{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "* This notebook was made to create TS initial files based on 11arp20arp with bathymetry6 for Susan's NEMO 3.6 run case." ] }, { "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,bathy_tools)\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": true }, "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": true }, "outputs": [], "source": [ "#old initial file\n", "initial_path = '/ocean/dlatorne/MEOPAR/SalishSea/results/spin-up/11apr20apr/SalishSea_01529280_restart.nc'\n", "T_S = nc.Dataset(initial_path, 'r')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "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']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#read in new bathymetry\n", "new_path = '/ocean/jieliu/research/meopar/river-treatment/bathy_meter_SalishSea6.nc'\n", "Fraser = nc.Dataset(new_path, 'r')\n", "bathy = Fraser.variables['Bathymetry'][:]" ] }, { "cell_type": "code", "execution_count": 7, "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": 10, "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": 12, "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 11apr20apr spin-up run restart file\n", "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n", "source: REQUIRED\n", "references: REQUIRED\n", "history: [2015-11-16 11:31:00] Created netCDF4 zlib=True dataset.\n", "comment: Salinity and Temperature conditions from 11apr20apr spin-up run restart file onto north extended Fraser bathymetry6\n" ] } ], "source": [ "# build nc file\n", "new_TS = nc.Dataset('TSApri.nc', 'w')\n", "nc_tools.init_dataset_attrs(\n", " new_TS, \n", " title='Salinity Temperature Initial Conditions based on 11apr20apr spin-up run restart file', \n", " notebook_name='CreateTSForSusan', \n", " nc_filepath='/ocean/jieliu/research/meopar/nemo-forcing/initial_strat/TSApri.nc',\n", " comment='Salinity and Temperature conditions from 11apr20apr spin-up run restart file onto north extended Fraser bathymetry6')\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 11apr20apr'\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 = 'SalishSea6, north 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": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jieliu/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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Il0nokxL3OkuwQ9EUiqJUp9VqodVqdV9v3LhxIO2O2x+cQ1Wf8a0kPwngGyhC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\nbMXCLG7Tx/xBAJtF5FgAPwPgDjR0zMZ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## temperature before, loos good, don't need to modify further\n", "plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jieliu/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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X1c0icjWwmcyuO19Vk66DcBbcXdWcgbX16b1uuTObgWDvC8trB+90LoOigQHN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\nqqFOg1j6pXcPhMI7Hi2LhNZ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## salinity before\n", "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## set salinity after New Westminster as 1,before as 0,river source cell as 0\n", "k = 0 \n", "i = 418\n", "j = 365\n", "vosaline[0, :, i-2, j-30: j-13] = 0.#16,335:351)(416 ,358:360)\n", "vosaline[0, :, i -2, j -7: j -4] = 0.\n", "vosaline[0, :, i -1,j -14:j -1] = 0.#(417, 351:363).. ..\n", "vosaline[0, :, i - 4, j - 18: j - 15] = 0.## for (414, 347:349)( 414, 355:357).. ..\n", "vosaline[0, :, i - 4, j - 10: j - 7] = 0.\n", "vosaline[0, :, i - 5,j - 16:j- 9]=0.## for (413, 349:355)\n", "vosaline[0, :, i - 3, j - 19:j-17] =0.#(415, 346, 347, 357, 358).. ..\n", "vosaline[0, :, i - 3, j - 8: j - 6] = 0.\n", "vosaline[0, :, i , j -5 : j +1] = 0.#for(418,360:365) .. ..New Westminster\n", "## plus north ones(all depth):\n", "vosaline[0, : , i: i+ 83 , j] = 0. ## for (418-500, 365).. ..\n", "vosaline[0, : , i + 82, j : j + 30] = 0. ## for (500, 365-394)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## make salinity(500, 395)==0(source) for all depth\n", "vosaline[0, : , 500, 395] = 0." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## make original salinity of freshwater source point as 4\n", "vosaline[0, 0:4 , 416, 334] = 1." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## modify damp salinity values but not for the widen part\n", "k = 0; i = 425; j = 302; d = 1.\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": 19, "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= 1.\n", "vosaline[0, k : k +4, i, j-1:j+18] = d" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jieliu/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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p4PO+QEROIwsKeJGq7haRlj0xraIO3paz6/RUoapeDFzc6qI7WckMmXj6Zbi+\n0wnsnFvWxBXqguaXqfSEnUxhUwb/tIp8vjPRtq87W1DHMIy+0mnoVk7k1e8D/8VFWKGqv2h1noF0\n3WxnVc1lELoOtj+Trc9sc2XbXPPCPhovsD6GvswgBE+ndYp8sjHe6o0t7R1BHb++ylm5282yNYx+\n0uOog2OBV4rIxWTf8P+oqrcXHTB4oX2mLrQ1gX3CNStONRiWFSWBaXVX7Qhm0b7QSvX/x/javr0/\nD8qedEvLLGgYi0Ke0N5f/Tn3Vx9r93QV4ABVPVlEfgO4Cji61QGLznZWsX1nZL1CXWC9sPplyhrM\nm+kWmmetJdpORSiUSAbW5IKYSqw/65Yz0fLJoK4+5Va2uKUf//HtgosbhtEpeUJ7zPQajpleU9v+\nu413lzndFuCrAKr6fRHZIyIHqeqTeQeMcNSnYRhGOWZ7G951DfAq4NsichwwWSSyMCCh3bZzf3Y8\nkYV31axYqFuw3rXspxcILdoil4GnEi39yyzlDogt4tQTiYfT+kiCvYM6/tw+iGJXVN7gJvD/Ex+C\n/EziooZh9Iouwrt85NVBQeTV5cDlInIPMAe8s9V5BiK0O7atSnd0xQL7S7d8NqgT5xRI4Z+pFz1/\nl2WOSbFXtO3Pu3eiLI4o8PcXuoF+uW9U2bsSVtCMhSgYRrd0KrQFkVfvaOc8g3EdbJtqtl6hbuj5\nfSmhjfMOeMLnGHdWTUXl4V3HT2AiWobs55ZeD/cP9vlrLER1Uk/4ZpceYr7o8Xc7btgwDM8oDsHt\nnm3AE2796aicYJ/fDgcj5A1pTQltPI137FII68RW60Sizj5u6QU3HNSWFwLmy0NRfqFbfjoeOBda\ntIuQMdwwxgSbysYwDKPPjHT2ro7ZRrP1Gq7H4V1hgv94gELc4QV1w9D/+vZW8F6JuvGMCLH1CnXL\n1ZcdENUNycvstSZYPyxxHGD+WMPoDya0v4zKU8tQaPPyvk4k6ni/aRwJEApk6P8N94U/9f2594nq\nhk/Pn9u31b80/ECFP7PRX4YxKGaHec6wvrGDuuUXCl2ewKbyBHjLNo4wSOHrpizQvEkZw7rxcF/P\nwcG6f5K+rX4sgk0kaxgDZzx9tLuoi1dRjGyneQz8XXlRjkW4aJrwbYk6vo0+KsJbq2uDOodGdf2E\nBnflttgwjEViPF0HhmEYi8h4Cu0M9Z/YRekN45FY0DqMKiSOm/XLcDReXurDsF8qTgzzcLQE8CmB\nvR/XW8YAOJNxAAAfxElEQVQP+1Ff4WTCf59orGEY/WI842hnKTfVi//JnxLjeOhyanhtHCPrjwk7\numJfrxfI0Hfshda32V/roaCOP+5fumVtUp99MQxjsIysj1ZE1gNnA3uAe4BzyAalfhk4Ejf7gqo2\nz/i3neLhsHlZtyDdoZVH/BJbHi1DVkTLkFrUg1P5eaf8M0HDqu6kPtf6S93yt93yK2bFGsagGEnX\ngcs4/j7g+ao6KyJfBt4KvAC4UVUvEZHzgQvcXyOpPK6QnwO2KDesJzVidSFapkaaTUR1vNXakDPd\n//zfGlUKGjZ/YLa8z/WKTbvyr1hYl2EMmrkBh3ct6/C4Z8ikbaWIVICVwKNk8+h8ztX5HPCmrlto\nGIbRJfNMlPrrFx1ZtKr6lIh8EvhnMvPuG6p6o4gcqqre7NtKPeipkRnSEx76bFh5o7+g2R0QD2CA\nZus2Dt1KPc84/nU+7A17yC19lq2ERevLnnC3/HtmyRrGsDCSPloROQb4Q7JI0qeB/yUiZ4d1VFVF\nJD1ZS+ifTWXS8gLrBTPUs3Z8tP44L6ypqXF8+kJ/7e1eRH8QVPJ5Y3e6ZWqorDuB5YIxjKFjJH20\nZF093/VZxUXkq8DLgMdE5DBVfUxEDqeuUI3cuaG+ftg0HD5d3LpQvPzz8hZoyn/rowp8lIEXVm9r\n/zSs7K1UL6L3uOUjQR0vrLGpHF7cNfLnZskaRqdUq1Wq1WrPzztooRXV9mcIFJEXA18CfoPsh/nf\nALeRRRs8qaqfEJELgP1V9YLoWOX9La4ZW7RlCKMFfNIWb/36YbA3+u3QIq26pRfW2D1Q9qI+kDZO\nfWgYSw/VixblOiKCqkqX59DX6dWl6n5d3tz19VJ06qO9W0Q+D9xOFt71j8BnyPJcXSUi78WFd/Wo\nnYZhGB0zkj5aAFW9BLgkKn4KOL3lwQdTzpfpLdIwHGs+2ufdA4cHdXxKQn/cQ25Z883eGlT2O32w\nbKepCs2SNYxhZdDhXYOR+f0pnlTRE8+QEK6vdctfyxb7rKkntl2Yz/wxM/ce0HhMbZ7Kp6jTrcAa\nhjHsjOcQ3Diva4y3RFOdYWuzxZqzMsfrKXwHaPxpcCsnAbBl1gntf/+S2+M9GaHQdjM3l4mzYYwC\nnboORORy4PXA46p6vCv7U+A3yWbA/TFwjqo+nX+WQVq0KRH1ZT4My+d7DYT5tadcC8A5XOFOlfkD\nrufMWp1aD+OpX6KRq7potGEYo0oXUQdXAJ8GPh+U3QCcr6p7ROTjwHpSI2ADOh0ZZhiGMTIsMFHq\nL0ZVb6ZxHhhU9UZV3eM2b6Vxoqokg7FoDw6unMretTZbrFib3d/v7vvXtV1//tQfASAuofaPXnVM\n0+G1B3b127Plm2PLNgzLchezqRAMY8nSxzja9wCbWlUavI82vH/nUj38uGxEwfFu8MDvUhda8ekL\nfzVbbHOjE8IHucIPPljrCj7qBPe/u+0nYuEFeG60bcJrGEuF2aa8qt0jIh8B5lT1ylZ1B2TRzsMu\nd+lKffDCmqN/DMBz8R1d3wXqYgo0DcH1YRs1cQXWsRmAmRNWAvD4fU6VfQjYEycEZ6i6ZZw3NhZe\nMPE1jNEkz6LdWf0+O6u3t30+EXk3cCbw6jL1ByK0B63ZysSyLOHBwp76AzjCTVngl4e6MbOrwmlw\nfd+ee0Ht4/Y9lx/Xqmx384P75eMvdEK71lW4JxTRh6Klj0hYSzNm9RrGKJIntFPTJzM1fXJt+6mN\n/6PluUTkDODDwKmqWjR1QY2BCO2KZTNMuHCDyrJ6hpn9nc/5ECewB7nA14kwC43P8OVuz4vy40Gi\nMC+wFXdc5UXZsvq7bjqZLUGOgju9dRtPh5sS0Vhozeo1jFGg0zhaEdkEnAocLCIPAxeRRRlMAjeK\nCMA/qOq5ReexyRkNw1jydBpHq6pvSxRf3u55BiK0E8yz2s10OBNEABzsLFgfG+ut3tBHq85lIC4v\n2KFPZb6EYw6sW5L+Z8KkS/E1yRwA28/KLN07Zl5eb8xfOkv45je4gu+45TcTLY+t1ZRF68vMsjWM\nYWHQ2bsGIrSr2F4T0RXB6CovsCtdmf/p74USYM51hk05F4K4lIfPrfysfgHXr+VdDqvcCIi1Lj/i\nurdsrlX92ht+C4Adf+tGR2xyIvzNfwxaHI4kCwnFtJVbwYTXMAbFWArtSmZ40g37Whskh60L7c6G\n+mFCiEdXZqEDq1dnFvGUy19Q+ed6/ef+qhPdff31svN5P81LuaNW900rvwbA5vesA+C77zkFgK9L\nnrjm0craTVm/w4C9AIylz+zcOCaVoS6q3oUAdUFs6Pyi8W20kyxk65cr9wPgsL1cGEKQYrzikoI/\n99hMcCv7ZufzsXT7B1MsnPJUJrr/+umvZ3UO/SQAe7Ghg7sKMQEzjGFhYX5E0yQahmGMCj6j36AY\niNDuZEXND7t/4zBioG7Begs09NHudJ1n+/um+yi28Je+s279zR3+wkcB2D6VdYatmq3H5dY8BM71\nMHVXmzdjGMbQM7JCKyL7A38NvABQ4BzgAeDLZFPaPAT8jqpui49dxQ6OcMp2aPCb3w9MmHU+2SkX\nNRD6aH0HWc2dsNrteDS4gF/PvAvs/XCW/2HqkMzNUAlnzvVCbZMqGsaSZX73iAot8OfA9ar62yJS\nIRtK8BHgRlW9RETOJ0sd1pQ+7FC21gQ29Mf69SlnwXqLdiJQQT8YwVu58/tl56nsF1zA96/9pHbi\nrE5KTOPpyl1z9KsbakXyrzdgGMbosmdhsF7STidn3A+4U1WPjsrvIxuWtlVEDgOqqvprUR19g15V\ny02wMgjv8p1hPleBD/0KoxBWO3PVH+9jb9c+Ve9Uk+vdyr21gzJ8a8O0Bl7nn46WoQD72N13bMAw\njNGbnJGflUzwf+Ty4ZmcETgK+IWIXAG8GLgD+EPgUFX1k3pvhWBcrGEYxqDYNZpRBxXgBODfq+r3\nReRSIheBqqqIJM3lH274Ws1NcMz0czh8+jgADnfWqt/3JAcBjT5ab9HOuDCvSRcetnPveg7zvQ/a\nQwPeDZyag8w/gdiFEOJSM+p/25CtLOTWrCEf2NC6kmEYDVSrVarVau9PPOA+mE5dB4eRJVI4ym2/\ngizRwtHAaar6mIgcDtyUch28STfVOr5WB71Y3u9ajyzI+tGOqmXWqieR8Z1hhzgVDd0LR33HuRH+\nryvw7gDvx11NHZ+kJs4pE4pp0b4cTGiNpczIuQ7uLqlzL+7+eik6smidkD4sIsep6v1kU4z/yP29\nC/iEW16TOn4lO2tW608T6Qh9Z5gfzBCmSYxDv/x2QyrF2HJ9lkbCHMC+TjyHWfgGXEjXNTE1jBFh\nwBZtN46L9wNfEpFJ3EyQZP37V4nIe3HhXXkHe6s1zKrjxfJ5/BNQ7xQL2Rq5fQ/iiaY6TWLpl949\nEApvJVoWCa2nyM1gGMbw0c1k1z2gY6FV1buB30jsOr3z5hiGYfSBEu6+fjKQrrjJoHsr9K36zjDv\nd/XpEScDE7Iea5uVef/twb/YUavTZMH60V/eHxvGLscWbXwOSE8gaRjG6DDCroOOmWS2NsIrHLDg\nM3rtcIMSfBxt6H/1nWd+9oX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## salinity after\n", "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jieliu/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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U2VbVh1T1Trt+ELgHU+axXsPXfr6iWRd6tYd+G7jSrm8GbvWO7bAdWkzScs5y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\nQ1LsCQkJCROG/wPtVdnQ/m+RSwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.subplot(1, 2, 1)\n", "plt.pcolormesh(votemper[0,0, 350: 520, 290 : 398])\n", "plt.colorbar()\n", "plt.subplot(1, 2, 2)\n", "plt.pcolormesh(vosaline[0,0, 350: 520, 290 : 398])\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "new_TS.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }