{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Average TS profiles over my tidal analysis period." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import netCDF4 as nc\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from nowcast import analyze \n", "from salishsea_tools import places\n", "\n", "import datetime\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grid = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')\n", "mesh_mask = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/mesh_mask_SalishSea2.nc')\n", "\n", "tmask = mesh_mask.variables['tmask'][:]\n", "gdept = mesh_mask.variables['gdept'][:]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/data/nsoontie/MEOPAR/tools/SalishSeaNowcast/nowcast/analyze.py:171: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if kss == 'None':\n" ] } ], "source": [ "PLACES = places.PLACES\n", "\n", "d1=datetime.datetime(2014,11,26)\n", "d2=datetime.datetime(2015,4,26)\n", "files = analyze.get_filenames(d1,d2,'1d','grid_T','/results/SalishSea/nowcast/')\n", "\n", "sals ={}\n", "temps = {}\n", "depths={}\n", "masks={}\n", "for name in ['Central node', 'East node']:\n", " j = PLACES[name]['NEMO grid ji'][0]\n", " i = PLACES[name]['NEMO grid ji'][1]\n", " sals[name], time = analyze.combine_files(files, 'vosaline', np.arange(40), j, i)\n", " temps[name], time = analyze.combine_files(files, 'votemper', np.arange(40), j, i)\n", " depths[name] = gdept[0,:,j,i]\n", " masks[name] = tmask[0,:,j,i]\n", " \n", " sals[name] = np.mean(sals[name], axis=0)\n", " temps[name] = np.mean(temps[name], axis=0)\n", " \n", " inds = np.where(masks[name]==1)\n", " sals[name] = sals[name][inds]\n", " temps[name] = temps[name][inds]\n", " depths[name] = depths[name][inds]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GqfEjdIGadr029HCRK+ZRF6018+bN49ChQ+zZs4cDBw4wf/58APbt28crr7zC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\nueeeYxphZWVl2rx586pGW6h618T+f3nnwAHV5s1VKyoSv+5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ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1,2,figsize=(10,5))\n", "for name in ['Central node', 'East node']:\n", " ax=axs[0]\n", " ax.plot(sals[name],depths[name],'o-', label=name)\n", " ax.set_ylim([300,0])\n", " ax.set_xlabel('Practical Salinity [psu]')\n", " ax.set_ylabel('Depth [m]')\n", " \n", " ax=axs[1]\n", " ax.plot(temps[name],depths[name],'o-', label=name)\n", " ax.set_ylim([300,0])\n", " ax.set_xlabel('Temperature [deg C]')\n", " ax.set_ylabel('Depth [m]')\n", "for ax in axs:\n", " ax.grid()\n", " ax.legend(loc=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save using pandas" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for name in ['Central node', 'East node']:\n", " data = pd.DataFrame({'Depth [m]': depths[name],\n", " 'Practical Salinity [psu]': sals[name],\n", " 'Temperature [deg C]': temps[name]})\n", " \n", " fname = '{}_meanTS_{}_{}.csv'.format(name[:-5], d1.strftime('%Y%m%d'), d2.strftime('%Y%m%d'))\n", " data.to_csv(fname)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Central_meanTS_20141126_20150426.csv East_meanTS_20141126_20150426.csv\r\n" ] } ], "source": [ "!ls *.csv" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Depth [m]Practical Salinity [psu]Temperature [deg C]
00.50000019.6815077.649859
11.50000321.7420397.993243
22.50001123.4478218.262228
33.50003124.5065788.452448
44.50007125.2507808.612563
55.50015125.8515958.760666
66.50031026.3477808.891456
77.50062326.7541669.002347
88.50123627.0902589.097125
99.50243327.3736159.179514
1010.50476627.6092079.251032
1111.50931227.8076369.312590
1212.51816727.9764189.365047
1313.53541228.1230759.410112
1414.56898228.2522399.448424
1515.63428828.3679059.480542
1616.76117328.4739349.507227
1718.00713528.5745019.529068
1819.48178528.6751659.545954
1921.38997828.7816589.555981
2024.10025628.9001859.552986
2128.22991629.0312759.525632
2234.68575729.1666519.460915
2344.51772329.2890669.372986
2458.48433329.3988099.314327
2576.58558729.5242639.334321
2698.06295829.7388469.455692
27121.86651629.9904239.615647
28147.08946230.1955329.720541
29164.99469030.2968659.763904
\n", "
" ], "text/plain": [ " Depth [m] Practical Salinity [psu] Temperature [deg C]\n", "0 0.500000 19.681507 7.649859\n", "1 1.500003 21.742039 7.993243\n", "2 2.500011 23.447821 8.262228\n", "3 3.500031 24.506578 8.452448\n", "4 4.500071 25.250780 8.612563\n", "5 5.500151 25.851595 8.760666\n", "6 6.500310 26.347780 8.891456\n", "7 7.500623 26.754166 9.002347\n", "8 8.501236 27.090258 9.097125\n", "9 9.502433 27.373615 9.179514\n", "10 10.504766 27.609207 9.251032\n", "11 11.509312 27.807636 9.312590\n", "12 12.518167 27.976418 9.365047\n", "13 13.535412 28.123075 9.410112\n", "14 14.568982 28.252239 9.448424\n", "15 15.634288 28.367905 9.480542\n", "16 16.761173 28.473934 9.507227\n", "17 18.007135 28.574501 9.529068\n", "18 19.481785 28.675165 9.545954\n", "19 21.389978 28.781658 9.555981\n", "20 24.100256 28.900185 9.552986\n", "21 28.229916 29.031275 9.525632\n", "22 34.685757 29.166651 9.460915\n", "23 44.517723 29.289066 9.372986\n", "24 58.484333 29.398809 9.314327\n", "25 76.585587 29.524263 9.334321\n", "26 98.062958 29.738846 9.455692\n", "27 121.866516 29.990423 9.615647\n", "28 147.089462 30.195532 9.720541\n", "29 164.994690 30.296865 9.763904" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] } ], "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }