d@0: 
d@0: #!/usr/bin/python
d@0: #
d@0: # processCSV.py: analyse the results and return results breakdown for a given question type
d@0: #
d@0: # Usage: processCSV.py questionType intonationPattern
d@0: # If no intonation pattern or question type is given, then all results are presented.
d@0: #
d@0: # Example: processCSV.py
d@0: # Example: processCSV.py 'Wh'
d@0: # Example: processCSV.py 'Wh' 'F'
d@0: # Possible Questions: 'Echo', 'Wh', 'YN'
d@0: # Possible Intonations: 'RF', 'FR', 'F', 'R', 'H'
d@0: 
d@0: 
d@0: 
d@0: import csv
d@0: import sys
d@0: import matplotlib.pyplot as plt
d@0: import numpy as np
d@0: 
d@0: 
d@0: if __name__=="__main__":
d@0:     #Load Command Line Parameters
d@0:     intPattern = None;
d@0:     qType = None;
d@0:     
d@0:     if len(sys.argv) > 2:
d@0:         intPattern = [sys.argv[2]]
d@0:     else:
d@0:         intPattern = ['RF', 'FR', 'F', 'R', 'H']
d@0:     if len(sys.argv) > 1:
d@0:         qType = [sys.argv[1]]
d@0:     else:
d@0:         qType = ['Echo', 'Wh', 'YN']
d@0: 
d@0: #print(intPattern)
d@0: #print(qType)
d@0: 
d@0: 
d@0: reader = csv.DictReader(open('DataSetSummary.csv'))
d@0: 
d@0: result = {}
d@0: for row in reader:
d@0:     for column, value in row.iteritems():
d@0:         result.setdefault(column, []).append(value)
d@0: #print result['QType']
d@0: 
d@0: for q in qType:
d@0:     qList = [j for j, x in enumerate(result['QType']) if x == q]
d@0: #    print "%s count = %d" % (q, len(qList))
d@0:     for i in intPattern:
d@0:         iList = [j for j, x in enumerate(result['Intonation']) if x == i]
d@0:         count = 0
d@0:         tracks = []
d@0:         if (iList) != None:
d@0:             for il in iList:
d@0:                 if il in qList:
d@0:                     count +=1
d@0:                     tracks.append(il)
d@0: #        print (i + ' vs. '+q+'=%d', count)
d@0: #        if count > 0:
d@0:             print "%s vs. %s = %d" % (q, i, count)
d@0:             print tracks
d@0: #            for t in tracks:
d@0: #                trackName = "T%d/01.csv" % (t)
d@0: #                trackReader = csv.reader(open(trackName))
d@0: #                xList = []
d@0: #                yList = []
d@0: #                for r in trackReader:
d@0: ##                    print "In trackReader"
d@0: ##                    print(r)
d@0: ##                    print(r[1])
d@0: #                    xList.append(r[0])
d@0: ##                    xList = [float(x) for x in xList]
d@0: #                    yList.append(r[1])
d@0: ##                    yList = [float(y) for y in yList]
d@0: #            #   Apply some smoothing and scaling
d@0: #            #   Vertical
d@0: #                print yList
d@0: #                print min(yList)
d@0: #                yList = np.subtract(yList, min(yList))
d@0: #                print yList
d@0: #                yList = np.divide(yList, max(yList))
d@0: #                print yList
d@0: #            #   Horizontal
d@0: #                xList = np.subtract(xList, min(xList))
d@0: #                xList = np.divide(xList, max(xList))
d@0: #                
d@0: #                
d@0: #                plt.plot(xList,yList)
d@0: #            plt.show()
d@0: 
d@0: #result = {}
d@0: #for row in reader:
d@0: #    print(row)
d@0: #    key = row[1]
d@0: ##    if key in result:
d@0: ##        # implement your duplicate row handling here
d@0: ##        pass
d@0: #    result[key] = row[1:]
d@0: #print result