{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make tidal boundary conditions for the Northern boundary in Johnstone Strait. Use data from Webtide and from Thomson & Huggett (1980) to make netcdf input files.\n",
    "\n",
    "Edited by Nancy in March, 2014. Modified measured grid rotation ($129^\\circ$ instead of $-51^\\circ$) and error in S2 elevation calculation.\n",
    "\n",
    "Edited by Susan in April, 2014.  Modified grid rotation to rotate versus across grid rather than up grid.  Corrects phases of currents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import webtide_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TSin =  {'North': {'M2': {'Amp': 1.2468500142849095, 'Pha': 5.8718032013900494}, 'Q1': {'Amp': 0.88018411744300051, 'Pha': 3.3676909436466511}, 'O1': {'Amp': 0.92187588787164776, 'Pha': -1.8419422569093058}, 'S2': {'Amp': 0.98646859951016619, 'Pha': -10.363961743466264}, 'K1': {'Amp': 1.056369887585026, 'Pha': -7.2574882761515624}, 'N2': {'Amp': 0.95930333678587432, 'Pha': -6.8740655239509989}}, 'West': {'M2': {'Amp': 0.8840061749307605, 'Pha': -7.8587198373167935}, 'Q1': {'Amp': 0.95936152535045738, 'Pha': 1.1445537519586795}, 'O1': {'Amp': 1.058133345863022, 'Pha': 0.92064288566727737}, 'S2': {'Amp': 1.0232932221404019, 'Pha': 0.7966194737248522}, 'K1': {'Amp': 1.0168859739167362, 'Pha': -3.8356999030330829}, 'N2': {'Amp': 1.0027750731988296, 'Pha': 2.5899670236940464}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# These are the corrections to be used.\n",
    "tidesetname = 'N36_AF'\n",
    "M2_amp_finecorrection = TSin['North']['M2']['Amp']\n",
    "M2_pha_finecorrection = TSin['North']['M2']['Pha']\n",
    "K1_amp_finecorrection = TSin['North']['K1']['Amp']\n",
    "K1_pha_finecorrection = TSin['North']['K1']['Pha']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#from Table 2, Thomson & Huggett (1980)\n",
    "#Tidal height harmonics\n",
    "#lats and lons found by estimating locations shown in Fig 4 with Google Maps\n",
    "\n",
    "TGS_Jun = {'O1amp':0.3249,'O1pha_PST':130.3,\n",
    "           'K1amp':0.5491,'K1pha_PST':138.9,\n",
    "           'M2amp':1.2196,'M2pha_PST':37.3,\n",
    "           'S2amp':0.3966,'S2pha_PST':56.6,\n",
    "           'Lat':50.470945,'Lon':-126.23826}\n",
    "\n",
    "TGN_Jun = {'O1amp':0.3250,'O1pha_PST':129.4,\n",
    "           'K1amp':0.5504,'K1pha_PST':138.5,\n",
    "           'M2amp':1.2086,'M2pha_PST':36.8,\n",
    "           'S2amp':0.3931,'S2pha_PST':56.6,\n",
    "           'Lat':50.498579,'Lon':-126.213026}\n",
    "\n",
    "TGW_Jan = {'O1amp':0.3161,'O1pha_PST':127.5,\n",
    "           'K1amp':0.5466,'K1pha_PST':138.7,\n",
    "           'M2amp':1.2453,'M2pha_PST':28.8,\n",
    "           'S2amp':0.4504,'S2pha_PST':51.2,\n",
    "           'Lat':50.574707,'Lon':-126.790431}\n",
    "\n",
    "TGS_Feb = {'O1amp':0.3198,'O1pha_PST':126.8,\n",
    "           'K1amp':0.5696,'K1pha_PST':139.4,\n",
    "           'M2amp':1.2218,'M2pha_PST':37.5,\n",
    "           'S2amp':0.4474,'S2pha_PST':59.1,\n",
    "           'Lat':50.470945,'Lon':-126.23826}\n",
    "\n",
    "TGE_Feb = {'O1amp':0.3376,'O1pha_PST':127.5,\n",
    "           'K1amp':0.5881,'K1pha_PST':140.8,\n",
    "           'M2amp':1.1690,'M2pha_PST':41.1,\n",
    "           'S2amp':0.4368,'S2pha_PST':62.1,\n",
    "           'Lat':50.442638,'Lon':-125.97064}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#need to convert phases from relative to 120 degrees W to GMT\n",
    "#for this conversion, we need to use the frequency of each component (in hours)\n",
    "freqs = {'M2':12.42, 'S2':12.00, 'O1':25.82, 'K1':23.93}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#For details, see Manual for Tide Heights Analysis and Prediction by M.G.G. Foreman, \n",
    "#Pacific Marine Science Report 77-10, IOS, 1977 (Revised 2004), \n",
    "#Section 2.3.1 Astronomical argument and Greenwich phase lag\n",
    "# available here: ftp://canuck.seos.uvic.ca/docs/MFTides/heights.pdf\n",
    "# or here: http://www.pac.dfo-mpo.gc.ca/science/oceans/tidal-marees/index-eng.html\n",
    "\n",
    "#Now, 120 degrees W (Pacific Standard Time) is 8 hours different to GMT \n",
    "#So to convert M2 phase from PST to GMT:\n",
    "#      GMT_pha = PST_pha - (-8hours)*(1/12.42hours)*360degrees\n",
    "\n",
    "#TGS_Jun\n",
    "TGS_Jun['O1pha_GMT'] = TGS_Jun['O1pha_PST']-(-8.0)*(1.0/freqs['O1'])*360\n",
    "TGS_Jun['K1pha_GMT'] = TGS_Jun['K1pha_PST']-(-8.0)*(1.0/freqs['K1'])*360\n",
    "TGS_Jun['M2pha_GMT'] = TGS_Jun['M2pha_PST']-(-8.0)*(1.0/freqs['M2'])*360\n",
    "TGS_Jun['S2pha_GMT'] = TGS_Jun['S2pha_PST']-(-8.0)*(1.0/freqs['S2'])*360\n",
    "\n",
    "#TGN_Jun\n",
    "TGN_Jun['O1pha_GMT'] = TGN_Jun['O1pha_PST']-(-8.0)*(1.0/freqs['O1'])*360\n",
    "TGN_Jun['K1pha_GMT'] = TGN_Jun['K1pha_PST']-(-8.0)*(1.0/freqs['K1'])*360\n",
    "TGN_Jun['M2pha_GMT'] = TGN_Jun['M2pha_PST']-(-8.0)*(1.0/freqs['M2'])*360\n",
    "TGN_Jun['S2pha_GMT'] = TGN_Jun['S2pha_PST']-(-8.0)*(1.0/freqs['S2'])*360\n",
    "\n",
    "#TGW_Jan\n",
    "TGW_Jan['O1pha_GMT'] = TGW_Jan['O1pha_PST']-(-8.0)*(1.0/freqs['O1'])*360\n",
    "TGW_Jan['K1pha_GMT'] = TGW_Jan['K1pha_PST']-(-8.0)*(1.0/freqs['K1'])*360\n",
    "TGW_Jan['M2pha_GMT'] = TGW_Jan['M2pha_PST']-(-8.0)*(1.0/freqs['M2'])*360\n",
    "TGW_Jan['S2pha_GMT'] = TGW_Jan['S2pha_PST']-(-8.0)*(1.0/freqs['S2'])*360\n",
    "\n",
    "#TGS_Feb\n",
    "TGS_Feb['O1pha_GMT'] = TGS_Feb['O1pha_PST']-(-8.0)*(1.0/freqs['O1'])*360\n",
    "TGS_Feb['K1pha_GMT'] = TGS_Feb['K1pha_PST']-(-8.0)*(1.0/freqs['K1'])*360\n",
    "TGS_Feb['M2pha_GMT'] = TGS_Feb['M2pha_PST']-(-8.0)*(1.0/freqs['M2'])*360\n",
    "TGS_Feb['S2pha_GMT'] = TGS_Feb['S2pha_PST']-(-8.0)*(1.0/freqs['S2'])*360\n",
    "\n",
    "#TGE_Feb\n",
    "TGE_Feb['O1pha_GMT'] = TGE_Feb['O1pha_PST']-(-8.0)*(1.0/freqs['O1'])*360\n",
    "TGE_Feb['K1pha_GMT'] = TGE_Feb['K1pha_PST']-(-8.0)*(1.0/freqs['K1'])*360\n",
    "TGE_Feb['M2pha_GMT'] = TGE_Feb['M2pha_PST']-(-8.0)*(1.0/freqs['M2'])*360\n",
    "TGE_Feb['S2pha_GMT'] = TGE_Feb['S2pha_PST']-(-8.0)*(1.0/freqs['S2'])*360\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Longitude   Latitude       amp         pha\n",
      "Constituent                                            \n",
      "O1           -126.7717  50.556435  0.311897  242.711639\n",
      "P1           -126.7717  50.556435  0.161873  256.887977\n",
      "Q1           -126.7717  50.556435  0.051117  232.075989\n",
      "K1           -126.7717  50.556435  0.522911  260.110718\n",
      "K2           -126.7717  50.556435  0.104906  283.723050\n",
      "N2           -126.7717  50.556435  0.260498  240.832452\n",
      "M2           -126.7717  50.556435  1.253263  263.723173\n",
      "S2           -126.7717  50.556435  0.358527  292.054419\n"
     ]
    }
   ],
   "source": [
    "#Compare the harmonics to Webtide harmonics at 50.556435 deg N, -126.7717 deg W\n",
    "import pandas\n",
    "#webtide = pandas.read_csv('/ocean/klesouef/meopar/tools/I_ForcingFiles/Tides/webtide_point_johnstone_strait.csv',\n",
    "                          #index_col=0,skiprows=2)\n",
    "webtide = pandas.read_csv('webtide_point_johnstone_strait.csv',\n",
    "                          index_col=0,skiprows=2)\n",
    "webtide = webtide.rename(columns={'Amplitude (m)':'amp','Phase (deg GMT)':'pha'})\n",
    "print (webtide)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbf0e008b38>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Iw1qGZQfJyMgoWE5PTyc9PT0GrRaReFDc1O79+vWr5FaJyJHIysoiKyurzP1iOpjWzJoA\ne939WzNLAZYA9wEXAtvc/X9KGEzbhaDLZxkaTCtSrem2WZHqobIG0x4LvGJmHwCrgCXuvhD4H+Bi\nM1sL/JggeMHdPwbmAh8DC4GRikhEqjdN7S5SvWnCNxGJa5dccglLly49qLxnz54sWbKkElokIrGg\nhxKKSELS1O4i1ZsyKiIS1zRGRaR6UEZFRBJS/iRkKSkpAKSkpJQ4GZkmhROpehSoiEjc69evHy1a\nBHM/tmjRosTJyDQpnEjVo0BFRBJCfgalpO4eTQonUjUpUBGRuDd//nxycoK5H3NycorNmBQ3KZyI\nJD4NphWRuBbNYFoNuBVJfBpMKyIJKZoJ3zQpnEjVpUBFROLaE088QV5eXqGyvLw8Hn/88UPaR0QS\nkwIVEYlr0Uz4pknhRKoujVERkbimMSoi1YPGqIhIQsqf8C0/Y5KamnrQhG/R7CMiiUkZFRGJe5EZ\nk5IyJdHsIyLxSxkVEUlY+RmTtLS0EjMl0ewjIomnRmU3QEQkGv369ePdd98tcfr8aPcRkcSirh8R\nERGpdOr6ERERkYSjQKUacHdun3C7HtImIiIJR4FKNTD/hflkLs9kwd80nbiIiCQWjVGp4tydrv27\nsqrjKrqs6cLKubplU0RE4o/GqFRx7s7ttx/cvTP/hflkp2WDQXa9bGVVREQkoShQqSLmz59PZmZm\noafFujuTZ04mr3XwsLa8NnlMmjFJY1VERCRhKFCpAtydyZMns3PnTiZNOhCIRGZTAGVVREQk4ShQ\nqQLmz59PdnY2ANnZ2QVZlSfmPlGQTcmX1yaPx+c8XuFtFBERORwKVBJcfjYlLy/s3snLK8iq3Nj/\nRlK/TC20f+r6VEYMGFEZTRURETlkClQSXGQ2JV9+VqXflf3otLMT5A9Jcei0qxN9r9D04iIikhhi\nGqiYWUszW25ma8ws28xuDstPN7M3zexDM3vOzOpFHDPOzNaZ2Sdm1jOW7asKnnjiiYJsSr68vDwe\nf/zx4CFtQ24ryKqkrk9l7FA9rE1ERBJHrDMq+4Ax7t4R6AqMNLP2wBPAr939dOAvwK8BzKwD0B9o\nD/QCMk3fqqW68cYbSU0t0r2TmsqIEUH3TmRWRdkUERFJNDENVNx9i7t/EC7vAv4BtABOdPfXw91e\nAvqFy1cBs919n7t/AawDzo1lGxNdv3796NSpU6GyTp06FTw9Nj+rkvZKmrIpIiKScCpsjIqZtQXO\nAN4C1pjZVeGm/kDLcLkFsCHisJywTEpgZtx2220FWZXU1FTGji0ckPS7sh8je4xUNkVERBJOhQQq\n4RiUecAtYWblBuAXZvYOUBfYUxHtqKoisyqR2ZR8ZsZ94+9TNkVERBJOjVifwMxqEAQpM939OQB3\nXwtcEm4/Ebg83D0HaBVxeMuw7CAZGRkFy+np6aSnp5dzyxNHflZl+PDhB2VTRERE4lFWVhZZWVll\n7hfzhxKa2Qxgq7uPiSg72t2/MbMk4EngFXefFg6mfRroQtDls4xgPIsXqVMPJSzC3Rk3bhwTJ05U\noCIiIgmnpIcSxjRQMbNuwKtANsFsHg7cAZwE/CJcX+Dud0QcM46ga2gvQVfR0mLqVaAiIiJShVRK\noBIrClRERESqlpICFc1MKyIiInFLgYqIiIjELQUqIiIiErcUqIiIiEjcUqAiIiIicUuBioiIiMQt\nBSoiIiIStxSoiIiISNxSoCIiIiJxS4GKiIiIxC0FKiIiIhK3FKiIiIhI3FKgIiIiInFLgYqIiIjE\nLQUqIiIiErcUqIiIiEjcUqAiIiIicUuBioiIiMQtBSoiIiIStxSoiIiISNxSoCIiIiJxS4GKiIiI\nxC0FKiIiIhK3FKiIiIhI3KpR1g5mlgScDjQHvgM+cvevY90wERERkRIDFTM7HvgN8BNgHfANUAc4\nyczygD8B0919f0U0VERERKqf0rp+7gGeAo5390vcfbC7X+PupwFXAQ2AIaVVbmYtzWy5ma0xs2wz\nuzksP93MVprZ+2b2tpmdHXHMODNbZ2afmFnPI3+LIiIikqjM3YvfYHaeu791RJWbNQOaufsHZlYP\neBfoAzwI3O/uS82sF/Brd7/IzDoATwPnAC2Bl4ATvUgjzaxokYiIiCQwM8PdrWh5aRmVzCM9qbtv\ncfcPwuVdwD8IxrrsJ8jIADQEcsLlq4DZ7r7P3b8g6HI690jbISIiIompzMG05cXM2gJnAKuA0cAS\nM7sfMOD8cLcWwMqIw3LCMhEREamGSgtUjjOz50va6O5XRXuSsNtnHnCLu+8ys5+Hy381s2uAqcDF\n0dYHkJGRUbCcnp5Oenr6oRwuIiIilSgrK4usrKwy9yttjMo64L9KOtDdV0TTEDOrAfwNWOTuD4Vl\nO9y9YcQ+O9y9oZndHlTt/xOWLwbGu/uqInVqjIqIiEgVUtIYldIyKruiDUbKMBX4OD9ICeWY2YXu\nvsLMfkwwFgXgeeBpM5tC0OVzAvB2ObRBREREElBpgcrnR1q5mXUDBgHZZvY+4MAdwI3AH80sGfge\nGAHg7h+b2VzgY2AvMFKpExERkeqrtK6fvqUd6O4LYtKiKKjrR0REpGopqeuntEBlP/BB+ILg7px8\n7u7Dy72VUVKgIiIiUrUczhiVvsBA4DTgOWCWu/8zRu0TEREROUiJGZWCHczqAr2BAUBj4M5yGmR7\n2JRRERERqVoOZ2bafN8D3wK5QD2CBxOKiIiIxFxpY1R6EHT9nEvwzJ3Z7v5uBbatRMqoiIiIVC2H\nO5j278DrBLcVF9rR3W+OQTujokBFRESkajmcwbTDKRKciIiIiFSkMgfTxiNlVERERKqWQx5Ma2Y/\nMrOhEevzzGx5+OoRq4aKiIiI5Cut62cCMCpi/WTgeqAuwTT4y2PXLBEREZHSb0+u7+4fR6yvc/f3\n3P1VIC3G7RIREREpNVBpGLni7pHP/mkam+aIiIiIHFBaoPIPM7u8aKGZXQGsjV2TRERERAKlzaNy\nAvAi8CawOiw+CzgfuMLdP62QFhbfNt31IyIiUoUc8oRv4UG1gUFAx7BoDfCMu38fk1ZGSYGKiIhI\n1XJYgUq8UqAiIiJStRzJQwlFRKQKmzR6NIvnzUN/AEo8UqAiIlLN7Vq9GoYNY3TXrgpYJO4oUBER\nqebMjEvz8piyapUCFok7ZQYqZpZtZn8v8nrNzKaYWeOKaKSIiMSegQIWiTvRZFQWEdymPCh8vQC8\nC2wBpsWsZSIiUikiA5Ylw4czady4ym6SVGOlPesn30/cvXPEeraZrXb3zmY2OFYNExGRyuHAktRU\nlnTqxKVjx9Kzb98yjxGJlWgClWQzO9fd3wYws3OA5HDbvpi1TEREKlTRAOWBvn0xO+huUZEKFU2g\n8l/AVDOrF67vBP7LzOoCE2PWMhERqRDuzmIFKBKnop7wzcwaALj7tzFtUXRt0YRvIiLlZNLo0Zz2\nox/RUwGKVKLDnpnWzJoC9wLN3b2XmXUAurr7n2PT1LIpUBEREalajmRm2mnAEqB5uP4pcGuUJ21p\nZsvNbE14m/OosHy2ma0OX5+b2eqIY8aZ2Toz+8TMekZzHhEREamaohmj0sTd55rZOAB332dmP0RZ\n/z5gjLt/EI5xec/Mlrn7wPwdzGwysCNcbg/0B9oDLYGXzOxEpU9ERESqp2gyKv8JJ3ZzADM7D4hq\nnIq7b3H3D8LlXcAnQIsiu/UHngmXewOz3X2fu38BrAPOjeZcIlL16Bk0IhJNoDIGeB443szeAGYA\now71RGbWFjgDWBVR1h3Y4u7/CotaABsiDsvh4MBGRKqJlc8+y/uDBnHreecpYBGppsrs+nH31WZ2\nIXAywYSFa91976GcJOz2mQfcEmZW8v0UmHUodeXLyMgoWE5PTyc9Pf1wqhGROJa2axdn7tnDorff\n5v1Bg1g0aRK9xo7lkn79dHeKSILLysoiKyurzP1KvOvHzEqditDdF0TTEDOrAfwNWOTuD0WUJxNk\nTDq7+6aw7Pagav+fcH0xMN7dVxWpU8NWRKqB6xs2ZNq33wYTkRE8z6NZrVpsOeOMgoBl8pgxdOrW\nTcGLSII75NuTzezJcPEY4Hxgebh+EfCmu18R5YlnAFvdfUyR8kuB37j7RRFlHYCngS4EXT7LgIMG\n0ypQEake8gOVfJEBy5Y6deh8883kvfUWXd99l8WdOnHpbbcpYBFJUId8e7K7/8zdfwbUBDq4ez93\n7wd0DMuiOWk3ggcZ9jCz98PbkS8NNw+gSLePu38MzAU+BhYCIxWRiEg+Ay4FHgSOMQMzzExP/BWp\nwqKZ8O0Td28fsZ4ErIksq2jKqIhUD8VlVF6sXZuXwq6fnn37MuGii8hYsaLQPktSU5VhEUkwJWVU\noplH5WUzW8KB7McA4KXybJyISHHy/yApGqBMKWWqdwMuzcvjklWrGDN8OH9/911+fd99FddoESlX\n0dz180sz6wNcEBY97u5/iW2zREQgOS2Nv+3eHVWAkq/oE4B79i31vgARiXMlBioW0b8SBiYHBSem\nPhgRiaH2115LzR/96LACFD0BWKRqKO2unyxgPvCcu38ZUV4L+BEwDHjF3afFvpkHtU3xkYgAMP7C\nC+n67ruFMigKUEQSz+GMUbkUGA7MMrN2BM/jSSG4U2gp8KC7vx+LxoqIRKte587YzTcrgyJSRZV5\n1w+AmdUEmgDfufuOmLeq7PYooyIiIlKFHPKEb/FMgYqIiEjVcsgTvomIiIhUNgUqIiIiEreiClTM\nrI2Z/SRcTjGztNg2S0RERCSKQMXMbgTmAX8Ki1oCf41lo0REREQguozKL4BuQC6Au68jeKKyiIiI\nSExFE6jsdvc9+StmVoNgEkgRERGRmIomUFlhZncAKWZ2MfAs8EJsmyVHavRvRzPveT3qXkREEls0\ngcrtwDdANnATsBD471g2So7c6vWrGfaXYXS9tqsCFhERSViHNOGbmR0FtHT3v8euSVG1QxO+lSH9\n+nRWtFsBDqnrU+m0sxO3Db2Nflf20zTjIiISdw57wjczyzKz+mGQ8h7whJlNiUUjJQYM8trmserU\nVcqwiIhIwomm66eBu+cCfYEZ7t4F+HFsmyXlLiJgGX7/cMbdPa6yWyQiIlKm0p6eXLCPmR0L9Afu\njHF7JFbyu4B2dWLsbWPpe0Xfym6RiIhImaIJVO4GlgCvu/s7ZnYcsC62zZJyExmgDA0CFI1RERGR\nRKGnJ1dRFw67kHeT3lWAIiIiCaGkwbRlZlTMrA5wA9ARqJNf7u7Dy7WFUq46t+nMzefcrABFREQS\nWjSDaWcCzYBLgBUEz/rZGctGyZGbcvcU3YosIoflsSmPVXYTRAqU2fVjZu+7+5lm9nd3P83MagKv\nuft5FdPEYtukrh8RkRjYuHEjPU7twStrXqFFixaV3RypRg57HhVgb/jvDjM7FWiAHkoYFyaNHs3i\neZoTRUTKz+zM2Yz+djSzM2dXdlNEgOgClcfNrBFwF/A88DHwh5i2SqKya/VqGDaM0V27KmARkcM2\nb9Y8up3ajYGdB/LZs5/Rnvb8c+4/Gdh5IN1O7ca8WfMqu4lSjZUZqLj7/7n7dndf4e7Hufsx7h5V\nB6aZtTSz5Wa2xsyyzezmiG2jzOyTsPy+iPJxZrYu3Nbz8N5W9WBmXJqXx5RVq8oMWKb8ryYTFpHi\nXfPTa5i7ZC7n9DqH/Xv3A+B7nXN6ncPcJXO55qfXVHILpTqLZgr92mZ2nZndYWa/zX9FWf8+YIy7\ndwS6Ar8ws1PMLB24Eujk7p2AyeG52hNMLNce6AVkmkaDlsmg1IBl48aNTPjDBHJyciq3oSJHSIM8\nY6dFixYM+PkA0nanAVD3+7oMHDlQ41Sk0kXT9fMc0Jsg6PhPxKtM7r7F3T8Il3cBnwAtgJ8D97n7\nvnDb1vCQ3sBsd9/n7l8QTCx3btTvppqLDFiWDB/OpHHBNPmZMzL5tue3ZM7MrNwGihyBjRs38sCE\nBxRwx9DszNkkf5vMtA7TqJFbg1mZsyq7SSJRBSot3X2Au//B3e/Pfx3qicysLXAGsAo4CbjAzN4y\ns1fM7KxwtxbAhojDcsIyicKjpLA4NZUxXbpQe8QNTF/+Ap2v6Myz7z0LLWHuO3PpfEVnTj33VGbN\n1S8gSSxzW+KeAAAgAElEQVSzM2dz3rfnaZBnDNWoVYMLZl7Akx89yQUzL6BGzWgmLxeJrWg+hW+a\nWSd3zz7ck5hZPWAecIu77zKzGkAjdz/PzM4BngWOO5Q6MzIyCpbT09NJT08/3OYlPAeeqlOHiXsb\nMnFKBg/ceCNmxqicHDJnZvL0W08DsPeHvfT6US9GDhmpdK4khHmz5jHl91NoVasVNbfX5E3exJ9x\nBi4ayIY9Gxh95+iDxk88NuUx/t/o/1dJLU5st2bcWrB8eb/LoV8lNkaqvKysLLKyssrcr8R5VMws\nm+A7sAZwIvAvYDdBD4O7+2nRNCQMSv4GLHL3h8KyhcD/uPuKcH0dcB5wI0Hl94Xli4Hx7r6qSJ2a\nRwUYf+GFdH33XZZ06sSe5p049S+dybsjj1/9/lcF+2zcuJFzxpzDlo5baPpRU9578D0FKZJQcnJy\nmJ05m4WPLOSa3GuYX38+vX7Zq9jxE5oDRCRxHc48KlcQDHjtBZwA9AzX88ujNRX4OD9ICf0V6BE2\n7CSglrv/m+D25wFmVsvM2oXnffsQzlWtrE9tyO1HHc/mPW3Zn12j2FsKM2dk8u0P39LhvQ7k7s8l\nc4bGqUjimDdrHv0v6c8bz79Bo/80oj3tabirIW8+/yb9L+l/0G2zmgNEpOopLVD5CugDjAUuBXLc\nfX3+K5rKzawbMAjoYWbvm9lqM7sUeBI4LszaPAMMBXD3j4G5BHO1LARGKnVSsmmLnuPFtxaVekth\nrZq1mHn9TD567iNmXj+TmjVqVnKrRaKXf9tscv1kkn4Ifl0l7U8iqX5SwWdcc4CIVG2ldf3MIZiV\n9jWCrMp6d7+lAttWInX9FLZx40YePOdBrthyBS80fYEx741R2luqlIwxGfz7yX/Tb0c/5jWYR5Ph\nTch4IKNge3730KdPf8pP1/+U2W1mc+KgE3V7rUgCOZyunw7uPtjd/wRcA3SPWevkiOiWQqnq6tWp\nR+ruVKZ1mEbdPXWpl1Kv0HbNASJSdZUWqOQ/44f8+U4kPumWQqnqovmMK2AXqZpK6/r5gQMTuxmQ\nAuRx4K6f+hXSwuLbpq4fESnkwYwHObHTiVzW9zIWLljIuux1hW63FZH4VlLXT4mBSjxToCIiIlK1\nHM4YFREREZFKpUBFRERE4pYCFREREYlbClREREQkbilQERERkbilQEVERETilgIVERERiVsKVERE\nRCRuKVARERGRuKVARUREROKWnl4nIiJxq23btqxfv76ymyHlqE2bNnzxxRdR769n/YiISNwKn/9S\n2c2QclTSz1TP+hEREZGEo0BFRERE4pYCFREREYlbClRERETi0PTp0+nevXtlN6PSKVARERE5DG3b\ntqVOnTps27atUPmZZ55JUlISX375JZMnT6ZTp07Ur1+f448/nsmTJx/SOcwOGlt6xHbt2sWYMWNo\n164daWlptG3blv79+/P2228X7JOUlESzZs3Yv39/Qdm+ffs45phjSE5OBuDUU0+lfv361K9fnxo1\napCSkkJaWhr169fnvvvuK7f2KlAREZGEMWn0aBbPm3dEdwKVRx0QBBHt2rVj1qxZBWUfffQR3333\nXaEAY+bMmezYsYNFixbxyCOPMHfu3CM675HYs2cPF110EWvWrGHhwoXk5ubyySefMHDgQBYvXlxo\n30aNGrFo0aKC9UWLFnHUUUcVrH/00Ufk5uaSm5tL9+7dyczMZOfOneTm5nL77beXW5sVqIiISMLY\ntXo1DBvG6K5dDzvYKI868g0ZMoTp06cXrE+fPp1hw4YVrN92222cccYZJCUlcdJJJ9G7d2/eeOON\ngu1JSUk8/PDDHH/88RxzzDH8+te/LlS/uzN27FiOOuoojj/++ELBxLRp0+jQoQP169fnhBNO4PHH\nHy+zvTNmzGDTpk0899xztG/fHjMjJSWFvn378tvf/rbU9zZjxoxC762oWN1GrkBFREQShplxaV4e\nU1atOuxgozzqyHfeeeexc+dO1q5dy/79+5kzZw6DBw8usa7XXnuNjh07Fir761//yurVq1m9ejXP\nPfccU6dOLdi2atUq2rdvz7///W/Gjh3LDTfcULCtadOmBVmRJ598ktGjR/PBBx+U2t6XX36ZSy65\nhDp16pS6n5lx9dVX8+qrr5Kbm8uOHTt4/fXX6d27d1mXpNwpUBERkYRjcOQBSznUAQcyD8uWLaN9\n+/Y0b9682P3Gjx+Pu/Ozn/2sUPntt99OgwYNaNmyJbfeemuhrqS2bdsyfPhwzIxhw4axZcsWvv76\nawB69epF27ZtAejevTs9e/bktddeK7WtW7dupVmzZgXrH374IY0aNaJBgwa0b9++0L516tThqquu\nYvbs2cyZM4errrqK2rVrR31dyktMAxUza2lmy81sjZllm9mosHy8mW00s9Xh69KIY8aZ2Toz+8TM\nesayfSIiktgig40lw4czady4Cq9j8ODBPPPMM0ybNo2hQ4cWu88jjzzCU089xcKFC6lZs2ahbS1b\ntixYbtOmDZs2bSpYjwwqUlJScHd27doFBGNGunbtSuPGjQvGk2zdurXUtjZu3JjNmzcXrJ9++uls\n376dBQsWsHv37oLy/GBtyJAhzJgxg5kzZ5b43mIt1hmVfcAYd+8IdAV+aWanhNsecPfO4WsxgJm1\nB/oD7YFeQKbFYsiziIhUCQ4sTk1lTJcuXPrkk4ydOLHC62jdujXt2rVj0aJF9O3b96DtU6dO5Q9/\n+APLly/n2GOPPWj7hg0bCpa//PLLEjMykfbs2cM111zDr3/9a7755hu2b99Or169yswG/fjHP2bp\n0qV89913UbyzIFOzefNmvv76a7p16xbVMeUtpoGKu29x9w/C5V3AJ0CLcHNxAUhvYLa773P3L4B1\nwLmxbKOIiCSeyODCZszggZUruaRfv0O6nbc86sg3depUli9fTkpKSqHyp59+mjvvvJNly5bRpk2b\nYo+dNGkSO3bsYMOGDTz00EMMHDiwzPPt2bOHPXv20KRJE5KSkli0aBFLly4t87ihQ4dy7LHH0qdP\nH9asWcP+/fvZvXs377zzTonH/O1vf+O5554rWK/oZy9V2BgVM2sLnAGsCot+aWYfmNn/mVmDsKwF\nsCHisBwOBDYiIlLNufuRByjlUAcUnuOkXbt2dO7c+aBtd911F9u2beOcc84pmGNk5MiRherp3bs3\nZ511Fp07d+bKK69k+PDhZZ6zXr16/PGPf+Taa6/lqKOOYvbs2VENdK1duzavvPIKHTp04PLLL6dB\ngwaccsopvPfee4Vum458b+3bty80fqW46xTLzo8KeXqymdUDsoDfuftzZnY0sNXd3czuAZq5+3+Z\n2cPASnd/Jjzu/4CF7r6gSH16erKISDVQ9Em7k0aP5rQf/Yieffse9pdjedRRXpKSkvjnP//Jcccd\nV6ntqEiH+vTkGhXQoBrAPGCmuz8H4O7fROzyBPBCuJwDtIrY1jIsO0hGRkbBcnp6Ounp6eXWZhER\niU9jp0yJizrkyGVlZZGVlVXmfjHPqJjZDILsyZiIsmbuviVcHg2c4+7XmVkH4GmgC0GXzzLgxKLp\nE2VURESqh5L++q4qkpOTWbduXblmVCZOnMi99957ULaoe/fuvPjii+V2nsN1qBmVmAYqZtYNeBXI\nJhi35MAdwHUE41X2A18AN7n7V+Ex44AbgL3ALe5+0OggBSoiItVDVQ9UqqO4ClRiRYGKiEj1oECl\n6jnUQEUz04qIiEjcUqAiIiIicUuBioiIiMQtBSoiIiIStxSoiIiIxKEVK1bQqlWrsnes4hSoiIiI\nHIFp06Zx2mmnUbduXZo3b87IkSP59ttvAVizZg2XXnopRx99NMnJyYdcdyxmzt27dy933303p5xy\nCmlpabRq1YrLL7+cZcuWFezTtm1b6tSpw7Zt2wode+aZZ5KcnMyXX37JZZddVvBYgFq1alG7dm3q\n169f7GMCjoQCFRERSUjuzu23/+GIbl8+0jruv/9+xo0bx/33309ubi5vvfUW69ev5+KLL2bfvn3U\nrFmTAQMGMHXq1MNuY3nr168fL7zwAk899RTbt2/n888/55ZbbmHhwoUF+5gZ7dq1Y9asWQVlH330\nUaGnLi9cuJCdO3eSm5vLoEGD+M1vfkNubi65ublkZmaWW3sVqIiISEKaP38JmZmbWbCg7KcGx6KO\nnTt3kpGRwSOPPMLFF19McnIyrVu3Zu7cuXzxxRc89dRTnHTSSfzsZz+jQ4cOxdbRrl077rvvPjp2\n7Ejjxo254YYb2LNnT8F2d+eBBx6gadOmtGjRgmnTphVsW7hwIZ07d6ZBgwa0adOGCRMmlNnml156\niZdffpnnn3+es88+mxo1alCjRg169uzJlCKPFhgyZAjTp08vWJ8+fTrDhg07xKt05BSoiIhIwnF3\nJk9ews6dDzBp0uLDyogcaR1vvvkmu3fvpk+fPoXK69aty2WXXVaoK6U0zzzzDMuWLeOzzz5j7dq1\n3HPPPQXbtmzZws6dO9m0aRP/93//xy9+8YuCbqV69eoxc+ZMvv32W1588UUee+wxnn/++VLP9fLL\nL9OlSxeOPfbYMtt13nnnsXPnTtauXcv+/fuZM2cOgwcPrvAJ+BSoiIhIwpk/fwnZ2ZcCRnb2JYeV\nETnSOrZu3UqTJk1ISjr4q/TYY49l69atUdUzatQomjdvTsOGDbnzzjsLdbfUqlWLu+66i+TkZHr1\n6kW9evVYu3YtABdccAEdO3YE4NRTT2XgwIGsWLGizDY3a9asYH379u00atSIhg0bkpKSctD++VmV\nZcuW0b59e5o3bx7VeypPClRERCSh5GdC8vJ6ApCXd8khZ0TKo44mTZqwdetW9u/ff9C2zZs306RJ\nk6jqadmyZcFymzZt2LRpU8F648aNCwVCqamp7Nq1C4BVq1bRo0cPjjnmGBo2bMif/vSnMoOjxo0b\ns3nz5oL1Ro0asX37dt57771CXU75Bg8ezDPPPMO0adMYOnRoVO+nvClQERGRhBKZCQkcekakPOro\n2rUrtWvXZsGCBYXKd+3axaJFi/jJT34SVT0bNmwoWF6/fn3UWYtBgwZx9dVXk5OTw44dO7jpppvK\nDLR+/OMf88477xQKhvIVd2zr1q1p164dixYtom/fvlG1q7wpUBERkYRRNBOS71AyIuVRB0D9+vX5\n7W9/y6hRo1iyZAn79u3jiy++YMCAAbRu3ZrBgwcDsHv3bnbv3o27s3v37oMyF//7v/9LTk4O27Zt\n495772XgwIFRnX/Xrl00atSImjVr8vbbb/PMM8+UeczFF1/MRRddxNVXX83bb7/N3r172bdvHytX\nrizxVuipU6eyfPnyYruGKoICFRERSRgHZ0LyRZ8RKY868o0dO5Z7772X2267jQYNGtC1a1fatGnD\nSy+9RM2aNVm/fj0pKSl06tQJMyMlJYVTTjmlUB3XXXcdPXv25IQTTuDEE0/kzjvvLPF8kcFEZmYm\nd911Fw0aNOCee+5hwIABUbX5L3/5C1dccQWDBw+mUaNGHHfcccyaNYulSw+878jztGvXjs6dOxe7\nrbSy8mKJ+PhsM/NEbLeIiBwaMyuU4Rg9ehKrV+8q9ovR3encuR5Tpowttc7yqKO8tGvXjj//+c/0\n6NGjQs4XD4r+TIuUH/RDUaAiIiJxq6QvtapCgcpB5QcFKur6ERERqSSx6DJ55plnCqa2z3+lpaXR\nqVOncj9XRVBGpRpwd8bdPY6Jv50Y035EEZHyVtUzKtWRMipykPkvzCdzeSYL/rag7J1FRETiiDIq\nVZy707V/V1Z1XEWXNV1YObfkW9BEROKNMipVjzIqUsj8F+aTnZYNBtn1spVVERGRhKJApQpzdybP\nnExe6zwA8trkMWnGJP11IgnJ3bn99j/o8ytSzShQqcIisymAsiqS0ObPX0Jm5ubDeviciCQuBSpV\nVNFsSj5lVSQR5U95vnPnA4f84DiRRNOuXTuWL19e2c2IGwpUqqiDsin5lFWRBBQ55XlpU5yre0gq\nUtGAYvbs2TRu3JjXXnuNm266iVNOOYXk5GRmzJhRia08YNmyZfTo0YP69etz9NFH07lzZyZNmlTw\n7KGMjAySkpJ4+OGHCx330EMPkZSUxN13311ojpbU1FSSk5ML5mmpX79+TNqtQKWKeuPdNzj7h7O5\n8PMLD3qdvf9sXn/n9cpuokhUij5ArrQHx6l7qPp5bMpjcVHH9OnTGTVqFAsXLqR79+6cccYZPPro\no5x11llHXHd5ePbZZ7n22msZPHgwX375Jd988w1z5sxh48aNBU9vNjNOPvnkgwKrGTNmcPLJJwPB\nc4l27txJbm4uixYtokWLFuTm5haUxYS7x+wFtASWA2uAbODmItt/BewHjoooGwesAz4BepZQr4tI\n9fDss4s8NXWxgxe8UlMX+bx5iwvtt3//fu/S5VaH4N/9+/dXUoulPJX2+37Dhg1+YoMTfePGjYdd\n/5HU0bZtW3/55Zf9scce86OPPtpXr1590D4/+tGPfPr06YXKMjIy/JprrvEBAwZ4Wlqan3XWWf7h\nhx8Wqnfy5Ml+2mmnecOGDX3gwIG+e/dud3ffvn27X3HFFX700Uf7UUcd5VdccYXn5OSU2dZWrVr5\nlClTSt0nIyPDBw8e7B06dPCPP/7Y3d3XrFnjHTp08CFDhviECRMK7Z+VleWtWrUq89xFlfQzDcsP\n+s6PdUZlHzDG3TsCXYFfmNkpAGbWErgYWJ+/s5m1B/oD7YFeQKZp0g+RasuLZFPyFZdVibZ7SKqO\n2ZmzGf3taGZnzq60OjIzM8nIyGD58uWceeaZUR/3/PPPM2DAALZv385Pf/pTrr76an744YeC7c8+\n+yxLly7l888/58MPP2TatGkA7N+/n+HDh7Nhwwa+/PJLUlNT+eUvf1nqudauXUtOTg59+/Yts11m\nxpAhQ5g+fToQZIqGDh1aqd2pMQ1U3H2Lu38QLu8iyJK0CDdPAYo+nrI3MNvd97n7FwSZlXNj2UYR\niV+RwUdhhYORogFNad1DktjmzZpHt1O7MbDzQD579jPa055/zv0nAzsPpNup3Zg3a16F1JHvpZde\n4rzzzuPUU089pPdx1lln0adPH5KTkxkzZgzff/89b731VsH2W265haZNm9KwYUOuvPJKPvjgAwCO\nOuoo+vTpQ+3atalbty7jxo1jxYoVpZ5r69atADRr1qyg7Kc//SmNGjWibt26PP3004X2HzRoELNn\nz2bfvn3Mnj2bwYMHH9J7K281KupEZtYWOANYZWZXARvcPbtIwqQFsDJiPYcDgY2IVDNvvJHN2Wfv\nwmzlQdvcnddfr0e/fpcUE9AcCGT69bukQtsssXXNT6+h6wVdmZ05m0+f/hQA3+uc0+sc7h95Py1a\nlP2VUR515Hv00Ue55557uOGGG/jzn/8c9XGtWrUqWDYzWrZsyaZNmwrKmjZtWrCcmprK5s2bAfju\nu++49dZbWbJkCTt27MDd2bVrF+5e4qzjjRs3BmDz5s20adMGgFmzZgHQvXv3Qpmc/LYdf/zx3HHH\nHZx00kmHdD1ioUIG05pZPWAecAvwA3AHML4izl1duO52kCpoypSxrFgxgaysjINeK1ZMYMqUsYfU\nPSRlS4TfJS1atGDAzweQtjsNgLrf12XgyIGH9IVaHnVAEFC8/PLLvPbaa4wcOTLq4/IHsEJwzTdu\n3BjVuSdPnsy6det455132LFjB6+++mpBHSU5+eSTadGiBQsWRH+359ChQ3nggQcYNmxY1MfESswD\nFTOrQRCkzHT354DjgbbAh2b2OcGA29VmdgxBBqV1xOEtw7KDZGRkFLyysrJi+A4Sg+52kOoq2u4h\niU6i/C6ZnTmb5G+TmdZhGjVyazArc1al1AFBl8rLL7/MkiVLGDNmDAB79+7l+++/x93Zs2cPu3fv\nLhRMvPfee/z1r3/lhx9+YMqUKdSpU4cuXbqUea5du3aRkpJC/fr12bZtGxkZGWUeY2ZMnjyZCRMm\n8Oc//5kdO3YAsG7dOr766qtijxkwYABLly7l2muvjeIKHJ6srKxC3+UlqYiun6nAx+7+EIC7fwQU\ndJSFwUpnd99uZs8DT5vZAwRdPicAbxdXaTQ/nOoi/y/KYDKsMfTt21MPHpRqI9ruISlbIv0uqVGr\nBhfMvIDL+l7GwgULWZe9rsLriLw2rVq14uWXX+bCCy8kJSWFN998kxUrVmBmrFy5kptuuolXXnmF\nCy64AIDevXszZ84chg4dyoknnsiCBQtITk4+qN6ibr31Vq677jqaNGlCixYt+NWvfsXzzz9fZlv7\n9+9Pw4YNuffeexk9ejS1a9emdevW3HTTTcUGI3Xq1KFHjx7Fvtfykp6eTnp6esH6hAkTit0vpk9P\nNrNuwKsEtyZ7+LrD3RdH7PMv4Gx33xaujwNuAPYCt7j7QWG9np5c2Lx5ixk2zMjLu4TU1MXMmGH6\nxSxVjrszbtwkJk4cG7dfnokuHn+XVMW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kmyT1SVpVGZlQ5lZJyyU9LumR3L6nJPVKugYY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf10521390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot them to check how it looks\n",
    "#the channel is mostly aligned east-west, so plot along the channel\n",
    "plt.figure(figsize=[9,5])\n",
    "pha = 'M2pha_GMT','O1pha_GMT','K1pha_GMT','S2pha_GMT'\n",
    "cols = 'r','b','m','k','g'\n",
    "marks = '>','^','*','d'\n",
    "\n",
    "for t in range(0,len(pha)):\n",
    "    plt.plot([TGW_Jan['Lon'],TGS_Feb['Lon'],TGS_Jun['Lon'],TGN_Jun['Lon'],TGE_Feb['Lon']],\n",
    "             [TGW_Jan[pha[t]],TGS_Feb[pha[t]],TGS_Jun[pha[t]],TGN_Jun[pha[t]],TGE_Feb[pha[t]]],\n",
    "             cols[t],marker=marks[t],markersize=8,linestyle='none')\n",
    "pha = 'M2','O1','K1','S2'\n",
    "nums = [6,0,3,7]\n",
    "marknum = 0\n",
    "for t in nums:\n",
    "    plt.plot(webtide['Longitude'][t],webtide.pha[t],color='green',marker=marks[marknum],markersize=8,linestyle='none')\n",
    "    marknum = marknum+1\n",
    "plt.xlim((-127, -125.5))\n",
    "plt.xlabel('Longitude (deg W)')\n",
    "plt.ylabel('Phase (deg GMT)')\n",
    "plt.legend(('M2pha_GMT','O1pha_GMT','K1pha_GMT','S2pha_GMT'),loc=4)\n",
    "plt.title('Phase lag for measured and Webtide points')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbf0d8f6860>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PnFRUBG+8sU9LSnnz/YhIzaeWExHJKJc3bcqUr78uWXZgLjAH+LRhQ7r++tcULl5Mz6VL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf0df470f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot them to check how it looks\n",
    "#the channel is mostly aligned east-west, so plot along the channel\n",
    "plt.figure(figsize=[9,5])\n",
    "amp = 'M2amp','O1amp','K1amp','S2amp'\n",
    "cols = 'r','b','m','k','g'\n",
    "marks = '>','^','*','d'\n",
    "\n",
    "for t in range(0,len(pha)):\n",
    "    plt.plot([TGW_Jan['Lon'],TGS_Feb['Lon'],TGS_Jun['Lon'],TGN_Jun['Lon'],TGE_Feb['Lon']],\n",
    "             [TGW_Jan[amp[t]],TGS_Feb[amp[t]],TGS_Jun[amp[t]],TGN_Jun[amp[t]],TGE_Feb[amp[t]]],\n",
    "             cols[t],marker=marks[t],markersize=8,linestyle='none')\n",
    "amp = 'M2','O1','K1','S2'\n",
    "nums = [6,0,3,7]\n",
    "marknum = 0\n",
    "for t in nums:\n",
    "    plt.plot(webtide['Longitude'][t],webtide.amp[t],color='green',marker=marks[marknum],markersize=8,linestyle='none')\n",
    "    marknum = marknum+1\n",
    "plt.xlim((-127, -125.5))\n",
    "plt.xlabel('Longitude (deg W)')\n",
    "plt.ylabel('Amplitude (cm)')\n",
    "plt.legend(('M2amp','O1amp','K1amp','S2amp'),loc=4)\n",
    "plt.title('Amplitude for measured and Webtide points')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Station  depth     U  angle  k1amp  k1pha_PST  m2amp  m2pha_PST\n",
      "0         1    120   3.0    145    5.3      170.2   28.4       11.9\n",
      "1         2     15  26.7    175    7.2      100.5   23.6       34.7\n",
      "2         2     15  23.6    174   11.0       88.6   21.8       40.8\n",
      "3         2     75  13.5    167    8.4       94.5   23.8       38.3\n",
      "4         2     75  15.0    170   12.5       83.0   22.2       50.7\n",
      "5         2    150   6.6    333   10.6       58.5   35.6       45.1\n",
      "6         2    150   7.3    344   15.9       39.8   36.9       47.2\n",
      "7         2    225  11.8    350   13.1       24.9   49.0       38.7\n",
      "8         2    295   7.4    351    9.7       19.4   33.2       38.3\n",
      "9         3     15  24.2    163    6.8       74.3   31.4       29.7\n",
      "10        3     15  23.8    176    NaN        NaN   25.6       37.3\n",
      "11        3     20  26.4    179    8.1       87.6   25.2       34.5\n",
      "12        3     23  29.6    176    7.4       94.6   28.0       32.4\n",
      "13        3     23  23.1    172    6.3       82.0   28.4       34.2\n",
      "14        3     75  14.7    171    7.4       92.4   24.0       34.3\n",
      "15        3    150  13.3    338   15.3       28.4   40.2       42.3\n",
      "16        3    200  15.8    349   10.9       24.5   44.1       40.7\n",
      "17        3    225  19.0    358   14.1       26.7   50.3       38.6\n",
      "18        3    225  17.9    357   18.3        9.0   46.7       37.6\n",
      "19        3    250  19.5    353   18.0        4.1   46.9       36.4\n",
      "20        3    275  14.6    348   16.2       47.1   46.1       40.7\n",
      "21        3    275  16.4    351   12.3       16.8   47.3       41.4\n",
      "22        4     75   5.7    155   10.4       47.6   31.7       41.3\n",
      "23        4    150  11.1    353   15.5       14.9   44.2       39.0\n",
      "24        4    250  21.0    340   16.9       -1.4   50.8       34.6\n",
      "25        5     80   4.0    193    8.8       13.6   41.4       12.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py3/lib/python3.5/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/sallen/anaconda/envs/py3/lib/python3.5/site-packages/ipykernel/__main__.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#get the measured tidal currents in Johnstone Strait\n",
    "#table 1 of Thomson & Huggett (1980)\n",
    "#thomson = pandas.read_csv('/ocean/klesouef/meopar/tools/I_ForcingFiles/Tides/thomson_obs_1980_johnstone_strait.csv')\n",
    "thomson = pandas.read_csv('thomson_obs_1980_johnstone_strait.csv')\n",
    "thomson = thomson.rename(columns={'K1 amp (cm/s)':'k1amp','K1 phase (deg relative to 120deg W)':'k1pha_PST',\n",
    "                                  'M2 amp (cm/s)':'m2amp','M2 phase (deg relative to 120deg W)':'m2pha_PST',})\n",
    "thomson.k1amp[thomson.k1amp>100] = float('NaN')\n",
    "thomson.k1pha_PST[thomson.k1pha_PST>1000] = float('NaN')\n",
    "print (thomson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Station  depth     U  angle  k1amp   k1pha_GMT  m2amp   m2pha_GMT\n",
      "0         1    120   3.0    145    5.3  290.551024   28.4  243.784058\n",
      "1         2     15  26.7    175    7.2  220.851024   23.6  266.584058\n",
      "2         2     15  23.6    174   11.0  208.951024   21.8  272.684058\n",
      "3         2     75  13.5    167    8.4  214.851024   23.8  270.184058\n",
      "4         2     75  15.0    170   12.5  203.351024   22.2  282.584058\n",
      "5         2    150   6.6    333   10.6  178.851024   35.6  276.984058\n",
      "6         2    150   7.3    344   15.9  160.151024   36.9  279.084058\n",
      "7         2    225  11.8    350   13.1  145.251024   49.0  270.584058\n",
      "8         2    295   7.4    351    9.7  139.751024   33.2  270.184058\n",
      "9         3     15  24.2    163    6.8  194.651024   31.4  261.584058\n",
      "10        3     15  23.8    176    NaN         NaN   25.6  269.184058\n",
      "11        3     20  26.4    179    8.1  207.951024   25.2  266.384058\n",
      "12        3     23  29.6    176    7.4  214.951024   28.0  264.284058\n",
      "13        3     23  23.1    172    6.3  202.351024   28.4  266.084058\n",
      "14        3     75  14.7    171    7.4  212.751024   24.0  266.184058\n",
      "15        3    150  13.3    338   15.3  148.751024   40.2  274.184058\n",
      "16        3    200  15.8    349   10.9  144.851024   44.1  272.584058\n",
      "17        3    225  19.0    358   14.1  147.051024   50.3  270.484058\n",
      "18        3    225  17.9    357   18.3  129.351024   46.7  269.484058\n",
      "19        3    250  19.5    353   18.0  124.451024   46.9  268.284058\n",
      "20        3    275  14.6    348   16.2  167.451024   46.1  272.584058\n",
      "21        3    275  16.4    351   12.3  137.151024   47.3  273.284058\n",
      "22        4     75   5.7    155   10.4  167.951024   31.7  273.184058\n",
      "23        4    150  11.1    353   15.5  135.251024   44.2  270.884058\n",
      "24        4    250  21.0    340   16.9  118.951024   50.8  266.484058\n",
      "25        5     80   4.0    193    8.8  133.951024   41.4  244.184058\n"
     ]
    }
   ],
   "source": [
    "#convert the GMT phase to PST phase and rename it to avoid confusion\n",
    "thomson.k1pha_PST = thomson.k1pha_PST-(-8.0)*(1.0/freqs['K1'])*360\n",
    "thomson.m2pha_PST = thomson.m2pha_PST-(-8.0)*(1.0/freqs['M2'])*360\n",
    "thomson = thomson.rename(columns={'k1pha_PST':'k1pha_GMT','m2pha_PST':'m2pha_GMT'})\n",
    "print (thomson)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the location of all measurements on our grid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbf0930c240>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E6kThW0RERESkThS+RURERETqROFbRERERKROFL5FREREROpknwzfX7v8a6M9BBERERGR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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf0d8fb240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import netCDF4 as NC\n",
    "#grid = NC.Dataset('/ocean/klesouef/meopar/nemo-forcing/grid/bathy_meter_SalishSea2.nc','r')\n",
    "#grid = NC.Dataset('../../../nemo-forcing/grid/bathy_meter_SalishSea2.nc','r')\n",
    "grid = NC.Dataset('../../../NEMO-forcing/grid/bathy_meter_SalishSea2.nc','r')\n",
    "bathy = grid.variables['Bathymetry']\n",
    "lat = grid.variables['nav_lat']\n",
    "lon = grid.variables['nav_lon']\n",
    "plt.figure(figsize=(12,9))\n",
    "plt.contourf(lon[:],lat[:],bathy[:],range(4,420))\n",
    "plt.xlim((-126.8,-125.4))\n",
    "plt.ylim((50.2,50.6))\n",
    "\n",
    "#I need the lat/long of each of the current measurement points\n",
    "#From Google Maps, I estimate the following locations:\n",
    "#Station 1\n",
    "plt.plot(-126.133747,50.482197,'m*',markersize=15)\n",
    "#Station 2\n",
    "plt.plot(-126.135936,50.478701,'m*',markersize=15)\n",
    "#Station 3\n",
    "plt.plot(-126.136451,50.472256,'m*',markersize=15)\n",
    "#Station 4\n",
    "plt.plot(-126.137567,50.464225,'m*',markersize=15)\n",
    "#Station 5\n",
    "plt.plot(-126.138554,50.458815,'m*',markersize=15)\n",
    "#Webtide point\n",
    "plt.plot(-126.7717,50.556435,'g*',markersize=15)\n",
    "#current measurement points\n",
    "plt.plot([TGW_Jan['Lon'],TGS_Feb['Lon'],TGS_Jun['Lon'],TGN_Jun['Lon'],TGE_Feb['Lon']],\n",
    "         [TGW_Jan['Lat'],TGS_Feb['Lat'],TGS_Jun['Lat'],TGN_Jun['Lat'],TGE_Feb['Lat']],'r*',markersize=15)\n",
    "\n",
    "#also mark the edge points\n",
    "import numpy as np\n",
    "j=np.array([37,48])\n",
    "plt.plot(lon[897,j],lat[897,j],'og')\n",
    "\n",
    "plt.title('Location of measurements around our grid')\n",
    "\n",
    "#so the current measurements are pretty much exactly at our boundary, YAY!\n",
    "#and the TGN and TGS stations are pretty close for the water elevation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to get depth integrated currents for putting into NEMO. Assume each current meter represents the water from halfway between it and the current meter above it, down to halfway between it and the current meter below it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbf06935320>,\n",
       " <matplotlib.lines.Line2D at 0x7fbf068fae10>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MNt8e+G3u+NYdCTXbtIYfA5ic8cbkjC8mZ2oQTniHscAmVS0FEJEXgAsB38ic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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf06a40ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xN1pJZSidqtKiLbREWjL6bo40Z7xvJt/NkeaO83TDeZsjzTS1NLFj/w6aI27i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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf069587b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for K1\n",
    "K1_amp_depth_int = np.zeros((5,1))\n",
    "K1_pha_depth_int = np.zeros((5,1))\n",
    "\n",
    "#Station 1: only one point, therefore this represents all depths\n",
    "K1_amp_depth_int[0,0] = thomson.k1amp[thomson.Station == 1]\n",
    "K1_pha_depth_int[0,0] = thomson.k1pha_GMT[thomson.Station == 1]\n",
    "\n",
    "#Station 2: depth integrate amplitude and phase\n",
    "K1_amp_depth_int[1,0] = np.sum([np.mean(thomson.k1amp[np.logical_and(thomson.Station == 2,thomson.depth == 15)])*45,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 2,thomson.depth == 75)])*67.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 2,thomson.depth == 150)])*75,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 2,thomson.depth == 225)])*72.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 2,thomson.depth == 295)])*40])/300.0\n",
    "K1_pha_depth_int[1,0]= np.sum([np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 15)])*45,\n",
    "                               np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 75)])*67.5,\n",
    "                               np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 150)])*75,\n",
    "                               np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 225)])*72.5,\n",
    "                               np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 295)])*40])/300.0\n",
    "\n",
    "#Station 3: depth integrate amplitude and phase\n",
    "K1_amp_depth_int[2,0] = np.sum([np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 15)])*17.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 20)])*4,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 23)])*27.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 75)])*63.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 150)])*62.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 200)])*37.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 225)])*25,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 250)])*25,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 3,thomson.depth == 275)])*37.5])/300.0\n",
    "\n",
    "K1_pha_depth_int[2,0] = np.sum([np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 15)])*17.5,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 20)])*4,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 23)])*27.5,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 75)])*63.5,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 150)])*62.5,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 200)])*37.5,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 225)])*25,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 250)])*25,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 275)])*37.5])/300.0\n",
    "\n",
    "#Station 4: depth integrate amplitude and phase\n",
    "K1_amp_depth_int[3,0] = np.sum([np.mean(thomson.k1amp[np.logical_and(thomson.Station == 4,thomson.depth == 75)])*112.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 4,thomson.depth == 150)])*87.5,\n",
    "                                np.mean(thomson.k1amp[np.logical_and(thomson.Station == 4,thomson.depth == 250)])*100])/300.0\n",
    "\n",
    "K1_pha_depth_int[3,0] = np.sum([np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 4,thomson.depth == 75)])*112.5,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 4,thomson.depth == 150)])*87.5,\n",
    "                                np.mean(thomson.k1pha_GMT[np.logical_and(thomson.Station == 4,thomson.depth == 250)])*100])/300.0\n",
    "\n",
    "#Station 5: only one point, therefore this represents all depths \n",
    "K1_amp_depth_int[4,0] = thomson.k1amp[thomson.Station == 5]\n",
    "K1_pha_depth_int[4,0] = thomson.k1pha_GMT[thomson.Station == 5]\n",
    "\n",
    "\n",
    "#for M2\n",
    "M2_amp_depth_int = np.zeros((5,1))\n",
    "M2_pha_depth_int = np.zeros((5,1))\n",
    "\n",
    "#Station 1: only one point, therefore this represents all depths\n",
    "M2_amp_depth_int[0,0] = thomson.m2amp[thomson.Station == 1]\n",
    "M2_pha_depth_int[0,0] = thomson.m2pha_GMT[thomson.Station == 1]\n",
    "\n",
    "#Station 2: depth integrate amplitude and phase\n",
    "M2_amp_depth_int[1,0] = np.sum([np.mean(thomson.m2amp[np.logical_and(thomson.Station == 2,thomson.depth == 15)])*45,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 2,thomson.depth == 75)])*67.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 2,thomson.depth == 150)])*75,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 2,thomson.depth == 225)])*72.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 2,thomson.depth == 295)])*40])/300.0\n",
    "M2_pha_depth_int[1,0]= np.sum([np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 15)])*45,\n",
    "                               np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 75)])*67.5,\n",
    "                               np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 150)])*75,\n",
    "                               np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 225)])*72.5,\n",
    "                               np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 2,thomson.depth == 295)])*40])/300.0\n",
    "\n",
    "#Station 3: depth integrate amplitude and phase\n",
    "M2_amp_depth_int[2,0] = np.sum([np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 15)])*17.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 20)])*4,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 23)])*27.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 75)])*63.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 150)])*62.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 200)])*37.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 225)])*25,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 250)])*25,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 3,thomson.depth == 275)])*37.5])/300.0\n",
    "\n",
    "M2_pha_depth_int[2,0] = np.sum([np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 15)])*17.5,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 20)])*4,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 23)])*27.5,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 75)])*63.5,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 150)])*62.5,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 200)])*37.5,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 225)])*25,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 250)])*25,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 3,thomson.depth == 275)])*37.5])/300.0\n",
    "\n",
    "#Station 4: depth integrate amplitude and phase\n",
    "M2_amp_depth_int[3,0] = np.sum([np.mean(thomson.m2amp[np.logical_and(thomson.Station == 4,thomson.depth == 75)])*112.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 4,thomson.depth == 150)])*87.5,\n",
    "                                np.mean(thomson.m2amp[np.logical_and(thomson.Station == 4,thomson.depth == 250)])*100])/300.0\n",
    "\n",
    "M2_pha_depth_int[3,0] = np.sum([np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 4,thomson.depth == 75)])*112.5,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 4,thomson.depth == 150)])*87.5,\n",
    "                                np.mean(thomson.m2pha_GMT[np.logical_and(thomson.Station == 4,thomson.depth == 250)])*100])/300.0\n",
    "\n",
    "#Station 5: only one point, therefore this represents all depths \n",
    "M2_amp_depth_int[4,0] = thomson.m2amp[thomson.Station == 5]\n",
    "M2_pha_depth_int[4,0] = thomson.m2pha_GMT[thomson.Station == 5]\n",
    "\n",
    "#measurements were in cm/s, convert to m/s\n",
    "K1_amp_depth_int = K1_amp_depth_int/100.0\n",
    "M2_amp_depth_int = M2_amp_depth_int/100.0\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(range(0,5),M2_amp_depth_int,'.-',range(0,5),K1_amp_depth_int,'.-')\n",
    "plt.legend(('M2','K1'))\n",
    "plt.title('Initial depth interpolated currents')\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(range(0,5),M2_pha_depth_int,'.-',range(0,5),K1_pha_depth_int,'.-')\n",
    "\n",
    "#after plotting them, the phase for K1 at Station 1 looks too high, so just take the value for Station 2\n",
    "K1_pha_depth_int[0,0] = K1_pha_depth_int[1,0]\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(range(0,5),M2_amp_depth_int,'.-',range(0,5),K1_amp_depth_int,'.-')\n",
    "plt.legend(('M2','K1'))\n",
    "plt.title('Depth interpolated currents after adjusting one point')\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(range(0,5),M2_pha_depth_int,'.-',range(0,5),K1_pha_depth_int,'.-')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot depths along the boundary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 49)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OJndUTRTROees6a4mIoXpscfgiCNKOxE0RMkgx1q0gD/+Efr2hS++iB2NiNSl3KuIQNVE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf10521208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = range(0,60)\n",
    "plt.plot(k,bathy[897,k])\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('grid point')\n",
    "plt.ylabel('depth [m]')\n",
    "plt.title('Depths at northern boundary')\n",
    "plt.xlim((36,49))\n",
    "#so the boundary is from j=37 to j=48 at i=897"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate of Thomson x-sectional area in mkm 693.0\n",
      "Our effective x-sectional area in mkm 856.341797805\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbf069d2b38>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf069d2b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare the size of the flux through the Thomson section and the size of the flux through\n",
    "# our section, remembering that our flux is at 39 degrees to the boundary.\n",
    "# Start with area\n",
    "print ('Estimate of Thomson x-sectional area in mkm', 0.55*0.5*300 + (2.4-0.55)*300 + 0.37*300*0.5)\n",
    "dx = 0.4155 # our grid size\n",
    "dxc = dx*np.cos(39.*np.pi/180.) # corrected for slant of boundary\n",
    "print ('Our effective x-sectional area in mkm', np.sum(bathy[897,37:48])*dxc)\n",
    "# Thomson bathymetry\n",
    "thomx = (0, 0.18, 0.55, 0.8, 1.4, 1.95, 2.4, 2.46, 2.77)/np.cos(0)+0.5\n",
    "thomy = (0, 100, 300, 300, 300, 300, 300, 150, 0)\n",
    "plt.plot (thomx,thomy,'x-')\n",
    "# Model bathymetry, corrected for slant\n",
    "krange = np.arange(0,49-36+1)*dxc\n",
    "depth = np.zeros(49-36+1)\n",
    "depth[0:49-36+1] = bathy[897,36:50]\n",
    "plt.plot (krange,depth,'*-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.373385]\n",
      "[ 257.164675] 314.773414644 [ 1.22401498] 1.2352092352092352\n",
      "[ 0.371089] 0.367725951687\n",
      "[ 0.66746198]\n"
     ]
    }
   ],
   "source": [
    "# Now estimate flux (M2)\n",
    "obs_flux = 0.\n",
    "model_flux = 0.\n",
    "# Station 5\n",
    "obs_flux = obs_flux + 0.55 * M2_amp_depth_int[4] * 0.5*300\n",
    "model_flux = model_flux + sum(depth[0:3] * dx * M2_amp_depth_int[4]*np.cos(39.*np.pi/180.))\n",
    "# Station 4\n",
    "obs_flux = obs_flux + (1.1-0.55) * M2_amp_depth_int[3] * 300\n",
    "model_flux = model_flux + sum(depth[3:5] * dx * M2_amp_depth_int[3]*np.cos(39.*np.pi/180.))\n",
    "# Station 3\n",
    "obs_flux = obs_flux + (1.7-1.1) * M2_amp_depth_int[2] * 300\n",
    "model_flux = model_flux + sum(depth[5:7] * dx * M2_amp_depth_int[2]*np.cos(39.*np.pi/180.))\n",
    "print (M2_amp_depth_int[2])\n",
    "# Station 2\n",
    "obs_flux = obs_flux + (2.4-1.7) * M2_amp_depth_int[1] * 300\n",
    "model_flux = model_flux + sum(depth[7:9] * dx * M2_amp_depth_int[1]*np.cos(39.*np.pi/180.))\n",
    "# Station 1\n",
    "obs_flux = obs_flux + (2.77-2.4) * M2_amp_depth_int[0] * 300 * 0.5\n",
    "model_flux = model_flux + sum(depth[9:13] * dx * M2_amp_depth_int[0]*np.cos(39.*np.pi/180.))\n",
    "print (obs_flux, model_flux, model_flux/obs_flux, 856/693.)\n",
    "print (obs_flux/693., model_flux/856.)\n",
    "M2_corr = obs_flux/model_flux\n",
    "M2_corr = M2_corr*M2_corr #reduce by an extra factor for a better match with observations\n",
    "print (M2_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 78.8998] 91.7139880465 [ 1.16241091] 1.2352092352092352\n",
      "[ 0.86028099]\n"
     ]
    }
   ],
   "source": [
    "# And flux K1\n",
    "obs_flux = 0.\n",
    "model_flux = 0.\n",
    "# Station 5\n",
    "obs_flux = obs_flux + 0.55 * K1_amp_depth_int[4] * 0.5*300\n",
    "model_flux = model_flux + sum(depth[0:3] * dx * K1_amp_depth_int[4]*np.cos(39.*np.pi/180.))\n",
    "# Station 4\n",
    "obs_flux = obs_flux + (1.1-0.55) * K1_amp_depth_int[3] * 300\n",
    "model_flux = model_flux + sum(depth[3:5] * dx * K1_amp_depth_int[3]*np.cos(39.*np.pi/180.))\n",
    "# Station 3\n",
    "obs_flux = obs_flux + (1.7-1.1) * K1_amp_depth_int[2] * 300\n",
    "model_flux = model_flux + sum(depth[5:7] * dx * K1_amp_depth_int[2]*np.cos(39.*np.pi/180.))\n",
    "# Station 2\n",
    "obs_flux = obs_flux + (2.4-1.7) * K1_amp_depth_int[1] * 300\n",
    "model_flux = model_flux + sum(depth[7:9] * dx * K1_amp_depth_int[1]*np.cos(39.*np.pi/180.))\n",
    "# Station 1\n",
    "obs_flux = obs_flux + (2.77-2.4) * K1_amp_depth_int[0] * 300 * 0.5\n",
    "model_flux = model_flux + sum(depth[9:13] * dx * K1_amp_depth_int[0]*np.cos(39.*np.pi/180.))\n",
    "print (obs_flux, model_flux, model_flux/obs_flux, 856/693.)\n",
    "K1_corr = obs_flux/model_flux\n",
    "print (K1_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So... we have current measurements close to the boundary, but not perfectly aligned with the boundary. But without any other well resolved data west of the NEMO boundary, we can't really interpolate to the boundary location. Plus, there isn't much of a change in tidal amplitudes or phases across the boundary. So just assume that the current measurements represent the currents at the boundary without any interpolation or change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is from Susan's notebook (JohnstoneStraitBoundary) where she looked at the boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#start and end points\n",
    "starti = 32\n",
    "endi = 62\n",
    "lengthi = endi-starti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's assume for currents (Kates original assumptions and Susan's suggestions)\n",
    "\n",
    "* Station 1 represents cells 47 - 62 (16 cells) -- SEA 45-49, 50-62 (18 cells)\n",
    "\n",
    "* Station 2 represents cells 44 - 46 (3 cells) --  SEA 43-44 (2 cells)\n",
    "\n",
    "* Station 3 represents cells 41 - 43 (3 cells) --- SEA 41-42 (2 cells)\n",
    "\n",
    "* Station 4 represents cells 39 - 40 (2 cells) --- SEA  39-40 (2 cells)\n",
    "\n",
    "* Station 5 represents cells 32 - 38 (7 cells) --- SEA 32-35, 36-38 (7 cells)\n",
    "\n",
    "Put all this into some new variables that are the same length as the boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>This is as far as I have gotten with the corrections.</b> The position of the fluxes, as given by SEA above needs to be corrected.  The impact of the correction from Stn 3 to Stn 4 needs to be propagated.  The currents need to be downgraded by the amount estimated above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nancy's continuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plan:\n",
    "\n",
    "1. Correct the currents and check.\n",
    "2. Correct grid positioning.\n",
    "3. Propogate S3-S4 changes through. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Current corrections:\n",
    "\n",
    "We adjust the currents by multiplying by the correction factors give above:\n",
    "\n",
    "$U_{mod} = corr*U_{obs}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.284     ]\n",
      " [ 0.33910833]\n",
      " [ 0.373385  ]\n",
      " [ 0.417125  ]\n",
      " [ 0.414     ]]\n",
      "[[ 0.1895592 ]\n",
      " [ 0.22634192]\n",
      " [ 0.24922029]\n",
      " [ 0.27841508]\n",
      " [ 0.27632926]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print (M2_amp_depth_int)\n",
    "\n",
    "K1_amp_depth_int_corr = K1_corr*K1_amp_depth_int[:];\n",
    "M2_amp_depth_int_corr = M2_corr*M2_amp_depth_int[:];\n",
    "print (M2_amp_depth_int_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 257.164675] 210.099287269 [ 0.81698347] 1.2352092352092352\n",
      "[ 0.371089] 0.245443092603\n"
     ]
    }
   ],
   "source": [
    "#Restimate Fluxes\n",
    "# Now estimate flux (M2)\n",
    "obs_flux = 0.\n",
    "model_flux = 0.\n",
    "# Station 5\n",
    "obs_flux = obs_flux + 0.55 * M2_amp_depth_int[4] * 0.5*300\n",
    "model_flux = model_flux + sum(depth[0:3] * dx * M2_amp_depth_int_corr[4]*np.cos(39.*np.pi/180.))\n",
    "# Station 4\n",
    "obs_flux = obs_flux + (1.1-0.55) * M2_amp_depth_int[3] * 300\n",
    "model_flux = model_flux + sum(depth[3:5] * dx * M2_amp_depth_int_corr[3]*np.cos(39.*np.pi/180.))\n",
    "# Station 3\n",
    "obs_flux = obs_flux + (1.7-1.1) * M2_amp_depth_int[2] * 300\n",
    "model_flux = model_flux + sum(depth[5:7] * dx * M2_amp_depth_int_corr[2]*np.cos(39.*np.pi/180.))\n",
    "# Station 2\n",
    "obs_flux = obs_flux + (2.4-1.7) * M2_amp_depth_int[1] * 300\n",
    "model_flux = model_flux + sum(depth[7:9] * dx * M2_amp_depth_int_corr[1]*np.cos(39.*np.pi/180.))\n",
    "# Station 1\n",
    "obs_flux = obs_flux + (2.77-2.4) * M2_amp_depth_int[0] * 300 * 0.5\n",
    "model_flux = model_flux + sum(depth[9:13] * dx * M2_amp_depth_int_corr[0]*np.cos(39.*np.pi/180.))\n",
    "print (obs_flux, model_flux, model_flux/obs_flux, 856/693.)\n",
    "print (obs_flux/693., model_flux/856.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 78.8998] 78.8998 [ 1.] 1.2352092352092352\n"
     ]
    }
   ],
   "source": [
    "# And flux K1\n",
    "obs_flux = 0.\n",
    "model_flux = 0.\n",
    "# Station 5\n",
    "obs_flux = obs_flux + 0.55 * K1_amp_depth_int[4] * 0.5*300\n",
    "model_flux = model_flux + sum(depth[0:3] * dx * K1_amp_depth_int_corr[4]*np.cos(39.*np.pi/180.))\n",
    "# Station 4\n",
    "obs_flux = obs_flux + (1.1-0.55) * K1_amp_depth_int[3] * 300\n",
    "model_flux = model_flux + sum(depth[3:5] * dx * K1_amp_depth_int_corr[3]*np.cos(39.*np.pi/180.))\n",
    "# Station 3\n",
    "obs_flux = obs_flux + (1.7-1.1) * K1_amp_depth_int[2] * 300\n",
    "model_flux = model_flux + sum(depth[5:7] * dx * K1_amp_depth_int_corr[2]*np.cos(39.*np.pi/180.))\n",
    "# Station 2\n",
    "obs_flux = obs_flux + (2.4-1.7) * K1_amp_depth_int[1] * 300\n",
    "model_flux = model_flux + sum(depth[7:9] * dx * K1_amp_depth_int_corr[1]*np.cos(39.*np.pi/180.))\n",
    "# Station 1\n",
    "obs_flux = obs_flux + (2.77-2.4) * K1_amp_depth_int[0] * 300 * 0.5\n",
    "model_flux = model_flux + sum(depth[9:13] * dx * K1_amp_depth_int_corr[0]*np.cos(39.*np.pi/180.))\n",
    "print (obs_flux, model_flux, model_flux/obs_flux, 856/693.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model and observed fluxes match now. Note that there are likely some integration errors in calculating the fluxes but we probably don't have to worry because these are estimates anways. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, correct the positioning based on Susn's corrections. Make sure  use the new array for corrected depth integrated currents.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So above is correct theoretically, but now given the model results, lets do some fine tuning.  Based on the run tide_M2phase, the average Kelsey Bay/Yorke Island M2 amplitudes are 3.4% high and the phase is 8.2 degrees low.  Add these corrections: here to the velocities and below to the elevations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fine tuning corrections at the top of the notebook\n",
    "M2_amp_depth_int_corr = M2_amp_depth_int_corr * M2_amp_finecorrection\n",
    "M2_pha_depth_int = M2_pha_depth_int + M2_pha_finecorrection\n",
    "K1_amp_depth_int_corr = K1_amp_depth_int_corr * K1_amp_finecorrection\n",
    "K1_pha_depth_int = K1_pha_depth_int + K1_pha_finecorrection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#K1 current amplitude\n",
    "amp_K1_bound = np.zeros((lengthi,1))\n",
    "amp_K1_bound[0:7,0]   = K1_amp_depth_int_corr[5-1,0]\n",
    "amp_K1_bound[7:9,0]   = K1_amp_depth_int_corr[4-1,0]\n",
    "amp_K1_bound[9:11,0]  = K1_amp_depth_int_corr[3-1,0]\n",
    "amp_K1_bound[11:13,0] = K1_amp_depth_int_corr[2-1,0]\n",
    "amp_K1_bound[13:,0]   = K1_amp_depth_int_corr[1-1,0]\n",
    "\n",
    "#K1 current phase\n",
    "pha_K1_bound = np.zeros((lengthi,1))\n",
    "pha_K1_bound[0:7,0]   = K1_pha_depth_int[5-1,0]\n",
    "pha_K1_bound[7:9,0]   = K1_pha_depth_int[4-1,0]\n",
    "pha_K1_bound[9:11,0]  = K1_pha_depth_int[3-1,0]\n",
    "pha_K1_bound[11:13,0] = K1_pha_depth_int[2-1,0]\n",
    "pha_K1_bound[13:,0]   = K1_pha_depth_int[1-1,0]\n",
    "\n",
    "#M2 current amplitude\n",
    "amp_M2_bound = np.zeros((lengthi,1))\n",
    "amp_M2_bound[0:7,0]   = M2_amp_depth_int_corr[5-1,0]\n",
    "amp_M2_bound[7:9,0]   = M2_amp_depth_int_corr[4-1,0]\n",
    "amp_M2_bound[9:11,0]  = M2_amp_depth_int_corr[3-1,0]\n",
    "amp_M2_bound[11:13,0] = M2_amp_depth_int_corr[2-1,0]\n",
    "amp_M2_bound[13:,0]   = M2_amp_depth_int_corr[1-1,0]\n",
    "\n",
    "#M2 current phase\n",
    "pha_M2_bound = np.zeros((lengthi,1))\n",
    "pha_M2_bound[0:7,0]   = M2_pha_depth_int[5-1,0]\n",
    "pha_M2_bound[7:9,0]   = M2_pha_depth_int[4-1,0]\n",
    "pha_M2_bound[9:11,0]  = M2_pha_depth_int[3-1,0]\n",
    "pha_M2_bound[11:13,0] = M2_pha_depth_int[2-1,0]\n",
    "pha_M2_bound[13:,0]   = M2_pha_depth_int[1-1,0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, as noted in the text of Thomson & Huggett (1980), the current amplitudes are in the along-strait direction. Across-strait currents are assumed to be negligible. From the direction of the mean currents, the average along-strait direction is approximately 170 degrees/350 degrees, measured counter-clockwise from east. \n",
    "\n",
    "So, we must convert the along-strait amplitude and phase into u and v amplitude and phase relative for NEMO. This means that we must rotate them because NEMO wants velocities relative to its grid orientation (not North South).\n",
    "\n",
    "Following the approach used to rotate the Webtide tidal constituents, we can do the following:\n",
    "\n",
    "* assume the postive along strait direction is approximately 10 degrees measured clockwise from East\n",
    "* 'up' in NEMO is rotated 29 degrees counter-clockwise of North\n",
    "* 'across' in NEMO is rotated 29 degrees counter-clockwise of East\n",
    "* the angle of rotation between along-strait direction and 'across' in NEMO is therefore $\\theta = 29^\\circ +10^\\circ=39^\\circ$\n",
    "* neglect across-strait velocity (i.e. $v_a = 0$, $\\phi_v = 0$)\n",
    "* $u_a$ and $\\phi_u$ are the depth averaged amplitude and phase at each station (calculated above)\n",
    "* following the approach described in the docs (docs/tides/tides_data_acquisition):\n",
    "    \n",
    "    so for U: \n",
    "\n",
    "    $Z1 = u_a\\cos\\theta\\cos\\phi_u$ \n",
    "    \n",
    "    $Z2 = u_a\\cos\\theta\\sin\\phi_u$\n",
    "    \n",
    "    and for V: \n",
    "\n",
    "    $Z1 = -u_a\\sin\\theta\\cos\\phi_u$ \n",
    "    \n",
    "    $Z2 = -u_a\\sin\\theta\\sin\\phi_u$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 126.69353554]\n"
     ]
    }
   ],
   "source": [
    "theta = 39\n",
    "\n",
    "#for K1 currents (these were corrected above)\n",
    "UZ1_K1 =  amp_K1_bound*np.cos(np.radians(theta))*np.cos(np.radians(pha_K1_bound))\n",
    "UZ2_K1 =  amp_K1_bound*np.cos(np.radians(theta))*np.sin(np.radians(pha_K1_bound))\n",
    "VZ1_K1 = -amp_K1_bound*np.sin(np.radians(theta))*np.cos(np.radians(pha_K1_bound)) \n",
    "VZ2_K1 = -amp_K1_bound*np.sin(np.radians(theta))*np.sin(np.radians(pha_K1_bound))\n",
    "\n",
    "#for M2 currents\n",
    "UZ1_M2 =  amp_M2_bound*np.cos(np.radians(theta))*np.cos(np.radians(pha_M2_bound))\n",
    "UZ2_M2 =  amp_M2_bound*np.cos(np.radians(theta))*np.sin(np.radians(pha_M2_bound))\n",
    "VZ1_M2 = -amp_M2_bound*np.sin(np.radians(theta))*np.cos(np.radians(pha_M2_bound)) \n",
    "VZ2_M2 = -amp_M2_bound*np.sin(np.radians(theta))*np.sin(np.radians(pha_M2_bound))\n",
    "\n",
    "print (pha_K1_bound[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's assume for surface elevation that the whole boundary is represented by the average between TGN and TGS. \n",
    "\n",
    "Then convert the amplitude and phase into cosine and sine format that NEMO likes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "252.026868877\n"
     ]
    }
   ],
   "source": [
    "#K1 surface elevation \n",
    "elev_amp_K1 = np.mean([TGN_Jun['K1amp'],TGS_Feb['K1amp'],TGS_Jun['K1amp']]) * K1_amp_finecorrection\n",
    "elev_pha_K1 = np.mean([TGN_Jun['K1pha_GMT'],TGS_Feb['K1pha_GMT'],\n",
    "                       TGS_Jun['K1pha_GMT']]) + K1_pha_finecorrection\n",
    "Z1_K1 = elev_amp_K1*np.cos(np.radians(elev_pha_K1))       \n",
    "Z2_K1 = elev_amp_K1*np.sin(np.radians(elev_pha_K1))\n",
    "\n",
    "#M2 surface elevation \n",
    "elev_amp_M2 = np.mean([TGN_Jun['M2amp'],TGS_Feb['M2amp'],TGS_Jun['M2amp']])*M2_amp_finecorrection\n",
    "elev_pha_M2 = np.mean([TGN_Jun['M2pha_GMT'],TGS_Feb['M2pha_GMT'],\n",
    "                       TGS_Jun['M2pha_GMT']]) + M2_pha_finecorrection\n",
    "print (elev_pha_K1)\n",
    "Z1_M2 = elev_amp_M2*np.cos(np.radians(elev_pha_M2))       \n",
    "Z2_M2 = elev_amp_M2*np.sin(np.radians(elev_pha_M2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start to make the netcdf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Tidal Boundary Conditions for Northern Boundary\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://github.com/SalishSeaCast/tools/blob/master/johnstone_strait_tides.ipynb\n",
      "references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/open_boundaries/north/SalishSea2_North_tide_K1_grid_U.nc\n",
      "history: [2017-04-25 18:13:27] Created netCDF4 zlib=True dataset.\n",
      "comment: Tidal current and amplitude data from Thomson & Huggett 1980\n",
      "(1, 30)\n",
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Tidal Boundary Conditions for Northern Boundary\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://github.com/SalishSeaCast/tools/blob/master/johnstone_strait_tides.ipynb\n",
      "references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/open_boundaries/north/SalishSea2_North_tide_K1_grid_V.nc\n",
      "history: [2017-04-25 18:13:27] Created netCDF4 zlib=True dataset.\n",
      "comment: Tidal current and amplitude data from Thomson & Huggett 1980\n",
      "(1, 30)\n",
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Tidal Boundary Conditions for Northern Boundary\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://github.com/SalishSeaCast/tools/blob/master/johnstone_strait_tides.ipynb\n",
      "references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/open_boundaries/north/SalishSea2_North_tide_M2_grid_U.nc\n",
      "history: [2017-04-25 18:13:28] Created netCDF4 zlib=True dataset.\n",
      "comment: Tidal current and amplitude data from Thomson & Huggett 1980\n",
      "(1, 30)\n",
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Tidal Boundary Conditions for Northern Boundary\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://github.com/SalishSeaCast/tools/blob/master/johnstone_strait_tides.ipynb\n",
      "references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/open_boundaries/north/SalishSea2_North_tide_M2_grid_V.nc\n",
      "history: [2017-04-25 18:13:28] Created netCDF4 zlib=True dataset.\n",
      "comment: Tidal current and amplitude data from Thomson & Huggett 1980\n",
      "(1, 30)\n",
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Tidal Boundary Conditions for Northern Boundary\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://github.com/SalishSeaCast/tools/blob/master/johnstone_strait_tides.ipynb\n",
      "references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/open_boundaries/north/SalishSea2_North_tide_K1_grid_T.nc\n",
      "history: [2017-04-25 18:13:28] Created netCDF4 zlib=True dataset.\n",
      "comment: Tidal current and amplitude data from Thomson & Huggett 1980\n",
      "(1, 30)\n",
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Tidal Boundary Conditions for Northern Boundary\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://github.com/SalishSeaCast/tools/blob/master/johnstone_strait_tides.ipynb\n",
      "references: https://bitbucket.org/salishsea/nemo-forcing/src/tip/open_boundaries/north/SalishSea2_North_tide_M2_grid_T.nc\n",
      "history: [2017-04-25 18:13:28] Created netCDF4 zlib=True dataset.\n",
      "comment: Tidal current and amplitude data from Thomson & Huggett 1980\n",
      "(1, 30)\n"
     ]
    }
   ],
   "source": [
    "#reload(webtide_tools)\n",
    "#wait.... what about the U and V being on different grids?\n",
    "#.... nothing really changes because we don't have great spatial resolution of measurements...\n",
    "#... perhaps we'll need to make an extra cell for the V grid....\n",
    "#currents\n",
    "webtide_tools.create_northern_tides(UZ1_K1, UZ2_K1,'U','K1', tidesetname, name='SalishSea')\n",
    "webtide_tools.create_northern_tides(VZ1_K1, VZ2_K1,'V','K1', tidesetname, name='SalishSea')\n",
    "webtide_tools.create_northern_tides(UZ1_M2, UZ2_M2,'U','M2', tidesetname, name='SalishSea')\n",
    "webtide_tools.create_northern_tides(VZ1_M2, VZ2_M2,'V','M2', tidesetname, name='SalishSea')\n",
    "#surface elevation \n",
    "webtide_tools.create_northern_tides(Z1_K1, Z2_K1,'T','K1', tidesetname, name='SalishSea')\n",
    "webtide_tools.create_northern_tides(Z1_M2, Z2_M2,'T','M2', tidesetname, name='SalishSea')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, remake the tides in the johnstone_strait_contd notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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