from os import listdir

dataFile = open('all_results.csv', 'w')
# Here, we're creating a program to combine all participant data into a .csv file from the Battleship experiment
# Note, data files must be stored in the current directory in order for their being in the created .csv file

allData = sorted(listdir('results'))  # Such that the same order of data is kept throughout .csv regardless of additions
for file in allData:
    condition = ''  # These (empty) variables will (1) store data for each participant in respect of their name;
    name = ''       # (2) appear in this order as columns in the .csv file. Each row will be one participant
    age = ''
    gender = ''
    targetHit = 0
    targetNearMiss = 0
    targetFullMiss = 0
    happiness = []
    happinessHit = 0
    happinessNearMiss = 0
    happinessFullMiss = 0
    willingnessHit = 0
    willingnessNearMiss = 0
    willingnessFullMiss = 0

    datum = open('results/' + file, 'r')
    allLines = datum.readlines()

    condition = allLines[0].strip('  ').strip('\n')  # Collating condition
    demographics = allLines[3].title().split(',')
    name = demographics[0]  # Collating name
    age = demographics[1]  # Collating age
    if demographics[2] == 'Male\n':  # Coding gender such that later statistical analysis is easier (i.e. ratio)
        gender = '1'  # Males coded as '1' and females coded as '2' (collated in gender)
    else:
        gender = '2'

    trialOutcome = allLines[5:]  # Collating the proportion of hits, near misses and full misses
    for trial in trialOutcome:  # Analysing each trial per participant (varying no. of trials per participant)
        ratings = trial.replace(',', ' ')
        ratings = ratings[-7:]  # Max. characters possible at the end for max. happiness and willingness (100)
        happinessRatings = ratings.split(' ')
        del (happinessRatings[0])
        if 'hit' in trial:
            targetHit += 1  # Collecting the number of times the target is hit etc.
            willingnessHit += int(ratings[-3:])  # Summing willingness ratings per participant to calculate average
            if len(happinessRatings) == 3:  # Length here indicative of no. of digits in happiness rating
                happinessHit += int(happinessRatings[1])
                happiness.append(happinessRatings[1])
            else:
                happinessHit += int(happinessRatings[0])
                happiness.append(happinessRatings[0])
        elif 'nearMiss' in trial:
            targetNearMiss += 1
            willingnessNearMiss += int(ratings[-3:])
            if len(happinessRatings) == 3:
                happinessNearMiss += int(happinessRatings[1])
                happiness.append(happinessRatings[1])
            else:
                happinessNearMiss += int(happinessRatings[0])
                happiness.append(happinessRatings[0])
        elif 'fullMiss' in trial:
            targetFullMiss += 1
            willingnessFullMiss += int(ratings[-3:])
            if len(happinessRatings) == 3:
                happinessFullMiss += int(happinessRatings[1])
                happiness.append(happinessRatings[1])
            else:
                happinessFullMiss += int(happinessRatings[0])
                happiness.append(happinessRatings[0])
        maximumHappinessTrial = happiness.index(max(happiness)) + 1  # +1 so that the first trial will appear as '1'
        maximumHappiness = max(happiness).strip('\n')  # Strip so items appear in the same row as associated items
        minimumHappinessTrial = happiness.index(min(happiness)) + 1
        minimumHappiness = min(happiness).strip('\n')
        total = targetHit + targetNearMiss + targetFullMiss  # Each to be divided by total; expressed as decimal

    dataFile.write(f'{condition}, {name}, {age}, {gender}, {targetHit / total:.2f}, {targetNearMiss / total:.2f}, '
                   f'{targetFullMiss / total:.2f}, {happinessHit / targetHit:.2f}, {willingnessHit / targetHit:.2f}, '
                   f'{happinessNearMiss / targetNearMiss:.2f}, {willingnessNearMiss / targetNearMiss:.2f}, '
                   f'{happinessFullMiss / targetFullMiss:.2f}, {willingnessFullMiss / targetFullMiss:.2f}, '
                   f'{maximumHappiness}, {maximumHappinessTrial}, {minimumHappiness}, {minimumHappinessTrial}\n')

dataFile.close()

# Here, we're creating a .csv file that organises the data files in respect of the date participation occurred
newDataFile = open('by_date_results.csv', 'w')
allData = sorted(listdir())
del (allData[0:3])
participationDate = {}
for file in allData:
    date = ''  # Collating individual date each participant performed experiment on
    luck = 0
    mixed = 0
    skill = 0
    if "results_" in file:
        datum = open(file, 'r')
        allLines = datum.readlines()

        day = allLines[1]
        day = day[6:14]
        date = day[:2] + '-' + day[2:4] + '-' + day[4:]  # Formatting such that day/month/year are easily distinguished
        participationDate[day] = 0

        if '04062013' in day and 'luck' in allLines[0]:
            luck += 1
        elif '04062013' in day and 'mixed' in allLines[0]:
            mixed += 1
        elif '04062013' in day and 'skill' in allLines[0]:
            skill += 1