Estuary+Python
Python codes for data analysis
2015/10/22 ROMS NetCDF, Vertical
(c) 2015-01-06 Teruhisa Okada
{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# vview (c) 2015 Teruhisa Okada\n", "\n", "% matplotlib inline\n", "\n", "import netCDF4\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import numpy as np\n", "import datetime\n", "import pandas as pd\n", "from geopy.distance import vincenty\n", "\n", "\n", "def vview(stafile, stations, vname, time):\n", " stations = np.asarray(stations)\n", " \n", " nc = netCDF4.Dataset(stafile, 'r')\n", " if type(time) == int:\n", " t = time\n", " time = netCDF4.num2date(nc.variables['ocean_time'][t], romspy.JST)\n", " elif type(time) == datetime.datetime:\n", " time2 = netCDF4.date2num(time, romspy.JST)\n", " time3 = nc.variables['ocean_time'][:]\n", " t = np.where(time3==time2)[0][0]\n", " else:\n", " print 'ERROR: your time type =',type(time)\n", " print '\\n',time, t\n", " \n", " cs_r = nc.variables['Cs_r'][:]\n", " h = nc.variables['h'][stations]\n", " zeta = nc.variables['zeta'][t,stations]\n", " lon = nc.variables['lon_rho'][stations]\n", " lat = nc.variables['lat_rho'][stations]\n", " var = nc.variables[vname][t,stations,:]\n", " \n", " depth = np.zeros([len(stations),len(cs_r)])\n", " dist = np.zeros([len(stations),len(cs_r)])\n", " for s in range(len(stations)):\n", " depth[s,:] = (h[s] + zeta[s]) * cs_r[:]\n", " if s == 0:\n", " dist[s,:] = 0\n", " else:\n", " back = [lon[s-1],lat[s-1]]\n", " fore = [lon[s],lat[s]]\n", " dist[s,:] = dist[s-1,:] + vincenty(back, fore).meters\n", "\n", " fig, ax = plt.subplots(figsize=[12,4])\n", " if vname == 'temp': \n", " cflevels=np.arange(7,30.1,1.0)\n", " clevels=cflevels\n", " if vname == 'salt': \n", " cflevels=np.arange(23,32.2,0.2)\n", " clevels=np.arange(23,32.2,1.0)\n", " if vname == 'chlorophyll': \n", " cflevels=np.arange(0,20.5,0.5)\n", " clevels=np.arange(0,21,1.0)\n", " origin = 'upper'\n", " #origin = 'lower'\n", " CF = plt.contourf(dist/1000, depth, var, levels=cflevels, extend='both', origin=origin)\n", " C = plt.contour(dist/1000, depth, var, colors='k', levels=clevels, origin=origin)\n", " plt.clabel(C, fmt = '%2.1f', colors = 'w')\n", " #plt.pcolor(dist, depth, var) ãããããããããã# ã°ãªããç¶ããããªã\n", " CB = plt.colorbar(CF)\n", " CB.ax.set_ylabel(vname)\n", " \n", " plt.xlim(0,20)\n", " plt.ylim(-20,0)\n", " \n", " plt.xlabel('distance(km)')\n", " plt.ylabel('depth(m)')\n", " plt.title(datetime.datetime.strftime(time, '%Y-%m-%d %H:%M:%S'))\n", " \n", "\n", "stafile = 'Z:/roms/Apps/OB500_fennelP/NL10_copy/ob500_sta.nc'\n", "stations = [13+i for i in range(20)] # 14 - 33\n", "\n", "#import romspy\n", "#romspy.get_time(stafile)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2012-06-01 12:00:00 305\n" ] }, { "data": { "image/png": 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TEsggtAZ1dfUyK52TD/9YrNerM9BtNleEEaanNCZCOFq5zHrtQk9JZveope0GSE8E3Xnn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\nkqThNNle4VJXrKza9ZAkSb2RZEmSK5PckuTmJO9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K0tAwWErqtauANyfZO8k+tOZO7pLk\n54H9qupy4L/RGjKH1jDzvm1NFwPbkiwCTmXqG2vWAW9vm4u5f1XdB2xN8hvNuSQ5pmm/BXj+mPf4\nLPAt4MIke3T8X9xyBHDzNF8jSfOawVJST1XVBuDrwI20Qtp17ZeBfYDLktwIXA2c0Vz7GvDeJDck\nORx4P3AtcA1PvVO8xh43w9WXAtcn2QCc2VxfCbwjyUZawe/E5vzlwK+NU/+fAxuAL6U1fj420D7l\nsxuvZPdhfUla8FI1o9U0JGnBSXIx8PtVdess3+fngPXA8rF3mUvSQmawlKRGkiOAg6vq6lm+z/OB\nQ6vqqu5UJknzg8FSkiRJXeEcS0mSJHWFwVKSJEldYbCUJElSVxgsJUmS1BUGS0mSJHXF/wcVo70c\n4rQHFAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d35f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#time = datetime.datetime(2012, 6, 1, 12)\n", "time = 305\n", "\n", "vview(stafile, stations, 'salt', time)\n", "\n", "plt.savefig('vview_salt.png', bbox_inches='tight', dpi=300)" ] }, { "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
Teruhisa Okada