{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from netCDF4 import Dataset, num2date, date2num\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wolski/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:9: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "ncfile=\"/work/data/saws/WC_all_accum-filled_2018.nc\"\n",
    "#ncfile=\"/work/data/saws/south_africa_2015.pr.nc\"\n",
    "_bnds=[15,20.5,-36,-30]\n",
    "ncdset=Dataset(ncfile)\n",
    "_lats=ncdset.variables['lat'][:]\n",
    "_lons=ncdset.variables['lon'][:]\n",
    "_ids=ncdset.variables['id'][:]\n",
    "_names=ncdset.variables['name'][:]\n",
    "data=ncdset.variables['pr'][:]\n",
    "if len(_bnds)>0:\n",
    "    x1,x2,y1,y2=_bnds    \n",
    "    _sel=(_lats<y2) & (_lats>y1) & (_lons>x1) & (_lons<x2)\n",
    "    _lons=_lons[_sel]\n",
    "    _lats=_lats[_sel]\n",
    "    _ids=_ids[_sel]\n",
    "    _names=_names[_sel]\n",
    "    data=data[:,_sel]\n",
    "_times=ncdset.variables['time']\n",
    "_dates = num2date(_times[:],units=_times.units)\n",
    "ncdset.close()\n",
    "_pr=np.ma.masked_invalid(data)\n",
    "_obsdates=pd.to_datetime(pd.to_datetime(_dates).date)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr=pd.DataFrame(_pr,_obsdates)\n",
    "count2=(~np.isnan(pr)).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(count2['1990':'2020'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncfile=\"/work/data/saws/south_africa_2015.pr.nc\"\n",
    "_bnds=[15,20.5,-36,-30]\n",
    "ncdset=Dataset(ncfile)\n",
    "_lats=ncdset.variables['latitude'][:]\n",
    "_lons=ncdset.variables['longitude'][:]\n",
    "_ids=ncdset.variables['id'][:]\n",
    "_names=ncdset.variables['name'][:]\n",
    "data=ncdset.variables['pr'][:]\n",
    "if len(_bnds)>0:\n",
    "    x1,x2,y1,y2=_bnds    \n",
    "    _sel=(_lats<y2) & (_lats>y1) & (_lons>x1) & (_lons<x2)\n",
    "    _lons=_lons[_sel]\n",
    "    _lats=_lats[_sel]\n",
    "    _ids=_ids[_sel]\n",
    "    _names=_names[_sel]\n",
    "    data=data[:,_sel]\n",
    "_times=ncdset.variables['time']\n",
    "_dates = num2date(_times[:],units=_times.units)\n",
    "ncdset.close()\n",
    "_pr=np.ma.masked_invalid(data)\n",
    "_obsdates=pd.to_datetime(pd.to_datetime(_dates).date)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr=pd.DataFrame(_pr,_obsdates)\n",
    "count=(~np.isnan(pr)).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5Hr2oB+bdN1J2m9qrxk49AM9iN/3UteztFnWj6nFDRIlwifwHQoiZUvF+IsoTQpRJrhn3FVAKIN/r8LYAAqZcCiGmAJgCACUlJXb7XmKKvTqXStMyYKo1wVd9qBSRQWiSFI9KHS4oozmnh/7Mle8v2hl0W882mbrr9V6hql+7LAwvaoHbRnTUXM/r87ar2u/6wYWa644kal2D/i3/sFb2svDBpSbqhgBMBbBBCPGc16bPAFwvvb4ewKde5ddJ0TcDAVS4XTyMPfFeo1MLZq5md3m/thjTW9vQjg1X19PMQ5+sDbm9ZVNtrgM5lu88gue/2xyQ291s3OKqtlVHAF652h5Be96X1tOX9VLcf1CH5rikuA2evKRxXyvHndS4boYAuBbAKCL6Vfo7H8BkAGcR0RYAZ0nvAeBLANsAbAXwOoCJxpvN2AEjLtzR3XJlyzNSEvGfq7Td5LGQFjjJwNQQf/ywMbInWKZRKyEinNfLfnEcA9orTw5LSojD81f0QX52agQsUkbRdSOEmI/gnY4zZfYXAG4P0y4mguj1mxmjq/KV6Kna+TJvHtOXBk5g8+dUjb5FPqIZuYClaPQzx2C0MhOKPA1ZI42Y1EQEDGifHVCup+4YaNCbxvZD8mGW7jVz6xsEqkNMvDIDO/2caq+tMSF6IETAjsljfMr835uFPWJ/GEvxngWp5eZq2sTEy0fHXR4Ls2nN8vO++Yv8IGsHr1DKpX8ZHdY5tP48pk6YUhteqbVim16C3KJnfFDr5541cTDaZoXvf0zwG0Ed2cUVhsmuG/tRpyMSyjk0Xl3RuGA4Cz3j02pRm3qguCBLeScV+Me+Z6UlAdA3sBoLg7FWzhVwryesF/evo1YozYxSMatuu16BLPSMD5F2f0wc2ZgSl0Cep45dbxireeP6EvQKI54+mrDDc1vuoaQ7eCE8U8KChZ7xuXIjfTGmJfu2UBu88n9rYXS33KjsUmslPzsVn9851BYpfGMJIx46VvY4WegZHyJ9MQbrQtuhNWdnnPpQi9QKTkYNxp7fy3cWdKj7p0vLjKDbzIaFnvG56I0U2N5ttbsYIilfkQptC0Y46+JGo8yraUS8O76/1/7m2aL1OeltivvYDjlpePnqfkH38yczNTHEVnNhoWd8yEkLf4q9mwoVqRX8b+bGFX3s06Q3S3BGd2tpTsU2ZbiU2C4jxSFR3WFeFz1aNzXGDhWw0DMecf3PVX2Ra0AuFTfZUgRNKAi+rdPGxZgNMyNslj90luF1fjRhIJ674jTD67Uzfx/bA/PvH+mzNrA/3q1+M92Iqqs2sev08R8GmVe5Hyz0jIf8bGMH+FS5Jgy8l826J81ogeZlpiA5QX+oZDS66BPj4wyZe2EEml03JqyKlZoUuZ6NQ/pQjB1R437x30fvIGM0Cl84qB1MjCa+/78zfK4GG3Xq/ND23b9xXQkKmlv7gGOhZ3xE0kj5UDP5KsBH7z7WTr4bE4i1B5Mv8r9txxbpvuk4bHAJhHqgypknVza6u/VjMey6YTx4T1gyAl1i7R6M1XBol5YZ+D8N69GqwTsXvn+aBiMI92u2y4Pi9yXa1lKNVryvgILsNPRum4knLlHOS28XWOgZS50APl110ueSmHPPcHQ3MIKhuKAZOrVIBwDcdWaRLVMraPmWmpoY5fLM5cYOKPsMxtrAeSP3QE1KiMNndwzFwA6Rifk3AhZ6xoPReqZmST1/ER3V1dXNdU8u6Z6nXsCNbOWa/fDLTlWOSApFODH4VqP2OrPT81W9zTYy2ovovVoYw/D2ixo1yHfRaa1x9YACxf38b4vL+7XFukfPQZEk9J/eMcQQe/Ri1n0b7uQZI1eaijR2cTvFEtF7tTC2plNuuqrWjdwuacmNrobEeIsuUburkQbz7NrKVMIOVtv9MlALCz2jWjPuGd1Z1X6vXN0XE0d01G+QTrT0Rr64c6iq/ezgJw4Xu+m83exRQ7RfBxxeyXhQugHVTtnWsqCzVTdQzyhP9euQhqbtccr3zC16xjeOPsSVbUpLjIB//u40XFGSj5J2xixmEi4vXNFH8QZvrWFtXTPQMrHM7EfpRae1NqdiDYYnaXTxaZ8ZG3zbi1f2sc21Gwxu0TMerGhdE7lyrD99ee+In1uOawYWoF3zNM/7YDd4u+Zp2FtRpescnXLTdR1nV4o0fh4zrrKEeEJNvQkVq2BsnzaobxBYtvOINQaogFv0DNR2UM1o0RtZZSQHzsKJTjJh/pWl2MG9YVYUkhl5/8/v1UpzDyRcuEXPeCCK7igDI00383tQM2O4XfNU7Dx8Kuh2LebZLepGyfbmaUk4fLJGUw/TjNnLZuGfwz4ScIue8SHSybKMFCEjxNm/jmDWmf1AjB7ZMu+70HJp9DVosXqnwkLPGHqjLn7wzJDb/zthoM97YwXNuA+i9MAzWtv+Na7YtPrt9tAwwp7Hxvbwef/ilcWqQ2a1EMUdXB9Y6BkPSi0oNV3plk1DR6M09Vt0wkivgpH5vZPiXbnig/p+DVaAdI35aOzkYjPaP67mo2Wk+F5HTZLiTQ2ZVXud2nUtX/bRM74rPBl8nb40rhgt0oOvWmVkpM97N/XHGf/4yZC6bj2jA6rr6nH94ELZ7eG4uLS6q2ZOHIzfDpzQfT6t9GzTFGv3HFO9/41DCvH01xsNt8MOPRGb6rZmWOgZD96im5GcgOPVdf47aMa0GGsZvMMiwyUlMR73nds16HajBcD/q/Wuv29BVoAP2syxlO552oQ+JVHbSllKDzo7torVNkjsNvDthl03jCrR6pbXFH3aNgsov3FIYVjnNuu+8Pfhurl2YDsMMiC9rNFSZD9pM4aurTLwx1GdfMrUCnk4ommV3trxIQWw0DNekPe6I343yqyJg5Els9j3wxfKC2qoc0SCawcV4tnfBeZKf+zinvjQb0DYG7W3aYPRN7SN9MHIj/b13cPxJ4MXhVHD9qfG+Ly/oHcedkweE2TvUFg3x8RIWOgZWTeA/3Vr1IWc7fewMPoGidRUdNPDKxW+F5s2HFWh6LqJkB1aiPbfg4We8RCJRkluRgrm3TfS65zqz7r4wTM9C5IE4/2bB2DJX0KHeBqB2fd1uMLhnYDO6Ifp2kfPMbbCIJh5Pap1sdhdwNXCQs+oSmpmZHRMfnZqY70qqs2UQjJbNk1BYgLJbnOTkhiP3AxXiKepDy6DFUDrgKYSZroS0pOtj+FokmTsALDy8eFttxrrfzHGNrguVpeAxdloSvkXdw7Fmj0VAICGhsbyl6/ui14hYqf1SLFa/Ta6oTewQzb+dkF3/P2L9erOb2JLM1TVzcJcGQtQfgCr+Wxnd2+p6Zx2HSSNFNyiZ2RvbLN89ErnkSM/OxXnSznu6xsarT2/V55P7yCSGD0YS0S4ymvpxUinovCxRec2owl1zUUqjNEpjwcWesYLirhPUusN++SlvdTXrdUYDXj3LLTibde1A9vhrxd0d5UbaPBjY3siP7uJrmOtFjel1vf9IeY3BCNs140tpm/pR1HoiehNIjpARGu9yvoQ0SIi+pWIlhFRf6mciOglItpKRKuJqK+ZxjPG477F/G8Msy5zrfX2s8kCD0aJ4WMX98RNQ9u76jRQYYsLsjDjtsG6jg29+Iz1gndbBJepdIrHR02L/m0A5/qVPQPgUSFEHwB/k94DwHkAiqS/CQBeMcZMxky8W1Du15Fy0dtAN3TRNktfazkU8TYaF3Eao7vl+rzXqt+qc91orDdSKAq9EGIugHL/YgDu+K1MAHul12MBvCtcLALQjIjULyDKmIa3bzsYvhezX4texZW++fHzNFplLmY+RHLSAyePhUtifBy+umuY4fUa2R+LqI/ewLON7dPGsLqiEb1RN3cDmENEz8L1sHD3EdsA2O21X6lUVqbbQiZsSo+cwtCnfwQAxdmB4bRI9GQxNNMVYGa3u3Wm8S16AEhTmYHTqiiSopbhL4OoOPko7DOosEHlfloHxe3aJ9M7GHsbgHuEEPkA7gEwVSqX+5yy3xQRTZD8+8sOHjyo0wxGDUt3+HfI5CGEiqOPPtTcop/dMUTHUb5+4jdvKMHndxifC92OvHZtSeROZqOLTv2DwZ7oFfrrAcyUXn8MoL/0uhRAvtd+bdHo1vFBCDFFCFEihChp0aKFTjMYJWrqGnDPf1d53r8xbxue+3azzz7e4u5ubUWr79wbNa3e3jKJ2tSQ4LXm56iuLdGrrbG50A1psOusI1Qr1n+CWrRitCDbPSpHr9DvBXCG9HoUgC3S688AXCdF3wwEUCGEYLeNhczf6ttbenz2Brz0/RbZfYkIT13aC+P6F2BYpxy/ba7/d47qhFkT9UVzRBq3WHbKDd/d4M074/sr76QTIx6wWanmLe6ixL/GFeOZy3qH3KcgOxXjh7QPvoONmsXqJ9DZyGgZFB2CRPQhgBEAcoioFMDDAG4B8CIRJQCogivCBgC+BHA+gK0ATgG40QSbGRXsLj+FA8erkRAn/yyfvnQ3MlMT8c9vNvmszJOX2QRPXdoL9/1vlexx/2dBJkK9uCc19SvIwlYDF+44o7N5PVAjZuY28UunoLl3EIZmXahi/QEiwt8u7I43f9musJ9+O4zGTrboQVHohRDjgmwKWMpcuPrKt4drFBM+w55xDb5Ou3mA7Pb7Zqz2vN683yWCIWdERuGV3r99NgBgbJ/W+O+y3Qp7Rw6zv8pwf6vidlmYuXKP4n63ntEhrPME445RnfDUVxsDHlhWoPaZZ3fXDee6cTj1Op29dr9w1dChRbrOHOTWYcZDQEudOyaPwc+blYMjzPxebz2jI249w7hJUY9epG3NBHmi+37gFAgOpKq23vP62qlLVB+nV2RCJRYLRoFFOWqUiETUotnn6CvNHra31zhyyM1DM/o3aN3MFW7btVVThT2tgVv0DqSmPoxELBJaBpem3TIA+49Vaar/8zuGovxUjVazooaMlAQcr6pT3jEISkIkt33xg2di3d4KDO6YE7hRhqsHFOCDxbt0WBddhOPKUjtfoX/7bHx6+xBdjZ5IwELvQPRe1nrdNRkpichI0RZ2l5maiEwDUt7aldDjHcaeKz6OUN8g0LJpClo2TVF9XGJ8HDKbJKKistZYg2xGnAFfuJoqTsvXF6obCVjoHYiKbAeKeIt+sIW2reKDmwfgZLX+1nK08Oo1fZHZRDnVwld3DcOS7cEnxQXTqCgcX9cFpxBioXcc7y/aic9+lZ2jpptrBxUaWl+4DOmkzjWhB6N8t0ZEKZ3bU12aqM4tM9A5xBKLoT5SKDPdPYVox4hFdKL9WcGDsQ7joU/WYonKlAf+yN30TpkJaRRqVzYyOxeNewxFrYb9+Rz5+Q9yESlu26Nd3NyMMHHeQ7TAQu8gzBCXSedpX+QhmlFqiE+5Tn+uF3c+HCNdJgsmKS+ETgBuH9lJppwwtk8bfDRhoPxxNld6/xnAwcjVMG7hTyzlo2eiBCMvSrvf5GYR7DvUuuBJqJ/CiN/pLKlnkZYcfFKR0nmKC0IPHjphLoVRROOEQW/YR+8gjF7HlGlE8wCw308xc+JgQx+ej1zYA38cVaQ52gkA1j16DnYfOeWJ+Q5qls21TY/4jumVh9lr1KffsnsOG7Vwi96mDHrqe7z4nXzysWCEe0l63zdZaa5oj/Rk57cFcjOSFfdJSYxH83Tl/dz4/xYZKr9HtdqVEB+n2yWRlpygamJPapL1KQiMxn09OyXPvFqcfxdHKWUVVXj+u824a3SR6mOMbNHfPboI+VmpGNPL+QuEfXL7EDw4aw1+2mTcugh1MquHW9Xh0rvQR++2zTBXRToEvbxxXQlKj5zCI5+vN2zQ/6u7huGINBFv1sTBAZ/N/V3EWueXhd4GrCmtwLsLd6B/+2z8+X+r0TytMXa6cNJsXDuwHR67uCeEEOjx8Bz8viQfZRWVmLNuPwBX5IUhsfNeipCcEI+rBhSEX2kU0LpZE5zboxV+2nTQsK66f1iit9hGubvXMEZ3b4kfNrqu4b4K4wVq6ZbX2FMpLgh/IXmnPBDYdWMDnpmzER8vL8Wf/+fKKHn4pG9qgPcW7QQAHKuqw6maery9YIdH5AFjRJ4xlnfHy2cNVaJV0xQMK8rB81f0CduGFhnJGN65BV66sljTcZFONj/sAAAW90lEQVS8nMIR0vukkNGurYLPIQiG1odttD+cWegt5tCJaszbckhxv5PVdbhj2gpTbYnya9lWDOrYHDsmj0HHFmmajkuIj8N7Nw3wpFgOh/g4wrvj+2NAh+Yh9ws6czZsC9SjZ2C1o7SgTCyMI4ULf0MWU/L4d6r26/HwHJMtiW3MarF5N1jzmrkGTy/u08ack8UoejoFqhd4cUhvmYXeAmrqGpCUEIeauvCzTBqJQ65pW+H21cfHxSEnPRmbHz8PifHcdzICPd+i2gd6bX0DKmvrcUIKqY32OQUs9Cqoqq1H179+jfvP7YrbRoS3IMK36/fjlneXGWSZsTSws99winLTsfPwKaRJoYpJCfb3lmaluoIBCpun4meLbbECIQSK/vKV1WYYiv2vOhtwrMqVxnXq/G1h1/X12n1h12EWdSz0hnfVX7iyGNNuHhDWNPxI0ye/Gd668XQ8OKab1aYYjprft1qmp90sLbpzPrHQh2D+lkMonDQbC387DCA8EVhdehR/mbUGtQYsCmIW9TKx37GCWV3z9OQEDDYx26YRyA2EjuySi+QEe0+Ycpsdzi+3fGc5CifNRuGk2Z4e7c7DpwL2i4/ysBsW+hBcM3UxAOCxL9YDCG9C0lWvL8YHi3ehi45QsHAZ119dPDy36GMTI5LhTb91EB6IcAK8PvlZuGFwIV64Un0oqr9e/+uHrZ7XJ2tc/vh3F+4IOM6IxUushIU+CK/+/Jvn9aETrrj2I6dqVeU7WbqjHP2f+A6vz92GuvoGTJ2/3TOo8485m8wx2IvsNN/FKp66tBduHFKoeJwTco8z1tC/fbahC3qrIT6O8MhFPdA2S/v6wweOV6Nw0myf2dD3frwKDQ0C+49VB+wf5TrPQh+MyV9tlC3/6ydrFY/93asLceB4NZ74cgOmzt/u6RFECu8WWndppuBZ3ZTzqLdtZs8FuyOJlkfduP75UZUPJrNJIi7r29ZqM3ywU/jinHX7MX3Zbny3YX/AtvgoX6aKo24khBDYUHYcmamJ2HOkMuh+3288gL1HK5GXmaJqkscCyb9vBf++qhgX9G4NAOjRWnnRYiev4WoGT13aG09d2ttqM1Sz6uGzrTbB9hw6EdiaB6LfdcNCL3Hvx6sxY0Wp4n4VlbUYPPkH/O2C7hg/tH3A9m0HT/i8/9nEpFBaSEwIfaEmxXPnDojN2cFW5lqP9KmVsna63bT+RPt1wXe3hJzIjx8SKORu5Lp3AHC0stYwm4wkNSn4M33+/SOx6EHllYpiARt5EhgTuGZgu5Db3aHU3sy4bbAh685aScy36IdM/gF7jsq7av52YXe8+ct22W2nauply81eK9QM9AxmMYwRRPp2UZLrmSv2BJRpXV3MjsR8iz6YyLt564bTZcvX7KmQLdcauHLfufKLNoeD24Ron7ZtBfyNWYOa733WxMH4z1V9TbfFicS00P+06YDiPgODZP4LFoqoNY3AxBGBizYbRZSPH0UUdx56/s7sS3FBFsb0Dm8hnOjrbxtDTLtu7py2Mug29wrzWm/8WL2Q7E7XVhm48vT8oNvdz2fuBVlDqPvmrO4tUZBtjHsxjoCc9KSgg65OJaaF/niIyU8Thrsmf2gNqzJ6ge7WmSnYW1Gl6RjhES3Gzdd3Dw+53f2dxcV0HzfyqLm9Xr+uxMDzEebdNwrd/va1YXVGA3xZB8F9AWqdKGH04JKVoW+xhDs1hRELfjiJds3NHaiPwtiFqISFPghueY2PIyx/aLTq47S06OfdN1KjVYxZ9GuXhSUPnolLiu01c9Rqvr5rOH66d4Tp54nsalYRPJlNiEmhF0KgcNLskPt4XwzN05ORoLJlr2UstmkT5ZmoclPsM1UcB8TmBR0O0ZRK2EhCXSdNkuJVX2+MfYlJoQ8WA28EauLo+7fPxstX9/XcQKGy/nXNCz2Tj2EYRomYEPr5Ww5h+rLdnvez15QpHqM3+kKN56ZFejLO79UYJhbKL6zHisaHDTfpGYaJkagbd17535e4wuvmqsg/o+T2uCBIPK8qH71f3VonWYWybVhRDogIczcfZNcNYwhmum6GdMrB4I7N8cD5zlvNyk4otuiJ6E0iOkBEa/3K7ySiTUS0joie8Sp/gIi2StvOMcNof45X1WLlriM+ZVW19Xj+283o9cgcT1nhpNl4cNYa/LL1kOZz+Gtx62ZNPK8X/HbIM4Fq8fZyxbq0hGxqFev3bhrACcoYQzEzz0uTpHhMu2UgOuWmm3YORp3r5m0A53oXENFIAGMB9BZC9ADwrFTeHcCVAHpIx7xMRKYn7L7t/RW45OUFqPTyvU+dvx0vfr8Fx6t8Y+WnLd6FI6eUE4/5hzX6+97dM2Dnbj6Iq15fjFd//g1bD5zAlLnK68oG3jbmxJiFuj2HFdl7eTuGYYxDUeiFEHMB+DdTbwMwWQhRLe3jziUwFsBHQohqIcR2AFsB9DfQXlkW/OZqoR86UY2T1XU4WV3nKdOLUhvG7W4pq3Dlytlx6CSOnlI3205LK92sttRjY3uaVDPD2JtYdGnq9dF3BjCMiJ4AUAXgXiHEUgBtACzy2q9UKjONrQeOe0R32DM/Glav/8Xg3+Z250ZxN/TX7j2Gj19dqK5u/7q0+ugV9+BZKAzDNKJX6BMAZAEYCOB0ANOJqAPkNUhWdYhoAoAJAFBQoG7xajl2lQeu2B4J3OLs/nAbyo6pPtbf5xlKlvXMjPWkQAhxLBHw470jDE/ZwEQvbbzGnRhnoVfoSwHMFC7H9RIiagCQI5V7Z45qC2CvXAVCiCkApgBASUmJLrWZv+UQxr+9TM+hiijJ69sLdmDu5oPYduik5roT/RKqmKW1Sp+hfU6aOSdmopJWmbExYSwWE9fpDc/4BMAoACCizgCSABwC8BmAK4komYjaAygCsMQIQ+VYVXrUrKpVtaT1iPy1A9thUogJUv7EEeHlq/ticMfmGNKpOWbcNkjRNm6jM1qIPdmLPRRb9ET0IYARAHKIqBTAwwDeBPCmFHJZA+B6qXW/joimA1gPoA7A7UII06ah9mzjWvB6aKccTL6sF4Y+Le+jz8tMQZnGDJABPnqD1POxiwMHQd0RPc1SE3HULyIoPg44v1eezwQrtcTioBPDMIEoCr0QYlyQTdcE2f8JAE+EY5RaukvpAcYPLQztjwZww+BCzFq5BxUq13SNpEaGeoZc3i94DvVwaJGRbEq9DMPYj6ieWdMiIxk7Jo/BqK4tFYX5kYt6YNXDZ0fELq14cqH7Pax2TB6jKW3upX3bSPUpdz9CLRbOMIyziGqh98ZwN4WN/R7BLPMfZLLxR3AkifHR+YXnpLt6d30LmllsCWMWjhH6UGkFvN06r16jbnHhyLpujBkAcH/MYLUt/Yv6vPqMdpY8OBoLHxhltRmaKcxJwzf3DMf956oPEmCiC8f030MJc7PUxqRM6cnqEjRpXUIwLDyuG4OqC7L+KfvlzSUrLclqE3TTuWWG1SYwJuIYoQ+l9FOvP71xN5ViarXb453x2jNHBJgcnZ4Exoa8dm0/xzQUjOpBRxOOEfpQkyD0TASxJuqm8ayhsvlZ/RBiYo9zerSy2gQmDBzjo9cjfr2kOHw5RnXL9Xn/z9+dpv0EKmlMWdBY1iLdnNbT6G4t8cczi0ypm2GiAf+Z6cG4oHcebh3ewWRrIoNzhF7HMW/ecLpseRwBuRm+vYBLis3LzSbXlUxK0P7TxEtO/lAd0zeuL8GfzuqsuW6GcQpq8+v/+6q+jlkQxTFCH4zRfi1zb+QW3gaAi2VE3Qh3SUayvKesIDsVADCySwuVNckb44m6kboI7OFhGAZwkNDXBVmP7+Wr+/m89xa/tOQEfDRhYMAxz1zWO6BMTe6br+8eFnL7kiDhje2ap2H5Q6Nx8zBt3cRx/f1nzfrH0bPUMwzjIKEPpmlKLpCs1MCQuASdS/E1TwvtV28SpAcBAM11+OQzUnxDRZ0SFcEwjLE4Ruj9fepKmJF7OyUx8l/nF3cO9by+Y2QnAN4pFSJuDsMwNsQxQq+V/Gxfoe/cMrzFidtmNQlwlSTrGFBVg/s0hMYMnkBj78W9mEhEJ30xDGNbHBNHbwT/GleMtXsqdB370JhuAYOfVumsW+hZ5xmGAWK4RS/Hhae1DhlOdV7P4JNGCrIDV2uyqkXdECQFAsMwsYljhb4oyMzSjlL5pcVtAWibDq2U/ddf180Segp44YvwuG5MOT3DMFGGY103024ZKBuF0rJpCnZMHhNQrqb1W6+g9AFpghVrNAd3iz6elZ5hGDi4Ra82AiZdmsTUNU85e1+HFsEX0xYQgT5xk3U22MPJPbDsnbWTYZjYxbEtev8Y82C0zUrF9FsHhcx74+bes7ugf2E2MlIS8fvXFvpsk2vsxxFh1sTBuOTlBapsMYqHL+yBsX3aoFMup55lGMbBQq8Ftcv1JcbH4cxuLVXX2yAEiguyVO+vtgOg5PpPSYzHwA7NVZ+XYRhn4yjXzUvjii09v78Aa/XchEpNHOp8fx/bAx//YZDGszEMEys4SugvOq21pef395lrXd5Ab26a6wYV4vRC9YuIMwwTWzhK6K0mIFTTpIVs3A8UjqlhGH3E2kLoLPQ66dG6qc97noXKMNHDzIlD8KyJiwnZDRZ6nQT64wnxUuGwohzX/845nu1a/e8MwzBGwVE3BpIQH4e5fx6J3KbJ2FdR5Vmr9pdJo5DZxPiYdu5FMAyjBhZ6nfgPvCYluN4XNHetFlWY0zi5yuiUyPuOVQEAVuw8ami9DMM4E8cJ/avX9EPbLONzzfvj35q2YnLSwm2HI35OhmGiD8cJ/bkhMkwyDMPEIjwYG8Xw0oEMw6iBhT6KcUf3MAzDhIKFPoqprTdpRhbDxABdW8VO0j8W+ihGKK2EwjBMUHq2ycTKv55ltRkRgYWeYZiYJSstyWoTIoLjom6s4Jt7hhtW10cTBiJPmmilBLfnGcZ4XryyT0CKk2iHhd4AOrUwLr2Bljzy7LphGOMpzs/yTHx0Cuy60YkdNNYONjAMY38UhZ6I3iSiA0S0VmbbvUQkiChHek9E9BIRbSWi1UTU1wyj7YZVOWdY6BmGUYOaFv3bAM71LySifABnAdjlVXwegCLpbwKAV8I3kQlGQP57hmHCxonJAhWFXggxF0C5zKbnAdwH3zHBsQDeFS4WAWhGRHmGWGpj9K4MFS7comcYRg26fPREdBGAPUKIVX6b2gDY7fW+VCpzHHZoTXtnyGQYxhhSEuOtNsFwNEfdEFEqgL8AOFtus0yZrCIS0QS43DsoKCjQaoZtePKSXpad+/rBhZadm2GcxoD22bj/vK6OzCGlp0XfEUB7AKuIaAeAtgBWEFEruFrw+V77tgWwV64SIcQUIUSJEKKkRYsWOsywluQE11O/d9tMy2xwoCuRYSwjIyURfQuyrDbDFDQLvRBijRAiVwhRKIQohEvc+woh9gH4DMB1UvTNQAAVQogyY022B/8aV4zbR3Z03MQKhok18rNd61fcMaqTxZaYh5rwyg8BLATQhYhKieimELt/CWAbgK0AXgcw0RArbUjrZk3w53O6WjYQC/DMWIYxAnfvPC3Jeb55N4o+eiHEOIXthV6vBYDbwzeLUUNCHDtvGIZRhmfGRjGJ8fzzMQyjDCtFFNPEgWFgDBNpYiFnFAt9FNPEwT5Fhok0TpwR64aFnmEYxuGw0DMMwzgcFnqGYRiHwwuPRCF3jy7Cvooqq81gGEfg/KFYFvqo5O7Rna02gWEciHNHY9l1wzAMA8DJbXsWeoZhYhrntuMbYaFnGCamcW47vhEWeoZhGABObtuz0DMMwzgcFnqGYRiHw0LPMAzjcFjoGYaJbWJgNJaFnmEYBpy9kmEYholiWOgZhmEcDgs9wzAxTYq0UpuDPTec1IxhmNhmynX9MGP5HrTPSbPaFNNgoWcYJqZpm5WKu0YXWW2GqbDrhmEYxuGw0DMMwzgcFnqGYRiHw0LPMAzjcFjoGYZhHA4LPcMwjMNhoWcYhnE4LPQMwzAOh4SwPkcnER0EsNNqO4KQA+CQ1UboJFptj1a7AbbdKmLV9nZCiBZKO9lC6O0MES0TQpRYbYceotX2aLUbYNutgm0PDbtuGIZhHA4LPcMwjMNhoVdmitUGhEG02h6tdgNsu1Ww7SFgHz3DMIzD4RY9wzCMw4k5oSeiN4noABGt9So7jYgWEtEaIvqciJpK5UlE9JZUvoqIRkjlqUQ0m4g2EtE6IpocLbZ7bZtCRJulz3BZBGzPJ6IfiWiD9J3dJZVnE9G3RLRF+p8llRMRvUREW4loNRH19arremn/LUR0fTTZLm1vSkR7iOjf0WI3ET0j1bFB2sfUBZl02N5Vug+qiehepXqiwXZpWzMi+p90n24gokG6jBJCxNQfgOEA+gJY61W2FMAZ0uvxAB6TXt8O4C3pdS6A5XA9HFMBjJTKkwDMA3BeNNguvX8UwOPS6zgAORGwPQ9AX+l1BoDNALoDeAbAJKl8EoCnpdfnA/gKrhXeBgJYLJVnA9gm/c+SXmdFg+1e9b0IYBqAf0eD3QAGA/gFQLz0txDACJvZngvgdABPALhXqZ5osF3a9g6Am6XXSQCa6bLJzA9s1z8AhfAVy2NoHK/IB7Beev0fANd47fc9gP4y9b0I4JZosR3AbgBpFv8GnwI4C8AmAHlSWR6ATdLr1wCM89p/k7R9HIDXvMp99rOz7dLrfgA+AnADTBZ6A7/zQXA1FJrA1chZBqCbnWz32u8Rf7GUqycabAfQFMB29/0dzl/MuW6CsBbARdLr38ElmACwCsBYIkogovZw3aT53gcSUTMAF8IlpFagyXbJXgB4jIhWENHHRNQykgYTUSGAYgCLAbQUQpQBgPQ/V9qtDVwPJDelUlmw8ogQju1EFAfgnwD+HCl73YRjtxBiIYAfAZRJf3OEEBsiY7lq27XWExHCtL0DgIMA3iKilUT0BhHpWtiWhd7FeAC3E9FyuLpaNVL5m3Bd7MsAvABgAYA690FElADgQwAvCSG2RdTiRrTangCgLYBfhBB94eqGPxspY4koHcAMAHcLIY6F2lWmTIQoNx0DbJ8I4EshxG6Z7aYRrt1E1AlAN7iumzYARhHRcOMtlTFIve0RqSfC50yAy1X7ihCiGMBJuFw+uiqKeYQQGwGcDQBE1BnAGKm8DsA97v2IaAGALV6HTgGwRQjxQuSs9UWH7YcBnAIwS9r0MYCbImErESXCdeF/IISYKRXvJ6I8IUQZEeUBOCCVl8K399QWwF6pfIRf+U9m2g0YZvsgAMOIaCKAdABJRHRCCKHr5o2g3dcAWCSEOCHV+RVcPvy5Ztmtw3at9ZiKQbaXAigVQrh7IP+DTqHnFj0AIsqV/scBeAjAq9L7VHdXiYjOAlAnhFgvvX8cQCaAuy0xWkKr7cLl/PscjWJ5JoD1EbCTAEwFsEEI8ZzXps8AuCNnrofLn+kuv06KBBkIoELq7s4BcDYRZUlRC2dLZba3XQhxtRCiQAhRCOBeAO+aLPJGfee7AJwhuQETAZwBwFTXjQ7btdZjGkbZLoTYB2A3EXWRivTfq5EclLDDH1yuljIAtXA9MW8CcBdcI+ObAUxG4+BmIVwDKBsAfAdXpjjA1dIRUvmv0t/N0WC7tK0dXK2x1XCNLRREwPah0ne22us7Ox9Ac8mGLdL/bGl/gmtA+TcAawCUeNU1HsBW6e/GaLLdq84bYH7UjSF2wxVp85p0La0H8JwNv/NW0j1xDMBR6XXTYPVEg+3Stj5wuV9XA/gEOiPMeGYswzCMw2HXDcMwjMNhoWcYhnE4LPQMwzAOh4WeYRjG4bDQMwzDOBwWeoZhGIfDQs8wDONwWOgZhmEczv8DhR1cao3ZTFkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(count['1990':'2020'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
