Extract_time_series_using_xarray.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://nomads.ncep.noaa.gov:9090/dods/nam/nam20161025/nam1hr_00z\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "from datetime import datetime, timedelta # \n",
    "\n",
    "dayFile = datetime.now() - timedelta(days=1)\n",
    "dayFile  = dayFile.strftime(\"%Y%m%d\")\n",
    "\n",
    "url='http://nomads.ncep.noaa.gov:9090/dods/nam/nam%s/nam1hr_00z' %(dayFile)\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specify desired station time series location\n",
    "# note we add 360 because of the lon convention in this dataset\n",
    "#lati = 36.605; loni = -121.85899   # west of Pacific Grove, CA\n",
    "lati = 41.4; loni = -100.8  # Georges Bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# extract a dataset closeste to specified point\n",
    "dsloc = ds.sel(lon=loni, lat=lati, method='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f473404ca50>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hssYThHNtNHMmrLde5bbnbRAuazxBONdGM2ZA//6V297668OcObBsWeW26Vw5PEE410aV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47340875d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select a variable to plot\n",
    "dsloc['dswrfsfc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-Oct-25 00:00\n",
      "2016-Oct-26 12:00\n",
      "-152.878623 -49.5860145742\n",
      "459 263\n",
      "0 36\n"
     ]
    }
   ],
   "source": [
    "import netCDF4\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta # \n",
    "\n",
    "dayFile = datetime.now() - timedelta(days=1)\n",
    "dayFile  = dayFile.strftime(\"%Y%m%d\")\n",
    "\n",
    "url='http://nomads.ncep.noaa.gov:9090/dods/nam/nam%s/nam1hr_00z' %(dayFile)\n",
    "\n",
    "# NetCDF4-Python can open OPeNDAP dataset just like a local NetCDF file\n",
    "nc = netCDF4.Dataset(url)\n",
    "varsInFile = nc.variables.keys()\n",
    "\n",
    "lat = nc.variables['lat'][:]\n",
    "lon = nc.variables['lon'][:]\n",
    "time_var = nc.variables['time']\n",
    "dtime = netCDF4.num2date(time_var[:],time_var.units)\n",
    "\n",
    "first = netCDF4.num2date(time_var[0],time_var.units)\n",
    "last = netCDF4.num2date(time_var[-1],time_var.units)\n",
    "print first.strftime('%Y-%b-%d %H:%M')\n",
    "print last.strftime('%Y-%b-%d %H:%M')\n",
    "\n",
    "# determine what longitude convention is being used\n",
    "print lon.min(),lon.max()\n",
    "\n",
    "# Specify desired station time series location\n",
    "# note we add 360 because of the lon convention in this dataset\n",
    "#lati = 36.605; loni = -121.85899 + 360.  # west of Pacific Grove, CA\n",
    "lati = 41.4; loni = -100.8 +360.0  # Georges Bank\n",
    "lati = 41.4; loni = -100.8   # Georges Bank\n",
    "\n",
    "# Function to find index to nearest point\n",
    "def near(array,value):\n",
    "    idx=(abs(array-value)).argmin()\n",
    "    return idx\n",
    "\n",
    "# Find nearest point to desired location (no interpolation)\n",
    "ix = near(lon, loni)\n",
    "iy = near(lat, lati)\n",
    "print ix,iy\n",
    "\n",
    "# Extract desired times.  \n",
    "\n",
    "# 1. Select -+some days around the current time:\n",
    "start = netCDF4.num2date(time_var[0],time_var.units)\n",
    "stop = netCDF4.num2date(time_var[-1],time_var.units)\n",
    "time_var = nc.variables['time']\n",
    "datetime = netCDF4.num2date(time_var[:],time_var.units)\n",
    "\n",
    "istart = netCDF4.date2index(start,time_var,select='nearest')\n",
    "istop = netCDF4.date2index(stop,time_var,select='nearest')\n",
    "print istart,istop\n",
    "\n",
    "# Get all time records of variable [vname] at indices [iy,ix]\n",
    "vname = 'dswrfsfc'\n",
    "\n",
    "var = nc.variables[vname]\n",
    "\n",
    "hs = var[istart:istop,iy,ix]\n",
    "\n",
    "tim = dtime[istart:istop]\n",
    "\n",
    "# Create Pandas time series object\n",
    "ts = pd.Series(hs,index=tim,name=vname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-25 00:00:00.000000      0.000000\n",
       "2016-10-25 01:00:00.000000      0.000000\n",
       "2016-10-25 02:00:00.000006      0.000000\n",
       "2016-10-25 03:00:00.000000      0.000000\n",
       "2016-10-25 04:00:00.000000      0.000000\n",
       "2016-10-25 05:00:00.000006      0.000000\n",
       "2016-10-25 06:00:00.000000      0.000000\n",
       "2016-10-25 07:00:00.000000      0.000000\n",
       "2016-10-25 08:00:00.000006      0.000000\n",
       "2016-10-25 09:00:00.000000      0.000000\n",
       "2016-10-25 10:00:00.000000      0.000000\n",
       "2016-10-25 11:00:00.000006      0.000000\n",
       "2016-10-25 12:00:00.000000      0.000000\n",
       "2016-10-25 13:00:00.000000      0.000000\n",
       "2016-10-25 14:00:00.000006     34.571644\n",
       "2016-10-25 15:00:00.000000     84.320755\n",
       "2016-10-25 16:00:00.000000    239.692627\n",
       "2016-10-25 17:00:00.000006    541.701721\n",
       "2016-10-25 18:00:00.000000    597.381653\n",
       "2016-10-25 19:00:00.000000    571.218872\n",
       "2016-10-25 20:00:00.000006    324.944275\n",
       "2016-10-25 21:00:00.000000    371.187897\n",
       "2016-10-25 22:00:00.000000    212.613312\n",
       "2016-10-25 23:00:00.000006     56.342171\n",
       "2016-10-26 00:00:00.000000      0.000000\n",
       "2016-10-26 01:00:00.000000      0.000000\n",
       "2016-10-26 02:00:00.000006      0.000000\n",
       "2016-10-26 03:00:00.000000      0.000000\n",
       "2016-10-26 04:00:00.000000      0.000000\n",
       "2016-10-26 05:00:00.000006      0.000000\n",
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       "Name: dswrfsfc, dtype: float32"
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