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#!/usr/bin/python
#
# processCSV.py: analyse the results and return results breakdown for a given question type
#
# Usage: processCSV.py questionType intonationPattern
# If no intonation pattern or question type is given, then all results are presented.
#
# Example: processCSV.py
# Example: processCSV.py 'Wh'
# Example: processCSV.py 'Wh' 'F'
# Possible Questions: 'Echo', 'Wh', 'YN'
# Possible Intonations: 'RF', 'FR', 'F', 'R', 'H'



import csv
import sys
import matplotlib.pyplot as plt
import numpy as np


if __name__=="__main__":
    #Load Command Line Parameters
    intPattern = None;
    qType = None;
    
    if len(sys.argv) > 2:
        intPattern = [sys.argv[2]]
    else:
        intPattern = ['RF', 'FR', 'F', 'R', 'H']
    if len(sys.argv) > 1:
        qType = [sys.argv[1]]
    else:
        qType = ['Echo', 'Wh', 'YN']

#print(intPattern)
#print(qType)


reader = csv.DictReader(open('DataSetSummary.csv'))

result = {}
for row in reader:
    for column, value in row.iteritems():
        result.setdefault(column, []).append(value)
#print result['QType']

for q in qType:
    qList = [j for j, x in enumerate(result['QType']) if x == q]
#    print "%s count = %d" % (q, len(qList))
    for i in intPattern:
        iList = [j for j, x in enumerate(result['Intonation']) if x == i]
        count = 0
        tracks = []
        if (iList) != None:
            for il in iList:
                if il in qList:
                    count +=1
                    tracks.append(il)
#        print (i + ' vs. '+q+'=%d', count)
#        if count > 0:
            print "%s vs. %s = %d" % (q, i, count)
            print tracks
#            for t in tracks:
#                trackName = "T%d/01.csv" % (t)
#                trackReader = csv.reader(open(trackName))
#                xList = []
#                yList = []
#                for r in trackReader:
##                    print "In trackReader"
##                    print(r)
##                    print(r[1])
#                    xList.append(r[0])
##                    xList = [float(x) for x in xList]
#                    yList.append(r[1])
##                    yList = [float(y) for y in yList]
#            #   Apply some smoothing and scaling
#            #   Vertical
#                print yList
#                print min(yList)
#                yList = np.subtract(yList, min(yList))
#                print yList
#                yList = np.divide(yList, max(yList))
#                print yList
#            #   Horizontal
#                xList = np.subtract(xList, min(xList))
#                xList = np.divide(xList, max(xList))
#                
#                
#                plt.plot(xList,yList)
#            plt.show()

#result = {}
#for row in reader:
#    print(row)
#    key = row[1]
##    if key in result:
##        # implement your duplicate row handling here
##        pass
#    result[key] = row[1:]
#print result