{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook outlines the steps needed to change the resolution of the 2D domain.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "\n",
    "from salishsea_tools import nc_tools\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Changing the Vertical resolution \n",
    "To change the vertical resolution, you need to work through the followinh steps.\n",
    "\n",
    "1. Set new resolution in NEMO.\n",
    "2. Interpolate initial conditions onto new grid.\n",
    "3. Interpolate bounday conditions onto new grid.\n",
    "\n",
    "\n",
    "## 1. Set new resolution in NEMO\n",
    "\n",
    "###a. Choose the grid levels.\n",
    "The vertical resolution is set through a number of options in the namelist.domain.\n",
    "\n",
    "    ppsur       = 999999.\n",
    "    ppa0        = 999999.\n",
    "    ppa1        = 999999.\n",
    "    ppkth       = 25.\n",
    "    ppacr       =  3.\n",
    "    ldbletanh   = .FALSE.   ! use double tanh true or false\n",
    "    ppa2        =  0  Double tanh function parameters\n",
    "    ppkth2      =  0     \n",
    "    ppacr2      =  0  \n",
    "    \n",
    "These parameters set the grid spacing and depth at each grid level as defined by:\n",
    "\n",
    "$$ z(k) = ppsur + ppa0k + ppa1 ppacr \\log(\\cosh(\\frac{k-ppkth}{ppacr})) + ppa2 ppacr2 \\log\\cosh(\\frac{k-ppkth2} {ppacr2})) $$ \n",
    "\n",
    "$$ e3(k) =  ppa0k + ppa1 \\tanh(\\frac{k-ppkth}{ppacr}) + ppa2\\tanh(\\frac{k-ppkth2}{ppacr2}) $$\n",
    "\n",
    "where $k$ is the grid level, $z(k)$ is the depth of the grid level, and $e3(k)$ is the grid spacing at that grid level (NEMO manual). Note that the second hyperbolic tan is only used if ldbletanh=.TRUE.\n",
    "\n",
    "Note: ppsur should be set so that z(1) = 0\n",
    "\n",
    "You should experirment with these settings in a notebook.  Here is an example of a double tanh profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def NEMO_tanh(ks, a, kth, acr):\n",
    "    \"\"\"A generic tanh used in NEMO\"\"\"\n",
    "    return a* np.tanh((ks-kth)/acr)\n",
    "\n",
    "def NEMO_tanh_integral(ks, a, kth, acr):\n",
    "    \"\"\"A generic integrated tanh used in NEMO grid\"\"\"\n",
    "    return a*acr*np.log(np.cosh((ks-kth)/acr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ppsur -83.1898878903\n",
      "ppa0 8.19973820108\n",
      "Surface:  1.42108547152e-14\n",
      "First grid cell thickness 1.0\n",
      "Bottom: 428.789503531\n"
     ]
    },
    {
     "data": {
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RtxrwRAQeT2yWM4WYyCUiHoiILdPbhIg4Nt3/ZETsGBGbRsTOfRt8WXNfZccuc+wIHgCd\nBbwPeDdwj8RnJVaSJkxK1oobes24sst6vHEEz8GFa/Vu8HWT1XTqVSwrWcauYp6zjl1t79wJvrZy\nM3V/Fc+bKua5qrGLVDdlNqbPzPIpgtuB3SXeAXwPLvsmvHcUzFx/+bOmbCJNoEJXlYY13hg6M+YY\n9lgl+dvdJ/rz46VRXREvtjReh8egesyx3+9Mt1Nd5HPMccckDbx3nQjfXQFIl2+oXN1vVhqZrdM3\nUmXoZmFWJNLe18MFW/d/pHVrxuWtXNfp3nkCQ4w3Tp/XkXxIE+cmC7b3Nf3fcNwtwPHA/0ZQd7ZP\nMxu+vNVPIzGSPAxcz3i9ULM8KET3TjMrkhf+VX9/OdeMk3Qh8CdgM0mLJH0GOA7YSdI9JN1fj8sy\njQNPp37pnsDJwFHAbRL7SKycQQLNrPDWWKX+/nLW/WZl50ZfA9xX2bGrGbtai/RGxF4RsWFErBwR\nYyLi53kbb5x0rbruEJg0FyZfDW/9C8ybFnHz/0YwC9gaOBT4FHCfxKFSe2bfy9/5Wu7YVcxz1rGr\nqzV1fxXPmyrmuaqxi1Q3udFnZkOod1XpK8/A46dmkx6DpOEXMXtixKwuuPnw2jE2EUQEl0ewI/Ax\nYDvgAYljJNbNKs1mViStW6DdzLLnMX1mNqQ+a8Y9C59bB3a7PoKDWnP8cpTrPOdDYlPgy8DHgf8G\nTozgoeSxCZNgzDSv+WfWX57L9XCNfMmGCZNg14vgwRtgydNeoN0sPxot1270mVnDJNYE5gEzIji9\n+eOVo1wXIR8SG5J0/dwfmA0HXQf/OSxZ8qGH1/wz61GEcj2UZvIg8QjwjggebnGyzKwJnsiljdxX\n2bEdOxHB08BuwDFSz1TelpVGzpkI/hbBV4FNgDtgjR/2bvBBI2v+FeF8LVPsKuY569hFIOlBSbdK\nulnSX1p8+Odg5BNCVfG8qWKeqxq7SHWT1+kzsxGJ4F6JTwK/lD59BDy+p7sHFkcES4BjpQcmAtv3\nf4Zn6DMrkAC6IuLJNhz7PzTR6DOzfHD3TjNrinT66fDAZ+D4mi8FjXUPLEu5LmI+Bl6L6/ML4PT3\nRPC3zqfKLD+KUK4lPQBsHRFPDPB4E2P6Jv4CHrsbHnvCP+iZ5Ye7d5pZh/12XO8GHzTSPdCyVm+G\nvs8vhK67gNslfiHxLolcf+k1q7gArpB0g6QprThg0uDb9mT4/hpwztbJj0PbnpzsN7OicffOBkjq\niojuKsWuYp4du9HYXsA3a82cMxHzZ0sTgEk9s7M+k8zQd/psibWAfYFzgX9KzAR+EcHL63cV73wt\nduwq5jnr2AWxXUT8XdKrgcsl3RUR19Y+QdLZwIPp5hLglp73tGdcUu/trY5ePt63O33ZGeNh0lRJ\n/+7//PrbtWOehvP8Vm73TUMH4x/KkO9ve7Yr+n53AVtGxI86md9Ov9+pLmAcI+DunY3FrtwHXhXz\n7NiNxR64e+CkuRGzJw4zbu67Tw1HVvlo9zkjsQLwAWAq8DbgTOC0CBYV7Xwteuwq5jkHsQtVP0k6\nClgWESfW7Gs4D9LkbphVZ6KuyVcn64MOOz2VO2+qmOeqxi5S3eRGn5k1ZXkXoNoZIA9YAPOmeUxf\n+UhsBhwEfBK4EpgJXBtBsT5MzIYp7+Va0mrAqIhYKml14DLgmIi4rOY5I2j0Nf+Dnpm1jxt9ZtZx\nyxdvf8u28MgCuPkbjQz2L0u5Lks+hkNiDWAf4GCSKd1nAhdE8O9ME2bWYnkv15JeB1yUbq4InB8R\nx/Z5zggafc3/oGdm7eOJXNooy7U4sopdxTw7duMi5s9Ofvk97utw3p3+QtBZWZwzESyN4BRY+UDg\nKyTrNj4kcYI0svEGjSpiWSlq3CrHzruIeCAitkxvE/o2+EZ+3Pmz4bpD4MD7YeoDMGnuSBp8VTxv\nqpjnqsYuUt3kiVzMrJX+B/iOxKoRPJN1YqwTno8ILgMuk9gE+AJwo8Q1JFf/rnLXT7NiSiZ64i3A\n2hEcnnV6zGzk3L3TzFpK4g/AzIiXuxsN4zXlKNdlyUezJEaTjPmbSjKV/CnAeRH8K9OEmY1AGcp1\nM3mQOBDYMoLPtThZZtYEj+kzs0xJp8yEez4Oj94NS58dzmK+ZSnXZclHq6Rr+72XpPG3PcnSDz+O\nYEGmCTNrQBnK9UjzkIzre9ux8OoN4PabvDi7WX54TF8bua+yYzv2UK+fMAlu+yDMWD+Z6tuL+XZC\nXs+ZCCKCP0SwO7A18DwwT+ISiZ2bXfA9r/kuY9wqx66q5RO5nPMW+MGrR1qfV/G8qWKeqxq7SHWT\nG31m1kJjpsFPXtd73xnjk5k9rcoieDAdE/Ra4LfA94E7JQ5OZwJFmjBJmjhXmtyd/PWPBWbZGTOt\n98yd4PrcrLjcvdPMWmaki/mWpVyXJR+dkF7l256k6+f74II/wrVbwmljlz9ryn1w3SHuTmZZKkO5\nznJxdjNrj1x175R0lqTFkm6r2beOpMsl3SPpMklr1Tx2hKR7Jd0laed2ps3M2mHps/X3L/NMntZL\n2vXzmgj2ALaEayb0bvCBryqYZcn1uVmZtLt758+BD/TZNx24PCI2Ba5Mt5G0BbAnsEX6mlMl5ar7\nqfsqO7ZjD2XRjOTqTK0DFsDCmc0d1wZT7HMGIlgETy6q/+joVdsZe6RcJ1cndnW1pj6v4nlTxTxX\nNXaR6qa2rtMXEddKGtdn965AT3eBc4BukobfbsCFEfE88KCk+4BtgHntTKOZtU6yptMEYPevwITt\n4MYrYeFMd8+zoQ10VWHMBIntI7i2s+kxq7bl9fmkqTDhHfC3++GWb7o+Nyumto/pSxt9l0TEm9Pt\npyJi7fS+gCcjYm1JM4F5EXF++tiZwJyI+HWf4xW+b71Z2UmsACwDXhPBsqGfX45yXZZ8ZGH5TIG1\nE0dMWQATLoFDdgPuB46O4I9ZpdGqqQzlutk8SJwC3BvByS1Mlpk1odFy3dYrfUOJiJA0WKuzWLPM\nmBkAEbwk8QDweuDWrNNj+df7qsLoVZNxQwtnRpwxW+KrwKeAcyUWAEdF8KdsU2xWKfcCb8g6EWY2\nclk0+hZLWj8iHpW0AfBYuv8RYEzN8zZO9/Uj6WzgwXRzCXBLRHSnj3UBtGO7tt9uJ+LVbvdNQwfj\nH0qH3t++2xV9v7uALSPiR53Mb6v/3/Cm1eD968I9v5UWPw7/OTr5Ut+r73sXMA5rmqSunv9BkWOn\n3cb6dR2L4HngLInzgH2ACyTuhs/+NuKnp7YidqOyes/L8r8uUmwD4D5g4kheWMXzpop5rmrsItVN\nWXTvPAF4IiKOlzQdWCsipqcTuVxAMo5vI+AKYHz0SWCW3Sx8Mju2Yw/nGHW76Q069X4Zuk9Bdvko\n+jnTeExWBvaFS78Fu9xM0u3zz51Ng+vkCsUufP3UfPfOKZ+FdU6CB65Pxt8umjHcsX1VPG+qmOeq\nxi5S3dTWRp+kC0kmbVkXWAx8k2RR3lnAWJKrdZMjYkn6/COB/YAXgEMi4tI6xyx85WtWZtLEuTBn\nl/6PTJobMbvuL8VlKddlyUdRpI2//YAjgfkk3T6vzzZVVjZlKNfN5CH5Ie9dM+Cnmyzf6zU0zbLW\naLlu65IIEbFXRGwYEStHxJiI+HlEPBkRO0bEphGxc0+DL33+9yJifERsXq/BZ2ZFsMYq9fcPPPV+\nXkgaI+kqSbdLmi9pWrp/wPVFLTsR/CeC00nGGl0C/EbifyW2zjhpZiUyZlrvBh94DU2z4snVOnh5\n12c8UiViVzHPjt2sQi/o+zxwWES8CXgncJCkNzLA+qJ5UfxzprnYETwXwWnAeGAOcLHE7yTe2u7Y\nnZaH97tqsa25H/KqeN5UMc9VjV2kusmNPjNrseIu0B4Rj0bELen9ZcCdJGOMdyVZV5T070eySaEN\nJm38/Zik8Xc5cInExRJbZZw0swIr9A95ZpZq+0QurVaGvvVmZZeMAdn0cNhiG7ipe6gF2vNYrtNJ\nqK4GJgALo876onVek7t8VJnEqsBngcOBP5NM+PLXbFNlRVOGct38mL6+k3MdsADmTfOYPrPs5Goi\nl3YoQ+VrVgUS6wJ3R/CqoZ+br3ItaTRJg+/bEXGxpKdqG3mSnoyIdeq8Llf5sETa+Ps88FXgT8Ax\nEV4/0oanDOW6+dk7J0xKxvBtuT3cfxvM/7YbfGbZytVELmXjvsqO7dgNeRp4pUShvixJWgn4NXBe\nRFyc7l4saf308dr1Reu9/mxJR6e3Q2vfU0ld7dju2deu4w+xfWiH49VuD+v9jeCZCE6CjfaFGY8C\nl0r8StrrMyON3/e972D+c/9+t2O7k+93ejs6LctnY0TMn53Mvvy9H8Evrm6kwVf7HndaVrGrmOeq\nxs4yz43ylb7GYnv9Ecd27IaOxzPAqyL49xBxc/FLuiSRjNl7IiIOq9lfd33ROq/3On0FiC2xOnAg\n8GXgGpIrf7d3Inazivh+lyB2LuqnZrQqDxJvB86JYIsGYlfuvKlinqsau0h1kxt9ZtYWSXegD10E\ni26EJ58ebDHfvJRrSe8maQTcCvRUjkcAf2GA9UX7vD4X+bDhSRt/BwFfAq4CvhXBHdmmyvKmDOW6\nhY2+FYCHgR0iuLf5lJnZSLnRZ2aZqz/wf+DFfMtSrsuSj6qRGA0cDHwRuAL4dgR3Zpsqy4sylOtW\n5kHiJ8A9EZzYiuOZ2cg0Wq49pq8B7qvs2I49XGOm9W7wgRfzbZ9ynDPZxY5gWQTHAZsAtwFXS5wv\nsVm7YzeqDO930WJbP78Fdhvuk6t43lQxz1WNXaS6yY0+M2uD5hbzNctCBEsjOJZknb87gD9KnCex\nacZJM8uRd60MR24r7f1/0sS5Sc8OM8s7d+80s5aTJs6FObv0f2TS3GQGuL7PL0e5Lks+LCHxSmBa\neptN0u3zvmxTZZ1WhnLdujF9jXXdN7P2cfdOM8uBRTOSLwK1DlgAC2dmkx6zxkXwzwi+DbwBuB+Y\nJ/FziU0yTppZRtx136yo3OhrgPsqO7ZjD0/yi+91h8Bhj8OU+TBpLsyb5l+C26MM50yeY0ewJIJj\nSLp9LgT+Il0wW+L1nYhfqwrvd95iW63Gu+5X8bypYp6rGrtIddOKWSfAzMopYv5siWuBCyL4ddbp\nMWtWBEuAoyR+BMt+BFwvcRHwHZiwRXIVZI1VYOmzgy1RYlZcS5+tv3/ZM51Nh5k1ymP6zKxtJC4E\nfhfBhYM/rxzluiz5sOGRWAf4IvxhGvw+4MQ1lz/qcU5lUYZy3d4xfV94BK75rM91s87yOn1mlgvJ\nl4P3ngHPPw0PPVSExdmbVZZ8WGOkXa+E372v/yP1Jy6yYilDuW7tOn0TJiVj+EavCqutDXu+AiZu\nEcFLrTi+mQ2PJ3JpI/dVdmzHHu5xen4NnrkhnL55MpPntid7au/2KMM5U+zYq4yq/6zWL1GSnzxX\nJ7b1FjF/dsTsiRGzuuDsrWDiMmDPgZ5fxfOminmuauwi1U1u9JlZG3iGN6uSgcY5rbpWZ9Nh1lnp\n1b0vAcdKDDDJi5nlgbt3mlnLSZO7YdYO/R+ZfHXy63Df55ejXJclH9aY+uOcDlwEH10RdroY+GIE\nAzQMLe/KUK7bnYd0QqN5ERzfrhhm1luj5dqzd5pZG3iGN6uOZKbaCcCkdJzTsmeSNSlP+z/gZ8Cf\nJPaIYEHGSTVrl8NJzvOzIng868SYWX/u3tkA91V2bMceLi/O3knlOGeKHbt2nFPyd/7sCP4J7AH8\nHLhO4mOtjttJVY1tQ4vgHuB84Oi+j1XxvKlinqsau0h1k6/0mVnLLb/yceApMCrg/ntg4UxP6W1V\nE0EAMyXmAbMkdgC+EsFzGSfNrNW+BdwlcUoEd2adGDPrra1j+iSdBXwQeCwi3pzuOxo4AF6+/H9k\nRMxJHzsC2A94EZgWEZfVOWbh+9abVYXEccCSCI4b/HnlKNdlyYe1h8TaJFf9NgT2jOCBjJNkw5CX\ncj3Ad6p1gF8CrwUeBCZHxJI6r+1IHiS+BHRF8OF2xzKrurwt2fBz4AN99gXww4jYKr31NPi2IJny\nd4v0NadKcvdTs2J7CRhgOnuzaongKWB34ELgzxK7ZZwkK5Z636mmA5dHxKbAlel2lk4BtpB4f8bp\nMLM+2tqHQew+AAAgAElEQVSoiohrgafqPFSvVbobcGFEPB8RDwL3Adu0MXkNc19lx3bshr2Ixw63\nXcnOmVLHjiAiOAnYFThZ4kSJldodtxWqGjsvBvhOtStwTnr/HOAjHU1UH2m35ekw96fSpEulyd3S\nW/+S1Rqt/u7k2GWNOxJZjembKunTwA3Al9KuCBsC82qe8zCwURaJM7PmJR/y2+0FK4+S7tsOFs3w\nmD6zRATzJN5K8kX9Gok9I1iYdbqscNaLiMXp/cXAelkmJjHhXzBpQ5j9+mS7Gzj/ZGkC/gwwy07b\n1+mTNA64pKb/+WtYPp7v28AGEbG/pJnAvIg4P33emcDsiPhNn+Plom+9mQ2s/rplU+6D6w6p96Ff\nlnJdlnxY50isQLK49ZeB/SL4fcZJsj7yVK7rfKd6KiLWrnn8yYhYp87rOpYHaeJcmLNL/0cmzY2Y\nPbETaTCrgtyv0xcRj/XcTxt2l6SbjwBjap66cbqvH0lnkwxYBlgC3BIR3eljXWkcb3vb2xltwwem\nJQ2+7mSTLpLttx4l6d81O8dhVmERvAR8X+JPwC8kLgC+HsHzGSfNimGxpPUj4lFJGwCPDfTEzn13\nWmOV3nU/JNv/3KAmLS2M521vV2M71cVIvztFOsCgXbc0YbfVbG9Qc/8w4IL0/hbALcDKwOuABZBc\niexzvGh3mgfJS1fVYlcxz47diuPs0Q0R/W97dA8QN7LKc4vfv0zyUYZzxrEDiFdDzIX4I8TGVchz\nQWJHVrHrpKXvd6oTgMPT+9OB47LOA3xgbu96/6r078Q5VTlvKlxWKhe7SHVTWydYkHQh8CdgM0mL\nJO0HHC/pVkl/BXYgafgREXcAs4A7gDnAFyLNkZkVzdJn6+9f9kxn02FWHBE8DkwCZgM3SP1marQK\nq/Od6jPAccBOku4B3pduZ2zRjKQ7f60vPAILZ2aTHjMD2j+mr9WUo771ZlZf/TF9ByyAedM8ps9s\naOki7ueTTPRyVAQvZJykyipDue50HpLPgLFTYfSqsPJo+Mzr4P3/FcHDnUqDWdk1Wq7d6DOztkg+\n9N91Eqy6Ktx7OyycOdDMbWUp12XJh+WDxHrAfwMrAXtH8LeMk1RJZSjXWedB4giSNSrfE8EAPUHM\nrBGNlmuvn9WAPgMpKxG7inl27NZIGng//TmcfGHE7Imeqrt9ynLOOHZvESwmWYz7DyTdPXcse57z\nGNuak/7vjgMeAk6T6q7V3M7YHVfVslLF2EWqm9zoM7N2CujcB7xZ2UTwYgTfAj4JnAs/2VdiVNbp\nMmtEBAF8BngbcFDGyTGrJHfvNLO2SMf1nQSrrQr33DHY4ux5KdeSVgGuBl5BMpPwbyPiCEnrAL8E\nXksy5fnkiFhS5/W5yIeVk8T6wIXAS8AnIng04yRVQhnKdV7yILEJyWQ0kyO4Ouv0mBWZx/SZWeaK\nvDi7pNUi4t+SVgT+SLJo9q7APyLiBEmHA2tHxPQ6r81NPqyc0qt8RwH7A5+M4KqMk1R6ZSjXecqD\nxM4kExS9I4KFWafHrKg8pq+N3FfZsR17uMZM693gg2R77NTWHL99IqJn8fiVgVHAUySNvnPS/ecA\nH8kgaQMqxznj2MOMvH0E3yTpKneBxNelznyWV/P9tmb1/d9FcBnwQ+A3Eqt2MnanVLWsVDF2keom\nN/rMrA3WWKX+/tFt/YBvBUkrSLoFWAxcFRG3A+tFxOL0KYuB9TJLoBkvf3HeGtgZmCPx6oyTZNaI\nHwD3Aj/p5MQuZlXm7p1m1nLSxLkwZ5f+j0yaGzF7Yv/n569cS3olcClwBPCbiFi75rEnI2KdOq8J\nkiuBD6a7lgC3RER3+ngXgLe93brt1UfBsvcDn4KDToBTb8tX+oq3neoCxqX398lb/dSofNaxrEYy\nvu/nEZycdXrMisZj+swsc2VZnF3SN4BngAOAroh4VNIGJFcAN6/z/Fzmw8pPYiLwc+BHwAkRvJRx\nkkqjDOU6r3mQGAfMA/by+FSzxnhMXxu5r7JjO/bwJA276w6BA+6CQx+GSXMHavDliaR1Ja2V3l8V\n2Am4GfgdsE/6tH2Ai7NJYX1lOGccu7m4EcwB3g58GLhE4lWdit0JRRo3Y70N9r+L4EHgE8CFaQOw\nY7HbqaplpYqxi1Q3rZh1AsysnCLmz5bYDBgbwWFZp2eYNgDOkbQCyY9i50XElZJuBmZJ2p90yYYM\n02hWVwSLJLqA7wI3SewVwZ8yTpbZoCK4UuJ44CKJ7SL495AvMrOGuXunmbWNxGHAayM4dPDnlaNc\nlyUfVnwSHwbOBL4PnJgujm0jUIZynfc8pJO5nAeIZCkSn69mQ3D3TjPLE39wm2UggkuAbYA9gN9K\n9Jt4yCwv0kbeFGBz4IsZJ8eslNzoa4D7Kju2YzdyrAmT4IDPwaEflybOTbatHcpyzjh2a+NG8BCw\nPbCApLvnOzoVu9WKNG7Gehvu/y6CZ4DdgS9L7NTJ2K1W1bJSxdhFqps8ps/MWq7O7J0bwZRNpAnk\nfTIXszKJ4D/AYRLXkEzw8j3gZHefszyKYKHEXsAvJbaN4P6s02RWFh7TZ2YtV4Z1+kaiLPmwcpJ4\nPTALWAjsF8GSjJNUCGUo10XLg8RUku6e20bwr6zTY5ZHHtNnZjmwxir1949etbPpMLMe6VWT7YBH\ngBsl3pZxkswGcgpwE/xmdjI8YHK3hwmYNceNvga4r7JjO/ZwLX22/v5lz7Tm+FarHOeMY3cibgTP\nRTAVmA7MkU798XC/VFfx/bbmjeR/l3Q/ftfF8Od3JL1GZu2Q/N325EYafkUtp45dnNhFqpuGPaZP\n0ipARMRzbUyPmZXCohkwZZOaMX3AAQtg4cxOpsL1lll9EfxK+sya8Jofw5xXLH/EY287xfXTUF75\neTj+Fb33nTEeJk0FfH6aNWjAMX3p4sQfAfYC3kVyVVDAi8B1wPnAxdHhQYFF65duVlXJr7Hv/AGs\nsSbcfRssnDnQF8lWleus6y3XT1YkjY69raoy1E9FrJukyd3JFb6+Jl8dMaur0+kxy5tGy/VgV/q6\ngWuBHwC39PwSJekVwFbArsBhwHtGnFozK62I+bMlxgPjI5jWobDduN4yGyaPve2wblw/NcDDBMxa\nabAxfTtFxNci4s+1XQ8i4rmImBcRR0Jr1lEpCvdVdmzHzr1K1ltVPWeqGLu1cRv7Ul3F97vFXD81\nZNEMmHJf731f/Q88/7P2x25OVctKFWMXqW4a8EpfbYUkaW1gLDCq5vGbhuqHLmkMcC7wGiCAn0bE\nDEnrAL8EXgs8CEyOiCXpa44A9iPp7jAtIi4bWdbMLEtJ9853HAhrriHdtSksmtHucUKtqLfMqqPe\n2NvpL8L6v84uTeXl+qkxSW+RCSRj+EavmvwY8ekX4YTPSfwuXYPSzIZpyHX6JH0b2Be4H3ipZ39E\nvHfIg0vrA+tHxC2SRgM3kvRn/wzwj4g4QdLhwNoRMV3SFsAFwNuBjYArgE0j4qWaYxauX7pZ1dRZ\nnJ3kF9vrDqnX8Gt1uW6m3moyrusnK5SkrI6t+VL9ofvhC9sA2/lLdaIM9VNZ6iaJFYHfAE8B+yaz\nfJpVU6PlejiNvnuACRHRdOU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8eoPkako+J8goQ/3kumnk6qw7C3z1WbhqcsT1lwz8SrP2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\nxtzoMzMzMzMzqzE3+szMzMzMzGrsfwGF24CNp8M+mAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75e29fed90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks = np.arange(1,41) # Remember Fortan starts counting at 1.\n",
    "\n",
    "#Parameters\n",
    "ppa1 = 0.5\n",
    "ppacr = 2.\n",
    "ppkth = 12\n",
    "ppa2 = 6.7\n",
    "ppacr2 =2.2\n",
    "ppkth2 = 13.\n",
    "#ensure first grid cell has spacing 1\n",
    "ppa0 =  1 -NEMO_tanh(ks[0], ppa1, ppkth, ppacr)  - NEMO_tanh(ks[0], ppa2, ppkth2, ppacr2)\n",
    "#ensure first grid point is at the surface (0)\n",
    "ppsur = - (ppa0 + NEMO_tanh_integral(ks[0],ppa1,ppkth,ppacr) + NEMO_tanh_integral(ks[0],ppa2,ppkth2,ppacr2))\n",
    "\n",
    "#define grid spacing (dzs) and grid levels (zs)\n",
    "dzs =   ppa0 + NEMO_tanh(ks, ppa1, ppkth, ppacr)  + NEMO_tanh(ks, ppa2, ppkth2, ppacr2)\n",
    "zs = ppsur + ppa0*ks + NEMO_tanh_integral(ks,ppa1,ppkth,ppacr) + NEMO_tanh_integral(ks,ppa2,ppkth2,ppacr2)\n",
    "\n",
    "#plot\n",
    "fig,axs=plt.subplots(1,3,figsize=(15,5))\n",
    "for ax in axs:\n",
    "    ax.plot(dzs,zs,'-o')\n",
    "    ax.grid()\n",
    "    ax.set_xlabel('Grid spacing (m)')\n",
    "    ax.set_ylabel('Depth (m)')\n",
    "axs[0].set_ylim([450,0])\n",
    "axs[1].set_ylim([80,0])\n",
    "axs[2].set_ylim([30,0])\n",
    "print 'ppsur', ppsur\n",
    "print 'ppa0', ppa0\n",
    "print 'Surface: ',zs[0]\n",
    "print 'First grid cell thickness', dzs[0]\n",
    "print 'Bottom:', zs[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###b. Generate mesh_mask.nc file for interpolation\n",
    "Now that that you have decided on a different vertical grid structure, add the above parameters to your namelist.domain and set up a \"fake\" simulation. The purpose of the fake simulation is to generate a new mesh_mask.nc file which contains the grids for your new domain. Once you have the new grid, you can interpolate the boundary and initial conditions. \n",
    "\n",
    "Run NEMO with nn_msh = 1 in namelist.domain. You only need to run a short simulation (one time step). This will generate a file called mesh_mask.nc\n",
    "\n",
    "Copy this file into the workspace where you'll be interpolating the boundary conditions and initial conditions.\n",
    "\n",
    "#2. Interpolate initial conditions\n",
    "\n",
    "The new mesh gives us the z-levels needed for interpolation. Choose your initial condition file to interpolate.\n",
    "\n",
    "Open old dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0.50000027    1.50000314    2.5000115     3.50003055    4.50007042\n",
      "    5.50015083    6.50031022    7.50062342    8.50123623    9.50243254\n",
      "   10.5047653    11.50931127   12.51816684   13.53541212   14.56898216\n",
      "   15.63428737   16.76117342   18.00713456   19.48178514   21.38997868\n",
      "   24.10025665   28.22991514   34.68575798   44.51772486   58.48433368\n",
      "   76.58558445   98.06295924  121.8665184   147.08945807  173.11448217\n",
      "  199.57304923  226.26030574  253.06663733  279.93454976  306.83419736\n",
      "  333.75016973  360.6745318   387.60320347  414.53408835  441.46610968]\n"
     ]
    }
   ],
   "source": [
    "#open old file\n",
    "fold = nc.Dataset('/data/nsoontie/MEOPAR/2Ddomain/initial_conditions/TS_uniform_36.nc')\n",
    "Sold = fold.variables['vosaline'][:]\n",
    "Told = fold.variables['votemper'][:]\n",
    "#masking\n",
    "Sold = np.ma.masked_values(Sold,0)\n",
    "Told = np.ma.masked_values(Told,0)\n",
    "#old grid\n",
    "f=nc.Dataset('/data/nsoontie/MEOPAR/2Ddomain/grid/mesh_mask.nc')\n",
    "gdept_old = f.variables['gdept_0'][0,:]\n",
    "xold = f.variables['nav_lon'][:]\n",
    "yold = f.variables['nav_lat'][:]\n",
    "print gdept_old"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open new mesh file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0.50003511    1.50045013    2.50187922    3.50584388    4.51615047\n",
      "    5.54231215    6.60804415    7.77197409    9.17587852   11.14406872\n",
      "   14.32758141   19.71521759   28.14229202   39.53160858   52.97109604\n",
      "   67.5062027    82.54394531   97.79561615  113.13538361  128.51092529\n",
      "  143.90090942  159.29672241  174.69488525  190.09399414  205.49346924\n",
      "  220.89311218  236.2928009   251.69252014  267.09225464  282.49200439\n",
      "  297.89172363  313.29147339  328.69119263  344.09094238  359.49069214\n",
      "  374.89041138  390.29016113  405.68991089  421.08963013  436.48937988]\n",
      "[   0.50003508    1.50045009    2.50187922    3.50584399    4.51615035\n",
      "    5.54231202    6.60804398    7.77197408    9.17587888   11.14406885\n",
      "   14.32758163   19.71521847   28.14229296   39.53160899   52.97109699\n",
      "   67.50620429   82.54394591   97.79561875  113.13538312  128.5109245\n",
      "  143.90091533  159.29672913  174.69488759  190.0939899   205.49347212\n",
      "  220.89310727  236.29280398  251.69252548  267.09225695  282.49199245\n",
      "  297.89172956  313.29146732  328.69120534  344.09094347  359.49068165\n",
      "  374.89041983  390.29015803  405.68989623  421.08963443  436.48937263]\n"
     ]
    }
   ],
   "source": [
    "mesh = nc.Dataset('/data/nsoontie/MEOPAR/2Ddomain/vertical_resolution/dbl_tanh/mesh_mask.nc')\n",
    "gdept = mesh.variables['gdept_0'][0,:,:,:]\n",
    "e3t = mesh.variables['e3t_0'][0,:,:,:]\n",
    "gdept_1d = mesh.variables['gdept_1d'][0,:]\n",
    "# multi dimensional because of patrial cells\n",
    "print gdept[:,0,0]\n",
    "print gdept_1d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Tnew = np.copy(Told)\n",
    "Snew = np.copy(Sold)\n",
    "for j in np.arange(Tnew.shape[2]):\n",
    "    for i in np.arange(Tnew.shape[3]):\n",
    "        Tnew[0,:,j,i] = np.interp(gdept[:,j,i], gdept_old, Told[0,:,j,i])\n",
    "        Snew[0,:,j,i] = np.interp(gdept[:,j,i], gdept_old, Sold[0,:,j,i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check it makes sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f75e079b1d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H8EjK3iGNugNn9WIvrN8AHDZIHbque7ZXN23/XpG6Duv/AXy0yeZQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75e29ed150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmin=0;vmax=14\n",
    "fig,axs =plt.subplots(1,2)\n",
    "mesh=axs[0].pcolormesh(np.arange(Tnew.shape[-1]),gdept[:,5,:],Tnew[0,:,5,:],vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[0])\n",
    "axs[0].set_title('New')\n",
    "mesh=axs[1].pcolormesh(np.arange(Told.shape[-1]),gdept_old[:],Told[0,:,5,:],vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[1])\n",
    "axs[1].set_title('Old')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f75e02b5d10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NAr4EfIxk2cd/lnSIma0hQm079aMPbReh62OBjcADkpYGXbekaw3AzL4H/LhV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\n4g7AcRxnRPn//z1biPtrH3YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75e29ed250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmin=20;vmax=32\n",
    "fig,axs =plt.subplots(1,2)\n",
    "mesh=axs[0].pcolormesh(np.arange(Snew.shape[-1]),gdept[:,5,:],Snew[0,:,5,:],vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[0])\n",
    "axs[0].set_title('New')\n",
    "mesh=axs[1].pcolormesh(np.arange(Sold.shape[-1]),gdept_old[:],Sold[0,:,5,:],vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[1])\n",
    "axs[1].set_title('Old')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save in a new file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def save_interopolated_netcdf_initial(filename):\n",
    "    \"\"\"Save the new interpolate T+S initial conditions in a netcdf file\"\"\"\n",
    "    new_TS = nc.Dataset(filename, 'w')\n",
    "    nc_tools.init_dataset_attrs(\n",
    "    new_TS, \n",
    "    title='2D T+S initialization - interpolated', \n",
    "    notebook_name='Changing Resolution.ipynb', \n",
    "    nc_filepath=filename,\n",
    "    comment='Interpolated T+S initial condtions for alternate vertical grid spacing')\n",
    "    new_TS.createDimension('y', 10)\n",
    "    new_TS.createDimension('x', 1100)\n",
    "    new_TS.createDimension('deptht',size = len(gdept_old))\n",
    "    new_TS.createDimension('time_counter', None)\n",
    "    nc_tools.show_dimensions(new_TS)\n",
    "    # variables\n",
    "    y = new_TS.createVariable('y', 'float32', ('y','x'))\n",
    "    y.long_name = 'spanwise'\n",
    "    y.units = 'km'\n",
    "    y[:] =yold\n",
    "    x = new_TS.createVariable('x', 'float32', ('y','x'))\n",
    "    x.long_name = 'streamwise'\n",
    "    x.units = 'km'\n",
    "    x[:] = xold\n",
    "    deptht = new_TS.createVariable('deptht', 'float32', ('deptht'))\n",
    "    deptht.long_name = 'Depth'\n",
    "    deptht.units = 'm'\n",
    "    deptht.positive = 'down'\n",
    "    deptht[:] = gdept_1d\n",
    "    time_counter = new_TS.createVariable('time_counter', 'float32', ('time_counter'))\n",
    "    time_counter.units = 'seconds since 2003-08-09 0:00:00'\n",
    "    time_counter.long_name = 'Time axis'\n",
    "    vosaline = new_TS.createVariable('vosaline', 'float32', \n",
    "                               ('time_counter','deptht','y','x'))\n",
    "    vosaline.units = 'none'\n",
    "    vosaline.long_name = 'Practical Salinity'  \n",
    "    vosaline.coordinates = 'x y deptht time_counter'\n",
    "    vosaline.grid = '2D bathy'\n",
    "    vosaline[0] =Snew\n",
    "    votemper = new_TS.createVariable('votemper', 'float32', \n",
    "                               ('time_counter','deptht','y','x'))\n",
    "    votemper.units = 'degC'\n",
    "    votemper.long_name = 'Temperature' \n",
    "    votemper.coordinates = 'x y deptht time_counter'\n",
    "    votemper[0] = Tnew\n",
    "        \n",
    "    new_TS.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: 2D T+S initialization - interpolated\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://bitbucket.org/salishsea/2d-domain/src/tip/Changing Resolution.ipynb\n",
      "references: REQUIRED\n",
      "history: [2015-09-04 11:43:14] Created netCDF4 zlib=True dataset.\n",
      "comment: Interpolated T+S initial condtions for alternate vertical grid spacing\n",
      "<type 'netCDF4._netCDF4.Dimension'>: name = 'y', size = 10\n",
      "\n",
      "<type 'netCDF4._netCDF4.Dimension'>: name = 'x', size = 1100\n",
      "\n",
      "<type 'netCDF4._netCDF4.Dimension'>: name = 'deptht', size = 40\n",
      "\n",
      "<type 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "save_interopolated_netcdf_initial('/data/nsoontie/MEOPAR/2Ddomain/vertical_resolution/dbl_tanh/TS_uniform_36.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick check that variables were saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0.50003511    1.50045013    2.50187922    3.50584388    4.51615047\n",
      "    5.54231215    6.60804415    7.77197409    9.17587852   11.14406872\n",
      "   14.32758141   19.71521759   28.14229202   39.53160858   52.97109604\n",
      "   67.5062027    82.54394531   97.79561615  113.13538361  128.51092529\n",
      "  143.90090942  159.29672241  174.69488525  190.09399414  205.49346924\n",
      "  220.89311218  236.2928009   251.69252014  267.09225464  282.49200439\n",
      "  297.89172363  313.29147339  328.69119263  344.09094238  359.49069214\n",
      "  374.89041138  390.29016113  405.68991089  421.08963013  436.48937988]\n",
      "[[  0.00000000e+00   5.00000000e-01   1.00000000e+00 ...,   5.48500000e+02\n",
      "    5.49000000e+02   5.49500000e+02]\n",
      " [  0.00000000e+00   5.00000000e-01   1.00000000e+00 ...,   5.48500000e+02\n",
      "    5.49000000e+02   5.49500000e+02]\n",
      " [  0.00000000e+00   5.00000000e-01   1.00000000e+00 ...,   5.48500000e+02\n",
      "    5.49000000e+02   5.49500000e+02]\n",
      " ..., \n",
      " [  0.00000000e+00   5.00000000e-01   1.00000000e+00 ...,   5.48500000e+02\n",
      "    5.49000000e+02   5.49500000e+02]\n",
      " [  0.00000000e+00   5.00000000e-01   1.00000000e+00 ...,   5.48500000e+02\n",
      "    5.49000000e+02   5.49500000e+02]\n",
      " [  0.00000000e+00   5.00000000e-01   1.00000000e+00 ...,   5.48500000e+02\n",
      "    5.49000000e+02   5.49500000e+02]]\n",
      "(1, 40, 10, 1100) 0.0 31.3702\n"
     ]
    }
   ],
   "source": [
    "f=nc.Dataset('/data/nsoontie/MEOPAR/2Ddomain/vertical_resolution/dbl_tanh/TS_uniform_36.nc')\n",
    "depth=f.variables['deptht'][:]\n",
    "x=f.variables['x'][:]\n",
    "S = f.variables['vosaline'][:]\n",
    "print depth\n",
    "print x\n",
    "print S.shape, S.min(), S.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Interpolate Boundary conditions\n",
    "\n",
    "The forcing files for  temperature/salinity all need to be interpolated onto the new vertical grid as well.\n",
    "\n",
    "Open old TS boundary condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'netCDF4._netCDF4.Dataset'>\n",
      "root group (NETCDF4 data model, file format UNDEFINED):\n",
      "    Conventions: CF-1.6\n",
      "    title: Temperature and Salinty Boundary Conditions 2D domain\n",
      "    institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "    source: https://bitbucket.org/salishsea/2d-domain/src/tip/Generate T+S Forcing - NEMO3.6.ipynb\n",
      "    references: REQUIRED\n",
      "    history: [2015-08-05 16:23:30] Created netCDF4 zlib=True dataset.\n",
      "    comment: based on average values across mouth of JdF and 3D weekly climatology\n",
      "    dimensions(sizes): xb(80), yb(1), time_counter(52), deptht(40)\n",
      "    variables(dimensions): float32 \u001b[4mdeptht\u001b[0m(deptht), float32 \u001b[4mtime_counter\u001b[0m(time_counter), float32 \u001b[4mvotemper\u001b[0m(time_counter,deptht,yb,xb), float32 \u001b[4mvosaline\u001b[0m(time_counter,deptht,yb,xb), int32 \u001b[4mnbidta\u001b[0m(yb,xb), int32 \u001b[4mnbjdta\u001b[0m(yb,xb), int32 \u001b[4mnbrdta\u001b[0m(yb,xb)\n",
      "    groups: \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#open old file\n",
    "fold = nc.Dataset('/data/nsoontie/MEOPAR/2Ddomain/boundary_conditions/TS_OBC.nc')\n",
    "Sold = fold.variables['vosaline'][:]\n",
    "Told = fold.variables['votemper'][:]\n",
    "print fold\n",
    "times = fold.variables['time_counter'][:]\n",
    "#Will use gdept_old for depths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolate onto new grid - gdept_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Tnew = np.copy(Told)\n",
    "Snew = np.copy(Sold)\n",
    "for t in np.arange(Tnew.shape[0]):\n",
    "    for j in np.arange(Tnew.shape[2]):\n",
    "        for i in np.arange(Tnew.shape[3]):\n",
    "            Tnew[t,:,j,i] = np.interp(gdept_1d[:], gdept_old[:], Told[t,:,j,i])\n",
    "            Snew[t,:,j,i] = np.interp(gdept_1d[:], gdept_old[:], Sold[t,:,j,i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f75dfaa5b90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kx5nLM6PZjxQkjUhaJ+mqNtdakp4Ir6+T9JcZS+badooxmLqeRd62yfnAJ8Lz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\n4zjzkP8PskgeOTCgkisAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75dfc0c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmin=0;vmax=14\n",
    "fig,axs =plt.subplots(1,2)\n",
    "mesh=axs[0].pcolormesh(np.arange(Tnew.shape[0]),gdept_1d,Tnew[:,:,0,10].T,vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[0])\n",
    "axs[0].set_title('New')\n",
    "mesh=axs[1].pcolormesh(np.arange(Told.shape[0]),gdept_old[:],Told[:,:,0,10].T,vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[1])\n",
    "axs[1].set_title('Old')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f75df7f3e90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZZZURJa0EzgRWkuybeoek48PaFo5TxSsTuTugrm2nK/SBtpui4WqgZvZDkuVI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\nAd5jcT1lH0H1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75e09b89d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmin=28;vmax=34\n",
    "fig,axs =plt.subplots(1,2)\n",
    "mesh=axs[0].pcolormesh(np.arange(Snew.shape[0]),gdept_1d,Snew[:,:,0,10].T,vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[0])\n",
    "axs[0].set_title('New')\n",
    "mesh=axs[1].pcolormesh(np.arange(Sold.shape[0]),gdept_old[:],Sold[:,:,0,10].T,vmin=vmin,vmax=vmax)\n",
    "plt.colorbar(mesh,ax=axs[1])\n",
    "axs[1].set_title('Old')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_interopolated_netcdf_boundary(filename):\n",
    "    Ny=8#remeber masked edges\n",
    "    nrim=10\n",
    "    ndepth=gdept_1d.shape[0]\n",
    "    \n",
    "    nemo = nc.Dataset(filename, 'w', zlib=True)\n",
    "\n",
    "    #start and end points\n",
    "    length_rim =nrim\n",
    "    lengthi=Ny*length_rim\n",
    "    #time and depth\n",
    "    depth_levels =ndepth\n",
    "\n",
    "    # dataset attributes\n",
    "    nc_tools.init_dataset_attrs(\n",
    "        nemo,\n",
    "        title='Temperature and Salinty Boundary Conditions 2D domain - interpolated to new vertical grid',\n",
    "        notebook_name='Changing resolution',\n",
    "        nc_filepath=filename,\n",
    "        comment='intepolated for new vertical grid spacing')\n",
    "\n",
    "    # dimensions \n",
    "    nemo.createDimension('xb', lengthi)\n",
    "    nemo.createDimension('yb', 1)\n",
    "    nemo.createDimension('time_counter', None)\n",
    "    nemo.createDimension('deptht', depth_levels)\n",
    "    \n",
    "    # variables\n",
    "    # deptht\n",
    "    deptht = nemo.createVariable('deptht', 'float32', ('deptht',))\n",
    "    deptht.long_name = 'Vertical T Levels'\n",
    "    deptht.units = 'm'\n",
    "    deptht.positive = 'down'\n",
    "    deptht[:]=gdept_1d[:]\n",
    "    # time_counter\n",
    "    time_counter = nemo.createVariable('time_counter', 'float32', ('time_counter'))\n",
    "    time_counter.long_name = 'Time axis'\n",
    "    time_counter.axis = 'T'\n",
    "    time_counter.units = 'weeks since beginning of year'\n",
    "    time_counter[:]=times[:]\n",
    "    # votemper\n",
    "    votemper = nemo.createVariable('votemper', 'float32', \n",
    "                               ('time_counter','deptht','yb','xb'))\n",
    "    votemper.units = 'degC'\n",
    "    votemper.long_name = 'Temperature'   \n",
    "    votemper[:]=Tnew[:]\n",
    "    # vosaline\n",
    "    vosaline = nemo.createVariable('vosaline', 'float32', \n",
    "                               ('time_counter','deptht','yb','xb'))\n",
    "    vosaline.units = 1\n",
    "    vosaline.long_name = 'Practical Salinity'  \n",
    "    vosaline[:]=Snew\n",
    "    # nbidta, ndjdta, ndrdta\n",
    "    nbidta = nemo.createVariable('nbidta', 'int32' , ('yb','xb'))\n",
    "    nbidta.long_name = 'i grid position'\n",
    "    nbidta.units = 1\n",
    "    nbjdta = nemo.createVariable('nbjdta', 'int32' , ('yb','xb'))\n",
    "    nbjdta.long_name = 'j grid position'\n",
    "    nbjdta.units = 1\n",
    "    nbrdta = nemo.createVariable('nbrdta', 'int32' , ('yb','xb'))\n",
    "    nbrdta.long_name = 'position from boundary'\n",
    "    nbrdta.units = 1\n",
    "    \n",
    "    for ir in range(length_rim):\n",
    "        nbidta[0,ir*Ny:(ir+1)*Ny] = ir\n",
    "        nbjdta[0,ir*Ny:(ir+1)*Ny] = range(Ny)\n",
    "        nbrdta[0,ir*Ny:(ir+1)*Ny] = ir\n",
    "    \n",
    "    nemo.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Temperature and Salinty Boundary Conditions 2D domain - interpolated to new vertical grid\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: https://bitbucket.org/salishsea/2d-domain/src/tip/Changing resolution.ipynb\n",
      "references: REQUIRED\n",
      "history: [2015-09-04 11:43:15] Created netCDF4 zlib=True dataset.\n",
      "comment: intepolated for new vertical grid spacing\n"
     ]
    }
   ],
   "source": [
    "save_interopolated_netcdf_boundary('/data/nsoontie/MEOPAR/2Ddomain/vertical_resolution/dbl_tanh/TS_OBC.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(52, 40, 1, 80) 29.5011 33.6646\n",
      "[   0.50003511    1.50045013    2.50187922    3.50584388    4.51615047\n",
      "    5.54231215    6.60804415    7.77197409    9.17587852   11.14406872\n",
      "   14.32758141   19.71521759   28.14229202   39.53160858   52.97109604\n",
      "   67.5062027    82.54394531   97.79561615  113.13538361  128.51092529\n",
      "  143.90090942  159.29672241  174.69488525  190.09399414  205.49346924\n",
      "  220.89311218  236.2928009   251.69252014  267.09225464  282.49200439\n",
      "  297.89172363  313.29147339  328.69119263  344.09094238  359.49069214\n",
      "  374.89041138  390.29016113  405.68991089  421.08963013  436.48937988]\n",
      "<type 'netCDF4._netCDF4.Dataset'>\n",
      "root group (NETCDF4 data model, file format UNDEFINED):\n",
      "    Conventions: CF-1.6\n",
      "    title: Temperature and Salinty Boundary Conditions 2D domain - interpolated to new vertical grid\n",
      "    institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "    source: https://bitbucket.org/salishsea/2d-domain/src/tip/Changing resolution.ipynb\n",
      "    references: REQUIRED\n",
      "    history: [2015-09-04 11:43:15] Created netCDF4 zlib=True dataset.\n",
      "    comment: intepolated for new vertical grid spacing\n",
      "    dimensions(sizes): xb(80), yb(1), time_counter(52), deptht(40)\n",
      "    variables(dimensions): float32 \u001b[4mdeptht\u001b[0m(deptht), float32 \u001b[4mtime_counter\u001b[0m(time_counter), float32 \u001b[4mvotemper\u001b[0m(time_counter,deptht,yb,xb), float32 \u001b[4mvosaline\u001b[0m(time_counter,deptht,yb,xb), int32 \u001b[4mnbidta\u001b[0m(yb,xb), int32 \u001b[4mnbjdta\u001b[0m(yb,xb), int32 \u001b[4mnbrdta\u001b[0m(yb,xb)\n",
      "    groups: \n",
      "\n"
     ]
    }
   ],
   "source": [
    "f=nc.Dataset('/data/nsoontie/MEOPAR/2Ddomain/vertical_resolution/dbl_tanh/TS_OBC.nc')\n",
    "depth=f.variables['deptht'][:]\n",
    "sal=f.variables['vosaline'][:]\n",
    "print sal.shape, sal.min(), sal.max()\n",
    "print depth\n",
    "print f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All done! Now try to run with the new vertical resolution!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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