fdbesanto2

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

	#########################################################################################
	# This code ingests binary GIMMS FAPAR and LAI data and converts them to GeoTIFFs.
	# The data was produced by the Climate and Vegetation Research Group of Boston University.
	# The GIMMS data are available from: http://cliveg.bu.edu/modismisr/lai3g-fpar3g.html

	# title : GIMMS3g_LAI_FPAR_Binary2Geotiff.r
	# purpose : Converts binary GIMMS FAPAR/LAI to GeoTIFFs
	# author : Abdulhakim Abdi (@HakimAbdi)
	# input : Binary 8-bit unsigned integer with ieee-be byte order
	# output : Georeferenced TIFF, ArcGIS-ready files

	#########################################################################################
	# title : ManhattanLST.R
	# purpose : Converts Landsat ETM+ thermal imagery to degrees Celsius
	# author : Abdulhakim Abdi (@HakimAbdi)
	# input : Landsat TM / ETM+ band 6
	# output : Land surface temperature map in degrees Celsius
	#########################################################################################

	# Install and load the required packages
	install.packages(c("rgdal" "sp", "raster"), repos='http://cran.r-project.org')

	# ##### BEGIN GPL LICENSE BLOCK #####
	#
	# This program is free software; you can redistribute it and/or
	# modify it under the terms of the GNU General Public License
	# as published by the Free Software Foundation; either version 2
	# of the License, or (at your option) any later version.
	#
	# This program is distributed in the hope that it will be useful,
	# but WITHOUT ANY WARRANTY; without even the implied warranty of
	# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

	library(xlsx)
	library(smatr)

	# get data, from publication Wright et al 2004. DOI: [10.1038/nature02403](http://doi.org/10.1038/nature02403)
	download.file("http://www.nature.com/nature/journal/v428/n6985/extref/nature02403-s2.xls", "wright-2004.xls")
	dat.Wright <- read.xlsx2("wright-2004.xls", sheetIndex=1, startRow=11, stringsAsFactors=FALSE, check.names=TRUE)

	## Clean data
	dat.Wright <- dat.Wright[names(dat.Wright) != " "] # Drop blank columns
	for(v in c("log.LMA","log.LL"))

	#################################################################################################
	# title : pload.R
	# purpose : Install and load uninstalled packages, or load installed ones.
	# author : Abdulhakim Abdi (@HakimAbdi)
	# input : Package name in quotes
	# output : Installation and loading of uninstalled packages, or loading of installed ones.
	#################################################################################################

	pload <- function(x){
	if(x %in% rownames(installed.packages()))

	#############################################################################################
	# title : Gridded Correlation of Time Series Raster Data (Gridcorts)
	# purpose : Pixelwise time series correlation and significance based on Pearson's r,
	# : Spearman's rho or Kendall's tau
	# author : Abdulhakim Abdi (@HakimAbdi)
	# input : Raster brick comprising two time series of equal number of layers
	# output : Raster object of pixelwise correlation, significance or both (i.e. brick)
	# : based on the chosen method
	# update : Minor based suggestion from Tao Xiong of Northeast Normal University in China.
	# data : Test data: https://1drv.ms/u/s!AsHsKb_qtbkwgvoQuHnPaFazPr_XnA?e=fJ9fue

	'''
	Created on May 26, 2015

	@author: vinnie, [email protected]

	Power-law results from:
	"DATA FORENSIC TECHNIQUES USING BENFORD’S LAW AND ZIPF’S LAW FOR KEYSTROKE
	DYNAMICS", Aamo Iorliam, Anthony T.S. Ho, Norman Poh, Santosh Tirunagari,
	and Patrick Bours. IWBF 2015.