jiaweih/SWE

## SWE
{
 "metadata": {
  "name": "Plot of SWE ( text file)"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%pylab inline",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Populating the interactive namespace from numpy and matplotlib\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pylab import *",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from datetime import datetime",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vic_path = '/usr1/jiawei/courses/computingWorkshop/shell/data/fluxes_13.7500_-1.2500'\nvic_header = ['year', 'month', 'day', 'vic_prec', 'vic_air_temp', 'vic_wind',\\\n              'vic_rel_humid', 'vic_shortwave', 'vic_net_short', 'vic_longwave', 'vic_SWE', 'vic_net_lon']\nvic_start = datetime(1990, 1, 1)\nvic_end = datetime(2012, 12, 17)\nvic_dates = pd.date_range(start = vic_start, end=vic_end, freq='D')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "data = np.genfromtxt(vic_path, delimiter='\\t')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "vic_df = pd.DataFrame(data, index = vic_dates, columns = vic_header)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print vic_df",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 8387 entries, 1990-01-01 00:00:00 to 2012-12-17 00:00:00\nFreq: D\nData columns:\nyear             8387  non-null values\nmonth            8387  non-null values\nday              8387  non-null values\nvic_prec         8387  non-null values\nvic_air_temp     8387  non-null values\nvic_wind         8387  non-null values\nvic_rel_humid    8387  non-null values\nvic_shortwave    8387  non-null values\nvic_net_short    8387  non-null values\nvic_longwave     8387  non-null values\nvic_SWE          8387  non-null values\nvic_net_lon      8387  non-null values\ndtypes: float64(12)\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#function for finding day of water year\ndef water_day(indate):\n    doy = indate.timetuple().tm_yday\n    if doy >= 274:\n        outdate = doy - 274\n    else:\n        outdate = doy + 91\n    return outdate",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nt = np.arange(365)\n\nvar = 'vic_SWE'\nxlab = 'Day of Water Year'\nylab = 'SWE (mm)'\n\nmax1 = vic_df[var].groupby(lambda x:water_day(x)).max()\nmin1 = vic_df[var].groupby(lambda x:water_day(x)).min()\n\nfill_between(t, min1.values, max1.values, color = 'grey')\n\nvic_df[var].groupby(lambda x:water_day(x)).mean().plot(style = 'w', lw = 3)\n\nxlabel(xlab)\nylabel(ylab)\nplt.xlim((0,366))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "(0, 366)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZNzY2chkxycnJmDp1KvecmAWE9Ho9fvnlF9GO5yjO9jUdhXSLixR1O1uzo4uM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\nYdiwYfjyyy+5YDxhwgQkJCSgtLQUw4cPx2effYb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       "text": "<matplotlib.figure.Figure at 0x3985c90>"
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}