{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from salishsea_tools import (\n",
    "    nc_tools,\n",
    "    viz_tools,\n",
    "    tidetools,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_t = nc.Dataset('/data/jpetrie/MEOPAR/NEMO-3.6-code/NEMOGCM/CONFIG/mygyre/EXP00/GYRE_5d_00010101_00011230_grid_T.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "       [-85.67404938, -85.        , -84.32595062, -83.65190887,\n",
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       "        -74.88928986, -74.21524811, -73.54119873, -72.86714935,\n",
       "        -72.1931076 , -71.51905823, -70.84500885, -70.17095947,\n",
       "        -69.49691772, -68.82286835, -68.14881897, -67.47477722,\n",
       "        -66.80072784, -66.12667847, -65.45262909, -64.77858734]], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = grid_t.variables['nav_lon'][:, :]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False]], dtype=bool)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(lats) == X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "netCDF4._netCDF4.Variable"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lats = grid_t.variables['nav_lat']\n",
    "lons = grid_t.variables['nav_lon']\n",
    "type(lats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-70.170959, -66.800728, -74.215248, -66.126678]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_list = [1,2,6,4]\n",
    "i_list = [3,9,2,12]\n",
    "\n",
    "points = [lons[j_list[n],i_list[n]] for n in range(len(j_list))]\n",
    "\n",
    "points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Dimension'>: name = 'axis_nbounds', size = 2\n",
      "\n",
      "<class 'netCDF4._netCDF4.Dimension'>: name = 'x', size = 32\n",
      "\n",
      "<class 'netCDF4._netCDF4.Dimension'>: name = 'y', size = 22\n",
      "\n",
      "<class 'netCDF4._netCDF4.Dimension'>: name = 'deptht', size = 31\n",
      "\n",
      "<class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 72\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nc_tools.show_dimensions(grid_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['nav_lat', 'nav_lon', 'deptht', 'deptht_bounds', 'votemper', 'time_centered', 'time_centered_bounds', 'time_counter', 'time_counter_bounds', 'vosaline', 'sosstsst', 'sosaline', 'sossheig', 'sowaflup', 'soshfldo', 'sosfldow', 'sohefldo', 'somxl010', 'somixhgt'])\n"
     ]
    }
   ],
   "source": [
    "nc_tools.show_variables(grid_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "name: GYRE_5d_00010101_00011230\n",
      "description: ocean T grid variables\n",
      "title: ocean T grid variables\n",
      "Conventions: CF-1.5\n",
      "production: An IPSL model\n",
      "timeStamp: 2016-May-06 09:45:44 PDT\n"
     ]
    }
   ],
   "source": [
    "nc_tools.show_dataset_attrs(grid_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "float32 vosaline(time_counter, deptht, y, x)\n",
      "    standard_name: sea_water_practical_salinity\n",
      "    long_name: salinity\n",
      "    units: 1e-3\n",
      "    online_operation: average\n",
      "    interval_operation: 7200 s\n",
      "    interval_write: 5 d\n",
      "    cell_methods: time: mean (interval: 7200 s)\n",
      "    _FillValue: 1e+20\n",
      "    missing_value: 1e+20\n",
      "    coordinates: time_centered deptht nav_lon nav_lat\n",
      "unlimited dimensions: time_counter\n",
      "current shape = (72, 31, 22, 32)\n",
      "filling on\n"
     ]
    }
   ],
   "source": [
    "nc_tools.show_variable_attrs(grid_t, 'vosaline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lons = grid_t.variables['nav_lon']\n",
    "lats = grid_t.variables['nav_lat']\n",
    "temp = grid_t.variables['votemper']\n",
    "salin = grid_t.variables['vosaline']\n",
    "time_s = grid_t.variables['time_centered']\n",
    "depth = grid_t.variables['deptht']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_s = time_s - time_s[0]\n",
    "time_d = time_s/(60*60*24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f11c9cb36d8>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vld7UtjDXMdgtXFjWnhs5sn1rC3HbbFMm5DEkS42zaBFcey1cdFGZwXPPPeHY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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11ca827eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(15, 15))\n",
    "ax[0].plot(time_d, temp[:,0,20,20])\n",
    "\n",
    "y,x = np.meshgrid(depth, time_d)\n",
    "mesh1 = ax[1].pcolormesh(x.T, y.T,5*temp[:,:,10,10].T)\n",
    "\n",
    "fig.colorbar(mesh1)\n",
    "\n",
    "\n",
    "mesh2 = ax[2].pcolormesh(lats, lons,temp[71,0,:,:])\n",
    "fig.colorbar(mesh2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.51905632,  16.19310379,  16.86715126,  17.54119873,\n",
       "        18.2152462 ,  18.88929176,  19.56333923,  20.2373867 ,\n",
       "        20.91143417,  21.58548164,  22.25952911,  22.93357468,\n",
       "        23.60762215,  24.28166962,  24.95571709,  25.62976456,\n",
       "        26.30381203,  26.97785759,  27.65190506,  28.32595253,\n",
       "        29.        ,  29.67404747,  30.34809494,  31.02214241,\n",
       "        31.69618797,  32.37023544,  33.04428482,  33.71833038,\n",
       "        34.39237595,  35.06642532,  35.74047089,  36.41452026], dtype=float32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lats[1]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}