{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check River Climatology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "\n",
    "from salishsea_tools import nc_tools\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def find_points(flow):\n",
    "    for i in range(390,435):\n",
    "        for j in range(280,398):\n",
    "            if flow[i,j] > 0:\n",
    "                print i,j, lat[i,j], lon[i,j], flow[i,j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = nc.Dataset('/ocean/sallen/allen/research/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')\n",
    "lat = grid.variables['nav_lat'][:,:]\n",
    "lon = grid.variables['nav_lon'][:,:]\n",
    "depth = grid.variables['Bathymetry'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 898, 398)\n"
     ]
    }
   ],
   "source": [
    "river = nc.Dataset('/ocean/sallen/allen/research/MEOPAR/NEMO-forcing/rivers/rivers_month.nc')\n",
    "print river.variables['rorunoff'][:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apr\n",
      "411 324 49.0993995667 -123.083885193 0.65822\n",
      "412 324 49.1033248901 -123.087242126 0.65822\n",
      "414 334 49.1300430298 -123.041992188 6.58978\n",
      "415 334 49.1339645386 -123.045349121 6.58978\n",
      "416 334 49.1378898621 -123.048713684 6.58978\n",
      "434 318 49.1783180237 -123.192321777 0.660519\n",
      "May\n",
      "411 324 49.0993995667 -123.083885193 0.93152\n",
      "412 324 49.1033248901 -123.087242126 0.93152\n",
      "414 334 49.1300430298 -123.041992188 9.32592\n",
      "415 334 49.1339645386 -123.045349121 9.32592\n",
      "416 334 49.1378898621 -123.048713684 9.32592\n",
      "434 318 49.1783180237 -123.192321777 0.934773\n"
     ]
    }
   ],
   "source": [
    "print 'Apr'\n",
    "find_points(river.variables['rorunoff'][4,:,:])\n",
    "print 'May'\n",
    "find_points(river.variables['rorunoff'][5,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mqf/ICb0RniRJktrEuG1GMW6bUYvvn37R9U2MpodWtM/KiB0rxyIzl9pusgNwW2Y+DxAR\nlwI7Ax2TlSeBDTvc34ClS8UapsirgQ1fdKWviBgCvA+YDFwJHFGddgRweVExSJIkSeq2+4BxETEk\nIgLYC5jZac6VwOEAETGOSqnYbApS5MrKusD5EbESlaTowsy8PiImA7+NiCOBR4CDCoxBkiRJ6hsK\n7rOSmVMj4gLgLmAhcDdwVkQcUz1/Zmb+KSL2jYgHgVeBTxYZU2HJSmZOB8bWGJ9LJUuTJEmS1EIy\n8/vA9zsNn9lpzud6Kx472EuSJEllsKJ7VkrIZEWSJEkqgzZMVgrtsyJJkiRJy8tkRVJL2O3w73H2\n729tdhh93qSps9j0Ayfx4sv/bHYokqSeKr6DfcuxDExS4Z5/8RVO+5/ruPlv9zNn7susvupgtth4\nBJ8+aDy7jt0MgMoVEotxyPG/4O2brMOEz+zX5bzjf3gJl14/eanxP/zs82w1at2iwmuYTT9w0uLb\nQwYNYP21h3LQPu/iyI/uunh8+zEbc8fFJzF0tZWbEaIkST1istLqLhlTe9zO9iqRz/zXRcx7YwHf\n+9K/sPF6b+O5F17hzukPF/7b/QVvvkm/lXqwgBzBrtttxqlfWfKK6mvW+GL/xvwFDBzQ+z9CFy5c\nCMBKdd7Xd7/wUfbcaUtee/0NrrppGief9SfeNnQV9t9zOwAG9O/H8KGrFhrjgjffpH+/foW+hiS1\npTbcs2KyIqlQ/3jlNe6a8SgXfvdI3r3NpgCst9ZQ3rnFBkvNnffGAv7vf1/GVTdPZbWVB/OJ/Xfm\nqI+9Z/H5J+e8yLf/31XcNuUhAHbZbjO+8R8fZp3hawBw2oXXcfVf7+FTH92Nn158A0/OfpH37zya\nO+95hDvveYQLr5oEwC3nf4X11x66dLCZDBzQv+aX+UOO/wWbbbw2QwYN5LLr7maDddbksv/+LGf/\n/lYuve5uHnt6LquvOpjdd3g7Jx61L6uvMrjy/l99nQk/u4Jb736QV/75OiPetjpHfGRnPrn/LovP\nn3L2n5h4+73Me2M+YzZdj5OO/iBbb74+AL+79u9884wr+clJH+eUs//MrCee5Y8/P5bNNx5R8+97\n9VUGL47/MweP55xLb2XqfY8vTlYmTZ3FoSeczd9/+zX6rbQSOx7yHX76fz/Oe3faavFz3Pr3+/nU\nNy7g9otOZNgaq/DMcy9x8ll/4ta/PwDA2NEb8Z+f/hAj1xte9+992qXfYMjggTVjlCQtp5KUbjWS\nyYqkQq08ZCCrDBnIdbfPZPvRGzNoYO0fO5nJuZf9hS8cthfHHLQ7N/3tPr55xh/YYcxItttqIxYu\nXMgx37yAIYMG8qvvHUUCE352Bcd880Ku+Mlbl3t/4pkX+MPNU/n51w5lQP9+rDN8DebMfZlNN1yL\n//PJvQEYtnr9Eqik/j8EV9wwhUM+sCO/OfUYFk3r128lvv7pD7PhusN4cvYLTPj5lXzz51dy6vGV\n1ZkfnX8t9z86m3O+dQTD11yVx5+ey/Mvvbr4PR/5n+exxmpDOOdbRzB0tZX5/cS/c+hXz+L6s7/M\nWsNWAypJ3M8uvpGTjzuAYUNXYa01V+si/oo331zI1X+9hxdffo2taySGAKutMpi9xm3FFTdMWSJZ\nueLGKew6djOGrbEKr73+Bh//6lnsMGYkv/7h0Qzo34+zfncrh51wDhPP+hKDBw2o+ffejFUnSVLf\n478mkgrVv18/vv+lj3HSf1/KxX++kzGbrsf2ozdm3/dszTZv33CJubttvzmHffjdABy+386cd8Vt\n3DblIbbbaiP+OuUh/vfh2dx03vGLV0VOO+Fg9vj3H3LblIfYedvKqs38BW9y6vEH8bYOqyMD+vdj\nyKAB3Sp/uuWu+9n6gG8svv+ud2zCud/+BAAbrjOME4/ad4n5i1ZIANZfeyhfPXIfjvnm/yxOVp6a\n8yJjNl1/8UrSemu9taJz+9RZ3Pfw09z1m68xaGDlS/8XD38f199xL5ddP5mjD6ysKr25MJnwmf0Y\ns9l6y4z/yz/4LV859RLmvbEAgG/8x4f56F5L9eddbP89t+PY717Mq6/NY5Uhg3h93nyuvW0m3zn2\nAACuunkaAN//0scWP+a/Pr8/Ox7yHW644z72fc/WQO2/d0lSg1kGJkmNt8+u72CPHbfkb/c8wuR7\nH+WWvz/A2Zf+hS8f8X4+c/B4oLLBfstNltzEPmLY6jz/0isAPPTYHNZ+22pLlG9tuM4wRgxbnQcf\nm704WVln+Oor9IV5x6034eTjPrr4/uDqSlBE8I7N1l9q/m1THuKM39zErMef5eV/vs6bby5kwZtv\n8uzcl1lr2Goc+qGd+Ox//Yp7HnySXbfbjPeO24odt94EgHseeJLX5s1nh3/9ryWec94bC3jsmbmL\n7/fvtxKjN+3eBv8TP/UBdt/h7Tw150W+feYfmHj7TA7f7911579nhy0YMngg1/51BgfsNZbrJt0L\nwPt3Hr04xieeeWGJBA7g9XlLxriif++SpG6wDEySijFoYH92HbsZu47djM8f+l5OPO1STr/oOo4+\ncLfFm7H791ty03hEkAu784P5rV81reg+icGDBrDRusNqnlt58IAl7j85+wWO/Pp5HLLvjnz5iPcx\ndPWVueeBpzjulF/zxoI3Adh9h7dz6wVf4ea/3c9fpzzEkV8/jw/stjXf/9LHWJjJ8KGr8ttTP73U\na6268qDFtwcO6Nftq6WtteZqbLTuMDZadxg//9qhvP/oH3PFjVP4yB7b1pw/oH8/9t1ta664cQoH\n7DWWK26Ywt47j1m80rMwk61GrctPTvr4Uo9dY9Uhi2+7P0WSVAT7rEhqik03XIsFby5cXK60zPkb\nrc2c51/mydkvLB577Om5zJ77DzbfeO0uHzugfz8WVK+i1UjTHniSBQsW8p/HfIhtt9yIkesN55nn\nXlpq3pqrr8L+792OH3z5Y3z3C//CpdfdzfwFbzJms/V47sVXiGBxgrHoGLbGKisc38brvY399tiW\nM35zU5fz9t9zW26b8hAPPjaHW+++n49UN+MDbL3Z+jz69PMMXX3lpWJcY7UhXTyrJKnhosFHCZis\nSCrUC/94lUO/ehaX3zCZe2c9zePPzOVPt0znF7+7hV223YxVhgyq+9jMt7a77zZ2c7bcZB2+8L3f\nMP2BJ5l2/xN88Xu/5h2brb/4KmP1bDBiTab+7xM8OfsF5r70Kpk9X0bvGMsim6w/nIWZnHPpX3j8\nmblceeMUzrvitiXm/PiCiUy8fSYPP/kcDz42h2v+eg8brTuMAf37sdvYzdl+9MYcPeFCbr7rf3n8\nmbncPfNRfnzhRP52zyM9jrGWIz+6Kw88Oocb77yv7pyxozdm/bWHctwpv2bYGquwy3Zv/X1+ZM9t\nGT50NY6ZcAF3Tn+Yx5+Zy53TH+bks/7EI08915AYJUmqxzIwSYVaZcggtttqI867/DYefep53pi/\ngBHDV2f/Pbfjc4fs0eVjI2KJX/ycOeEwvnXGVRz61bOAyqWLOzZ6jICo8auiT31sN47/4SW8/+gf\nM2/+Am4+r86liyNqPr5WLABbbrIOX//0hzjzkpv50QUT2X70xpz0qX059pSLF88ZOLA/p553LY8/\nM5dBA/uz3VYbcfaEIxafP/fbn+DU86/lpNMu4/mXXuFtQ1dlhzEj+dj7tl/itZfXlpusw67bbcZZ\nv7uVPXbcctHbXMp+e27Lzy6+kX8/YNclXm/woAH8+odH8/1zr+az3/kVL7/6OiPethrv3mbTxY0l\n6/29S5IarA33rMTy/IaxN0RE0sUlRNtevaaQo5fjN51j5vRw/rM9mz+6h/Pb0Kxraq8ujNp7Xi9H\nsmz1YpUkqYxG7XMimdnyv3GJiOSTkxv7pL/cruXfu2VgkiRJklqSZWCSJElSGbRhGZgrK5IkSZJa\nkisrkiRJUhm09O6SYpisSJIkSWVgGZgkSZIktQZXViRJkqQyaMMyMFdWJEmSJLUkV1ZUvJlr1R7v\nw80iG9U4sRWbRbZio0pV2LBTkvq4NtyzYrIiSWX1x/vh9Dtg3gIY1J8bdtmVPXfastlRSZKK0oZl\nYCYrklRGf7wfjvszPPTC4qFvTa/cNmGRJPUV7lmRpDI6/Y4lEhWAx56eywVX3takgCRJhYts7FEC\nrqxIUhnNW1Bz+PU3ao9LkvqANiwDc2VFkspoUO3fNQ0e6O+gJEl9h8mKJJXRsTvBpmsuMbTRusM4\nfL+dmxSQJKlwloFJkkrhg1tU/vzJHfD6Ahjcn6/v7NXAJEl9i8mKJJXVB7d4K2kB9rTPiiT1bW24\nZ8Vkpa+ZObz2+Ojn6j9mxtq1x8fMqTO/TpPHesb03eaPzdLT5n82cpQkqQ8oSelWI7lnRZIkSVJL\ncmVFksrKDvaS1F4sA5MklYId7CVJbcAyMEkqIzvYS1L7acNLFxearETEhhFxY0TMiIh7IuLY6viE\niHgiIiZXj32KjEOS+hw72EtS+4kGHyVQdBnYfOCLmTklIlYF/h4RE4EEfpSZPyr49SWpb7KDvSSp\nDSxzZSUiDoqI1au3/zMiLouIsd158sx8JjOnVG+/AtwLrL/oqZczZkmSHewlqf20YRlYd34F95+Z\n+duI2BV4L/BD4Axgp568UESMBLYDJgG7AJ+PiMOBu4AvZ+aLPXk+SWprdrCXJJVIRAwGMjN71Pyt\nO8nKm9U/PwSclZl/iIhv9zC4VYHfAcdl5isRcQbwrerpbwOnAkcu/cgJHW6Prx6SJMAO9pK0HCZN\nncWkabOaHcbyKVFdUkSsBOwPHALsTKWiKyLiTeB24CLg8szscomnO8nKkxHxC+B9wCnVrKjbG/Mj\nYgDwe+B/MvNygMyc0+H82cBVtR89obsvo2Wp19ke6ne372ln+56auVadeOx4r6XNatAX8VF79+gX\nOpKkPmbcNqMYt82oxfdPv+j6JkbTQyUp3aq6CbiVSlXWlEUrKhExiEq11X7AF4H3dPUk3UlWDgQ+\nAPwgM1+MiHWB47sTYUQEcA4wMzNP6zC+bmY+Xb17ADC9O88nSerAppCSpNb1vlolX9WxScCkauLS\npe4kK2dm5mEdXuDpiPg+cG03HrsL8G/AtIiYXB07CTgkIralclWwh4FjuvFckqRFbAopSe2nRGVg\nHROViFgT2Ajo1+H83d3Zv9KdZOUdHe9ERH9g+24G+Rdql4z9uTuPlyTV0UVTSJMVSeqjylUGBkB1\nr/sngFnAwg6n9ujO4+smKxFxEnAiMCQiXu5waj7wix5HKklqHJtCSpLK4V+BTTPzjeV5cN2N8pl5\ncmauBvwwM1frcAzLzBOWN1pJUgPYFFKS2k/BfVYi4u0RMbnD8VJEHNtpzqERMTUipkXEXyPincuI\negaw5jLm1LXMf9Uy84SIWB/YuOP8zLxleV9UkrSCjt0JHpq7RCmYTSElSSsiM/+XypW6Fl16+Eng\nsk7TZgHvycyXImIfKhVX47p42pOByRFxD7Boj0pm5n7diWmZyUpEfI/K8s1M3uq5AmCyIknNYlNI\nSWo/vbvBfi/gocx8vONgZt7e4e4dwAbLeJ4LgFOAe3hrz0q3N990p17gAODtPe02KUkqmE0hJam9\n9O4G+4OBXy1jzpHAn5Yx55XMPH15g+hOsvIQMJC3lm3UCopu2NjM167XLLIem0j2KY1q/tis54fG\nNZ7sjVglSW3kscmVYxkiYiDwYeCrXczZA/h3Kq1KunJrRHwXuJIO+URm3t2dkLuTrLwGTImI61my\nzuzYLh4jqSQ2/sRljHzkxaXGHxk5lEfPO6AJEUmSpJpWtAxs4+0qxyJ//WW9mR8A/p6ZNX8jXN1U\nfxawT2a+UGtOB2OplH113teyYpcu7uDK6tFR+S7yLKmmkY+8yE03P7rU+Hhg6VFJktQGDgEurnUi\nIjYCLgX+LTMfXNYTZeb4FQmkO1cDO29FXkCSJElSA/TCBvuIWIXK5vqjOowdA5CZZwJfp3Ip4jMi\nAmB+Zu5Y43k+AfxPZtZsAFYtNTs0M+su70DXTSEvycwDI2J6jdOZmcu6prIkSZKkRumFDfaZ+Sow\nvNPYmR1ufwr4VDeealXgbxFxH3AX8DSVdGsdYAdgSyqlZF3qamXluOqfH+5GMJIkSZIEQGb+NCJ+\nRmUD/q76xIXvAAAciElEQVTVAypV5j8FbsvMZWZfdZOVzHyq+ucjETEC2JHKXpU7M7MXLjklSZIk\nabHe7bOywqrJyF+qx3LpTlPIg4AfADdXh34aEcdn5iXL+6KSWscjI4cyvs64JElSM3XnamBfA961\naDUlItYCrgdMVqQ+4NHzDvCqX5IklUHvNoVsCd1JVgLoeI3l5yndIlQfVK9xYr3xA2cUF8uyzKgT\n05geNnMsUfPHek0Bm9Xkr97rNqp5YSP1hYaKNnOUJBWiDb+BdydZuRq4JiJ+ReWv6F+BPxcalSRJ\nkqTSi4jBwL8AI3kr98jM/FZ3Ht+dPivHR8RHeWsH/5mZedlyxCpJxfnj/XD6HTBvAQzqzw277Mqe\nO23Z7KgkSWqccpaBXQG8CPwdeL2nD+6qz8oWVDbWbwZMA47PzCeWM0hJKs4f74fj/gwPvbB46FvT\nK7dNWCRJfUY5y8DWz8y9l/fBK3Vx7lzgD1SWbe4GTl/eF5GkQp1+xxKJCsBjT8/lgitva1JAkiSp\n6raIWO5m8l2Vga2amYu6St4XEZOX90UkqVDzFtQcfv2N2uOSJJVSicrAImJ69WY/4JMR8TCw6Co6\nmZndSmC6SlYGR8TYRa8HDKnej+oL3L0ccUtS4w2q/aNs8MDuXENEkiQV4MPVP5OlC9i6nXV19S/5\nM8CpXdzfo7svIkmFOnYneGjuEqVgG607jMP327mJQUmS1GAl2rOSmY8ARMSFmXlYx3MRcSFwWK3H\ndVY3WcnM8SsQnyT1ng9uUfnzJ3fA6wtgcH++vrNXA5Mk9TElKgPr4B0d70REf2D77j7YGglJfcMH\nt3graQH2tDGjJElNExEnASdS2UrycodT84FfdPd5TFYEM4f3bP7o52qPz1i79viYOT17/j7MzvaS\nJGm5lasM7GTg5Ig4JTNPWN7nMVmR1BQbf+IyRj7y4lLjj4wcyqPnHdDzJ7QppCRJrejEiPgXKg3m\nFwJ/6UmD+a6aQm7PW7v3lyqQ82pgklbEyEde5KabH11qfDyw9Ogy2BRSktQOyrln5efApsDFVPKK\nT0fE+zLzM915cFcrK6dSSVKGUNkEM606/k7gLuDdyxuxJDVUF00hTVYkSX1GicrAOtgDGJ2ZCwEi\n4jxgZncfXLeDfWaOz8w9gKeAsZm5fWZuD2xXHZOk1mBTSEmSWtWDwEYd7m9UHeuW7uxZ2TIzF3Wg\nJDPviYituh+fJBXMppCSpHZQzjKw1YF7I+JOKlVbOwJ/i4irqDSa36+rB3fnX/JpEXE28D9UFp8+\nDkxdsZglqYFsCilJagflLAP7ehfnlpl9dSdZ+STwH8Bx1fu3AGd043GSVNcjI4cyvs54j9kUUpKk\nlpSZN0XESGCzzLwuIlYG+mfmP7rz+GUmK5n5GvCj6iFJDfHoeQf0/KpfXbEppCSprythGVhEHA0c\nBQyjclWwDagsfLy3O4/v6tLFl2TmgRFxD0sv0WRmvnP5QlZTXDKm/rkDZ/ReHB3NWKv2+Jhna4/P\nrDN/dJ35JdKsJoz1mkI2q0mlJEnqcz5LZZ/KJIDMvD8i6nQSX1pXKyuLyr4+SFkr5CRJkqS+opzf\nyOdl5ryISvAR0Z9u7FVZpG6ykplPVZ/svOoljCVJkiQ1SwnLwICbI+L/AitHxPuAzwBXdffBdfus\nAGTmAmBhRCzHjldJkiRJbe4E4FlgOnAM8Cfga919cHeuBvYqMD0irgX+WR3LzDy2h4FKkiRJWl4l\nLAPLzDcj4nLg8syc09PHdydZubR6LPG63XnyiNgQuABYu/qYX2Tm6RExDPgNsDHwCHBQZr7Y3aAl\nSZIkta6obFL5BvA5oF917E3gJ8C3MrNb+UR3Ll183vKHyXzgi5k5JSJWBf4eEROp9G6ZmJnfj4iv\nUlkeOmEFXkeSJEnq28q1Z+WLwC7AuzLzYYCIGAX8v+q5brVFqbtnJSL2j4jPdbh/Z0Q8XD0O7M6T\nZ+YzmTmlevsV4F5gfWA/4PzqtPOB/bvzfJIkSVLbigYfxToc+PiiRAUgM2cBh1bPdUtXG+y/AlzZ\n4f5AYAdgdyod7Xuk2rlyO+AOYERmzq6emg2M6OnzSZIkSWpZ/TNzqWZ41bHubEWpPEkX5wZm5mMd\n7v8lM58Hno+IVbofJ1RLwH4PHJeZLy+6znI14Iyot6Y1ocPt8dVDDTejTl+eMT3eAyVJktTSJk2d\nxaRps5odxvIpVxnY/OU8t4SukpU1O97JzM91uFunlfjSImIAlUTlwsy8vDo8OyLWycxnImJdoM63\n4gndfRlJkiRpmcZtM4px24xafP/0i65vYjR92jsj4uU654Z090m6KgO7IyKO7jwYEZ+mUsq1TNWr\nAJwDzMzM0zqcuhI4onr7CODyzo+VJEmS1EGJ9qxkZr/MXK3O0ZAysC8Cl0fEx4G7q2NjgcF0f0P8\nLsC/AdMiYnJ17ETgFOC3EXEk1UsXdzdgSZIkqS2VqwysIeomK5k5OyJ2BvYExlDpk/KHzLyhu0+e\nmX+h/urNXj0JVJIkSVJ76XIJptqs5frqIUmSJKlZStjBfkV1u15MkiRJUhO1YRlYVxvsJUmSJKlp\nXFmRJEmSyqANy8BcWZEkSZLUklxZUc/NHN7sCLpn9LPNjqAws64Z1OwQJElSb2vDPSsmK5IkSVIZ\ntGGyYhmYJEmSpJbkyookSZJUBm6wlyRJktSuImJoRPwuIu6NiJkRMa7OvHdFxIKI+GiR8biyIkmS\nJJVB7+xZ+W/gT5n5sYjoD6yyVBgR/YDvAVdT8HqPKyuSJElSGUSDj85PH7EGsFtmnguQmQsy86Ua\nkXwe+B1Q+KVXTVYkSZIkAWwCPBsRv4yIuyPirIhYueOEiFgf+AhwRnWo0OUey8AkSZKkMljRgqv/\nnQ73T+9qRn9gLPC5zPxbRJwGnAB8vcOc04ATMjMjos4aTeOYrAhmrlV7fMyc3o2j0eq9rz7cLFKS\nJKmut29dORb5w8WdZzwBPJGZf6ve/x2VZKWj7YFfV/IUhgMfiIj5mXllESGbrEiSJEllUPAG+8x8\nJiIej4gtMvN+YC9gRqc5oxaHE/FL4KqiEhUwWZEkSZLKoXf6rHweuCgiBgIPAf8eEccAZOaZvRJB\nByYrkiRJkgDIzKnAuzoN10xSMvOTRcdjsiJJkiSVQe/0WWkpJiuSJElSGfROGVhLsc+KJEmSpJbk\nyookSZJUBm1YBubKiiRJkqSW5MqK6rtkTO3xA2fUHpckSVJx2nDPismKJEmSVAaWgUmSJElSa3Bl\nRZIkSSqDNiwDc2VFkiRJUktyZUWSJEkqgzbcs2KyIkmSJJWBZWCSJEmS1BpcWZEkSZLKwDIwqRsa\n1SxyzJwVj0WSJEl9lsmKJEmSVAZtuGfFZEWSJEkqgzYsA3ODvSRJkqSW5MqKJEmSVAZtWAZW6MpK\nRJwbEbMjYnqHsQkR8URETK4e+xQZgyRJktQnRDb2KIGiy8B+CXRORhL4UWZuVz2uLjgGSZIkSSVU\naBlYZt4aESNrnGrDRSxJkiRpBbThN+hmbbD/fERMjYhzImJok2KQJEmS1MKascH+DOBb1dvfBk4F\njqw9dUKH2+OrhyRJkrR8Jk2dxaRps5odxvIpyT6TRorMYt90tQzsqszcuofnsrK9RaX3jZt7Nr+n\nne3HPNuz+aN7OL9EZl0zqNkhSJJUKqP2OZHMbPkCq4hILvltY5/0wINa/r33ehlYRKzb4e4BwPR6\ncyVJkiS1r0LLwCLiYmB3YHhEPA58AxgfEdtSWTZ5GDimyBgkSZKkPqENy8CKvhrYITWGzy3yNSVJ\nkiT1DXawlyRJksqgpXeXFMNkRZIkSSqDNiwDa1afFUmSJEnqkisrkiRJUhm0YRmYKyuSJEmSWpIr\nKyreN3evPd7TZpGSJEntrA33rJisSJIkSWVgGZgkSZIktQZXViRJkqQysAxMkiRJUkuyDEySJEmS\nWoMrK5IkSVIZtGEZmCsrkiRJklqSKyuSJElSGbThyorJipqnp80ix8wpLhZJkqRW5wZ7SZIkSWoN\nrqxIkiRJZdCGZWCurEiSJElqSa6sSJIkSWXQhntWTFYkSZKkMuiFZCUiHgH+AbwJzM/MHWvMGQ/8\nGBgAPJeZ44uKx2RFkiRJ0iIJjM/MubVORsRQ4GfA3pn5REQMLzIYkxVJkiSpDHpvg31XazgfB36f\nmU8AZOZzRQbiBntJkiSpDKLBR20JXBcRd0XEUTXObw4Mi4gbq3MOa8h7q8OVFUmSJKkdTL23cnRt\nl8x8OiLWAiZGxH2ZeWuH8wOAscB7gZWB2yNiUmY+UETIJisqvxlr1R4f82zvxtECRu09r0fzZ10z\nqKBIJElSw61oGdi2W1aORS68fKkpmfl09c9nI+IyYEegY7LyOJVN9a8Br0XELcA2QCHJimVgkiRJ\nkoiIlSNitertVYD3A9M7TbsC2DUi+kXEysBOwMyiYnJlRZIkSSqD4i9dPAK4LCKgkidclJnXRsQx\nAJl5ZmbeFxFXA9OAhcBZmWmyIkmSJLW1gq8GlpkPA9vWGD+z0/0fAj8sNJgqy8AkSZIktSRXViRJ\nkqQy6IUO9q3GlRVJkiRJLcmVFUmSJKkMeq+DfcswWZEkSZLKoA3LwExW1Hq+uXvP5v/2kp7Nn1mn\niSTA6PZrJClJktSqTFYkSZKkMmjDMjA32EuSJElqSa6sSJIkSWXgnhVJkiRJLckysMaKiHMjYnZE\nTO8wNiwiJkbE/RFxbUQMLTIGSZIkSeVU9J6VXwL7dBo7AZiYmVsA11fvS5IkSepKNPgogUKTlcy8\nFXih0/B+wPnV2+cD+xcZgyRJktQnRDb2KIFmXA1sRGbOrt6eDYxoQgySJEmSWlxTN9hnZkZ0ldZN\n6HB7fPWQOjnowNrjuz9ae/znfywuliabdc2gZofQVkbtPa8hz+PnJkm9Z9LUWUyaNqvZYSyfkpRu\nNVIzkpXZEbFOZj4TEesCc+pPndBbMUmSJKkNjNtmFOO2GbX4/ukXXd/EaLQszSgDuxI4onr7CODy\nJsQgSZIklYt7VhorIi4GbgPeHhGPR8QngVOA90XE/cCe1fuSJEmSutKGVwMrtAwsMw+pc2qvIl9X\nkiRJUvnZwV6SJEkqg5KUbjVSM/asSJIkSdIyubIiSZIklUFJ9pk0ksmKJEmSVAaWgUmSJElSa3Bl\nRX3XzRvXHj/sgPqPOXR67fEv3b7i8TSQHc/f0qgu8s1U7z34OUuSltCGZWCurEiSJElqSa6sSJIk\nSWXQhntWTFYkSZKkMrAMTJIkSZJagysrkiRJUhlYBiZJkiSpJVkGJkmSJEmtwZUVSZIkqQwsA5Pa\nwN3r9vzcqwNqj//nLSseDzb/646+0Pyxp5r1nv3vUZLUKkxWJEmSpDJowz0rJiuSJElSGbRhGZgb\n7CVJkiS1JFdWJEmSpDJowzIwV1YkSZIktSRXViRJkqQyaMM9KyYrkiRJUhm0YbJiGZgkSZKkluTK\nitQdX9+j9vgq82uPf+n24mLpI9qxyWNZLM9nU6+RZNk/ZxtkSmopbrCXJEmSpNbgyookSZJUBm24\nsmKyIkmSJJWBG+wlSZIkqTWYrEiSJEllEA0+6r1MRL+ImBwRV9U4Nzwiro6IKRFxT0R8olFvrxaT\nFUmSJKkMIht71HccMBOoNelzwOTM3BYYD5waEYVtLTFZkSRJkgRARGwA7AucTe31l6eB1au3Vwee\nz8wFRcXjBntJkiSpDHrnamA/Bo7nrYSks7OAGyLiKWA14KAigzFZkbpjwMLa4/+wYdyylL0poLqn\nr37OjXxfNpiU1HSTHoA7Hqx7OiI+BMzJzMkRMb7OtJOAKZk5PiI2BSZGxDaZ+XLjAzZZkSRJksph\nRS9d/O7NKscip1/decbOwH4RsS8wGFg9Ii7IzMM7zfkOQGY+FBEPA28H7lqx4Gpzz4okSZJUBgVf\nDSwzT8rMDTNzE+Bg4IZOiQrAfcBeABExgkqiMquB73IJrqxIkiRJqiUBIuIYgMw8EzgZ+GVETKWy\n8PGVzJxbVAAmK5IkSVIZ9GIH+8y8Gbi5evvMDuPPAR/urTialqxExCPAP4A3gfmZuWOzYpEkSZLU\nepq5spLA+CKXjSRJkqQ+o3cuXdxSml0G1oZ/5ZIkSdJy6MUysFbRzKuBJXBdRNwVEUc1MQ5JkiRJ\nLaiZKyu7ZObTEbEWlWYy92XmrUtOmdDh9vjqIUmSJC2fSVNnMWlaYVfaLVYb1iQ1LVnJzKerfz4b\nEZcBOwJdJCtSE82vswg5aYMePY0drKX2NWrveT2a788LqRjjthnFuG1GLb5/+kXXNzEaLUtTysAi\nYuWIWK16exXg/cD0ZsQiSZIklUJkY48SaNbKygjgsohYFMNFmXltk2KRJEmSWp9lYL0jMx8Gtm3G\na0uSJEkqh2ZfuliSJElSd5SkdKuRTFYkSZKkMmjDMrBm9lmRJEmSpLpcWZEkSZLKoA3LwFxZkSRJ\nktSSXFmRVsTQ13o0vV5TuL7c/K3ee+tpgzyp3bTjzwtJy9CGe1ZMViRJkqQysAxMkiRJklqDKyuS\nJElSGbRhGZgrK5IkSZJakisrkiRJUhm04Z4VkxVJkiSpDCwDkyRJkqTW4MqKJEmSVAaWgUnqkd+8\no/b4kZNrj79vVnGxSGoLPW2oahNJSWVmsiJJkiSVQRvuWTFZkSRJksqgDcvA3GAvSZIkqSW5siJJ\nkiSVgWVgkiRJklqSZWCSJEmS1BpcWZEkSZLKoA3LwFxZkSRJktSSXFmRJEmSyqAN96yYrEhFmO+i\nZdPNXKtn80c/W0wckiQ1imVgkiRJktQaXFmRJEmSyqANy8BcWZEkSZLUklxZkSRJksqgDfesmKxI\nkiRJZWAZmCRJkiS1BldWJEmSpDKwDEySJElSS2rDMjCTFakIp7279vi+D/ZuHO1sRp2mkGPqNH+s\n10TSZpEqiVnXDGp2CJLUcCYrkiRJUhm0YRmYG+wlSZIktSRXViRJkqQycM+KJEmSpJZkGVjviYh9\nIuK+iHggIr7arDjUbDc1OwAVbNLUWc0OQb3hpoebHYF6gf8/931+xgKIiH4RMTkirqpz/vTqd/ip\nEbFdkbE0JVmJiH7AT4F9gNHAIRGxVTNiUbPd1OwAVLBJ0/yHry3c9EizI1Av8P/nvs/PuMVFNvao\n7zhgJrDUpIjYF9gsMzcHjgbOKOS9VjVrZWVH4MHMfCQz5wO/Bj7SpFgkSZIkARGxAbAvcDa1C8/2\nA84HyMw7gKERMaKoeJqVrKwPPN7h/hPVMUmSJEm1RIOP2n4MHA8srHO+1vf4DZbvDS1bZPb+VQUi\n4l+AfTLzqOr9fwN2yszPd5jTfpc7kCRJUq/LzJbful7Ud+OO7z0iPgR8IDM/GxHjgS9n5oc7xXEV\ncEpm/rV6/zrgK5l5dxHxNetqYE8CG3a4vyGVrGyxMvxHI0mSJPWGXvpuvDOwX3VfymBg9Yi4IDMP\n7zCn8/f4DapjhWhWGdhdwOYRMTIiBgL/ClzZpFgkSZKktpeZJ2Xmhpm5CXAwcEOnRAUq39kPB4iI\nccCLmTm7qJiasrKSmQsi4nPANUA/4JzMvLcZsUiSJEmqKQEi4hiAzDwzM/8UEftGxIPAq8Aniwyg\nKXtWJEmSJGlZmtYUsh6bRfZNEbFhRNwYETMi4p6IOLY6PiwiJkbE/RFxbUQMbXasWjGdG0n5Gfc9\nETE0In4XEfdGxMyI2MnPue+JiBOrP7OnR8SvImKQn3P5RcS5ETE7IqZ3GKv7uVb/O3ig+t3s/c2J\nWu2spZIVm0X2afOBL2bmGGAc8NnqZ3sCMDEztwCur95XuXVuJOVn3Pf8N/CnzNwKeCdwH37OfUpE\njASOAsZm5tZUSrYPxs+5L/glle9ZHdX8XCNiNJV9xaOrj/l5RLTUd0f1fa32H5zNIvuozHwmM6dU\nb78C3EvlOt2LGwtV/9y/ORGqEeo0kvIz7kMiYg1gt8w8Fyp7EDPzJfyc+5p/UPkl08oR0R9YGXgK\nP+fSy8xbgRc6Ddf7XD8CXJyZ8zPzEeBBKt/VpF7TasmKzSLbQPU3dtsBdwAjOlxBYjZQWAdU9Ypa\njaT8jPuWTYBnI+KXEXF3RJwVEavg59ynZOZc4FTgMSpJyouZORE/576q3ue6Hku2lvB7mXpdqyUr\n7vbv4yJiVeD3wHGZ+XLHc1m52oP/DZRUtZHUnMycTJ2+uH7GfUJ/YCzw88wcS+VKMEuUAvk5l19E\nbAp8ARhJ5QvrqtUGzov5OfdN3fhc/czVq1otWVlms0iVV0QMoJKoXJiZl1eHZ0fEOtXz6wJzmhWf\nVtiiRlIPAxcDe0bEhfgZ9zVPAE9k5t+q939HJXl5xs+5T9kBuC0zn8/MBcClwLvxc+6r6v2c7tXm\nf1ItrZas2Cyyj4qIAM4BZmbmaR1OXQkcUb19BHB558eqHOo0kjoMP+M+JTOfAR6PiC2qQ3sBM4Cr\n8HPuS+4DxkXEkOrP772oXDjDz7lvqvdz+krg4IgYGBGbAJsDdzYhPrWxluuzEhEfAE7jrWaR321y\nSGqAiNgVuAWYxltLyCdS+aH3W2Aj4BHgoMx8sRkxqnEiYnfgy5m5X0QMw8+4T4mIbahcRGEg8BCV\nhmD98HPuUyLiK1S+uC4E7gY+BayGn3OpRcTFwO7AcCr7U74OXEGdzzUiTgL+HVhApYT7miaErTbW\ncsmKJEmSJEHrlYFJkiRJEmCyIkmSJKlFmaxIkiRJakkmK5IkSZJaksmKJEmSpJZksiJJkiSpJZms\nSFIviIgREfGriHgoIu6KiNsiYv86c9eLiEvqnLspIravM35fREyJiL90aNrYo+fvNO+kZc2RJKlI\nJiuSVLBqB/DLgZsyc9PM3AE4GNigxtz+mflUZh5Y5+mStxqrdh7/eGZuC5wP/KBePMt4/o5O7MYc\nSZIKY7IiScXbE5iXmb9YNJCZj2XmTwEi4hMRcWVEXA9MjIiNI+Ke6rkhEfHriJgZEZcCQ4BYxuvd\nCmxWffwPImJ6REyLiIOqYyMjYnqH1740Iv4cEfdHxPeq46cAQyL+fzv3zhpVFIVh+P0khSjYpbDS\nWo0xFkIg2oiVCKKF4C8waJtgY2chKAgiCDYWGlsrG0FB7bwFE4yIrTaxEW94QZfFzJBRZxIhGTPg\n+1RzLmvvfarhY519Mp3kapJ1SW42OzezrbEkSeqlgdVegCT9B7YCT5a4ZwQYqqq3STaz0D0ZBz5U\n1ZYkQ81xOnVWYCHEHABmkhwChoHtwCDwMMndDnXDwA7gK/AiyYWqOpnkeFWNACQ5DLyuqv3N4w1L\nPbQkSctlZ0WSeu+XcJHkYrND8aDt9K2qetuhdjdwDaCqZoGZLnMEmEoyDYwCE8AYcL0a5oG7wK4O\ntber6n1VfQHmgE0d7pkB9iU5k2Ssqt51fVpJklaIYUWSeu8ZsLN1UFUngL00uh0tnxapX+q1L1jY\nszJSVYeq6lWX2k5dmS9tv7/ToeteVS9pdH9mgdNJTv3FmiRJWhbDiiT1WFXdAdYmOdZ2ev1flt8D\njgIk2Ubjla5ufg8m94EjSdYkGQT2AA/+LOvqW5KB5twbgc9VNQWcoy18SZLUK+5ZkaR/4yBwPskk\n8Ab4CEw2r3X6wlfr+BJwJckc8Bx4tMgcv4xRVTeSjAJPm9cmqmr+tz0x3b4uBnCZxt6Xx8BV4GyS\nHzT2towvsg5JklZEqrr9R0mSJEnS6vE1MEmSJEl9ybAiSZIkqS8ZViRJkiT1JcOKJEmSpL5kWJEk\nSZLUlwwrkiRJkvqSYUWSJElSX/oJLr7XrF81ZFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8007ca790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ik = 425; jk = 302; d = 6\n",
    "fig, ax = plt.subplots(1,1,figsize=(15,7.5))\n",
    "imin = 390; imax = 435; jmin = 280; jmax = 398\n",
    "cmap = plt.get_cmap('winter_r')\n",
    "cmap.set_bad('burlywood')\n",
    "mesh = ax.pcolormesh(depth[imin:imax,jmin:jmax], vmax = 10., cmap=cmap)\n",
    "ax.set_xlim((0,110))\n",
    "ax.set_xlabel('Grid Points')\n",
    "ax.set_ylabel('Grid Points')\n",
    "ax.text(40, 28, \"Short Fraser River\", fontsize=14)\n",
    "cbar=fig.colorbar(mesh)\n",
    "cbar.set_label('Depth (m)')\n",
    "ax.plot(np.array((324,324,334,334,334,318))-jmin+0.5,np.array((411,412,414,415,416,434))-imin+0.5,'ko');\n",
    "ax.plot(np.array((322-1,318-2))-jmin+0.5,np.array((412-1,414-1))-imin+0.5,'rs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'nav_lat', u'nav_lon', u'deptht', u'deptht_bounds', u'sossheig', u'time_centered', u'time_centered_bounds', u'time_counter', u'time_counter_bounds', u'votemper', u'vosaline']\n"
     ]
    }
   ],
   "source": [
    "data = nc.Dataset('/ocean/sallen/allen/research/MEOPAR/myResults/NEMO36_Tides/base_run/E1/SalishSea_1d_20030421_20030424_grid_T.nc')\n",
    "nc_tools.show_variables(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 40, 898, 398)\n"
     ]
    }
   ],
   "source": [
    "S = data.variables['vosaline'][:]\n",
    "print S.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/anu/sallen/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:3: RuntimeWarning: divide by zero encountered in log10\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": 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sGAZZ87K4qjbNjquqbyX5rVEFJEnTZZNKSdJ2ocEF+1W17xT7TgFOGeQ8ST4H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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8010d4490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=3; d = 0\n",
    "imin = 390; imax = 435; jmin = 280; jmax = 398\n",
    "lS = np.log10(S[t,d,imin:imax,jmin:jmax])\n",
    "fig, ax = plt.subplots(1,1,figsize=(15,7.5))\n",
    "cmap = plt.get_cmap('spectral')\n",
    "cmap.set_bad('burlywood')\n",
    "mesh = ax.pcolormesh(lS, vmax=1.2, vmin=-40, cmap=cmap)\n",
    "ax.set_xlim((0,110))\n",
    "ax.set_xlabel('Grid Points')\n",
    "ax.set_ylabel('Grid Points')\n",
    "ax.text(40, 28, \"Short Fraser River\", fontsize=14)\n",
    "cbar=fig.colorbar(mesh)\n",
    "cbar.set_label('Salinity (practical)')\n",
    "ax.plot(np.array((324,324,334,334,334,318))-jmin+0.5,np.array((411,412,414,415,416,434))-imin+0.5,'wo');\n",
    "ax.plot(np.array((322-1,318-2))-jmin+0.5,np.array((412-1,414-1))-imin+0.5,'rs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datau = nc.Dataset('/ocean/sallen/allen/research/MEOPAR/myResults/NEMO36_Tides/base_run/E1/SalishSea_1d_20030421_20030424_grid_U.nc')\n",
    "u = datau.variables['vozocrtx'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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33sX+JwLPqO+fD1zKjEQGIDOnrmO6lOriuLc3NZaZL63/XDfXDpnISFIHrd26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\nJElS5/wPyr9/Yj7pAZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8009716d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 3; d = 0\n",
    "imin = 390; imax = 435; jmin = 280; jmax = 398\n",
    "fig, ax = plt.subplots(1,1,figsize=(15,7.5))\n",
    "cmap = plt.get_cmap('spectral')\n",
    "cmap.set_bad('burlywood')\n",
    "mesh = ax.pcolormesh(u[t,d,imin:imax,jmin:jmax], cmap=cmap)\n",
    "ax.set_xlim((0,110))\n",
    "ax.set_xlabel('Grid Points')\n",
    "ax.set_ylabel('Grid Points')\n",
    "ax.text(40, 28, \"Short Fraser River\", fontsize=14)\n",
    "cbar=fig.colorbar(mesh)\n",
    "cbar.set_label('Cross-page velocity')\n",
    "ax.plot(np.array((324,324,334,334,334,318))-jmin+0.5,np.array((411,412,414,415,416,434))-imin+0.5,'wo');\n",
    "ax.plot(np.array((322-1,318-2))-jmin+0.5,np.array((412-1,414-1))-imin+0.5,'rs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datav = nc.Dataset('/ocean/sallen/allen/research/MEOPAR/myResults/NEMO36_Tides/base_run/E1/SalishSea_1d_20030421_20030424_grid_V.nc')\n",
    "v = datav.variables['vomecrty'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oVNNOkbs3/J9jzMw/XOWv7beYSl67mbVIekrS4ZKWSnpQ0nHuvqCozXskPenu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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8007f4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 3; d = 0\n",
    "imin = 390; imax = 435; jmin = 280; jmax = 398\n",
    "fig, ax = plt.subplots(1,1,figsize=(15,7.5))\n",
    "cmap = plt.get_cmap('spectral')\n",
    "cmap.set_bad('burlywood')\n",
    "mesh = ax.pcolormesh(v[t,d,imin:imax,jmin:jmax], cmap=cmap)\n",
    "ax.set_xlim((0,110))\n",
    "ax.set_xlabel('Grid Points')\n",
    "ax.set_ylabel('Grid Points')\n",
    "ax.text(40, 28, \"Short Fraser River\", fontsize=14)\n",
    "cbar=fig.colorbar(mesh)\n",
    "cbar.set_label('Up-page velocity')\n",
    "ax.plot(np.array((324,324,334,334,334,318))-jmin+0.5,np.array((411,412,414,415,416,434))-imin+0.5,'wo');\n",
    "ax.plot(np.array((322-1,318-2))-jmin+0.5,np.array((412-1,414-1))-imin+0.5,'rs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh =  data.variables['sossheig'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U1X0zG1V1b/dmYgNpDGKS/CydBf1PAG4AXl9Vt+/MSCVpHJZfv5znr3k+57/3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\nbxxgH0mSFpxBjCSN3/OAH1fV+2Yaqur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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa800f30050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imin = 390; imax = 435; jmin = 280; jmax = 398\n",
    "fig, ax = plt.subplots(1,1,figsize=(15,7.5))\n",
    "cmap = plt.get_cmap('spectral')\n",
    "cmap.set_bad('burlywood')\n",
    "mesh = ax.pcolormesh(ssh[t,imin:imax,jmin:jmax], cmap=cmap)\n",
    "ax.set_xlim((0,110))\n",
    "ax.set_xlabel('Grid Points')\n",
    "ax.set_ylabel('Grid Points')\n",
    "ax.text(40, 28, \"Short Fraser River\", fontsize=14)\n",
    "cbar=fig.colorbar(mesh)\n",
    "cbar.set_label('Sea Surface Height')\n",
    "ax.plot(np.array((324,324,334,334,334,318))-jmin+0.5,np.array((411,412,414,415,416,434))-imin+0.5,'wo');\n",
    "ax.plot(np.array((322-1,318-2))-jmin+0.5,np.array((412-1,414-1))-imin+0.5,'rs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.05899e-05 5.74376e-05\n"
     ]
    }
   ],
   "source": [
    "# point of interest is 413, 317\n",
    "print S[0,0,413,317], S[0,1,413,317]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.245697 7.27951e-05\n"
     ]
    }
   ],
   "source": [
    "print u[0,0,413,317], S[0,0,413,318]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.233229 6.46897e-05\n"
     ]
    }
   ],
   "source": [
    "print u[0,0,413,316], S[0,0,413,316]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.132482 0.000106054\n"
     ]
    }
   ],
   "source": [
    "print v[0,0,413,317], S[0,0,414,317]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.119515 4.91381e-05\n"
     ]
    }
   ],
   "source": [
    "print v[0,0,412,317], S[0,0,412,317]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.79804e-06\n",
      "-8.17751e-06\n"
     ]
    }
   ],
   "source": [
    "print -u[0,0,413,317]*S[0,0,413,318]+u[0,0,413,316]*S[0,0,413,316]\n",
    "print -v[0,0,413,317]*S[0,0,414,317]+v[0,0,412,317]*S[0,0,412,317]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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