#!/usr/bin/env python
############################################################################
#
# MODULE:		r.mcda.topsis
# AUTHOR:		Gianluca Massei - Antonio Boggia
# PURPOSE:		Generate a MCDA map based on TOPSIS algorthm. 
#				Based on Hwang C. L. and Yoon K. Multiple Objective Decision 
#				Making Methods and Applications, A State-of-the-Art Survey . 
#				Springer - Verlag , 1981.	 
# COPYRIGHT:  c) 2015 Gianluca Massei, Antonio Boggia  and the GRASS 
#				Development Team. This program is free software under the 
#				GNU General PublicLicense (>=v2). Read the file COPYING 
#				that comes with GRASS for details.
#
#############################################################################

#%Module
#% description: Generates a MCDA map based on TOPSIS algorthm.
#% keyword: raster
#% keyword: Multi Criteria Decision Analysis (MCDA)
#%End
#%option
#% key: criteria
#% type: string
#% multiple: yes
#% gisprompt: old,cell,raster
#% key_desc: name
#% description: Name of criteria raster maps 
#% required: yes
#%end
#%option
#% key: preferences
#% type: string
#% key_desc: character
#% description: preference (gain,cost)
#% required: yes
#%end
#%option
#% key: weights
#% type: double
#% description: weights (w1,w2,...,wn)
#% multiple: yes
#% required: yes
#%end
#%option
#% key: topsismap   
#% type: string
#% gisprompt: new_file,cell,output
#% description: Ranked raster map
#% required: yes
#%end


import sys
import grass.script as gscript
from time import time  

def standardizedNormalizedMatrix(attributes,weights): #step1 and step2
	criteria=[]
	for criterion,weight in zip(attributes,weights):
		gscript.mapcalc("critPow=pow(${criterion},2)",criterion=criterion,overwrite='True')
		stats=gscript.parse_command("r.univar",map='critPow',flags='g')
		nameMap="_%s" % criterion
		gscript.mapcalc("${nameMap}=(${criterion}/sqrt(${sum}))*${weight}", \
			nameMap=nameMap,criterion=criterion,sum=stats['sum'],weight=weight,overwrite='True')
		criteria.append(nameMap)
	return criteria


def idealPoints(criteria,preference): #step3
	idelaPointsList=[]
	for c,p in zip(criteria,preference):
		stats=gscript.parse_command("r.univar",map=c,flags='g')
		if p=="gain":
			ip=float(stats['max'])
		elif p=="cost":
			ip=float(stats['min'])
		else:
			ip=-9999
			print "warning! %s doesn't compliant" % p
		idelaPointsList.append(ip)
	return idelaPointsList

def worstPoints(criteria,preference):
	worstPointsList=[]
	for c,p in zip(criteria,preference):
		stats=gscript.parse_command("r.univar",map=c,flags='g')
		if p=="gain":
			wp=float(stats['min'])
		elif p=="cost":
			wp=float(stats['max'])
		else:
			wp=-9999
			print "warning! %s doesn't compliant" % p
		worstPointsList.append(wp)
	return worstPointsList
	  
	  
def idealPointDistance(idelaPointsList,criteria): #step4a
	distance=[]
	i=0
	for c,ip in zip(criteria,idelaPointsList):
		mapname="tmap_%s" % i
		gscript.mapcalc("${mapname}=pow((${c}-${ip}),2)",mapname=mapname,c=c,ip=ip,overwrite='True')
		distance.append(mapname)
		i=i+1
	mapalgebra2="IdealPointDistance=sqrt(%s)" % ("+".join(distance))
	gscript.mapcalc(mapalgebra2,overwrite='True')
	gscript.run_command("g.remove", flags='f', type='raster', name=",".join(distance))
	return 0

def worstPointDistance(worstPointsList,criteria): #step4b
	distance=[]
	i=0
	for c,wp in zip(criteria,worstPointsList):
		mapname="tmap_%s" % i
		gscript.mapcalc("${mapname}=pow((${c}-${wp}),2)",mapname=mapname,c=c,wp=wp,overwrite='True')
		distance.append(mapname)
		i=i+1
	mapalgebra2="WorstPointDistance=sqrt(%s)" % ("+".join(distance))
	gscript.mapcalc(mapalgebra2,overwrite='True')
	gscript.run_command("g.remove", flags='f', type='raster', name=",".join(distance))


def relativeCloseness(topsismap): #step5
	gscript.mapcalc("${topsismap}=WorstPointDistance/(WorstPointDistance+IdealPointDistance)",\
		topsismap=topsismap,overwrite='True')


	
def main():
	"main function for TOPSIS algorithm"
	#try:
	start=time()
	attributes=options['criteria'].split(',')
	preferences=options['preferences'].split(',')
	weights=options['weights'].split(',')
	topsismap = options['topsismap']

	criteria=standardizedNormalizedMatrix(attributes,weights)
	idelaPointsList=idealPoints(criteria,preferences)
	worstPointsList=worstPoints(criteria,preferences)

	idealPointDistance(idelaPointsList,criteria)
	worstPointDistance(worstPointsList,criteria)
	relativeCloseness(topsismap)
	gscript.run_command("g.remove", flags='f', type='raster', name=",".join(criteria))
	gscript.run_command("g.remove", flags='f', type='raster', name="IdealPointDistance,WorstPointDistance,critPow")
	end=time()
	print "Time computing-> %.4f s" % (end-start)
	
	
if __name__ == "__main__":
    options, flags = gscript.parser()
    main()