exercises/plot_temperature_Tarifa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "1. download a netCDF file from [EMODnet-Physics](https://www.emodnet-physics.eu/Map/).\n",
    "2. read the time and the temperature from one of the downloaded files (hint: create a function).\n",
    "3. loop on all the files to read all the data.\n",
    "4. plot the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add here all the \"import\" commands for the modules you need\n",
    "import os\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datadir = \"../data/Tarifa-coast-buoy/\"\n",
    "datafile1 = os.path.join(datadir, \"IR_TS_MO_Tarifa-coast-buoy_20190830.nc\")\n",
    "os.path.exists(datafile1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data reading\n",
    "Hint: use the [`netCDF4`](https://unidata.github.io/netcdf4-python/netCDF4/index.html) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_temp_nc(datafile):\n",
    "    \"\"\"\n",
    "    Read the time and the temperature from a netCDF file\n",
    "    \"\"\"\n",
    "    with netCDF4.Dataset(datafile) as nc:\n",
    "        time = nc.variables[\"TIME\"][:]\n",
    "        depth = nc.variables[\"DEPH\"][:]\n",
    "        temperature = nc.variables[\"TEMP\"][:,1]\n",
    "        \n",
    "    return time, temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "time, temperature = read_temp_nc(datafile1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple plot to check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5cjejUlwsGN/fp++tWrRB0OmbsBY2Qd/YaPjRa5/x8ieH+MnyXKvLCTm7ispZuvEw33px\nI0VlNT5732VbCskrqeSBrAwidDTvc7pJuIIwCvoV3umBaUMTWJydz5Ls9jbKUmfKLWjaOKy0up57\n/raJ+obGHr9nfUMjT63ezbiBvbl07FnbHCgfcMd7747VEX1YC4ug9zQ08sSq3WSkxPPKnedxfno/\nHlmWQ25hqdWlhYzcwlLioh386qsT2HDgJL96e2eP33Np9mHyT1SzMGsUIjqa94fT/W50RB/WwiLo\nl28pJK+4kgezMohyRPD01ybTNzaKu1/eRGl1vdXlhYTcwjLG9O/NVyYP4tYZQ/m/9/fzds6Rbr9f\nTX0D/7tmD5OH9OWSUbqdpL8kxjW1QdC7Y8Ob7YO+aXpgD2MH9ObSsalA0wWqZ26cQsHJah5aulWX\nnnWgsdGw/UgZYwf0BuA/549h4uC+PLR0G/tLKrv1nn//9BBHSmv43jwdzftTpCOChNhoHdGHOdsH\n/WsbD3PoRBUL52V8IVCmpSXyw8tH8+72Ip5fn2dhhcFv//FKquoaGDuwDwDOSAeLbppCpEO466WN\nVHdx6V51XQPPrN3HucMSOT+9nz9KVi0kxTt11U2Ys3XQ13oaeHr1HiYN7susUclnPX/HhcO4Ynwq\nv3pnF5/kHbegwtCQW9h0IbZ5RA8wsG8vnrxhMruKynl4WU6Xfit68eMDFJfXslBH8wHhdumIPtzZ\nOugXb8insLTmrNF8MxHhl9dMYGhiLPe8spljPlw2aCe5haVEOYSRya4vPH5xRhL3zR7Ja5sO8/cN\nnVvFVFHr4dl1ecwc6eacYYn+KFedwR3v1J70Yc62QV9T38Dv1uzlnGGJXDjC3eZxrpgoFt08hfKa\neu55ZTMeHywbtJvcgjIyUlxER57943LfnJHMHOnmJ2/k8tnhjlcx/eXDA5yorGPhvFH+KFW1wh3v\npKRcL8aGM9sG/UsfH+RYeS0Ls1ofzbc0OrU3v7h6PJ/uP8Gv39kVoApDgzGG3MJSxg3o0+rzjgjh\nqRsm446L5q6XN3Kqqu1AKa2u57l1+5g7JplJg/v6q2R1Bne8k+r6BiprPVaXoixiy6CvrPXw+7X7\nmDnSzbnDO3ex7yuTB3HTuUN4bn0e7+Qe9XOFoeNIaQ0nq+oZO7B3m8ckxkXzzE1TKCqr4cElW2ls\nbH2+/o8f7KesxsMDWRn+Kle1ovmmKZ2nD1+2DPo/f3iA45V1PNjFQHn0ykwmDOrD95Zs5UA3lw3a\nTWsXYlszeUgCjyzIZM3OY/x+3b6znj9ZWccLH+znivGpjG3jtwPlH9oGQdku6Mtq6nl+fR6zRycz\neUhCl17rjHTwzI1TiIgQ7np5kzY/A3IKShFpmt7qyC3nDeVLEwfw23d38e+9JV947rn1eVTWefju\nXB3NB1pzYzNtgxC+bBf0L3ywn9Lq+i6P5psNTozlyesnseNIGY8sy/FxdaEnt7CM4e444pyRHR4r\nIvzi6vEMT4rnvlc2c7S0aRVTcXktf/nwAF+aOICMFFcH76J8rXlEr3fHhi9bBf2pqjr++P5+Lhub\nyriB3Z8emDU6mXtnj2DpxsMs3nDIhxWGnu2FpV2aaolzRvLszVOorm843fzs92v3UdfQyP1zRvqx\nUtWWxDjvHL2O6MOWrYL++fV5VNT55mLfd+dmcOEIN48szyWnIDybn52orKOwtKbD+fkzjUh28ctr\nJpB98CQ/eHUbL31ykKsnD2R4UryfKlXtiXJEkBAbpXP0Ycw2QV9SUcufPzzAlRMGMCq159MDTcsG\nJ5EY27RssLwm/JqfNXf37M5vR1dOHMBt56fxj80FGGO4T0fzltJNwsObbYL+2bX7qKlv4P65vguU\nfvFOnrh+EvknqlmxrfudGkNVZ1fctOXHV4xhXmYK984eyeDEWF+Wprqoae9YnaMPV7YI+qKyGl78\n+CBXTxlEuo+nB84bnsjAvr1Yub3Ip+8bCnILyxjYtxd9Y6O79froyAiev3WajuaDgDveqatuwpgt\ngv6Z9/bS0Gj8crFPRMjKTOGDvSVU1YXXnYW5haVkdnM0r4JL04hegz5chXzQF5yq5u+f5nPttMF+\nmx6Yl5lCraeR9/eUdHywTVTWethfUtlm6wMVWtyuaKrqGsJusKKahHzQ/27NHgDunT3Cb58xfVgi\nvWMiw2r6ZseRMozp/vy8Ci6ntxTU5mZhKaSD/uDxSpZkH+bGc4cwoG8vv31OlCOCWaOTWbPzGA1t\n9HGxm9MXYtvpcaNCh/v0TVM6fROO2g16EXGJyFIRyRORRd7HviEit4vIQyJy1utF5GkROSoib/ur\n6GZPrd5DlEO4+5J0f38UWZkpnKisY+PBk37/rGCQU1BKYlw0qb1jrC5F+UCStkEIax2N6M8DbgPG\nAXNEZDpwkTHmT0ARcG3Lg0VkILDZGJNqjLnMD/WetvdYBcs2F3DrjDSSAxBGF2ckEeUQVm4Pj86W\nuYVNe8TqDlD20NzvRi/IBo+TlYGbRms36I0xK40xlcaYKiAHuALY43061/t9S7OBR0TkTRFpe7cP\nH3hy1W5iohx866Lh/vyY01wxUcxId7Nye5HtNxOv8zSy51i5dpm0kX7aqjho1NQ38Mu3d3LBL9ew\nr7giIJ/ZqTl6EXEBh4B6oHnuogZIbXmcMeZFIB1YDTzezvvdKSLZIpJdXFzcnbqZNzaVH14+mn7e\nkUogZGWmcOB4VcD+5Vhld1E59Q1GL8TaSJQjgr7aBsFyGw6c4Iqn3uf3a/cxf3x/3HGBya/OXoy9\nBXgUKAaa1zC6gLN21DZNngDavMvGGPO8MWaaMWZaUlJSF0tu8qWJA7h1Rlq3Xttdc8c0bTD+rs1X\n3zS3PtCgt5ck3VLQMhW1Hh5dnsO1z35EXUMjL95xDr++diJ9YqMC8vkdBr2IXAUsM8aUA+8CY71P\nZQJve49J8P5TvP+MBjb4o2Ar9e/Ti/ED+9h+mWVuYRlx0Q7S+sVZXYryIb1pyhrrdhdz6RPrefHj\ng9x+QRrvfPciZo7s3gC3u9ptMi4idwMPAce94f0ksEFE7qBp2uZ/RKQf8DpwEbBERE4Cm4Fn/Vq5\nRbIyU3hi1W6OldeQ7LLnipTcwjIyB/QmIkIvxNqJ2+Vk2+FTVpcRNk5V1fHYm9v5x6YCRiTH8+q3\nz2fq0K5thuQr7Qa9MWYRsKiD9zhOU8hjjLm2g2NDXlZmCo+v3M3qHcf42jlDrC7H5xoaDTuOlHHd\ntMFWl6J8zB0frT3pA8AYw1s5R3l0eQ6nquq5d/YI7pk9Amekw7KaOt42SH3B6FQXgxKampzZMej3\nl1RSVdeg8/M25I53UlnXQHVdA72irQsdOztWVsMjy3N4J7eI8QP78Nf/ODco+kVp0HdRc5Ozlz85\nRFWdh9hoe/0Vfn4hVpdW2k3LTcK1bbRvGWNYuvEw//Xmdmo9jfzw8tF848JhRDqCo/lAcFQRYrIy\nU6jzNLJ+t/2anG0vLCPaEcHIFN0Nym5O3x2rF2R96mhpDbf88VO+/+o2RvfvzVv3z+TbF6cHTciD\nBn23TE+zb5Oz3MIyMlLjiQqiH1LlG25tg+AXP30jl40HT/Kzq8bx92+eF5RbZup/zd0Q5Yhg9uhk\n1uwswtPQaHU5PmOMIaewVFsT25TbpXfH+lpxeS2rdhRxy4yh3HLe0KBdqaZB301ZmamcrKq3VZOz\nwtIaTlXV64VYm+oXp62KfW3Z5gI8jYbrpg2yupR2adB308Wjkoh2RNhq+ia3oOlCbKaO6G0pOlLb\nIPiSMYbF2flMGdKXEckuq8tplwZ9N8U7I5mR3o+VO+zT5CynsAwRGNM/uH9oVffp3bG+szn/FHuP\nVYTEPSca9D2QlZnCweNV7D1mjyZn2wtLSU+Kt92SUfU5d3y0Xoz1kSUb8omNdrBg4gCrS+mQBn0P\nzB2TAtinyVlzD3plXzqi942qOg//3FrI/PH9iXcG/8BIg74HUvvEMGGQPZqcHa+o5UhpjQa9zTUF\nvV6M7akV245QWdfAddODf9oGNOh7LGtMClvyT3GsrMbqUnqkeY9YXVppb0kuJxW1HmrqG6wuJaQt\nyc5nuDuOaRY1KesqDfoeyhrbNH2zascxiyvpmeagD4a+HMp/dO/YnssrrmDDgZNcO21wyGy1qUHf\nQ6NSXAxO7BXye8nmFpYysG8v+sa2uV+MsgG9aarnlmQfxhEhXDNloNWldJoGfQ+JCFljUvn3vuNU\n1nqsLqfbtuuF2LCgbRB6xtPQyGubDjNrVBLJvUNnPwoNeh+Ym5lMnaeR9/d0b/9bq1XUesgrqWTc\nQJ2ft7vmoNcLst2zdlcxxeW1IbF2viUNeh84Jy2RPr2iQnaZ5Y4jTfPzOqK3v37xOnXTE0uy83HH\nO5k1OtnqUrpEg94HIk83OTsWkk3OmlsfaA96+3NGOujTS9sgdEdxeS1rdh7jmikDQ667a2hVG8Sy\nMlM4VVVPdgg2OcstLKNfXDQpvZ1Wl6ICwB0frUHfDa9vPoyn0XBtiE3bgAa9z1yUEbpNznIKyxg7\nsE/ILBVTPeOOd+rF2C4yxrB4Qz5ThyYwIjn4+s13RIPeR+KdkZw/oh+rQqzJWa2ngT1F5To/H0bc\nLr07tqs2HTrFvuLKoG9H3BYNeh9qbnK2J4SanO0pqsDTaDTow0hSvJMSHdF3SXMDs/kTgr+BWWs0\n6H2ouclZKE3f5OiF2LCT5HJSrm0QOq2y1sOb2wpZMCE0Gpi1RoPeh1J6xzBxUJ+QWmaZW1hGvDOS\noYmxVpeiAsStSyy7ZMVn3gZmIXgRtpkGvY9lZaawNf8URSHS5Cy3sJTM/r2Ddq9L5Xt601TXLM3O\nZ3hSHFNDpIFZazTofSwrMxWAVTuCf1Tf0GjYcaRcG5mFGW2D0Hn7vA3MrguhBmat0aD3sYyUeIYk\nxrIqBKZv9pdUUF3foK0Pwozb1Tyi16DvyJLsfBwRwtUh1MCsNRr0PiYiZGWmhESTs+bWxLriJryc\nnqPXEX276hsaeW1jAbNGJZPsCp0GZq3RoPeDuWNSqPM0sn53cDc5yy0sIzoyIiRvAFHd54x00Dsm\n0tYjemMMf3g/j40HT3T7PdbuKqakopbrQ2QXqfZo0PvB9LQE+sZGBf0yy9zCUkaluEKub4fqObvf\nNLV8SyH/tWIHX332I376Rm63frtubmB2yagkP1QYWPpfuB9EOiKYPSqZNbuCt8mZMYacgjLGDdRp\nm3Bk5zYIlbUefvHWDiYM6sPXZ6Txl48OMO+J9azrwm/Yx8prmhqYTQ29BmatCf0zCFLNTc42HAjO\nJmcFp6opra4nU2+UCktJ8U7bTt08u24fRWW1/OTKsfz0S2NZ+q0ZxERF8PUXPmXhkq2cqur4N5nX\nNxXQ0Gi4dmroT9uABr3fXJSRRHRkRNAus9QLseEtyeWk2IZBn3+iiufW53HVpAGn171PS0tkxX0z\nuWfWCJZtKWDu4+t567Mjbb6HMYbF2flMC9EGZq3RoPeTOGckF6T3Y+X24GxylltYRoTAmFQN+nDk\njo+mvMZ+bRB+8dYOHCL84PLRX3g8JsrB9y4dxRv3XEBKbyd3vbyJb7+4kWOt3Ni46dBJ8oorQ/pO\n2DNp0PvR3MwUDp2oYndR8DU5yy0oJT0pnl7RDqtLURZovmnqeKV9Lsh+nHecf312lLsuSad/n16t\nHjN2QB+Wf+cCfnDZaNbsOsbcx9exNDv/C4OxxRvyiYt2MH9C/0CV7nftBr2IuERkqYjkicgi72Pf\nEJHbReQhEYk44/hkEXlMRO4WkfP9WXgo+LzJ2VGLKzlbrm4GHtZOt0GwyQXZhkbD//vndgb27cWd\nFw1v99hIRwR3XZLOW/fPZFSqi4de3catL3xK/okqbwOzIyyYMIC4EG1g1pqORvTnAbcB44A5IjId\nuMgY8yegCLj2jOP/G3jJGLMI+JGE8j3DPpDSO4aJg/sG3TLLkopajpbVaMfKMNZ8d6xdVt4s3pDP\njiNl/PiKMcREde631PSkeBbfOYOffXksmw6e5NIn1/Pgki1U1TVw3fTQ7DvflnaD3hiz0hhTaYyp\nAnKAK4A93qdzvd+3NK/F8wBpPqozZM3LTGHr4dKganK2Nf8UoBdiw5mdOliWVtfzm3d3cc6wRK4Y\nn9ql10ZECLfMSOPdBy9meloi7+QWkZ4Ux5QhodvArDWdmqMXERdwCKgHmtcL1gBn/q1Gmc8nu1p7\nvvn97hSRbBHJLi4O7rtHeyrWrM9hAAARv0lEQVQrM/h61C/bUkifXlFMCeFufKpnPu9gGfpB//Tq\nPZysquPRBZndbjw2sG8v/nz7dJ6/ZSpP3TA5pBuYtaazF2NvAR4FioHmxuUu4PgZx7W86tja8wAY\nY543xkwzxkxLSgr9u87aMzI5nqH9YoNmmeWpqjreyT3KVyYP7PSvuMp+YqIcuGIiQ/7u2H3FFfzl\nwwPcMH1wj5vziQjzxqbasslfh0EvIlcBy4wx5cC7wFjvU5nA295jmoeGa0VkhPdrpzFmt4/rDTki\nwtwxKXy49zgVQdDkbPmWQuo8jVwbontfKt9Jig/9tfT/9eZ2ekU5WDhvlNWlBLWOVt3cDTwBvCEi\n24A5wAYRuQPoD7wsIv2A5d6X/AS4Q0Tu936taJq+qWsIjiZnS7LzGTewt16IVSHfBuG9ncd4b1cx\n988deXoqSrWu3fVD3tUzizp4j+PARd7jC4Ef+aY0+5g29PMmZ1eMt25tbk5BKbmFZTz25bEdH6xs\nz+2KZufRcqvL6JY6TyM/W7Gd4e44bp2RZnU5QU9vmAqASEcEs0cns2bnMeotbHK2NDuf6MgIvjwx\ntDdRUL6RFO8M2XX0f/3oAHnFlTy8YAzRkRpjHdG/oQCZl5lCaXU9Gw50vz92T9TUN7BsSyGXjU2l\nT2yUJTWo4OKOd1JW46HWE1ptEI5X1PLU6j1cnJHErFHJVpcTEjToA2TmSG+Ts+3HLPn8d7cXUVpd\nb4tNFJRvNN80dTzEVt78duVuqusaeGTBGNstg/QXDfoAOd3kbMdRS5qcLdmQz6CEXswY3i/gn62C\nUyhuEp5bWMornx7i1hlpjEh2WV1OyNCgD6CszFTyT1SzqyiwF8DyT1Tx730lXDt1MBEROgJSTULt\n7lhjDI/9czsJsdHcP2ek1eWEFA36AJo7pmk+cWVuYG+eenXjYQC+qmvnVQtJrtC6O/atnKN8sv8E\nC+dl6HWmLtKgD6Dk3jFMGtyXlQG8S7ah0fDqxsNcOMLNwL6tt25V4enzNgjBP0dfU9/Az1fsYHSq\nixumD7G6nJCjQR9gWZkpbDtcytHSwDQ5+3BfCQWnqm21iYLyjZgoBy5nZEjM0f/h/TwKTlXz6JWZ\nOHT6scs06AOsuclZoHrfLN6QT9/YKOaNTQnI56nQ4nYF/96xR0treOa9fVw+LpXz091WlxOSNOgD\nrLnJWSC6WZ6qquPd3CKumjQQZ6Q2MFNnc8dHB/2I/pdv76TBGH58xRirSwlZGvQBJiJkjUnho33+\nb3K2bHMBdQ2NOm2j2uSOD+4R/caDJ3l9cwF3zhzO4MTYjl+gWqVBb4HmJmfrdvm3ydmS7MOMH9iH\nTN1gRLUhyeUM2ouxjY2Gx/6ZS7LLyV2XpFtdTkjToLfA1KEJJMRG+XUv2ZyCUrYfKeM6XVKp2uGO\nd1JaXU+dx7oeTG35x+YCth4u5YeXj7bV/q1W0KC3QKQjgll+bnK2JDsfZ2QEX5qkDcxU25qXWB6v\nDK7pm4paD796eyeTBvflKv0Z7jENeovMy0yhrMbjlyZnNfUNLNtcwGXjUunTS28sUW1rvjs22C7I\nLnpvL8fKa/nJlZl6N7cPaNBbpLnJmT9W37yTe5SyGg/X60VY1QF3EN4de+h4FX94fz9XTxnIZJtt\n0m0VDXqLxDkjuXCEm5Xbi3ze5GxJdj6DE3txnjYwUx1Iar47tjx4Lsj+/F/biXQIP7hstNWl2IYG\nvYWyMlM4fLLap7v85J+o4t97j2sDM9Upzf1ugmXv2A/3lvBObhHfmTWClN4xVpdjGxr0Fpoz2tvk\nzIfTN0s3HkYErpmqq21Ux2KiHMQ7I4Ni6sbT0Mhjb25nUEIv7rhwmNXl2IoGvYVONznzUdA3NBpe\nzc5n5sgkbWCmOs0dHx0Ua+lf2ZDPzqPlPDx/DDFReie3L2nQWywrM4XPCko5Ulrd4/f6994SCktr\ndO286hJ3vJPi8sA02WvLqao6Hn93FzOG9+PSsamW1mJHGvQWm3e6yVnPtxhcnN3UwKy5cZpSnREM\nd8c+uWoPpdX1PHplpm4P6Aca9BYbkRxPmg+anJ2srGOlNjBT3WB1v5s9ReW8+PFBvnbOEMb013Yd\n/qBBbzERISszhY/2lVBeU9/t91m2RRuYqe5xxzs5VVXvt7u022OM4bE3txMX7eDBrIyAf3640KAP\nAnPHpFDfYFi3u3tNzowxLN6Qrw3MVLe4XU13xx63YPpmzc5jvL+nhO/OzaCfd02/8j0N+iDweZOz\n7k3f5BSUsfNoOddN19G86rrmfjeBboNQ52nkZ29uJz0pjltmDA3oZ4cbDfogEOmIYPboFN7rZpOz\n0w3MJg7wQ3XK7qzaJPzPH+7nwPEqHr1yLFEOjSJ/0r/dIJHV3ORsf9eanNXUN7BsSwGXawMz1U3N\nbRACeXdscXktT6/ey+zRyVyckRSwzw1X2uQ5SFyU4SY6MoL/fmsHY1I7P89eUlFLeY1HL8KqbrNi\n6uY37+yipr6Bh+fr9oCBoEEfJGKjI7n1vKH867Mj/HtvSZdee1FGkjYwU93WK9pBWr9Y3so5wt2X\npPt9HXtOQSlLNubzjQuHMTwp3q+fpZpo0AeRhxdk8vCCTKvLUGHontkj+d7SrbyTW8Rl4/x3Z6ox\nhv/3z1wSY6O5d85Iv32O+iKdo1dKcdWkAQx3x/HEyt00Nvq2bXZLb247woYDJ3no0lH0jtFrSoGi\nQa+UItIRwXezMthVVM6bnx3xy2dU1zXwi3/tILN/b67Va0oBpUGvlAJgwfj+jEpx8eSq3Xj8cJfs\nc+v3UVhaw0+uzMSheyUElAa9UgqAiAjhgayR5BVXsmxLoU/fu/BUNc+u28f8Cf05VxcOBFyngl5E\nLhKR1d6v+4jID0XkWyJyfyvHRojIpyJyVEQe9XXBSin/uXRsKmMH9Oap1bt92vvmf97aiTHwo8t1\ne0ArdCrojTHrgeadLL4PrDfGPAfMFpERZxx+NXCrMSbVGPOY70pVSvmbiLBwXgb5J6pZmn3YJ++5\n4cAJ3thayLcuTmdQQqxP3lN1TVembpo7Hk1p8fVO4NwzjrsAeE9EfiMiOjWkVIiZNSqZyUP68r9r\n9lBT39Cj92psbFpOmdo7hm9fPNxHFaqu6k4Q7wLmer+OBSpaPmmMeQBIBwYBt7X2BiJyp4hki0h2\ncXH3OjYqpfxDRFiYNYojpTX8/dNDPXqvVzceJqegjB9dMZrYaL1txyrdCfrHgCEicidwCfDemQcY\nY6qA+4BJrb2BMeZ5Y8w0Y8y0pCTtc6FUsLlgRD/OGZbIM2v3UV3XvVF9eU09v3pnJ1OHJmjDPYt1\nOeiNMSeMMXcDBcDTxpgyEXGIiAtAPr9/uh+w2nelKqUCpWlUn0FxeS0vfXywW+/xu/f2UlJRx090\ne0DLdXbVzXggXUTGichQEZkPRBpj/s97yOXAwyISC2wRkZ8Dk4wxy/1TtlLK384d3o+ZI938ft0+\nKmo9XXrt/pJKXvhgP9dOHcSEQX39VKHqrE5NmhljPgNa3sp28Izn3wTe9H470TelKaWs9mBWBl9Z\n9CF/+fAA35l15gK7tv18xQ6iHRE8dNkoP1anOktXxSil2jR5SAJzRifz3Lp9lFZ3bk/j9buLWbWj\niHtmjyTZFePnClVnaNArpdr1QFYGZTUe/vjB/g6PrW9o2h5waL9Y/uPCNP8XpzpFg14p1a5xA/tw\n+bhUXvhgPycr299A/OWPD7LnWAX/ecUYnJGOAFWoOqJBr5Tq0ANZGVTWeXhufV6bx5ysrOOJVXu4\ncISbrMyUAFanOqJBr5TqUEaKiy9NHMBfPjzQ5paDT6zaTUWth0cW6HLKYKNBr5TqlPvnjKTW08Dv\n1+4767mdR8t46eOD3HTuEEaluiyoTrVHg14p1SnDk+K5ZsogXvrkIEdLa04/bozhsX9uxxUTxQNz\nMyysULVFg14p1Wn3zRlJY6Phd+/tOf3Yu9uL+HDfcR7MyiAhLtrC6lRbNOiVUp02ODGW66YPZvGG\nfA6frKLW08DPV+wgIyWem84dYnV5qg0a9EqpLrl39ghEhP9dvZcXPjjAoRNVPLpgLJEOjZNgpX1D\nlVJd0r9PL248ZwgvfnwQZ2QEc8ekcOFIt9VlqXbo/4KVUl1296x0ohxCfUMjD88fY3U5qgM6oldK\ndVmyK4bHr5tEQ6MhzR1ndTmqAxr0SqluuWJ8f6tLUJ2kUzdKKWVzGvRKKWVzGvRKKWVzGvRKKWVz\nGvRKKWVzGvRKKWVzGvRKKWVzGvRKKWVzYoyxtgCRYuBgN1/uBkp8WE6oCefzD+dzh/A+/3A+d/j8\n/IcaY5I68wLLg74nRCTbGDPN6jqsEs7nH87nDuF9/uF87tC989epG6WUsjkNeqWUsrlQD/rnrS7A\nYuF8/uF87hDe5x/O5w7dOP+QnqNXSinVsVAf0SullOqABr1SStlcSAS9iCSKyJ9FJEdErj/juQwR\neUREFopIhlU1+lMH5/81EflERHaIiO2WnLV37i2O+aOIXBLg0gKio/MXkdEi8k0RmWFFff7Uwc/9\nV0TkG94/N1pVYyB4/x2vOOOxruWeMSbo/wCjaPqf0gDgH2c89xYQDzjPfM4uf9o6f0CAL3u/vgNY\nYXWtgfx3733+SmAxcInVtQb6/L3P/dbqGi0697Utvl5lda1+/DtwAve1PF/v413KvZAY0Rtjdhlj\nGoHBwNPNj4tILyDdGFNhjKkFhomI7bZHbOv8TZPl3m83AEesqM+f2jp3ABEZRtN2mDusqC0Q2jt/\n7/cHReQpEbkw8NX5VwfnvlFEHvP+Frso8NUFzO3AH1o+0J3cC4mgBxCR4cD/AHe3eDgBKGvxvQfo\n1C3BoaaN829pLvB44CoKnNbO3fuDfbkx5nXLCguQNs4/DkgDfgf8FlgqItGWFOhH7fzcPwKkA78G\n1ge6rkAQkbnA+8aYqjOe6nLuhUzQG2PygDlApog0n9RxIKbFYbHAqUDXFghtnD8AIjICOGiM2W5J\ncX7WxrlfBNwsImuB24AnRWSgNRX6VxvnHw1UG2MajTGHgEIg1aoa/aWdn/tfAd8GfgP83YraAuCb\nwDPen/FJIvKf3se7nHshNc1hjGkUkY+BEyLSxxhTKiIHRSQWaATyjTHVFpfpN22cfwow0RjzmojE\nNx1mKi0u1edaOfc1wPkAIvJTmuYwC6ys0Z9aOf+TIlIrIvHGmAqgGLDl+bf2cw+MNcaUAytE5PsW\nl+gXxpjTF6C9Yf/f3c29kLhhSkS+C2QC/wa2A3XAD40xXxORccBXgVpguR1HtW2dP3APsJqmX92g\n6eLsNBMK/1I7qb1/9y2O+SlNQb/Wihr9qYOf/enAVcBnQJEx5j3rKvW9Ds59ATCMpv+5xRhj/mZd\npf7nDfr76WbuhUTQK6WU6r6QmaNXSinVPRr0Sillcxr0Sillcxr0Sillcxr0Sillcxr0SinVCS1v\nyBOROSLyvojkicjlrRzrEpG9InJURG5v8XgvEfnsjGPPeqyNz7/Fu5S4yzTolVKqc7JafN3bGDOT\nprtXf9PKsbcA5xhjUo0xf2rx+B3AmS0NWnvsC0RkJNDt7rQa9Eop1UUteiy11UzwYmC7iCxsfkBE\nLgPWANUdPHa9iNwmIstExO3tYXQp0O2+TiHVAkEppQJNRG6iqWnYOSLS1/vwOmPMZuAKmpqufYEx\n5noRSQBeF5HNwF6a7uDdLiLN7zuklccmABcCK4AtNN0ZnAH8CZje7XPQO2OVUqpjInKbMebPLb53\n07QfxB/bec05NAW3k6ZROcAkYCWwqZXHXgUyjDE/874+jqbunOVAX++f7xhjvrARSYe1a9ArpVTH\nWga9N4CvMcb81dsyuw9QCWCMqRERMcYYEbkUKDbGbGrxPmuNMZec8d5rjTGXiMhUYClwDuAAhhpj\nPvUecwlNG+z8tKu169SNUkp1zgoAEXF6v+4tIvfRFPJTaLqoarzb/r0hIq8Cm1uGfEeMMRtF5DUg\nB1hijLnPF4XriF4ppWxOV90opZTNadArpZTNadArpZTNadArpZTNadArpZTNadArpZTNadArpZTN\nadArpZTN/X/Xm6fXSlNgNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf8212c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time, temperature)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Loop on the files\n",
    "Hint: create a file list using [`glob`](https://docs.python.org/2/library/glob.html) module.     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190830.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190831.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190901.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190902.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190903.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190904.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190905.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190906.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190907.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190908.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190909.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191016.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191017.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191018.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191019.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191020.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191021.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191022.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191023.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191024.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191025.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191026.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191027.nc',\n",
       " '../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191028.nc']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafilelist = sorted(glob.glob(datadir + \"*.nc\"))\n",
    "datafilelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190830.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190831.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190901.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190902.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190903.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190904.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190905.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190906.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190907.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190908.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20190909.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191016.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191017.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191018.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191019.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191020.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191021.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191022.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191023.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191024.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191025.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191026.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191027.nc\n",
      "Working on ../data/Tarifa-coast-buoy/IR_TS_MO_Tarifa-coast-buoy_20191028.nc\n"
     ]
    }
   ],
   "source": [
    "timeall = np.array([])\n",
    "tempall = np.array([])\n",
    "for datafile in datafilelist:\n",
    "    print(\"Working on {}\".format(datafile))\n",
    "    time, temperature = read_temp_nc(datafile)\n",
    "    timeall = np.append(timeall, time)\n",
    "    tempall = np.append(tempall, temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Plot of the whole time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OAz61ozJgE/Dn3fbzgGdPu749tmW740belvOAn5uFtkzaDuBE4IHd9q8CjxvrOQFOAN7W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\nGfKS1DBDXpIa9v+4zCrSoHUvqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf81d51dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(timeall, tempall)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tempall_masked = np.ma.masked_where(tempall < 0.0, tempall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LLh6aj4tO74rSHYcwf0U5HvviB/xr6Y+4RCu61asjF93yhZG1U+RxZSnYQMXx\nwnJOnUhcDRJjVsSJaBCAnkQ0EEANgIEAEoQQ84wcXCj85YIBANx/2uj1xrTxxCbTCogIY3rmYEzP\nHGw/UIMFKyvw7te78NqanZjYryOmlxRidFE2F93ywMjFPvK48h4RCf1M0PHEj5902Tgxu+xeCPGt\nEKKbEGKTEKJSCPGxEOIDowfXGtQvkd6HR74J/FVjWkuvjum4/5JBWDVrIn4zuTc2VB3F1fO+wvlP\nrsR763ehqSW6TQJiiSVbq/Ht7mOBd2wFMhKX3/NILNrSm9g8eMJVz376i2UAgK37anDh0186FwcZ\niSnT8J+7sRjzri8OuJ/ezzg5U84RE9NWstsl4TeT++DLWRMx55JBaGi247dvbEDJP5fgmWU/4thJ\nLrplJPLrLb/KkZhUtDjtFJc4q01J9mqTrA9+9j02VB3Fqu0HDR+TKZenTewXXGlYvVlxuY01nAkX\nyYlWXDmiO64o7oZl26oxf+UOzPnkezyx6AdcUdwNN48tRLes1GgPMy5Qv7cWj0g8En60tEtqlUhc\nr7NUgpZhEYnmyaYU8WDRi8RlbWnum8mEG4uFMKFfR0zo1xGb9xzDghXleGV1JV4qrcDUAXmYXlKE\n4T246FZbUIVafoMjGYlLp0yd2PTMVCqY9bHzcSSstVNCxNUl9leO6IYUm9Wt3gHDhJsBXTLwyE9P\nx93T+uHF0gr8Z3UlPtm0D8O6d8D0kiJMHZAX9x1vJBP7dTTkuFKyZbAbCRGX51DXC5z0s3YgEk2U\n41rE5c8tKxGatX/8zhkp+MVZPaM5LOYUIi8jGTOn9cOvJvTC2+t2YcHKctz2n6/RLStFK7rVzdnL\nNR4hAgZ0aR9wv9qGZlgt5OxPeehEg3Oxni+kZsuMNCOdi5r6JiRaLc7JU7WvZlOL7xNv219j3KA0\nTDmxGQwF2alO/9tChBytBC3DRIO0pATcMKYAS+4cj2euHYZO6cn463+/w+j7F+H+T7Zg77GT0R6i\nIQgRXBLBgD9/hjFzFgMAyioOY/h9X2D8Q0sDHR2AKyslUM/ctjDoL59j6mPLnZG4v+hbZd6KcsPG\nJInLEGDlzAnISEnEt7scqU1EwKI7xkck3Ydh/GG1EKYN7IxpAztj/c4jmL+iHPOW78CCFeX4yZAu\nuHlsIQZ2zQh8IBMgRTVY10hOEK7feRQAsOuI941NvR9IzW6RkbjBzkXloTrnr4pILPEPlrgU8fxM\nRyaA006xEDJSEgP+PGOYSDK0eyaeviYTVYfr8PyXFXhj7U68t343RhdlY8a4Qozv09HU/V2lvRHq\nYp/GICcDnZ64XdopgSPxxmarZRY4AAAY8UlEQVQ7Nuw6ijMKskIak0RG/YdrY6fsQtzaKYBrYjOe\n6hkz8Ue3rFTc85P+WDV7Emaf0w8Vh2px0wtlOPvRZXj1K/MW3ZKiGuq3r8HP9arHEs4JTff/++Ph\nhVtx+TOlzl/pwaDaNPL+EktFLeNaxJ3Lck0czTCnDhkpifj5WT2x/O4JePzK05Fis+L3732LMXMW\n45GF23DQZEW3pPaF+v1r8BOJC7fH7p54MJF45UFHo+ydITTMVicxjfTdW0tci3iCczFAlAfCMCGQ\naLXgwtO74r+/GovXbxmFYd074IlFP2DMnMWY9c5G/BCBjIdw4IzEQ/z+NTT5FvEWnWJT8jzBLKyR\nlQ5PNLh72m+s3emzMmWtMpfWEoMiHpeeuMSztgLDmAkiwqiibIwqysaP1Sfw3MpyvL1uF15fW4Xx\nfXMxo6QIY3rGbtEt0UpPvNnPDGWzTjqfFPaWIGY2U2yOFEa19smeoycx851vMbzHLrxz6xiv19Q2\nKBUL/aQT6nH3tL4h7d8a4joSZzuFiRd65rbD3y8ehNLZk/C7s/tg0+5juGb+Vzj3iZV4Z92uiCwq\nCZXWeuL+gl01Evb0xP3la0ukJqhBu6x2uv2AV38bAHDLagt1fuK28cZ3r4xrEZeRePsIFItnmEiQ\nlWbDryf1xsqZE/HApYPRYrfjjrc2oOSBxZi7dDuO1nnX8YgWUic9I/Gt+2pw/ydbWuUvq9G29MSd\ndoqHlz536XY8u/xHtNgF/vzBJuw6UufUBLtOTzXPgmWrdxzCv5f9iI27jjq3VYbgpUeKuFa37lmp\nmHxaJ1w/uke0h8IwYSU50YorzuiGy4vzsWxbNRasLMcDn27Fk4u244rifNw0thA9stOiOkZfnvh1\nC77CgZoGzCgpQk67pJCOqdeAQdopnp74A586em4O7Z6JF0sr8f2+GgzOz3AbG+Db577y2dUA3C2R\n6prAk8tXj+yOV7/aGXC/cBHXIp5is2L+DYFL1jKMWSEijO/bEeP7dsSWvccxf0U5Xl2zEy+trsSU\n/p0wQyu6FQ3fXNhdY1RpS2W/Fj+euK9iU3bnik6l4qH6fIDhhPqD4bTOgcsMhJO4FnGGOZU4rXN7\nPHzFEMyc1hcvllbgldU78dnm/Ti9WwdMLynEtAF5SLBGzkGVdofnlJRw2h+hi7leJG4PcDx1q7yh\nqFkuqrVS39SCn7+8DiW9c7zGGyyRvl2yiDNMnNGxfTLumtoPv1SKbv3q1fXIz0zBz84sxE/PiEzR\nrUArNvUiZyH8NzhzSzH08MTVCUi7jtiDXDeURrfcb9fxG5rsWLatGsu2Ves+Dzh6eTYEmEh+8aYR\nsEXohskizjBxSqotAdePLsA1I3vgiy37sWBFOe796Ds8tnAbrhrZHTeOKUCXDimGnV+Kq2ckLqNh\nveX1gVqs6WWnyMMcUSZ19USWoG+9qP54k06aoueQbFb/Ik4EnNUn1+fz4YZFnGHiHKuFMHVAHqYO\nyMM3VUcxf8UOLFhZjudWluO8wZ0xfWwRBuWHv+iWUxw9InFZ6EovLbJFCP8phi1qJK6dR1PZI3VN\nEEKAiNxSAVU7RNox6rlVEdezZB79Ypvb33oN2KNJsN3uxwH4sxBiEhF1AnArgI0AhmvbuTwgw5iA\n07t1wFNXD8OuI3V44csKvL62Ch98swcjC7Mwo6QIE/uFseiW007Rf1rPTgkUibt74toiH+ES5pNN\nLUi1JbiVipURP5GvSFw9fuB8+0jOKwRDsN3ulwOQv7uuBrBeCPEuABuAQQaNjWEYg8jPTMUfz++P\nVbMn4g/nnoaqw3WY/lIZJj+yDK+srnRrP9ZaVE+8qcWOdZVH3CJkPRHXW7CjtlLcd9xVntbV2cf1\nGhnlq+eRUXeLXaDyUK1jW4t+PZRgJlsDtXakCE9thnJLkYbTMgC/J6LuAI4A2OC5IxHdQkRlRFRW\nXV3t+TTDMDFC++REzBhXhGV3T8ATVw1Fu+QE/PH9TRgzZxEe/nwrDtTUt/rYqif+/vrduPRfqzB3\n6Y/O5/V85ZdLK7y2qbKqVh90ZqcoobSs860Xia+tOIIlWx161NisnyfuKxJXHaFAEh3pbM6QfxcI\nIb4G8A6A1wCsE0J4XbUQ4lkhRLEQojg3N3IGP8MwrSPRasEFQ7rgg1+eiTduGYXigiw8tWQ7xs5Z\ngrvf3oCt+0IvuuVadk/O1ZA7tUgY0G/i8E3VUa9taqTcLilBEUlpp7j2lZObaiRep/Orws1Osavb\n9SNxVZcD2U0xn2JIRGcCOArgfABLiWiHEOKHsI+MYZiIQ0QYWZSNkUXZ2FF9As996Si69WbZLozr\nk4sZJYUY2ysnqMVD6rzmRi2Crqn3XxEwKdEKNfa2WsjNLhEAOqUnY9/xet1I/IgWidcrlRCPn/Tu\nwhPKxKbjGlzd1mOtwXVrHPqRAPYKIY4AeB7AgPAOiWGYWKAotx3uu2gQSmdNwp1T+mDL3uO4bsEa\nnPP4CrxVVoWGZv++uVrF8MMNewC4R8V2nUnMXrnt3P62Erllq9iFcIqo6onL+kg19Q7BVsf2Vflh\nr/M0tejnifuyU1TdjrUmM0GJOBENAtCTiAYCeAnAmUR0DhyTnf8zcHwMw0SZzDQbfjWxN1bOnIAH\nLxsMIYC73t6Isf9cgqeXbMeRWv2iW05PXFEZVf/0MlE6pLq3UPSMeu3CtU2tnZKoZYzIbWpEfUyn\nH2ajjzxxXyUB1MnKQBoeaY0Pyk4RQnwLoJuyaZb2/0/CPiKGYWKSpAQrLi/uhsuG52PFDwcxf2U5\nHvxsK55avB2XDXcU3SrMcRXdUj1xyYkADRaaWuxukbHMBNm6rwab9xyD3S6c26RXrhedt7hZLN43\nGV92iq/6K6rox5qdwot9GIYJCSLCuD65GNcnF1v31WD+ih14Y20VXvmqEpNPcxTdOqMg0ymoamS6\nUcku0bNTPBcAWbWFNVMfWw4AyExNdE4sqoLtKezNAUS8odlHnngINcl9EekUQxZxhmFaTd+8dDx4\n+RDcNa0vXi6txMurK7Hwu/0Ykp+BKQPyAPgWPTUSt5BDTBs9RNTTf7YL1za1KYQUe8/ytIBrslNF\nzV6xB5Fi6DamGIvEY2vpEcMwpqRjejLumNIXpbMm4d6LBuJ4fTMe/MxRz9tXo4ol31fjphfW4kBN\nvTMabmx2t1O8PHG7ap247JQEi/TEHdteW+Oq563nvbuJuF21UwJH4gFFPBY9cYZhmGBIsVlx3age\nuGZEdyz6/gA+37wP31Qd0933na93AQAeXeiqTdJit0OtY+i5OlL1v6FE3Z6euJqRold18KRbJO7a\nHoydoifir84YiavnfQUg8nniHIkzDBN2LBbC2f074cHLh6Bje//de9Scc7twVSUEXDaJpMXHJKan\nxSJJsBDOG9wZE/t1dNuu5pGLEO0UPZEe0DkDlwzrGvC1RsAizjCMoQRyHxKVHYRwF1UvT9zunWIo\n1LRDj2NnpCSitqEZiR43g5NNLVi+rRpn/P0LbN5z3Lk9GDtFD7IAWak2AIhIrXYVtlMYhjGUQNkc\nqp1hF8LN+vC0Lprtdq+bQosQzvKwnl14khOtaLELbSUo0C8vHbnpSVj14yF8vHEvqmsasL7qiOtY\nwUTiOtdjIcKdU/uiR04apg3MC3iMcMKROMMwhnLIx2IgiVvuuF3geL0rmyTB4i5Rbot9ZO0UtxRD\n92MnWgl2AWeXnVFF2RjarQNa7MLZAEJ9jWckrlexUO+WZCHHDeO6UT0i3s+UI3GGYQwlUOd3WU8l\nJdGKl1dXuj2nN4n4nWZ/qOmENi3S9mzulmi1wC6E006xEDlF9t2vdwPwrJ3iHonr6bHetkC/NoyE\nI3GGYaLKiYYmEAGpNqvXc0mJ3hJ1XBN9Kb3HTjahfYpjub53JG6BXcC5LN9C3oKrFuXyXHbvb+FO\nv7x0p7UTzXIqLOIMw0SVmvpmpCRadUu8piR6C7tECIGjdY3YebgOWWmaiHvsk5hggV2prWKxkJen\nri4G8qqdoheJaxvH9cl13hAivUpThUWcYZiocqKhGak2q24WS7I/EQdw1oNLAQAdtMwQr0hcK2Wr\n2imeNwu1VG2TRz65njTL+jAE4NJh+dpxfQ7TcFjEGYYxFGk1SMHz5ER9M1JsVl1f2V8kDgFns4nk\nBG3FphaL52emOPuFOkTct52iTqw2BejxCQAFssgXAX+/eCA2/HlKVPtusogzDGMoiVqGSbKOvw0A\nNQ3NSE1M0BVxf5H41v2ubkMyhVAIR03xXUdOOqJurSaLFHEi76j5eH2Tc9sTiwL3t1GrMyZYLchI\nSQzwCmNhEWcYxlBmntMPgEtIPWlstiPFZsXuoye9nvMl/AAw55PvnY+TEly1U95f78g6+WLLfliI\nIIRw3gwam+1eNwshgPRkfSHWm7CcfFonAMB5gzr7HFskYRFnGMZQbh5biIo553ktxFHRy0wB/Efi\nKrYEV1MIdbGQhRx54rLZxPGTzbrC3D5FP9ta79dB37x0VMw5D4PyM4Iam9FwnjjDMBHBV9ccwLeI\n+/XEFeRiHgHg8837nduJgHWVR5Cd5pj4PHqyUVeYaxv0W81FM/87WDgSZxgmIuiVhJWk2NzjyaLc\nNIwszEK3rBSvff92oXdbX7WeypoKRwXDR64Y4hThPcccVk1tQ4tuJslhH6tKf3d2H59jjhVYxBmG\niQh+I3GPiPv8QZ3xxs9HI9VD3J+5dhiGdc/0er1chamu2Lx4aFenYKcmOo7T2GzXzUf3RY/s1KD3\njRYs4gzDRAT/kbi7iEuh9Vx2n2Cx6HraerVT1BomqUmO4ze0eE9s+iNu7BQiGkdEi7TH1xHRPiKq\nIKJDRHS2sUNkGCYe8CfinhOYUjw9RXxCv466qyPP6pMLwFXFUC7ukfWs0pKUSFxHmF+bMUp3XKpX\n/8y1w/H8jWf4vIZoEWy3++VEJM2p7UKIPAAgogcALDVobAzDxBH+RFymCEqkdqv1xPMzU2C1kG4k\nnplmA2k54UTAL87qCcAl6u00W6ahWd8TLy7wtmjkcSWRLjEbLKHYKY0AIIQoBQAisgIgIYR3F1KG\nYRgP/Iq4Rz54brqjG5AaiUurxLPBg4QAPLVkO4RwRd7ynB202ipdO6ToRuKezSekPZOliHis0hZP\nvATAcr0niOgWIiojorLq6uo2nIJhmHhBtj67ZmR3r+c8RfTy4d0AwG0Ssq7RsTy+KKedc1tSggWL\n7jgLgLsHnqbZIFLEu3ZIwbPXDceTVw0Nqrzs/24vwUOXD0FOO/+t5WKBtoj42QA+13tCCPGsEKJY\nCFGcm5vbhlMwDBMvyOwUz36XgLeISvFWmzLIkrEWCyFdi7TPKMhCz1yHqKuHSEpwiLhcIp9gsWDK\ngDx0SLXpRuKejRz6dErHZcP1a73EGq0ScXJcsU0I0RDm8TAME6cMye8AAOia6Z377auUq2qnjO6Z\n7XwsRV7VXvVxhrZCUzo4CYoF49EsCAUmSCP0R1ATm0Q0CEBPIhoohNgEYASANYaOjGGYuOLXk3rj\nnEF56JfXPujXqE2Hn7l2uPOxFHc1qnbcCByqLeubSDtF9dE9I/HXbxkd9HhikWCzU74F0E35+ysA\nXxk1KIZh4g+rhUIScABorxWmys9McU5WAi4htuhE4r07tlNWcLrsFM/XAkBGSiLyMpJDGlOswYt9\nGIaJWWSWyiVDu7ptlwUR3SJxmZaoKHtzgEhcb5LVk76d0kMbdIThAlgMw0SNG8cU4IVVFT57VKbY\nrNh23zleaYUysiYvO8VdpBu1iobqYiKp8e2SEnDX1L4Bx/i/20v8VmCMNiziDMNEjWCWtdsSvA0D\n6Y7oTWyqE5eNLd4iTspqUM+sFD0ckX3sLr9nEWcYJuKM6ZmNwpw0t4JVoWDV88Q9ngNcE5tqSVtp\nt9hjOLoOBRZxhmEizqtarZK//ndzq15v0ctOkcKuKLuMwNVoXuae24Pop2kGeGKTYZioE4ytoeKK\nxF2vkw2P1Ug8RWnLJpE54/5K45oJFnGGYaKGnqMxc1q/gK+z6iz2kajC/sRVQ3HJsK7o38WV2ign\nRf3VcjETbKcwDBN1VC0+d1DgaoF6i30k6sRmYU4aHrnidLfnEzkSZxiGCS+qFidaA8uSS8R9Pxfo\ntfECizjDMDFFMCJu0fHEw3l8MxFfV8MwjOnRywv3xOWJe4t4Q5Pda5tKgo965GaFRZxhmKjRtYOj\nomHHdFf9ElsIkbKeM1JWecTvaxI8yxiaHJ7YZBgmatw0thCFOWmYdJqrxrivzj0qPbJTsa7ySKsm\nJxPYE2cYhgkPVgthcv9ObrZIQhCR+LDujp6Yx06G3h2S7RSGYRgDKOmdE/S+mamO3pfHWyHigSY2\nx/YKfhyxANspDMPEBPOuLw5alDtonXv0zJRv/zLF72sDZbTMv6E4qDHECiziDMPEBMmJVrdqg/7w\nF02na40kfBHIEg92DLEC2ykMw5iOwpw0AMDVI9ybOkwd0Cnga1uTWx7LcCTOMIzpyE1PQsWc89y2\nef7ti3gTcY7EGYY5paA4U72gLoeIxhHRIuVvGxHdSkSTiSjbuOExDMOEl3iLxIPtdr+ciFIAgBwJ\nnU8C+IsQYq+Rg2MYhgk3cbbWJyQ7pVH7/5UAsgDcQEQzKdRq7gzDMFEk3iLx1rhDFwF4UggxB8BE\nABd77kBEtxBRGRGVVVdXt3WMDMMwYYNFHEgGcFx7/BGAgZ47CCGeFUIUCyGKc3Nz2zI+hmGYsHIq\n2ymSlQCGao8TAawN33AYhmGMJd4i8aAmNoloEICeRDQQwGMAHiOiagCNQohPjBwgwzBMOPGl4a/O\nGIn0JP+rPWORYLNTvgXQTdn0S2OGwzAMYyy+cjHG9DRX4StJnKW9MwzDnFqwiDMMw5gYFnGGYRgT\nwyLOMAxjYljEGYZhTAyLOMMwjIlhEWcYhjExLOIMwzAmhkWcYRjGxLCIMwzDmBgWcYZhGBPDIs4w\nDGNiuNs9wzCnHA9cOhhFuWnRHkZYYBFnGOaU44ozugXeySSwncIwDGNiWMQZhmFMDIs4wzCMiWER\nZxiGMTEs4gzDMCaGRZxhGMbEsIgzDMOYGBZxhmEYE0NCCGNPQFQDYKuhJ4kcOQAORnsQYYKvJfaI\nl+sA+FrCQQ8hRG6gnSKxYnOrEKI4AucxHCIq42uJPeLlWuLlOgC+lkjCdgrDMIyJYRFnGIYxMZEQ\n8WcjcI5IwdcSm8TLtcTLdQB8LRHD8IlNhmEYxjjYTmEYhjExLOIMwzAmJmQRJ6J0InqLiHYQ0Vxt\nWwER7SGifUQ0Wdm3IxGVerzebRsRTSeinxHRXUQU0ZtKW65Fbz+zXou27TIiuoiIiqJ5La29DiLq\nQUSHiaiCiHYT0d+jeR1tvBYLEf2ZiC4lonuJKF/bfjER3UpEs4go1QzXov39OyK6mogeJSKrti3m\n35dgv+PRvBYAgBAipP8AnA0gDUAqHIt4zgAwE0Cix34E4A4Ay31tA1AA4CXt8fUAfhrqeNryXxuv\nxW0/k1/LHQAmxMK1tPY6AAwDYNMeXwtguFnfEwBDATyqPT4PwG8BtAPwP23bOAAzTXItZwP4vfb4\nLgA3muh9Cfgdj/a1CCFCj8SFEAuFELVCiDoAmwAcADAVQCURXaPsejWAVwDY/WybAuAH7fFmAOeG\nOp620NprISKbzn5mvZYeAK4D0IuIHieiDETxWlp7HUKIr4UQjdpzQ4UQ62DS9wQOYZlMRMMA9ALw\nKoAxAPZqz5vpWoYBkO/LJgBnwgTvSwjf8aheC9CGFZtElA5gpxCiEsBE7Sffx0S0Fo67XJUQYj8R\nyf2Hem6DYznrEe1xPYC81o6nLYR6LZpYeO5nymuB40P3rhBiHhHNBnAvgH2I8rW04jrk61IB1Gl/\nmvI9EULUEdEsAH8H8JX2nCmvBY4b0i8APARH5HsCJnhfhBDbPLf5GHfUr6Uty+6vA3CP/EMIsUvz\nIQcCOAvAEO2NPJ2InoTjzuy5bSOATO0Q6QAOtWE8bSGkaxFC/J/OftUw4bUA2AHguLb7RwAeB/At\non8trXpPAJwD4BPtsVnfkz/CEQWeC+BVIroejihc+uBmupZfAxhMRL8HMBrAI3D8uoj192Wbzja9\nz1P0P2Ot9JQuAtBFe9wJrnzzG+R2Zd+lOq9fqv2/O4AXtcc3Abg+0n5Sa6/Fcz+zXguAEQBe0B4P\nA/DPaF9LWz5fAB5Q9jfrezIMwMPa49MBPAaHgH+sbZsI4B4zXIvydz6Ap8z0vgTzHY+Fawk5Eiei\n2+CYoDik+UZvALiIiN4GUCqE2BPssYQQO4loLRHdDMfPkDmhjqcttPZaiGg0gCc89zPjtQgh1hDR\n2UR0NYCOAP4hhDgWrWtpy+dL279JaN8os36+AKwHcAkRXQigEMDjwmGxvKIdMxfAwxG4BCdt+K5k\nARgCx3X8BjDH+xLsd1wI0RLNawF4xSbDMIyp4cU+DMMwJoZFnGEYxsSwiDMMw5gYFnGGYRgTwyLO\nMAxjYljEGYZhTAyLOMMwjIlhEWcYhjEx/w9GiS8Ys/pdhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf824c30f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(timeall, tempall_masked)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Diva-python3.6",
   "language": "python",
   "name": "diva-python3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}