWtr\na9Chtew7gOlf5XMcaHzh7CZSkDIXrgc7ReR2u+F2u/3bDocDk+bMC1WsoERERMBsNhsdow8VMwFq\n5lIlU4/ZjI8hYTablcl0IWYKnqq5qqqq/F/b7XbY7faA+wQsElarFS0tLf5tr9cLq9Xar43H4/Fv\nezyefm0uZrFY4PP5/P+NiYkZsN1AB9LW1hYodkiZzWZmCpKKuVTJJM9+Ar2rC21tbcpkuhAzBU/F\nXGazGQ6HY8j7BbwmkZKSgsbGRjQ3N6O7uxu1tbVIT0/v0yY9PR0HDhwAANTX1yM6OrrPqSYpZe/9\nCxeYMWMGampqAAA1NTX93pPoihOmAZLXJEgtAUcSmqYhNzcXhYWFkFJi7ty5SEpKQnV1NYQQyM7O\nRlpaGpxOJ5YtWwaTyYT8/Hz//sXFxThy5Aja2tqQn58Ph8Phn0a7efNm7N+/HzabDQUFBaN6oETK\n4wJ/pCAhL/4Tfww4deqU0RH6UHVoqVomQM1cqmSSntPQNzyBsGdfUibThZgpeCrmSkgY3tM1A55u\nIqIQEbxPgtTDIkGkCt5xTQpikSBShcYiQephkSBSRZjGZ1yTclgkiFTBZTlIQSwSRKrgNQlSEIsE\nkSp4TYIUxCJBpIpPlwofg7cu0TjGIkGkCCFE770SXJqDFMIiQaQSznAixbBIEKmE1yVIMSwSRCrh\ng4dIMSwSRCrRwnhNgpTCIkGkEo2L/JFaWCSIVMIb6kgxLBJEKtHCOLuJlMIiQaQSTeNIgpTCIkGk\nEj7ClBQT8BnXAOByuVBZWQkppf/51BerqKiAy+VCZGQklixZguTk5Evuu3PnTuzbtw8xMTEAgIUL\nF2L69OkjdFhEYxSvSZBiAhYJXddRXl6O1atXIzY2FitXrsTMmTORmJjob+N0OtHU1ISSkhIcPXoU\n27Ztw9q1awPuO3/+fMyfP3/0jo5orOHpJlJMwNNNDQ0NiI+Ph81mQ3h4ODIyMlBXV9enTV1dHWbP\nng0ASE1NRUdHB3w+X8B9uZAZ0UXC+EwJUkvAIuH1ehEXF+fftlqt8Hq9QbUJtO+ePXvw2GOP4YUX\nXkBHR8dlHQjRuMDZTaQYwy5cz5s3D6WlpdiwYQMsFgu2b99uVBQidfCaBCkm4DUJq9WKlpYW/7bX\n64XVau3XxuPx+Lc9Hg+sViu6u7sH3XfSpEn+f8/KysK6desG/Hy32w232+3fdjgcMJvNgWKHVERE\nBDMFScVcKmVqmxCBiZGRSmU6j5mCp2quqqoq/9d2ux12uz3gPgGLREpKChobG9Hc3IzY2FjU1tbi\noYce6tMmPT0de/fuxaxZs1BfX4/o6GhYLBZMmjRp0H19Ph8sFgsA4ODBg7jmmmsG/PyBDqStrS3g\ngYWS2WxmpiCpmEulTD1SoqPtDMLPnVMm03kq9dN5KmYC1MxlNpvhcDiGvF/AIqFpGnJzc1FYWAgp\nJebOnYukpCRUV1dDCIHs7GykpaXB6XRi2bJlMJlMyM/Pv+S+ALBjxw4cO3YMQgjYbDbk5eUNOTzR\neCOumgTZfsboGER+Qo7BKUanTp0yOkIfqv7VoFomQM1cKmXSX3kJsNoQ893vK5PpPJX66TwVMwFq\n5kpISBjWfrzjmkglFivg8wRuRxQiLBJEKomxAj5v4HZEIcIiQaQQYbFCskiQQlgkiFRiiePpJlIK\niwSRSiyxgM/LJWtIGSwSRAoRpqjeRf7Ofmx0FCIALBJE6rFYoXtbArcjCgEWCSLVWOKgf8TrEqQG\nFgkixQiLFfIjjiRIDSwSRKqJ4ekmUgeLBJFqYnm6idTBIkGkGJ5uIpWwSBCpJsbKkQQpg0WCSDWc\nAksKYZEgUk1M7/pNUuezrsl4LBJEihETJkBERQN8+BApgEWCSEEilgv9kRpYJIgUpMV+js+VICWw\nSBApSLN+js+VICWEB9PI5XKhsrISUkpkZmYiJyenX5uKigq4XC5ERkZiyZIlSE5OvuS+7e3t2LJl\nC5qbmzF58mQUFBQgKipq5I6MaAzj6SZSRcCRhK7rKC8vx6pVq1BUVITa2lqcPHmyTxun04mmpiaU\nlJQgLy8P27ZtC7jv7t27MW3aNBQXF8Nut2PXrl2jcHhEYxNPN5EqAhaJhoYGxMfHw2azITw8HBkZ\nGairq+vTpq6uDrNnzwYApKamoqOjAz6f75L7Hjp0yL/PnDlz+r0n0ZWMp5tIFQGLhNfrRVxcnH/b\narXC6/UG1eZS+7a2tsJisQAALBYLWltbL+9IiMYRnm4iVQR1TSIUhBBGRyBShma1AY0n0fPcT42O\n4tceHo6e7m6jY/ShYiZgdHJp33sAwnb1iL5nMAIWCavVipaWz5YI8Hq9sFqt/dp4PJ/91ePxeGC1\nWtHd3T3ovhaLBT6fz//fmJiYAT/f7XbD7Xb7tx0OBxISEoI8vNAxm81GR+hHxUyAmrlUzBSz+89G\nR6Bxpqqqyv+13W6H3W4PuE/A000pKSlobGxEc3Mzuru7UVtbi/T09D5t0tPTceDAAQBAfX09oqOj\nYbFYLrnvjBkzUFNTAwCoqanp954XHojD4fD/78KDVAUzBU/FXMwUHGYKnoq5qqqq+vwuDaZAAEGM\nJDRNQ25uLgoLCyGlxNy5c5GUlITq6moIIZCdnY20tDQ4nU4sW7YMJpMJ+fn5l9wXAHJycrB582bs\n378fNpsNBQUFl3H4REQ0GoK6JjF9+nQUFxf3+bevf/3rfbZzc3OD3hcArrrqKjz55JPB5iQiIgOE\nPfXUU08ZHWKoJk+ebHSEfpgpeCrmYqbgMFPwVMw1nExCSilHIQsREY0DXLuJiIgGxSJBRESDUuZm\nuotdzqKCRmU6cuQI1q9fj89//vMAgK985Su48847Ry1PWVkZDh8+jJiYGGzcuHHANqHuo2Byhbqf\ngN57d0pLS9Ha2gohBLKysnD77bf3axfK/gomU6j7qqurC2vWrEF3dzd6enpw8803Y8GCBf3ahbKf\ngslkxPcU0Ls+3cqVK2G1WrF8+fJ+rxvx83epTMPqJ6mgnp4euXTpUnn69GnZ1dUlH330UXnixIk+\nbQ4fPix/9rOfSSmlrK+vl0888YThmdxut3z22WdHNceF3n77bfnBBx/IRx55ZMDXQ91HweYKdT9J\nKeVHH30kP/jgAymllGfPnpUPPvig4d9TwWQyoq86OzullL3f80888YQ8evRon9eN+L4KlMmIfpJS\nyldffVUWFxcP+NlG/fxdKtNw+knJ002Xs6igkZkAQIZwHsDUqVMRHR096Ouh7qNgcwGh7Seg9w7/\n83/FmUwmJCYm9luDLNT9FUwmIPR9FRkZCaD3L/ienp5+rxvxfRUoExD6fvJ4PHA6ncjKyhrwdSP6\nKVAmYOj9pOTppoEWBmxoaAjYxuv1+hcNNCITABw9ehSPPfYYrFYrFi1a5L950Aih7qOhMLKfTp8+\njePHjyM1NbXPvxvZX4NlAkLfV7quY8WKFWhqasK8efOQkpLS53Uj+ilQJiD0/bR9+3YsWrQIHR0d\nA75uRD8FygQMvZ+UHEmMVV/84hfx/PPPY8OGDfjmN7+JDRs2GB1JSUb2U2dnJzZt2oT77rsPJpMp\nZJ97KZfKZERfaZqG9evXo6ysDEePHsWJEydG/TMvN1Oo++n8Nbfk5GRIKUM+ihlupuH0k5JF4nIW\nFTQyk8lk8g+Lb7rpJnR3d6O9vX3UMgUS6j4KllH91NPTg6KiItx6662YOXNmv9eN6K9AmYz8noqK\nioLdbofL5erz70Z+Xw2WKdT99M477+DQoUNYunQpiouL4Xa7UVpa2qdNqPspmEzD6Scli8TlLCpo\nZKYLzzeePxV11VVXjVomAJf8KybUfRRsLiP6CeiddZWUlDTgrCbAmP4KlCnUfXXmzBn/qYpz587h\nrbfe6rfqcqj7KZhMoe6nu+++G2VlZSgtLcXDDz+MG264AUuXLu3TJtT9FEym4fSTktckLmdRQSMz\nvfnmm6iurkZYWBgiIiLw8MMPj2qm4uJiHDlyBG1tbcjPz4fD4UB3d7dhfRRsrlD3E9D7V9Ybb7yB\nKVOm4PHHH4cQAgsXLkRzc7Nh/RVMplD3lc/nw9atW6HrOqSUmDVrFtLS0gz92QsmkxHfUwMxsp+C\nyTScfuKyHERENCglTzcREZEaWCSIiGhQLBJERDQoFgkiIhoUiwQRkQLKysrwwAMP4NFHHw3Y9u23\n38by5cuxcOFCHDx4sN/rZ8+eRX5+PioqKi47F4sEEZECMjMzsWrVqqDa2mw2LFmyBLfccsuAr7/y\nyiu4/vrrRySXkvdJEBFdaaZOnYrm5uY+/9bU1ITy8nK0tbUhIiICP/jBD5CQkIDPfe5zAAAhRL/3\nef/999Ha2orp06fjvffeu+xcHEkQESnqxRdfxP33349nnnkGixYtwksvvXTJ9lJK/OIXv8CiRYtG\nbD0pjiSIiBTU2dmJd999F5s3b/b/wh9smfTz9u7di5tuumlE14hikSAiUpCUEtHR0Vi3bl3Q+9TX\n1+Pdd9/FH/7wB5w9exY9PT0wmUy4++67h52DRYKISBEXLow5ceJETJ48GW+++SZuvvlmAMDx48fx\nhS98od8+5z344IP+r2tqavD+++9fVoEAuHYTEZESLlwYMyYmBg6HAzfccAO2bduGjz76CLquY9as\nWbjzzjvx3nvvYePGjfj4448xYcIEWCwWFBUV9Xm/80Xi/vvvv6xcLBJERDQozm4iIqJBsUgQEdGg\nWCSIiGhQLBJERDQoFgkiIhoUiwQREQ2KRYKIiAbFIkFERIP6/wC07Z7ChKWRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe9c8aaf748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(f.variables[ik][:,5,2,2], index= pd.to_timedelta(t - min(t), \"s\"), columns = [ik])\n",
    "plt.figure()\n",
    "plt.plot(df.index, df.PHY2, '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}