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Extract NOAA's Climate Normals data
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import urllib2\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Climate data from NOAA\n",
"\n",
"* Dani Arribas-Bel ([@darribas](http://twitter.com/darribas))\n",
"\n",
"This notebook presents the process of accessing NOAA's servers to extract average climate data at the station level. In particular, I am interested in yearly averages, but the logic would be the same for any other metric in the dataset.\n",
"\n",
"Data come from the [1981-2010 Normals Data](ttp://www.ncdc.noaa.gov/land-based-station-data/climate-normals/1981-2010-normals-data), and I will extract average values to characterize the climate of each weather station, which will also be plotted at the end.\n",
"\n",
"The dataset can be accessed through FTP on the following url:\n",
"\n",
"[`ftp://ftp.ncdc.noaa.gov/pub/data/normals/1981-2010/products/temperature/`](ftp://ftp.ncdc.noaa.gov/pub/data/normals/1981-2010/products/temperature/)\n",
"\n",
"Information about the data is found in the entre file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"readme = urllib2.urlopen('ftp://ftp.ncdc.noaa.gov/pub/data/normals/1981-2010/readme.txt')\n",
"print readme.read()[:200]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"README FILE FOR NOAA'S 1981-2010 CLIMATE NORMALS\n",
"\n",
"OUTLINE\n",
"\n",
"I. CONTENTS\n",
"II. FILENAMING\n",
"III. FILE FORMATS\n",
"IV. UNITS\n",
"V. SPECIAL VALUES\n",
"VI. FLAGS\n",
"\n",
"I. CONTENTS\n",
"\n",
" readme.txt - this file\n",
" statu\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to extract annual (`ann`) average (`normal`) for the following variables:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vars2xtr = {\n",
" 'cldd': 'cooling degree days', \\\n",
" 'cldh': 'cooling degree hours', \\\n",
" 'clod': 'clouds', \\\n",
" 'dewp': 'dew point temperature', \\\n",
" 'dutr': 'diurnal temperature range', \\\n",
" 'hidx': 'heat index', \\\n",
" 'htdd': 'heating degree days', \\\n",
" 'htdh': 'heating degree hours', \\\n",
" 'prcp': 'precipitation', \\\n",
" 'pres': 'sea level pressure', \\\n",
" 'snow': 'snowfall', \\\n",
" 'snwd': 'snow depth', \\\n",
" 'tavg': 'mean temperature', \\\n",
" 'temp': 'temperature', \\\n",
" 'tmax': 'maximum temperature', \\\n",
" 'tmin': 'minimum temperature', \\\n",
" 'wchl': 'wind chill', \\\n",
" 'wind': 'wind'\n",
" }"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Locating files\n",
"\n",
"Copied from the `source_path` is a table of the file directory:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"source_path = 'ftp://ftp.ncdc.noaa.gov/pub/data/normals/1981-2010/products/temperature/'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
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"cell_type": "code",
"collapsed": true,
"input": [
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"File:ann-cldd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-cldd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-cldd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-cldd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-cldd-base70.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-cldd-base72.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-cldd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-dutr-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-htdd-base40.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-htdd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-htdd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-htdd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-htdd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-htdd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-htdd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tavg-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-grth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-grth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-grth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-grth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-grth080.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-grth090.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-grth100.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmax-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth000.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth010.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth020.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-avgnds-lsth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:ann-tmin-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-base70.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-base72.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-cldd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-dutr-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-htdd-base40.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-htdd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-htdd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-htdd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-htdd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-htdd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-htdd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tavg-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-grth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-grth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-grth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-grth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-grth080.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-grth090.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-grth100.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmax-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth000.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth010.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth020.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-avgnds-lsth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:djf-tmin-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-base45.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-base50.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-base55.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-base57.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-base60.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-base70.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-base72.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-cldd-normal.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-dutr-normal.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-dutr-stddev.txt \t17535 KB \t30/06/11 \t00:00:00\n",
"File:dly-htdd-base40.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-htdd-base45.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-htdd-base50.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-htdd-base55.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-htdd-base57.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-htdd-base60.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-htdd-normal.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-tavg-normal.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-tavg-stddev.txt \t17535 KB \t30/06/11 \t00:00:00\n",
"File:dly-tmax-normal.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-tmax-stddev.txt \t17535 KB \t30/06/11 \t00:00:00\n",
"File:dly-tmin-normal.txt \t20658 KB \t30/06/11 \t00:00:00\n",
"File:dly-tmin-stddev.txt \t17535 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-base70.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-base72.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-cldd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-dutr-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-htdd-base40.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-htdd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-htdd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-htdd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-htdd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-htdd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-htdd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tavg-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-grth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-grth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-grth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-grth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-grth080.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-grth090.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-grth100.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmax-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth000.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth010.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth020.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-avgnds-lsth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:jja-tmin-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-base70.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-base72.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-cldd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-dutr-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-htdd-base40.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-htdd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-htdd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-htdd-base55.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-htdd-base57.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-htdd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-htdd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tavg-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-grth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-grth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-grth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-grth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-grth080.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-grth090.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-grth100.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmax-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth000.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth010.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth020.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-avgnds-lsth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mam-tmin-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-base45.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-base50.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-base55.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-base57.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-base60.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-base70.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-base72.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-cldd-normal.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-dutr-normal.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-dutr-stddev.txt \t635 KB \t30/06/11 \t00:00:00\n",
"File:mly-htdd-base40.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-htdd-base45.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-htdd-base50.txt \t748 KB \t30/06/11 \t00:00:00\n",
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"File:mly-htdd-base57.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-htdd-base60.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-htdd-normal.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tavg-normal.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tavg-stddev.txt \t635 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmax-avgnds-grth040.txt \t748 KB \t30/06/11 \t00:00:00\n",
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"File:mly-tmax-avgnds-grth090.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmax-avgnds-grth100.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmax-avgnds-lsth032.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmax-normal.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmax-stddev.txt \t635 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmin-avgnds-lsth000.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmin-avgnds-lsth010.txt \t748 KB \t30/06/11 \t00:00:00\n",
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"File:mly-tmin-normal.txt \t748 KB \t30/06/11 \t00:00:00\n",
"File:mly-tmin-stddev.txt \t635 KB \t30/06/11 \t00:00:00\n",
"File:son-cldd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-cldd-base50.txt \t184 KB \t30/06/11 \t00:00:00\n",
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"File:son-cldd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-dutr-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-htdd-base40.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-htdd-base45.txt \t184 KB \t30/06/11 \t00:00:00\n",
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"File:son-htdd-base60.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-htdd-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tavg-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-grth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-grth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-grth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-grth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-grth080.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-grth090.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-grth100.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmax-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth000.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth010.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth020.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth032.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth040.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth050.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth060.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-avgnds-lsth070.txt \t184 KB \t30/06/11 \t00:00:00\n",
"File:son-tmin-normal.txt \t184 KB \t30/06/11 \t00:00:00\n",
"'''"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"paths = []\n",
"for file in files.split('\\n'):\n",
" file = file.strip('File:').split(' ')[0]\n",
" if ('ann' in file) and ('normal' in file):\n",
" paths.append(file)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We get to the files we can access on annual averages:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in paths:\n",
" name = vars2xtr[i.strip('.txt').split('-')[1]]\n",
" print '%s | %s'%(i, name)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ann-cldd-normal.txt | cooling degree days\n",
"ann-dutr-normal.txt | diurnal temperature range\n",
"ann-htdd-normal.txt | heating degree days\n",
"ann-tavg-normal.txt | mean temperature\n",
"ann-tmax-normal.txt | maximum temperature\n",
"ann-tmin-normal.txt | minimum temperature\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extracting the data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def load_txt_file(url):\n",
" dat = pd.read_table(url, header=None, squeeze=True)\n",
" dat = dat.apply(parse_line)\n",
" return dat\n",
"\n",
"def parse_line(line):\n",
" stnid = line[:11]\n",
" val = line[11:-1]\n",
" flag = line[-1:]\n",
" return pd.Series({'stnid': stnid, 'val': val, 'flag': flag})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"db_path = 'weather_data_stations.csv'\n",
"try:\n",
" db = pd.read_csv(db_path)\n",
"except:\n",
" db = []\n",
" for var in paths:\n",
" print 'Downloading: ', var\n",
" dat = load_txt_file(source_path + var)\n",
" dat['var'] = var\n",
" db.append(dat)\n",
" db = pd.concat(db, ignore_index=True)\n",
" db.to_csv(db_path, index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Geo-referencing the stations\n",
"\n",
"Station info is given in a different file also accessible via FTP. The structure of this file is (as copied from the README file):\n",
"\n",
"```\n",
" ------------------------------\n",
" Variable Columns Type\n",
" ------------------------------\n",
" ID 1-11 Character\n",
" LATITUDE 13-20 Real\n",
" LONGITUDE 22-30 Real\n",
" ELEVATION 32-37 Real\n",
" STATE 39-40 Character\n",
" NAME 42-71 Character\n",
" GSNFLAG 73-75 Character\n",
" HCNFLAG 77-79 Character\n",
" WMOID 81-85 Character\n",
" METHOD*\t 87-99 Character\n",
" ------------------------------\n",
"```\n",
"\n",
"We can parse it and read it in to link it up to `db`, keeping only data values with quality enough (i.e. `C`, `S`, `R`)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def load_stations(url):\n",
" dat = pd.read_table(url, header=None, squeeze=True)\n",
" dat = dat.apply(parse_stn)\n",
" return dat\n",
"\n",
"def parse_stn(line):\n",
" d = {\n",
" 'stnid': line[:11], \\\n",
" 'lat': float(line[12:20]), \\\n",
" 'lon': float(line[21:30]), \\\n",
" 'ele': float(line[31:37]), \\\n",
" 'state': line[38:40], \\\n",
" 'name': line[41:71], \\\n",
" 'gsnflag': line[72:75], \\\n",
" 'hcnflag': line[76:79], \\\n",
" 'wmoid': line[80:85], \\\n",
" 'method': line[86:99]\n",
" }\n",
" return pd.Series(d)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"stations_url = 'ftp://ftp.ncdc.noaa.gov/pub/data/normals/1981-2010/station-inventories/allstations.txt'\n",
"try:\n",
" geodb = pd.read_csv('weather_geo.csv')\n",
"except:\n",
" stations = load_stations(stations_url)\n",
" geodb = db.join(stations.set_index('stnid')[['lon', 'lat', 'ele']], on='stnid')\n",
" filt = geodb['flag'].apply(lambda x: x in ['C', 'S', 'R'])\n",
" geodb = geodb[filt]\n",
" geodb.to_csv('weather_geo.csv', index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both `stations` and `db` can be linked up by the station id:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"geodb.info()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 38047 entries, 0 to 38046\n",
"Data columns (total 8 columns):\n",
"Unnamed: 0 38047 non-null int64\n",
"flag 38047 non-null object\n",
"stnid 38047 non-null object\n",
"val 38047 non-null int64\n",
"var 38047 non-null object\n",
"lon 38047 non-null float64\n",
"lat 38047 non-null float64\n",
"ele 38047 non-null float64\n",
"dtypes: float64(3), int64(2), object(3)"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use PySAL plotting capabilities to get a quick plot of the station locations:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pysal as ps\n",
"from pysal.contrib.viz import mapping as maps"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, ax = plt.subplots(figsize=(12, 6))\n",
"sc = plt.scatter(stations.lon, stations.lat, \\\n",
" linewidth=0, c='blue', s=0.2, marker='.')\n",
"ax = maps.setup_ax([sc], ax)\n",
"plt.title('Location of NOAA weather stations')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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erqA6aVL2zwMAAGgDwGmMG2e2apWqlYmJClnR0YFRT+7IKTeohoVpCf/rr/Xc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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x5c62550>"
]
}
],
"prompt_number": 26
}
],
"metadata": {}
}
]
}
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