{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigate a Restart File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as NC\n",
    "import numpy as np\n",
    "from salishsea_tools import viz_tools\n",
    "from salishsea_tools import tidetools\n",
    "from salishsea_tools import nc_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = '/ocean/sallen/allen/research/MEOPAR/myResults/NEMO36_Tides/base_run/E5s/SalishSea_00055981_restart.nc'\n",
    "fT = NC.Dataset(filename,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'netCDF4.Dimension'>: name = 'x', size = 398\n",
      "\n",
      "<type 'netCDF4.Dimension'>: name = 'y', size = 898\n",
      "\n",
      "<type 'netCDF4.Dimension'>: name = 'z', size = 40\n",
      "\n",
      "<type 'netCDF4.Dimension'> (unlimited): name = 't', size = 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check Dimensions\n",
    "nc_tools.show_dimensions(fT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'nav_lon', u'nav_lat', u'nav_lev', u'time_counter', u'kt', u'ndastp', u'adatrj', u'rnf_b', u'rnf_hc_b', u'rnf_sc_b', u'utau_b', u'vtau_b', u'qns_b', u'emp_b', u'sfx_b', u'en', u'avt', u'avm', u'avmu', u'avmv', u'mxln', u'ub2_b', u'vb2_b', u'sbc_hc_b', u'sbc_sc_b', u'qsr_hc_b', u'fraqsr_1lev', u'fse3t_b', u'fse3t_n', u'rdt', u'rdttra1', u'ub', u'vb', u'tb', u'sb', u'rotb', u'hdivb', u'sshb', u'un', u'vn', u'tn', u'sn', u'rotn', u'hdivn', u'sshn', u'rhop']\n"
     ]
    }
   ],
   "source": [
    "# Check Variables\n",
    "nc_tools.show_variables(fT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(898, 398)\n"
     ]
    }
   ],
   "source": [
    "sshn = fT.variables['sshn'][0,:]\n",
    "print sshn.shape\n",
    "me = sshn == 0\n",
    "etan = np.ma.array(sshn,mask=me)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb539525d88>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DLMf6+nrW19f3tA9ju1RUrW0dllTVWUmOtNYeqqrHJXlfkquT/PMkn2qtXVNVVyU5o7X2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb539473ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imin = 700; imax = 898; jmin = 100; jmax=300\n",
    "imin = 750; imax = 800; jmin = 100; jmax=150\n",
    "fig, ax = plt.subplots(1,1,figsize=(10,10))\n",
    "mesh = ax.pcolormesh(etan[imin:imax,jmin:jmax])\n",
    "fig.colorbar(mesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 898, 398)\n"
     ]
    }
   ],
   "source": [
    "vvel =  fT.variables['vb'][0,:]\n",
    "print vvel.shape\n",
    "me = vvel == 0\n",
    "v = np.ma.array(vvel,mask=me)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 4.1103789126 -7.68411912978\n"
     ]
    },
    {
     "data": {
      "image/png": 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g79f+X3vkqt4uj69rlvr5upKSByW5vKruka31J69trV1ZVVcleUNVPS+LS4KXNUEA4Njm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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5394da510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "level = 0; imin = 00; imax = 898; jmin = 0; jmax=398\n",
    "imin = 750; imax = 800; jmin = 100; jmax=150\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(10,10))\n",
    "mesh = ax.pcolormesh(v[level,imin:imax,jmin:jmax])\n",
    "fig.colorbar(mesh)\n",
    "print level, v[level].max(), v[level].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(898, 398)\n"
     ]
    }
   ],
   "source": [
    "sshb = fT.variables['sshb'][0,:]\n",
    "print sshb.shape\n",
    "me = sshb == 0\n",
    "etab = np.ma.array(sshb,mask=me)\n",
    "eta = etan - etab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb5393cc6c8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSYt6+vOf4+n/5/OjSqrjpg71XN/1wG9U1ceS/BLwAeAfHeoYbHIkSeqx/ftW\nMMk5Z2bu56D3vGthxW5g47z3G5lLV0bVnNzUHDli3bOr6vzm9V8A143Y1m5aeHWVJElaqjuBTUlO\nSXIUc5OCty2o2Qb8CkCSzcDeqtozZt2dSc5tXr8a+Pq8bb0xyVFJTgU2AV9sG5xJjiRJPfb0gdU7\nlFfV/iRXALcC64Drq2pHksuaz6+tqo8n2dJMEn4cuHTUus2mfw344yRHA08276mqe5LcDNwD7Acu\nr6rW01UZ8dmyJRn13dJgdLmE/AleMLamy9VVP8VXxta8kY+NrZF0aLpcQr4VqKqpXWuepNj9/Wl9\nHWz4kan+/pbLJEeSpD7zAZ2tnJMjSZIGySRHkqQ+M8lpZZIjSZIGySRHGqB38jtja36P35/CSKR+\nWKvPpdLy2ORIE/ACnhhbs44DY2ue32E7e1k/8vOHOGnsNiQNyH4btDaerpIkSYNkkiNJUp/tX+0B\nrF0mOZIkaZBMciRJ6jOTnFYmOZIkaZBMciRJ6jOTnFYmOZIkaZBMciRJ6rN9qz2AtcskR5IkDZJN\njiRJGiRPV0lryDE8tuxtvIi/mcBIJPXG+CfGHLZMciRJ0iCZ5EiS1GdeQt7KJEeSJA2SSY4kSX1m\nktPKJEeSJA2SSY4kSX1mktPKJEeSJA2SSY4kSX1mktPKJEeSJA2SSY40xp9z4diao6cwDklLc1Wy\n2kNYWSY5rWxypAn4QYc25wDrxtYc0eH+7D/gqJGfn8E9Y7chSYcDT1dJkqRBMsmRJKnPPF3VyiRH\nkiQNkkmOJEl9tm+1B7B2meRIkqRBMsmRJKnPxl+UedgyyZEkSYNkkiNJUp95dVUrkxxJkjRIJjmS\npN76/aE/sqELk5xWNjnSGF0ex9Clposf47GxNY9w3MjPH+OYiYxFkvrO01WSJGmQTHIkSeozT1e1\nMsmRJEmDZJIjSVKfmeS0MsmRJEmDZJIjSVKfmeS0MsmRJEmDZJIjSVKfmeS0MsmRJEmDZJIjHaY+\nwz8YW3Me/+8URqK14r0dHpEwydDgt6smuLXD2L7VHsDaZZMjjfFGPja25gYuHltzBAcmUrNuzGHm\nQIc/1sfxN2NrJD3Xvx7TmG31WVprik2OJEl9Nv7fRoct5+RIkqRBssmRJEmD5OkqSZL6zEvIW5nk\nSJKkQbLJkSSpz/ZP8WcRSS5Icm+S+5K8vaXm6ubzu5OcOW7dJDcl2d78fDPJ9mb5KUmenPfZn4za\nNZ6ukiTR7bEUAAAOxElEQVRJS5JkHfB+4HxgN3BHkm1VtWNezRbgtKralOQc4Bpg86h1q+qN89b/\nQ2DvvK/dWVVn0oFNjiRJfba6c3LOZq7p2AVzCQxwEbBjXs2FwA0AVXV7kvVJTgBOHbdukgBvAM5b\nyuA8XSVJkpZqA/DAvPcPNsu61JzUYd1fAPZU1f3zlp3anKqaTfLKUYMzyZHU6s+5cGzNL7FtCiM5\nvHV53MIQ/f4a/H1ftQbHtMqPdej6bI6l7rhLgI/Me/8QsLGqHk1yFnBLkpdV1WOLrWyTI01Al0cp\nHM1THbazbmzNC3hy5OdPcdTYbfyAo8fWPMYxY2ukPuhyoPMq7Bb/dRYemB1VsRvYOO/9RuYSmVE1\nJzc1R45aN8kRwOuBsw4uq6qnYO4v06q6K8n9wCbgrsUGZ5MjSVKfreRjHTbMzP0c9IWrFlbcCWxK\ncgpzKcvFzKUv820DrgBuSrIZ2FtVe5I8Mmbd84EdVfXQwQVJjgMeraoDSV7CXIPzjbbh2+RIkqQl\nqar9Sa4AbgXWAddX1Y4klzWfX1tVH0+yJclO4HHg0lHrztv8xcCNC77yVcDvJdkHPA1cVlV7aWGT\nI0mSlqyqPgF8YsGyaxe8v6LruvM+u3SRZR8FPtp1bDY5kiT1mROKWnkJuSRJGiSTHEmS+swkp5VJ\njiRJGiSTHEmS+mx1bwa4ppnkSJKkQTLJkSSpz1byZoA9Z5IjSZIGySRHmoAuz4taN6FLII7iByM/\nf4IXjN3Gi3hkbM0xLPq8O2mQev18qzU7sNVnkiNJkgbJJEeSpD4zyWllkiNJkgbJJkeSJA2Sp6sk\nSeozbwbYamySk+QDSfYk+cq8ZccmuS3J15N8Msn6lR2mJEnSoelyuuqDwAULll0J3FZVpwOfbt5L\nkqRpOzDFn54Z2+RU1eeARxcsvhC4oXl9A/C6CY9LkiRpWZY6J+f4qtrTvN4DHD+h8UiSpEPhJeSt\nlj3xuKoqSbV9vnXr1mdez8zMMDMzs9yvlCQNwHuS1R7Csn0T2LXag1CrpTY5e5KcUFXfTnIi8HBb\n4fwmRxqqA6zrUDP+j9vx7Blb0+WxDeMcPebRENDt0Q/SSjqyQ81qX1h0avNz0GdXYxAmOa2Wep+c\nbcCbmtdvAm6ZzHAkSZImY+w/LZPcCJwLHJfkAeCdwLuAm5O8mbmk7g0rOUhJktRiteOsNWxsk1NV\nl7R8dP6ExyJJkjQx3vFYkqQ+6+H9a6bFZ1dJkqRBssmRJEmD5OkqSZL6zEvIW5nkSJKkQTLJkSSp\nz0xyWpnkSJKkQTLJkaZkXYd/bq3rcC3oRh4Y+fle1nce0yjr2TuR7Uh90OswxJsBtjLJkSRJg2SS\nI0lSn3kzwFYmOZIkaZBMciRJ6rNeTyhaWSY5kiRpkGxyJEnSIHm6SpKkPvN0VSuTHEmSNEgmOZIk\n9Zk3A2xlkiNJkgbJJEeagLdw3US28x85Z2zNSTy0rM8BHmDj2Jouj5jQsDi1o6f8o9rKJEeSJA2S\nSY4kSX1mBNfKJEeSJA2SSY4kSX1mktPKJEeSJA2SSY4kSX3mfXJameRIkqRBssmRJEmD5OkqSZL6\nzJsBtjLJkSRJg2SSI60hr+L2sTU7xzyS4e8ceHjsNvauWz+25gU8MbZG0/FbVcvexnuTCYxkuiY1\nn3bwV1gP/je4dCY5kiRpkExyJEnqM5OcViY5kiRpkGxyJEnqs31T/FlEkguS3JvkviRvb6m5uvn8\n7iRnjls3yU1Jtjc/30yyfd5n72jq703ymlG7xtNVkiRpSZKsA94PnA/sBu5Isq2qdsyr2QKcVlWb\nkpwDXANsHrVuVb1x3vp/COxtXp8BXAycAWwAPpXk9Kp6erHxmeRIktRnB6b481xnAzuraldV7QNu\nAi5aUHMhcANAVd0OrE9yQpd1kwR4A3Bjs+gi4Maq2ldVu4CdzXYWZZMjSZKWagPwwLz3DzbLutSc\n1GHdXwD2VNX9zfuTmrpR6zzD01WSJGlxNQvMjqzouKWl3qjpEuAjY2pax2CTI0lSny3/XpEjzDQ/\nB121sGA3POsOpRt5dtKyWM3JTc2Ro9ZNcgTweuCsMdva3TZ6mxxJ6rk+3s1Yg3EnsCnJKcBDzE0K\nvmRBzTbgCuCmJJuBvVW1J8kjY9Y9H9hRVQ8t2NZHkryXudNUm4Avtg3OJkfqmdOedQp7Ed8cf8A7\n5dRdY2te+N2nug3oxd3KtLq8X5xWQlXtT3IFcCuwDri+qnYkuaz5/Nqq+niSLUl2Ao8Dl45ad97m\nL+aHE44Pft89SW4G7mHuf+vLq9qfe5IRny1bklHfLWkl7Bzf5Hzv1KPG1nRvcvwzvtq6JDl9bHL6\n+OyqrUBVTS1aS1IrfL5q4TdO9fe3XF5dJUmSBskmR5IkDZJNjiRJGiSbHEmSNEheXSVJUq9Naor2\n8JjkSJKkQTLJkSSp1/p4g4DpMMmRJEmDZJMjSZIGydNV0tA8NL7khd/vcDfj+zp+3+s71kmrpMuB\nrt8nfJx43MYkR5IkDZJJjiRJvdbvHGolmeRIkqRBMsmRJKnXnJPTxiRHkiQNkkmOJEm9ZpLTxiRH\nkiQNkkmOJEm95tVVbUxyJEnSINnkSJKkQfJ0ldQ3t2e1R/BsH+swntfXyo9DOmw58biNTY40NH87\noe083qHmRyf0XVqW36rxTeR7M5nmeJqzP47sUOPhXaPY5EiS1GtOPG7jnBxJkjRIJjmSJPWaJ+3a\nmORIkqRBMsmRJKnXnJPTxiRHkiQNkkmOJEm95pycNiY5kiRpkExyJEnqNefktDHJkSRJg2SSI/XN\nD8Z83uUfdd/vUOMjG7SIaR40uvyvPKlHP3gwHCb/u0qS1GtOPG7j6SpJkjRIJjmSJPWaE4/bmORI\nkqRBMsmRJKnXnJPTxiRHkiQNkkmOJEm95pycNiY5kiRpkExyJEnqNefktDHJkSRJg2SSI/XNI2M+\nf7zDNib1J9+/QbSCuvzvNc1HP6h//CtKkqRes0Vr4+kqSZI0SCY5kiT1mpeQtzHJkSRJg2SSI0lS\nrzknp41JjiRJGiSTHEmSes05OW1MciRJ0iCZ5EiS1GvOyWljkyOtJX+R8TXrVn4YkjQENjlS3zw0\n5vMfdNjGwx1qju1QA/Cr1bFQq6nLow26WGuZgY9+AOfktHNOjiRJWrIkFyS5N8l9Sd7eUnN18/nd\nSc7ssm6Sf5FkR5L/nOTdzbJTkjyZZHvz8yejxmaSI0mSliTJOuD9wPnAbuCOJNuqase8mi3AaVW1\nKck5wDXA5lHrJjkPuBB4eVXtS/LieV+7s6rOpAObHEmSem1VT6SdzVzTsQsgyU3ARcCOeTUXAjcA\nVNXtSdYnOQE4dcS6vw78QVXta9b7zlIG5+kqSZK0VBuAB+a9f7BZ1qXmpBHrbgJeleQ/JZlN8jPz\n6k5tTlXNJnnlqMGZ5EiS1GurOvG465UHHS4dfZYjgP+uqjYn+VngZuAlzF16sbGqHk1yFnBLkpdV\n1WNtG1myJBcA72Puotbrqurdy9meJElaS+4HvjGqYDewcd77jcwlMqNqTm5qjhyx7oPARwGq6o4k\nTyd5UVU9AjzVLL8ryf3MpT53LTa4JZ+umjdh6ALgDOCSJC9d6va0NLOzs6s9hMFzH0+H+3nl3bfa\nA9AK2beCPz8BzMz7eY47gU3NVU9HARcD2xbUbAN+BSDJZmBvVe0Zs+4twKubdU4HjqqqR5Ic1/Qf\nJHkJcw1Oaxe2nDk5z0w2aiYGHZwwpCnywLDy3MfT4X5eeTY5mrSq2g9cAdwK3AP8WXN11GVJLmtq\nPg58I8lO4Frg8lHrNpv+APCSJF8BbqRpkoBXAXcn2Q78OXBZVe1tG99yTlctNpHonGVsT5IkHbLV\nvRlgVX0C+MSCZdcueH9F13Wb5fuAX15k+UdpTmN1sZwkx9ucSpKkNStVS+tVmvNqW6vqgub9O4Cn\n508+TmIjJEk6rFTVoV5JtGSrcZyd5u9vuZbT5BwBfA34h8xd0vVF4JL5dzmUJElaLUuek1NV+5Mc\nnDC0DrjeBkeSJK0VS05yJEmS1rIVeaxDlyeS6tAl+UCSPc0ldQeXHZvktiRfT/LJJOtXc4x9l2Rj\nks8k+Wrz5NvfaJa7nyckyY8kuT3Jl5Lck+QPmuXu4wlLsq65/f1/aN67jycsya4kX2728xebZe7n\nNWLiTY43CVxRH2Ruv853JXBbVZ0OfLp5r6XbB/xmVb0M2Ay8pfn/1/08IVX1feC8qnoF8HLgvOb5\nM+7jyXsrc/cfORjZu48nr4CZqjqzqs5ulrmf14iVSHK8SeAKqarPAY8uWPzM012bX1831UENTFV9\nu6q+1Lz+b8w9DXcD7ueJqqonmpdHMTen71HcxxOV5GRgC3AdP3xukPt4ZSy82sj9vEasRJPT5Ymk\nmpzjm9tjA+wBjl/NwQxJklOAM4HbcT9PVJLnJfkSc/vyM1X1VdzHk/ZHwNuAp+ctcx9PXgGfSnJn\nkn/eLHM/rxEr8RRyZzKvkqoq7000GUl+DPhL4K1V9Vjyw3+ouZ+Xr6qeBl6R5MeBW5Oct+Bz9/Ey\nJPknwMNVtT3JzGI17uOJ+fmq+laSFwO3Jbl3/ofu59W1EklOlyeSanL2JDkBIMmJwMOrPJ7eS3Ik\ncw3Oh6vqlmax+3kFVNXfAn8F/H3cx5P0D4ALk3yTuef+vDrJh3EfT1xVfav59TvAx5ibsuF+XiNW\nosnp8kRSTc424E3N6zcx9+RWLVHmIpvrgXuq6n3zPnI/T0jzFOH1zevnA/8I2I77eGKq6l9W1caq\nOhV4I/DXVfXLuI8nKskLkhzTvP5R4DXAV3A/rxkrcp+cJK8F3scPbxL4BxP/ksNQkhuBc4HjmDvP\n+07g3wM3Az8B7ALeMOqJrBqtucrnPwJf5oenXt/B3B293c8TkOSnmJuM+bzm58NV9W+THIv7eOKS\nnAv871V1oft4spKcylx6A3PTP/60qv7A/bx2eDNASZI0SCtyM0BJkqTVZpMjSZIGySZHkiQNkk2O\nJEkaJJscSZI0SDY5kiRpkGxyJEnSINnkSJKkQfr/ARi9HQn0PrbBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb53965fed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imin = 750; imax = 800; jmin = 100; jmax=150\n",
    "fig, ax = plt.subplots(1,1,figsize=(10,10))\n",
    "mesh = ax.pcolormesh(eta[imin:imax,jmin:jmax])\n",
    "fig.colorbar(mesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}