{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coordinates and Bathy for SalishSeaLake\n",
    "## May 8, 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from salishsea_tools import timeseries_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coordinates = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/grid/coordinates_seagrid_SalishSea201702.nc',decode_times=False)\n",
    "bathy = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 1, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * time     (time) float32 9.96921e+36\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    nav_lon  (y, x) float32 -123.429 -123.424 -123.419 -123.413 -123.408 ...\n",
       "    nav_lat  (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 46.8691 ...\n",
       "    glamt    (time, y, x) float64 -123.4 -123.4 -123.4 -123.4 -123.4 -123.4 ...\n",
       "    glamu    (time, y, x) float64 -123.4 -123.4 -123.4 -123.4 -123.4 -123.4 ...\n",
       "    glamv    (time, y, x) float64 -123.4 -123.4 -123.4 -123.4 -123.4 -123.4 ...\n",
       "    glamf    (time, y, x) float64 -123.4 -123.4 -123.4 -123.4 -123.4 -123.4 ...\n",
       "    gphit    (time, y, x) float64 46.86 46.86 46.86 46.87 46.87 46.87 46.87 ...\n",
       "    gphiu    (time, y, x) float64 46.86 46.86 46.86 46.87 46.87 46.87 46.87 ...\n",
       "    gphiv    (time, y, x) float64 46.86 46.86 46.87 46.87 46.87 46.87 46.87 ...\n",
       "    gphif    (time, y, x) float64 46.86 46.86 46.87 46.87 46.87 46.87 46.87 ...\n",
       "    e1t      (time, y, x) float64 454.9 454.9 454.9 454.8 454.8 454.8 454.8 ...\n",
       "    e1u      (time, y, x) float64 454.9 454.9 454.8 454.8 454.8 454.8 454.8 ...\n",
       "    e1v      (time, y, x) float64 454.7 454.7 454.7 454.7 454.7 454.7 454.7 ...\n",
       "    e1f      (time, y, x) float64 454.7 454.7 454.7 454.7 454.7 454.7 454.7 ...\n",
       "    e2t      (time, y, x) float64 407.2 419.5 428.0 434.3 439.1 443.1 446.4 ...\n",
       "    e2u      (time, y, x) float64 413.4 423.7 431.1 436.7 441.1 444.8 447.8 ...\n",
       "    e2v      (time, y, x) float64 396.9 415.5 426.2 433.3 438.6 442.7 446.2 ...\n",
       "    e2f      (time, y, x) float64 406.2 420.9 429.8 436.0 440.7 444.5 447.6 ...\n",
       "Attributes:\n",
       "    history:  [2017-02-14] Compressed Fraser river region"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5cbb678dd8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nLAsWmTdLRNtjrP12NY+9eCBjHrd5/K+XbXW/wy6cT6TTIwF88Esv8J8j/FJe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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5cc24855f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bathy.Bathymetry.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "strat = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/TSforDeepenByGridThicknessDec.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, time_counter: 1, x: 398, y: 898)\n",
       "Coordinates:\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "  * time_counter  (time_counter) float32 9.96921e+36\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (time_counter, deptht, y, x) float32 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    votemper      (time_counter, deptht, y, x) float32 0.0 0.0 0.0 0.0 0.0 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        Salinity Temperature Initial Conditions based on Dec 2002 r...\n",
       "    institution:  Dept of Earth, Ocean & Atmospheric Sciences, University of ...\n",
       "    source:       https://bitbucket.org/salishsea/tools/src/tip/Find TS for n...\n",
       "    references:   REQUIRED\n",
       "    comment:      Salinity and Temperature conditions from Dec 2002 file for ...\n",
       "    history:      [2016-02-01 Deepened]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5cbb3e23c8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kkUeq559/fscb+gywKYwUIPPxY3ANiC1Po7d171SP53dDU/mFqGIRLRpBFYpI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ggCt/0vfb4+hr1NEJH3icgVEfnijn3zIvKkiDxd/Zzb8btfFJFnROQvReTErb0JaGQnG9aE1JI1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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5cbb5b50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chopped_bathy = bathy.where(bathy.y > 335, drop = True)\n",
    "chopped_bathy.Bathymetry.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5cbb293518>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VIq2OECcjYzGyLVOJrSqQHR2l+okVADvd33mgmF13IfiBFgixKKrsIurSYEho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mjIndFjpM7Qc/DW+uOwAJ8YXs9vHn8zzSjjiXSBG6LzLGrDptUocRwvZgGwTpFIwbW1dWoLBY/X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5cbb2e8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bathy2 = chopped_bathy.where(chopped_bathy.x > 115,drop = True)\n",
    "bathy2.Bathymetry.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 1, x: 282, y: 562)\n",
       "Coordinates:\n",
       "  * time     (time) float32 9.96921e+36\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    nav_lon  (y, x) float32 -123.914 -123.909 -123.904 -123.899 -123.894 ...\n",
       "    nav_lat  (y, x) float32 48.4059 48.4078 48.4098 48.4117 48.4137 48.4156 ...\n",
       "    glamt    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    glamu    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    glamv    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    glamf    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    gphit    (time, y, x) float64 48.41 48.41 48.41 48.41 48.41 48.42 48.42 ...\n",
       "    gphiu    (time, y, x) float64 48.41 48.41 48.41 48.41 48.41 48.42 48.42 ...\n",
       "    gphiv    (time, y, x) float64 48.41 48.41 48.41 48.41 48.42 48.42 48.42 ...\n",
       "    gphif    (time, y, x) float64 48.41 48.41 48.41 48.41 48.42 48.42 48.42 ...\n",
       "    e1t      (time, y, x) float64 439.5 439.5 439.5 439.5 439.5 439.4 439.4 ...\n",
       "    e1u      (time, y, x) float64 439.5 439.5 439.5 439.5 439.5 439.4 439.4 ...\n",
       "    e1v      (time, y, x) float64 439.5 439.5 439.5 439.5 439.4 439.4 439.4 ...\n",
       "    e1f      (time, y, x) float64 439.5 439.5 439.5 439.5 439.4 439.4 439.4 ...\n",
       "    e2t      (time, y, x) float64 504.7 504.6 504.6 504.6 504.6 504.6 504.5 ...\n",
       "    e2u      (time, y, x) float64 504.6 504.6 504.6 504.6 504.6 504.5 504.5 ...\n",
       "    e2v      (time, y, x) float64 504.7 504.6 504.6 504.6 504.6 504.6 504.5 ...\n",
       "    e2f      (time, y, x) float64 504.6 504.6 504.6 504.6 504.6 504.5 504.5 ...\n",
       "Attributes:\n",
       "    history:  [2017-02-14] Compressed Fraser river region"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords1 = coordinates.where(coordinates.y > 335, drop = True)\n",
    "coords2 = coords1.where(coords1.x > 115, drop = True)\n",
    "coords2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "syncing\n"
     ]
    }
   ],
   "source": [
    "bathy2.to_netcdf('/data/vdo/MEOPAR/SalishSeaLake/revised_bathymetry_201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "syncing\n"
     ]
    }
   ],
   "source": [
    "coords2.to_netcdf('/data/vdo/MEOPAR/SalishSeaLake/revised_coordinates_seagrid_SalishSea201702.nc',engine='scipy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_bathy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_coords = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/grid/revised_coordinates_seagrid_SalishSea201702.nc',decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 1, x: 282, y: 562)\n",
       "Coordinates:\n",
       "  * time     (time) float32 9.96921e+36\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    nav_lon  (y, x) float32 -123.914 -123.909 -123.904 -123.899 -123.894 ...\n",
       "    nav_lat  (y, x) float32 48.4059 48.4078 48.4098 48.4117 48.4137 48.4156 ...\n",
       "    glamt    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    glamu    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    glamv    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    glamf    (time, y, x) float64 -123.9 -123.9 -123.9 -123.9 -123.9 -123.9 ...\n",
       "    gphit    (time, y, x) float64 48.41 48.41 48.41 48.41 48.41 48.42 48.42 ...\n",
       "    gphiu    (time, y, x) float64 48.41 48.41 48.41 48.41 48.41 48.42 48.42 ...\n",
       "    gphiv    (time, y, x) float64 48.41 48.41 48.41 48.41 48.42 48.42 48.42 ...\n",
       "    gphif    (time, y, x) float64 48.41 48.41 48.41 48.41 48.42 48.42 48.42 ...\n",
       "    e1t      (time, y, x) float64 439.5 439.5 439.5 439.5 439.5 439.4 439.4 ...\n",
       "    e1u      (time, y, x) float64 439.5 439.5 439.5 439.5 439.5 439.4 439.4 ...\n",
       "    e1v      (time, y, x) float64 439.5 439.5 439.5 439.5 439.4 439.4 439.4 ...\n",
       "    e1f      (time, y, x) float64 439.5 439.5 439.5 439.5 439.4 439.4 439.4 ...\n",
       "    e2t      (time, y, x) float64 504.7 504.6 504.6 504.6 504.6 504.6 504.5 ...\n",
       "    e2u      (time, y, x) float64 504.6 504.6 504.6 504.6 504.6 504.5 504.5 ...\n",
       "    e2v      (time, y, x) float64 504.7 504.6 504.6 504.6 504.6 504.6 504.5 ...\n",
       "    e2f      (time, y, x) float64 504.6 504.6 504.6 504.6 504.6 504.5 504.5 ...\n",
       "Attributes:\n",
       "    history:  [2017-02-14] Compressed Fraser river region"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timerange1 = ['2017-01-01','2017-02-01']\n",
    "Jan_time_series = timeseries_tools.make_filename_list(timerange1,'T',model = 'nowcast-green',resolution='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Jan_data = xr.open_mfdataset(Jan_time_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (axis_nbounds: 2, deptht: 40, nvertex: 4, time_counter: 31, x: 398, y: 898)\n",
       "Coordinates:\n",
       "    nav_lat               (y, x) float32 46.8597 46.8615 46.8634 46.8653 ...\n",
       "    nav_lon               (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "  * deptht                (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 ...\n",
       "    time_centered         (time_counter) datetime64[ns] 2017-01-01T12:00:00 ...\n",
       "  * time_counter          (time_counter) datetime64[ns] 2017-01-01T12:00:00 ...\n",
       "Dimensions without coordinates: axis_nbounds, nvertex, x, y\n",
       "Data variables:\n",
       "    bounds_lon            (time_counter, y, x, nvertex) float32 -123.429 ...\n",
       "    bounds_lat            (time_counter, y, x, nvertex) float32 46.8597 ...\n",
       "    area                  (time_counter, y, x) float32 180762.0 189238.0 ...\n",
       "    deptht_bounds         (time_counter, deptht, axis_nbounds) float32 0.0 ...\n",
       "    sossheig              (time_counter, y, x) float64 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    time_centered_bounds  (time_counter, axis_nbounds) float64 3.692e+09 ...\n",
       "    time_counter_bounds   (time_counter, axis_nbounds) float64 3.692e+09 ...\n",
       "    votemper              (time_counter, deptht, y, x) float64 0.0 0.0 0.0 ...\n",
       "    vosaline              (time_counter, deptht, y, x) float64 0.0 0.0 0.0 ...\n",
       "    buoy_n2               (time_counter, deptht, y, x) float64 0.0 0.0 0.0 ...\n",
       "    mixed_depth           (time_counter, y, x) float64 0.0 0.0 0.0 0.0 0.0 ...\n",
       "Attributes:\n",
       "    name:         SalishSea_1d_20170101_20170101\n",
       "    description:  ocean T grid variables\n",
       "    title:        ocean T grid variables\n",
       "    Conventions:  CF-1.5\n",
       "    production:   An IPSL model\n",
       "    timeStamp:    2017-Jan-02 00:31:31 PST"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jan_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (axis_nbounds: 2, deptht: 40, nvertex: 4, x: 398, y: 898)\n",
       "Coordinates:\n",
       "    nav_lat               (y, x) float32 46.8597 46.8615 46.8634 46.8653 ...\n",
       "    nav_lon               (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "  * deptht                (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 ...\n",
       "Dimensions without coordinates: axis_nbounds, nvertex, x, y\n",
       "Data variables:\n",
       "    bounds_lon            (y, x, nvertex) float32 -123.429 -123.429 -123.429 ...\n",
       "    bounds_lat            (y, x, nvertex) float32 46.8597 46.8597 46.8597 ...\n",
       "    area                  (y, x) float32 180762.0 189238.0 194104.0 197344.0 ...\n",
       "    deptht_bounds         (deptht, axis_nbounds) float32 0.0 1.0 1.0 2.00001 ...\n",
       "    sossheig              (y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    time_centered_bounds  (axis_nbounds) float64 3.694e+09 3.694e+09\n",
       "    time_counter_bounds   (axis_nbounds) float64 3.694e+09 3.694e+09\n",
       "    votemper              (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    vosaline              (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    buoy_n2               (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    mixed_depth           (y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averaged_Jan_data = Jan_data.mean(dim='time_counter')\n",
    "averaged_Jan_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timerange2 = ['2016-06-01','2016-07-01']\n",
    "Jun_time_series = timeseries_tools.make_filename_list(timerange2,'T',model='nowcast-green',resolution='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Jun_data = xr.open_mfdataset(Jun_time_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (axis_nbounds: 2, deptht: 40, nvertex: 4, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht                (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 ...\n",
       "    nav_lat               (y, x) float32 46.8597 46.8615 46.8634 46.8653 ...\n",
       "    nav_lon               (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "Dimensions without coordinates: axis_nbounds, nvertex, x, y\n",
       "Data variables:\n",
       "    area                  (y, x) float32 180762.0 189238.0 194104.0 197344.0 ...\n",
       "    bounds_lat            (y, x, nvertex) float32 46.8597 46.8597 46.8597 ...\n",
       "    bounds_lon            (y, x, nvertex) float32 -123.429 -123.429 -123.429 ...\n",
       "    buoy_n2               (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    deptht_bounds         (deptht, axis_nbounds) float32 0.0 1.0 1.0 2.00001 ...\n",
       "    mixed_depth           (y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    sossheig              (y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    time_centered_bounds  (axis_nbounds) float64 3.675e+09 3.675e+09\n",
       "    time_counter_bounds   (axis_nbounds) float64 3.675e+09 3.675e+09\n",
       "    vosaline              (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    votemper              (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "Attributes:\n",
       "    name:         SalishSea_1d_20160601_20160601\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-Jul-12 02:02:05 PDT\n",
       "    history:      Tue Jul 12 02:19:15 2016: ncks -4 -L4 -O SalishSea_1d_20160...\n",
       "    NCO:          \"4.5.2\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averaged_Jun_data = Jun_data.mean(dim='time_counter',keep_attrs=True)\n",
    "averaged_Jun_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "example_strat = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/TSforDeepenByGridThickness.nc',decode_times=False)\n",
    "example2 = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/TSforDeepenByGridThicknessDec.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, time_counter: 1, x: 398, y: 898)\n",
       "Coordinates:\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "  * time_counter  (time_counter) int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    votemper      (deptht, time_counter, y, x) float64 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    vosaline      (deptht, time_counter, y, x) float64 0.0 0.0 0.0 0.0 0.0 ..."
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_variables = ['area','bounds_lon','bounds_lat','deptht_bounds','sossheig',\n",
    "                     'time_centered_bounds','time_counter_bounds','buoy_n2','mixed_depth']\n",
    "\n",
    "new1 = averaged_Jan_data.drop(dropped_variables)  #.shift(1)\n",
    "new3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "insert_number_here = 3\n",
    "new3 = new1.assign_coords(time_counter = insert_number_here).expand_dims('time_counter',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "USE_THIS_ONE_Jan = new3.squeeze('time_counter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    votemper      (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    vosaline      (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..."
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USE_THIS_ONE_Jan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "syncing\n"
     ]
    }
   ],
   "source": [
    "USE_THIS_ONE_Jan.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter_Jan2017.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    votemper      (deptht, y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n",
       "Attributes:\n",
       "    name:         SalishSea_1d_20160601_20160601\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-Jul-12 02:02:05 PDT\n",
       "    history:      Tue Jul 12 02:19:15 2016: ncks -4 -L4 -O SalishSea_1d_20160...\n",
       "    NCO:          \"4.5.2\""
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USE_THIS_ONE_Jun = averaged_Jun_data.drop(dropped_variables).assign_coords(time_counter = insert_number_here).expand_dims('time_counter',1).squeeze('time_counter')\n",
    "USE_THIS_ONE_Jun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "syncing\n"
     ]
    }
   ],
   "source": [
    "USE_THIS_ONE_Jun.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer_Jun2016.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 5.041 5.041 5.041 5.041 5.041 5.041 ...\n",
       "    votemper      (deptht, y, x) float64 3.092 3.092 3.092 3.092 3.092 3.092 ..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer_Jun2016.nc')\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 5.824 5.824 5.824 5.824 5.824 5.824 ...\n",
       "    votemper      (deptht, y, x) float64 1.308 1.308 1.308 1.308 1.308 1.308 ..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2 = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter_Jan2017.nc')\n",
    "test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_test = test.mean(dim = ('x','y'))\n",
    "saline = new_test.vosaline\n",
    "temp = new_test.votemper\n",
    "empty_of_variables = test.drop(['vosaline','votemper'])\n",
    "#new_summer = xr.merge([empty_of_variables,saline,temp])\n",
    "#new_summer.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer_Jun2016.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_test2 = test2.mean(dim=('x','y'))\n",
    "empty2 = test2.drop(['vosaline','votemper'])\n",
    "saline2 = new_test2.vosaline\n",
    "temp2 = new_test2.votemper\n",
    "#new_winter = xr.merge([empty2,saline2,temp2])\n",
    "#new_winter.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter_Jan2017.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new_summer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-0e320d3f94eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_summer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'new_summer' is not defined"
     ]
    }
   ],
   "source": [
    "new_summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 1, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array = new_test.vosaline.values\n",
    "array = np.expand_dims(array,axis=1)\n",
    "array = np.expand_dims(array,axis=2)\n",
    "array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 898, 398)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones = np.ones((40,898,398))\n",
    "ones.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 898, 398)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_array = array*ones\n",
    "new_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 898, 398)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2 = new_test.votemper.values\n",
    "array2 = np.expand_dims(array2,axis=1)\n",
    "array2 = np.expand_dims(array2,axis=2)\n",
    "array2 = array2*ones\n",
    "array2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_summer = xr.Dataset({'vosaline':(['deptht','y','x'], new_array),\n",
    "                          'votemper':(['deptht','y','x'],array2)},\n",
    "                         coords={'deptht':(['deptht'], deptht),\n",
    "                                 'nav_lat':(['y','x'], nav_lat),\n",
    "                                 'nav_lon':(['y','x'], nav_lon),\n",
    "                                 'time_counter': 3})\n",
    "#new_summer.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer_Jun2016.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nav_lon = test.nav_lon.values\n",
    "nav_lat = test.nav_lat.values\n",
    "deptht = test.deptht.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "saline = new_test2.vosaline.values\n",
    "saline = np.expand_dims(saline,axis=1)\n",
    "saline = np.expand_dims(saline,axis=2)\n",
    "saline = saline*ones\n",
    "\n",
    "temp = new_test2.votemper.values\n",
    "temp= np.expand_dims(temp,axis=1)\n",
    "temp = np.expand_dims(temp,axis=2)\n",
    "temp = temp*ones\n",
    "\n",
    "nav_lon2 = test2.nav_lon.values\n",
    "nav_lat2 = test2.nav_lat.values\n",
    "deptht2 = test2.deptht.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_winter = xr.Dataset({'vosaline':(['deptht','y','x'], saline),\n",
    "                         'votemper':(['deptht','y','x'],temp)},\n",
    "                        coords={'deptht':(['deptht'], deptht2), \n",
    "                                'nav_lat':(['y','x'], nav_lat2),\n",
    "                                'nav_lon':(['y','x'], nav_lon2),\n",
    "                                'time_counter': 3})\n",
    "#new_winter.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter_Jan2017.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 5.824 5.824 5.824 5.824 5.824 5.824 ...\n",
       "    votemper      (deptht, y, x) float64 1.308 1.308 1.308 1.308 1.308 1.308 ..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_winter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 5.041 5.041 5.041 5.041 5.041 5.041 ...\n",
       "    votemper      (deptht, y, x) float64 3.092 3.092 3.092 3.092 3.092 3.092 ..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9e55f362e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x7rpt8q+lmgXtLqpkf4Ckv7B9aSeCiIgYCqZj0xpI2gfYVhL8Yqo8O+kU7pJeBBwMLHoy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\niAZLko+IaLD/Dwt/phjnsiA0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e5d2ce3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_summer.vosaline.isel(y=600).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'tmask' (z: 40, y: 898, x: 398)>\n",
       "[14296160 values with dtype=int8]\n",
       "Dimensions without coordinates: z, y, x"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh_mask = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')\n",
    "t_mask = mesh_mask.tmask.squeeze('t')\n",
    "t_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898, z: 40)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y, z\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 5.041 5.041 5.041 5.041 5.041 5.041 ...\n",
       "    votemper      (deptht, y, x) float64 3.092 3.092 3.092 3.092 3.092 3.092 ...\n",
       "    tmask         (z, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ..."
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_mask = xr.merge([new_summer,t_mask])\n",
    "with_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_summer = with_mask.where(with_mask.tmask == 1).drop('tmask')\n",
    "#final_summer.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer_Jun2016.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "syncing\n"
     ]
    }
   ],
   "source": [
    "winter_with_mask = xr.merge([new_winter,t_mask])\n",
    "final_winter = winter_with_mask.where(winter_with_mask.tmask == 1).drop('tmask')\n",
    "final_winter.to_netcdf('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/winter_Jan2017.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 nan nan nan nan nan nan nan nan nan ...\n",
       "    votemper      (deptht, y, x) float64 nan nan nan nan nan nan nan nan nan ..."
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/summer_Jun2016.nc')\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9e5579ca90>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G5fNHa4EXAp+zfUPHd6+YMgz7x1WCmK4m/3NgDfDmMpAxm4D/WOUEERE7qMdsL51qA9sj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e558525f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.vosaline.isel(y=500).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9e55683860>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gKDN7KoY/jbBA8zrgZDNbWKR8TrWo+4S0tOQ9Sa1bPg7ag2splX8DAyvpqCFJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e55945898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.votemper.isel(deptht=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, time_counter: 1, x: 398, y: 898)\n",
       "Coordinates:\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "  * time_counter  (time_counter) float32 9.96921e+36\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (time_counter, deptht, y, x) float32 0.0 0.0 0.0 0.0 0.0 ...\n",
       "    votemper      (time_counter, deptht, y, x) float32 0.0 0.0 0.0 0.0 0.0 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        Salinity Temperature Initial Conditions based on Mar 20, 20...\n",
       "    institution:  Dept of Earth, Ocean & Atmospheric Sciences, University of ...\n",
       "    source:       https://bitbucket.org/salishsea/tools/src/tip/FindTSforSmoo...\n",
       "    references:   REQUIRED\n",
       "    comment:      Salinity and Temperature conditions from  Mar 20, 2016 23:0...\n",
       "    history:      [2016-07-06 Created]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2 = xr.open_dataset('/home/vdo/MEOPAR/NEMO-forcing/initial_strat/TS20mar2016DeepSmooth.nc',decode_times=False)\n",
    "test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f9e54cf4438>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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viYhDtnQ/ERFS/x4adbGPmVlZvS3zn8yDknYHyH9Xb12CW3PwNzPrQp9f8roI\nOD5/Ph74Ti/SPBkHfzOzbvQo5y/pXOBnwLMlrZB0AvBx4FWS7gBemcf7wmX+ZmZd6FUpfES8ucWs\nw3uzh/Yc/G1ojUZt/HNz2zrdarS1025+VRrfT7v9NZZttNtT60OVX6Ndodcu2lQfOVkbPJMtN5mt\nPX8zipt3MDMbMjF7mndw8DczK8k9eZmZDasORXwzhYO/mVkXnPM3Mxs2W/cC10Bx8Dcz64IrfM3M\nhpCDv5nZsAlc4WtmNoxc4WtmNowc/M3MhstsesnLrXraUPr2yqXUqTMaNUajRi1ii4fRqFMn2g7A\n+PKb2uyZfHuNeWU1tldWo72e5s+90O6YZoUIVC83DLq+Bf9ue6aXdKqk5ZJuk3REv9JlZrZV+tuZ\ny5TpZ87/LEr2TC9pf+A44IC8zuclVfuYNjOzLdLnzlymTN+Cf5c90x8DnBcRGyLiLmA5cGi/0mZm\ntkUCqEe5YcBNdZl/q57pFwH3FZZbkadtRtKJkpZJWjbKhv6l1MxsMrOk2GfanvbZ0p7pI2IJsARg\nBy2cAafYzGaTmVCkU8ZUB/8HJe0eEauaeqZfCexVWG7PPM3MbKDMhCd5ypjqYp9WPdNfBBwnaZ6k\nfYDFwNIpTpuZWXtli3xmwPWhbzn/3DP9y4BdJK0APkzqif783Ev9PcCxABFxk6TzgZuBMeCkiEIH\nq2ZmAyC95DUDInsJfQv+3fZMHxFnAmf2Kz1mZj3hVj3NzIbPbMn5u3kHG0qjUZ+0WYZR6uNDY7xU\n0w1EywHYbJ2yTR4076e+FYXJ3165dLxZi+K/RvMQjaYuikNjmW+vbF8F164Zh6q0xWkeOC7zNzMb\nRjOj3Z4yHPzNzLoxS4p9HPzNzMqK3nbjKOluYB1QA8Yi4pDebb09B38zs270Puf/8ohY0+uNduLg\nb2bWjdlR6uOnfczMuqF6vdRAesF1WWE4cZLNBfBDSVe3mN83zvmbmZUVdPOS15oSZfgviYiVkp4K\nXCrp1twcft85529mVpIIFOWGMiJiZf67GriQKezHxMHfhlLxJazmF7uKL3w1/k728lZxepl9Fcc7\nvTjWSE/zi2KNv80ve31jxc/LHXeL/nUvWHHVpC+Q9aIf3lnVhy+kCt8yQweSFkjavvEZ+APgxvZr\n9Y6LfczMutG7i9luwIVKb0DPAc6JiO/3auOdOPibmZXVXZl/+01F3Akc1Jutdc/B38ysC/lJnhnP\nwd/MrLRaBLZJAAAMF0lEQVRy5fkzgYO/mVlZgYO/mdlQmh2lPg7+ZmbdmC2duTj429D50r1Xsq5F\n7q3a1O9IlcaE9INvfjGm8Qx+o8OSCq07Lml+1r+V4vP2je0Vl2+kqduOXUajc5a13Ta/seLnvHHP\nw1qu1+7YZxUHfzOzIRMBtdlR7uPgb2bWDef8zcyGkIO/mdmQCcB9+JrNTE9EhdFIVbe1XElZLVay\nKsbHG38rm1UEx3jlb1Wb4kGFGK/8LSpWhrarVG1uBK3WtL0K6tiQ3JaoEYXK7S3TqtK324rpwRZQ\nouJ8JnDwNzMrK3CFr5nZUHKZv5nZEHLwN5uZ1tXnAhPL+0cL8ysTXqjKt/iF33uVoKJNdQLVCCqC\nEYKqYDSCkeaybzW2nV/aiqDOxJfGJitMqLCpHqAqMRr1Ui+UTaZMpzPtlKkTmLRDmFlX5j87jsfB\n38ysrADcpLOZ2RByzt9sZlofI1SoU6fCiMbGi38aaqHxop3aJN1c12C8GKhKPT/2GYwSqegn97tb\n1aaiktGoU0WMaFMxTo1gY7E4qZCMWjTGNZ6C0UbQafwtLF+mCKjVI5cVtNm8ybbXTfFNc7/Ds4eb\ndzCbsWqIGtUUpPPz/lVt/oOuR4kydVWoAdWo50BXZxQxoqAemzpfH0UT3iWoEdQiTW8YjYnvDzSW\no6lxtxr5olKoCygTZFt2pK5J5k1y6GUbrYPZGPSzgPBz/mZmQ8hv+JrNTKNRTU/0qD5+B7BZRrXp\nTqD5DqExTqRpo1SoEmwkqFKnlouDRvJTQevz8qNRZ0TB+qhQy3cDo1FhI1Wq1BlRnfmqT0xT3sam\noqB859BIXDDpW8WbHfckufEqol4ohmqeBpueSGpX7DNZc9GNKZsXnM1wLvM3m7nqCKJCVfVc5l+h\nksNVncp4UIdCoC98rhdD2niQrqfPqlCNAEE1ghoaL0LaqAq1iPHmJepovM5hNKoAzKUOEhSKgGox\n8W9RVXQVkBrbqGrzgF4Lxi82m6hjAG9XEDI7CkmyCD/tYzZTbarErcN4jr42IaA3LgATxidsQ4XP\nKWiPRrqLqESdukSFoJ7z56meoUI16qwntS2UKoabK5vTT3I+tVzuHuOlDLXN6g3S+MaIzTqhmfS4\nY/yo8wVp8gtGsVSjQqN9ofbb3thiW2XSNeM45282M9XGc/wVGvnSYmBtfG4E/BqakPuHfOcw2bYb\nDbFFfhIo76ceKYc/SqP4Zw7rC9toXAiq+WmhVGRUm3BxqAcgTSxyzncIZYqhG5XL9UjHtD4XVVXb\nFOc0GrRrVEZ32nZRNadrdhX7BFGrdV5sBnDwt6E0Hswbb95GTCjnn5CzjwqjOXc/6TaazGUMtKkY\np7Hs+hihqjrrY2TC9Krq428SF980HmkqR68h6nWN30XMpcYI9fGcfCdP1KtUFNRDrM93GKkF07Sf\nEdXHi6cabzCXfWhnfb6TKWpcENpdNGYcN+lsNnNNKN4Zb9p5Uzl/IxhuzAGyToWNsXnwr47fNUy8\nCKyPkZzrD2pKwX00qmyMOYzWq4zGpp9dY91U2TsGwOPMY0Q1Hq2P5aKjRnMOjWfnxYhqjOQipVbp\na7ay9hRqUeGejbswvzLKPI0yV7VUHDV+ARgbT9dozGFEY4zGnAkXssncN7YjVerjaWzUlxQvdLOG\nH/XsD0lHAv8LqAL/GhEfn+Yk2RBoXBDqUaFWqPithcanT7ZORfVJ5yEg6jzBPCo5WIxGlUdrC6iT\n6gPmV0aphRiNOVRVZ2NUqSpYXx9hfYzwWG0+8yqj/GZsWwC2rW6gSvD0uQ9NWA4Yr6xu54q1z+Gx\n2jzG6lVGo8ITY/NYX5vDDiPr2X5kPTvNeQKAJ+pz2VCfw1i9Op7WOZVavitYP+m2/3vdvsyrjKV6\nD9XHj29ENbavPtkxbTNFANGjnP90x7qBCv6SqsDngFcBK4BfSLooIm6e3pTZbNcI8lXFhKCfyutb\nl1q3mze+XaWc+SNj27FqdEdGlHLsu8xZx/oYoR7pIrKhPsK8yii/HlvAr0e35fHaPB7esICHnlxA\nhKhW6oxUaize4SH2mPcb7npyZ54Ym0c9xMZ6FXiwbVouvP0g6nUxMlJjw5MjzNtmlGq1TrVSp1IJ\n5lZrjNUr1Orp2LcZGUNKbzqvH5tDhNiZ2ybd9sX3HsCcap251Roj1Rpr18+nWqkzVqsgwa7c2jZt\nM0b0pjOXQYh1AxX8gUOB5RFxJ4Ck84BjAAd/66v1kVr6XKANE6aPxhxqIeaqfSVfsfimVnhsdH3M\nhYB7Nu4MwBO1uYxGle2qG/j52mew89yU2955ZB1VgtUbd+DHq59FrV5hu7kbePiJBax9bD5Rr6RH\nMEPctWoXdtrpcZ5YPzcF/idHIOBZHYL/xrXpGMcYQdXgySe3gUqgOemdghgTGqsQc+rMWTDG45V6\nulCsH6E+VqFSrbNzi20/+sAOVNZXiLl1mFunsnYOlScrqA4dTt2M06MK32mPdYoBemxJ0huAIyPi\nHXn8rcALI+I9hWVOBE7Mo88GHgbWTHVam+wyAGkAp2PQ0gBOxyCl4ekRsevWbEDS90nHUcZ8JpaT\nLYmIJXk7HWNdvw1azr+jfPKWNMYlLYuIQ6YxSQORBqdj8NLgdAxeGrZWRBw53WnolUF7BHclsFdh\nfM88zcxsNpn2WDdowf8XwGJJ+0iaCxwHXDTNaTIz67Vpj3UDVewTEWOS3gP8gPT401ci4qYOqy3p\nMH8qDEIawOkoGoQ0gNNRNAhpGAhbGOt6aqAqfM3MbGoMWrGPmZlNAQd/M7MhNGODv6QjJd0mabmk\nU6Z433dLukHStZKW5WkLJV0q6Y78d6c+7PcrklZLurEwreV+JZ2az89tko7oYxpOl7Qyn49rJR3d\n5zTsJelySTdLuknS+/L0qT4XrdIx1edjvqSlkq7L6TgjT5+y89EmDVN6LqwLETHjBlIFya+AZwBz\ngeuA/adw/3cDuzRN+yRwSv58CvCJPuz394DnAzd22i+wfz4v84B98vmq9ikNpwN/Pcmy/UrD7sDz\n8+ftgdvzvqb6XLRKx1SfDwHb5c8jwFXAYVN5PtqkYUrPhYfyw0zN+Y+/Gh0RG4HGq9HT6Rjg7Pz5\nbOB1vd5BRPwEeKTkfo8BzouIDRFxF7CcdN76kYZW+pWGVRHxy/x5HXALsIipPxet0tFKv9IREfFY\nHh3JQzCF56NNGlrpy7mw8mZq8F8E3FcYX0H7H12vBfBDSVfn5iYAdouIVfnzA8BuU5SWVvud6nP0\nXknX52KhRvFC39MgaW/geaSc5rSdi6Z0wBSfD0lVSdcCq4FLI2LKz0eLNMA0fTesvZka/KfbSyLi\nYOAo4CRJv1ecGRFB6W4weme69gt8gVQEdzCwCvj0VOxU0nbABcDJEbG2OG8qz8Uk6Zjy8xERtfyd\n3BM4VNKBTfP7fj5apGFavhvW2UwN/tP6anRErMx/VwMXkm5XH5S0O0D+u3qKktNqv1N2jiLiwfzD\nrwNfYtPte9/SIGmEFHC/HhHfypOn/FxMlo7pOB8NEfEocDlwJNP03SimYTrPhbU3U4P/tL0aLWmB\npO0bn4E/AG7M+z8+L3Y88J2pSE+b/V4EHCdpnqR9gMXA0n4koBFgsteTzkff0iBJwJeBWyLiM4VZ\nU3ouWqVjGs7HrpJ2zJ+3IbURfytTeD5apWGqz4V1YbprnLd0AI4mPV3xK+BDU7jfZ5CeUrgOuKmx\nb2Bn4DLgDuCHwMI+7Ptc0q3zKKmM9IR2+wU+lM/PbcBRfUzDvwM3ANeTftS79zkNLyEVYVwPXJuH\no6fhXLRKx1Sfj+cC1+T93Qj8fafvZK/T0SYNU3ouPJQf3LyDmdkQmqnFPmZmthUc/M3MhpCDv5nZ\nEHLwNzMbQg7+ZmZDyMHfzGwIOfibmQ0hB3+bNSS9IDcgNj+/iX1Tcxs3Zpb4JS+bVSR9FJgPbAOs\niIiPTXOSzAaSg7/NKrmtp18A64EXRURtmpNkNpBc7GOzzc7AdqSeteZPc1rMBpZz/jarSLqI1LPb\nPqRGxN4zzUkyG0hzpjsBZr0i6c+A0Yg4R1IV+KmkV0TEj6Y7bWaDxjl/M7Mh5DJ/M7Mh5OBvZjaE\nHPzNzIaQg7+Z2RBy8DczG0IO/mZmQ8jB38xsCP1/n/H66WQmWOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e54d75940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1)\n",
    "test2.vosaline.isel(y=500,time_counter=0).plot(ax=ax)\n",
    "#ax.set_ylim((0,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (deptht: 40, x: 398, y: 898)\n",
       "Coordinates:\n",
       "  * deptht        (deptht) float32 0.5 1.5 2.50001 3.50003 4.50007 5.50015 ...\n",
       "    nav_lat       (y, x) float32 46.8597 46.8615 46.8634 46.8653 46.8672 ...\n",
       "    nav_lon       (y, x) float32 -123.429 -123.424 -123.419 -123.413 ...\n",
       "    time_counter  int64 3\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    vosaline      (deptht, y, x) float64 nan nan nan nan nan nan nan nan nan ...\n",
       "    votemper      (deptht, y, x) float64 nan nan nan nan nan nan nan nan nan ..."
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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