{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Smooth bathymetry  -grab from Susan's notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "from salishsea_tools import (nc_tools,viz_tools,tidetools)\n",
    "from salishsea_tools.nowcast import figures\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.125"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bathy_3 = nc.Dataset('bathy_meter_SalishSea3.nc','r+' )\n",
    "bathyy_3 = bathy_3.variables['Bathymetry'][:]\n",
    "bathyy_3[418,395]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_max(bathy):\n",
    "    i,j = np.unravel_index(bathy.argmax(), bathy.shape)\n",
    "    return i,j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_slope(bathy,di,dj):\n",
    "    imax, jmax = bathy.shape\n",
    "    Da = 0.5*(bathy[di:,dj:]+bathy[0:imax-di,0:jmax-dj])\n",
    "    Dd = bathy[di:,dj:]-bathy[0:imax-di,0:jmax-dj]\n",
    "    return Dd/Da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def smooth(gamma,bathy,i,j,di,dj):\n",
    "    a = 0.5*(bathy[i,j]+bathy[i+di,j+dj])\n",
    "    if bathy[i,j] < bathy[i+di,j+dj]:\n",
    "        change = gamma\n",
    "    else:\n",
    "        change = -gamma\n",
    "    bathy[i,j] = bathy[i,j] + gamma*a\n",
    "    bathy[i+di,j+dj] = bathy[i+di,j+dj] - gamma*a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n",
      "--\n"
     ]
    }
   ],
   "source": [
    "gamma = 0.2\n",
    "maxslope = 0.8\n",
    "\n",
    "slopei = find_slope(bathyy_3,1,0)\n",
    "i,j = find_max(slopei)\n",
    "slopej = find_slope(bathyy_3,0,1)\n",
    "k,l = find_max(slopej)\n",
    "while np.maximum(slopei[i,j],slopej[k,l]) > maxslope:\n",
    "    if slopei[i,j] > slopej[k,l]:\n",
    "        smooth(gamma,bathyy_3,i,j,1,0)\n",
    "    else:\n",
    "        smooth(gamma,bathyy_3,k,l,0,1)\n",
    "    slopei = find_slope(bathyy_3,1,0)\n",
    "    i,j = find_max(slopei)\n",
    "    slopej = find_slope(bathyy_3,0,1)\n",
    "    k,l = find_max(slopej)\n",
    "\n",
    "print (slopei[i,j])\n",
    "print (slopej[i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bathy_3.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create new t,s file for new bathymetry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from salishsea_tools import nc_tools\n",
    "from salishsea_tools import tidetools\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#old bathymetry inforamtion\n",
    "old_path = '../nemo-forcing/grid/bathy_meter_SalishSea2.nc'\n",
    "old_bathy = nc.Dataset(old_path, 'r')\n",
    "old_depth = old_bathy.variables['Bathymetry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#old initial file\n",
    "initial_path = '/data/dlatorne/MEOPAR/SalishSea/nowcast/14jun15/SalishSea_02401920_restart.nc'\n",
    "T_S = nc.Dataset(initial_path, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 14.77021985  14.34337293  14.20415199  13.84946759  13.67585241\n",
      "  13.59131301  13.64740562  13.5873905   13.43705051  13.34423781\n",
      "  13.15459748  13.03363     12.96472808  12.91797194  12.89496075\n",
      "  12.87349044  12.84061662  12.78115335  12.70208798  12.59582154\n",
      "  12.51719442  12.35120913  12.09723483   0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.           0.        ]\n",
      "[   0.5000003     1.5000031     2.50001144    3.50003052    4.50007057\n",
      "    5.50015068    6.50031042    7.50062323    8.50123596    9.50243282\n",
      "   10.50476551   11.50931168   12.51816654   13.53541183   14.56898212\n",
      "   15.63428783   16.76117325   18.00713539   19.48178482   21.38997841\n",
      "   24.10025597   28.22991562   34.68575668   44.51772308   58.48433304\n",
      "   76.58558655   98.06295776  121.86651611  147.08946228  173.11448669\n",
      "  199.57304382  226.26029968  253.06663513  279.93453979  306.834198\n",
      "  333.75018311  360.67453003  387.60321045  414.53408813  441.46609497]\n"
     ]
    }
   ],
   "source": [
    "#nc_tools.show_variables(T_S)\n",
    "old_T = T_S.variables['tb'][0] # omit the first dimension\n",
    "old_S = T_S.variables['sb'][0]\n",
    "depths = T_S.variables['nav_lev']\n",
    "lon = T_S.variables['nav_lon']\n",
    "lat = T_S.variables['nav_lat']\n",
    "print (old_T[:, 427, 292])\n",
    "print(depths[0:40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(898, 398)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb826829b00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8DDgYmCMiXwM2i8gRqrpJRBZSiHCD+OzKQjo8rWc2z+uZweGxeS9LgGpJ1bLg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\nk63jOE4b8MnWcRynDfxvpdv+3Df5OZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb851744b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in new bathymetry\n",
    "new_path = '/ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea3.nc'\n",
    "Fraser = nc.Dataset(new_path, 'r')\n",
    "bathy = Fraser.variables['Bathymetry'][:]\n",
    "print(bathy.shape)\n",
    "plt.pcolormesh(bathy[350: 440, 280 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "floor = np.empty_like(depths)\n",
    "ceil = np.empty_like(depths)\n",
    "ceil[0] = 0.\n",
    "floor[0] = 2*depths[0]\n",
    "for k in range(1,40):\n",
    "    ceil[k] = floor[k-1]\n",
    "    floor[k] = 2*depths[k] -floor[k-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "S = np.empty_like(old_S)\n",
    "T = np.empty_like(old_T)\n",
    "#for every cell with top of cell depth < bathymetry, use old TS, if old TS=0, find closest point and use that.\n",
    "for k in range(40):\n",
    "    for j in range(398):\n",
    "        for i in range(898):\n",
    "            if ceil[k] < bathy[i,j]:\n",
    "                if old_S[k,i,j] <> 0:\n",
    "                    S[k,i,j] = old_S[k, i, j]\n",
    "                    T[k,i,j] = old_T[k, i, j]\n",
    "                else:\n",
    "                    # closest neighbour thing\n",
    "                    masked_array = np.ma.array(old_S[k], mask = old_S[k] == 0)\n",
    "                    X, Y = tidetools.find_closest_model_point(lon[i,j], lat[i,j], lon, lat, masked_array)\n",
    "                    S[k, i, j] = old_S[k, X, Y]\n",
    "                    T[k, i, j] = old_T[k, X, Y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file format: NETCDF4\n",
      "Conventions: CF-1.6\n",
      "title: Salinity Temperature Initial Conditions based on Nowcast June 14, 2015 for extended Fraser river channel\n",
      "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
      "source: REQUIRED\n",
      "references: REQUIRED\n",
      "history: [2015-06-27 16:03:00] Created netCDF4 zlib=True dataset.\n",
      "comment: Salinity and Temperature conditions from nowcast June 14, 2015 onto extended Fraser bathymetry\n",
      "<type 'netCDF4.Dimension'>: name = 'y', size = 898\n",
      "\n",
      "<type 'netCDF4.Dimension'>: name = 'x', size = 398\n",
      "\n",
      "<type 'netCDF4.Dimension'>: name = 'deptht', size = 40\n",
      "\n",
      "<type 'netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# build nc file\n",
    "new_TS = nc.Dataset('TSforextendedFraserRiver.nc', 'w')\n",
    "nc_tools.init_dataset_attrs(\n",
    "    new_TS, \n",
    "    title='Salinity Temperature Initial Conditions based on Nowcast June 14, 2015 for extended Fraser river channel', \n",
    "    notebook_name='Smooth bathymetry & Create New TS file', \n",
    "    nc_filepath='/ocean/jieliu/research/meopar/nemo-forcing/initial_strat/TSforextendedFraserRiver.nc',\n",
    "    comment='Salinity and Temperature conditions from nowcast June 14, 2015 onto extended Fraser bathymetry')\n",
    "new_TS.createDimension('y', 898)\n",
    "new_TS.createDimension('x', 398)\n",
    "new_TS.createDimension('deptht',size = len(depths))\n",
    "new_TS.createDimension('time_counter', None)\n",
    "nc_tools.show_dimensions(new_TS)\n",
    "# show variables\n",
    "nav_lat = new_TS.createVariable('nav_lat', 'float32', ('y','x'))\n",
    "nav_lat.long_name = 'Latitude'\n",
    "nav_lat.units = 'degrees_north'\n",
    "nav_lat = lat\n",
    "nav_lon = new_TS.createVariable('nav_lon', 'float32', ('y','x'))\n",
    "nav_lon.long_name = 'Longitude'\n",
    "nav_lon.units = 'degrees_east'\n",
    "nav_lon = lon\n",
    "deptht = new_TS.createVariable('deptht', 'float32', ('deptht'))\n",
    "deptht.long_name = 'Depth'\n",
    "deptht.units = 'm'\n",
    "deptht.positive = 'down'\n",
    "deptht.valid_range = np.array((4., 428.))##minimum depth 4m\n",
    "deptht = depths\n",
    "time_counter = new_TS.createVariable('time_counter', 'float32', ('time_counter'))\n",
    "time_counter.units = 'seconds since 2015-06-14 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 = 'nav_lon nav_lat deptht time_counter'\n",
    "vosaline.grid = 'SalishSea3, extended Fraser'\n",
    "vosaline[0] = S\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 = 'nav_lon nav_lat deptht time_counter'\n",
    "votemper[0] = T\n",
    "new_TS.history = \"\"\"[2015-06-14] Created\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb826550a28>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RTSJyOjmBBCKyv4j8K4CqzgFnAN8iS61wVVFEA/SjvHsQo+tNCvFKtdD0ECfS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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8266a9f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## temperature before\n",
    "plt.pcolormesh(votemper[0,0, 350: 440, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## modify temperature for some grid cells\n",
    "k = 0 \n",
    "i = 413\n",
    "j = 351\n",
    "votemper[0, k : k + 4, i, j : j + 5] = 14. ## for(413, 351:355) from 0 to 4m\n",
    "votemper[0, k : k + 4, i + 4, j + 4: j +13] = 14. ## for (417, 355:363).. ..\n",
    "votemper[0, k : k + 4, i + 1, j + 4: j + 7] = 14. ## for (414, 355:357).. ..\n",
    "votemper[0, k : k + 4, i + 2, j + 6: j + 8] = 14. ## for (415, 357:358).. ..\n",
    "votemper[0, k : k + 4, i + 3, j + 7: j + 10] = 14. ## for (416, 358:360).. ..\n",
    "votemper[0, k : k + 4, i + 5, j + 9: j + 47] = 14. ## for (418, 360:397).. .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb82643aa70>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EOmMJL5FfxeYT3Xgnm4Sfxj+mp+JQoTEVYq/Y3717p1qYFjJ+7I/rq8XbUG6F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\nAdeBCmtNjj5bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8266e38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## temperature after\n",
    "plt.pcolormesh(votemper[0,0, 350: 440, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb8262ad170>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UKDZUPZW0TYwgQH3a2oqLlk2L88IN9ggLSjKzzDPzvok0zXYrO5/y4ukVXSuS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\n7eUsflUpAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8263f6e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## salinity before\n",
    "plt.pcolormesh(vosaline[0,0, 350: 440, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## set salinity after New Westminster as 1,before as 4,river source cell as 0\n",
    "k = 0 \n",
    "i = 418\n",
    "j = 365\n",
    "vosaline[0, k : k + 4, i-2, j-30: j-13] = 4.#16,335:351)(416 ,358:360)from 0 to 4m\n",
    "vosaline[0, k : k + 4, i -2, j -7: j -4] = 4.\n",
    "vosaline[0, k : k + 4, i -1,j -14:j -1] = 4.#(417, 351:363)..  ..\n",
    "vosaline[0, k : k + 4, i - 4, j - 18: j - 15] = 4.## for (414, 347:349)( 414, 355:357).. ..\n",
    "vosaline[0, k : k + 4, i - 4, j - 10: j - 7] = 4.\n",
    "vosaline[0, k : k + 4, i - 5,j - 16:j- 9]=4.## for (413, 349:355)\n",
    "vosaline[0, k : k + 4, i - 3, j  - 19:j-17] = 4.#(415, 346, 347, 357, 358).. ..\n",
    "vosaline[0, k : k + 4, i - 3, j  - 8: j - 6] = 4.\n",
    "vosaline[0, k : k + 4, i , j -5 : j +1] = 4.#for(418,360:365) .. ..\n",
    "vosaline[0, k : k + 4, i , j +1: j + 32]=1.# (418, 366:397)  .. after New Westminster\n",
    "vosaline[0, k : , i , j + 32] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## make original salinity of freshwater source point as 4\n",
    "vosaline[0, 0:4 , 416, 334] = 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## modify damp salinity values\n",
    "k = 0; i = 425; j = 302; d = 4.\n",
    "vosaline[0, k: k +4, i, j+1] = d\n",
    "vosaline[0, k: k +4, i-1, j:j+3] = d\n",
    "vosaline[0, k: k +4, i-2, j+1:j+5] = d\n",
    "vosaline[0, k: k +4, i-3, j+3:j+7] = d\n",
    "vosaline[0, k: k +4, i-4, j+5:j+9] = d\n",
    "vosaline[0, k: k +4, i-5, j+7:j+11] = d\n",
    "vosaline[0, k: k +4, i-6, j+9:j+13] = d\n",
    "vosaline[0, k: k +4, i-7, j+11:j+14] = d\n",
    "vosaline[0, k: k +4, i-8, j+12:j+16] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## modify salinity values of straight channel connect damp and further extended channel\n",
    "k = 0; i = 416; j = 317; d= 4.\n",
    "vosaline[0, k : k +4, i, j-1:j+18] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb8257508c0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HL54FvzINNgXt1ZY/FkcO5rODdnh/8LcyxenEyEVqeghcfVbo++8S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\nMSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb825b54150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## salinity after\n",
    "plt.pcolormesh(vosaline[0,0, 350: 440, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar instance at 0x7fb82550d1b8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wFKYxK9rMSc8iotdC3g3K5AmImGSIIx7dbbTrlZgwQhcO6PKiODF3uXzAODm3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb825708190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.pcolormesh(votemper[0,0, 350: 440, 290 : 398])\n",
    "plt.colorbar()\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.pcolormesh(vosaline[0,0, 350: 440, 290 : 398])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_TS.close()"
   ]
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}