// userStatistics provides functions to generate statistics about stored user profiles.
// The user chooses the attribute(s), and the gui displays a chart with a button to view the statistics data.
// See statisticsGui.go to see the gui code.

package userStatistics

//TODO: Add the ability to control for confounding variables
// Example: Wealth, controlled for age and sex

import "seekia/internal/badgerDatabase"
import "seekia/internal/helpers"
import "seekia/internal/profiles/calculatedAttributes"
import "seekia/internal/profiles/profileStorage"
import "seekia/internal/profiles/attributeDisplay"
import "seekia/internal/translation"

import "slices"
import "strings"
import "errors"
import "math"


type StatisticsItem struct{

	// The label for the statistics item
	// For a bar chart, this represents the name of an X axis bar.
	// For a donut chart, this represents the name of a donut slice
	// Example: "Man", "100-200"
	// This will never be translated, unless it is the Unknown/No Response item, in which case
	//		the label will be "Unknown"/"No Response" translated to the user's current app language
	Label string

	// This is the formatted, human readable version of the label
	// This will be translated into the application language
	// Sometimes, the LabelFormatted will be identical to Label
	// This does not include units (Example: " days", " users")
	// Example:
	//	-"1000000-2000000" -> "1 million-2 million"
	LabelFormatted string

	// The value corresponding to the label
	// For a bar chart, this represents the Y axis value for a bar.
	// For a donut chart, this represents the value (size) of one of the donut slices
	// This will never be translated
	// For example, the value could be 500 if 500 men responded Yes.
	Value float64

	// This is the formatted version of the value
	// This does not include units (Example: " days", " users")
	// Examples: 
	//	-5 -> "5/10"
	//	-1500000000000 -> "1.5 trillion"
	ValueFormatted string
}


//Outputs:
//	-int: Number of users analyzed in statistics
//	-[]StatisticsItem: Statistics items list (sorted, not grouped)
//	-bool: Grouping performed
//	-[]StatisticsItem: Grouped items list
//	-func(float64)(string, error): Function to format y axis values
//		-This is used because the values must be passed to the chart code as pure floats, but they must be formatted after to be human readable
//		-Example: "1000000" -> "1 million"
//	-error
func GetUserStatisticsItemsLists_BarChart(identityType string,
										networkType byte,
										xAxisAttribute string,
										xAxisIsNumerical bool,
										formatXAxisValuesFunction func(string)(string, error),
										xAxisUnknownLabel string,
										yAxisAttribute string)(int, []StatisticsItem, bool, []StatisticsItem, func(float64)(string, error), error){

	isValid := helpers.VerifyNetworkType(networkType)
	if (isValid == false){
		networkTypeString := helpers.ConvertByteToString(networkType)
		return 0, nil, false, nil, nil, errors.New("GetUserStatisticsItemsLists_BarChart called with invalid networkType: " + networkTypeString)
	}

	getYAxisRoundingPrecision := func()int{
		if (yAxisAttribute == "Number Of Users"){
			return 0
		}
		if (yAxisAttribute == "Average Height"){
			return 0
		}
		if (yAxisAttribute == "Average Age"){
			return 0
		}
		return 1
	}

	yAxisRoundingPrecision := getYAxisRoundingPrecision()

	//Outputs:
	//	-int: Total analyzed users
	//	-[]StatisticsItem: Statistics items list
	//	-map[string]int: Response Counts map (X axis attribute response -> Number of y axis responses)
	//	-bool: yAxisIsAverage
	//	-map[string]float64: Response Sums map (X axis attribute response -> all y axis responses summed) (If yAxisIsAverage == true)
	//	-func(float64)(string, error): Function to format statistics values
	//	-bool: Include a No Response/Unknown value item
	//		-This is only needed if at least 1 user did not respond to the X axis attribute on their profile.
	//	-StatisticsItem: The unknown value item.
	//	-error
	getStatisticsItemsList := func()(int, []StatisticsItem, map[string]int, bool, map[string]float64, func(float64)(string, error), bool, StatisticsItem, error){

		//TODO: Add "Probability Of ..."
		// This will allow viewing of choice attribute probabilities
		// For example: Probability of being Male, probability of being Female, etc.
		// We need to add this for each canonical option

		if (yAxisAttribute == "Number Of Users"){

			totalAnalyzedUsers, statisticsItemsList, responseCountsMap, numberOfUnknownValueUsers, err := getProfileAttributeCountStatisticsItemsList(identityType, networkType, xAxisAttribute, formatXAxisValuesFunction)
			if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }

			formatValuesFunction := func(input float64)(string, error){

				// input will be an whole number representing the number of users

				result := helpers.ConvertFloat64ToStringRounded(input, 0)

				return result, nil
			}

			if (numberOfUnknownValueUsers == 0){
				return totalAnalyzedUsers, statisticsItemsList, responseCountsMap, false, nil, formatValuesFunction, false, StatisticsItem{}, nil
			}

			unknownValueFormatted := helpers.ConvertIntToString(numberOfUnknownValueUsers)

			unknownItem := StatisticsItem{
				Label: xAxisUnknownLabel,
				LabelFormatted: xAxisUnknownLabel,
				Value: float64(numberOfUnknownValueUsers),
				ValueFormatted: unknownValueFormatted,
			}

			return totalAnalyzedUsers, statisticsItemsList, responseCountsMap, false, nil, formatValuesFunction, true, unknownItem, nil
		}

		userIdentityHashesToAnalyzeList, err := getUserIdentityHashesToAnalyzeList(identityType)
		if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }

		yAxisIsAverage := strings.HasPrefix(yAxisAttribute, "Average ")
		if (yAxisIsAverage == true){

			// This function will return the attribute name
			// We must convert the attribute title to the attribute name
			getAttributeToGetAverageFor := func()string{

				attributeTitle := strings.TrimPrefix(yAxisAttribute, "Average ")
		
				if (attributeTitle == "Wealth"){
					return "WealthInGold"
				}
				if (attributeTitle == "23andMe Neanderthal Variants"){
					return "23andMe_NeanderthalVariants"
				}
				if (attributeTitle == "Body Fat"){
					return "BodyFat"
				}
				if (attributeTitle == "Body Muscle"){
					return "BodyMuscle"
				}
				if (attributeTitle == "Fruit Rating"){
					return "FruitRating"
				}
				if (attributeTitle == "Vegetables Rating"){
					return "VegetablesRating"
				}
				if (attributeTitle == "Nuts Rating"){
					return "NutsRating"
				}
				if (attributeTitle == "Grains Rating"){
					return "GrainsRating"
				}
				if (attributeTitle == "Dairy Rating"){
					return "DairyRating"
				}
				if (attributeTitle == "Seafood Rating"){
					return "SeafoodRating"
				}
				if (attributeTitle == "Beef Rating"){
					return "BeefRating"
				}
				if (attributeTitle == "Pork Rating"){
					return "PorkRating"
				}
				if (attributeTitle == "Poultry Rating"){
					return "PoultryRating"
				}
				if (attributeTitle == "Eggs Rating"){
					return "EggsRating"
				}
				if (attributeTitle == "Beans Rating"){
					return "BeansRating"
				}
				if (attributeTitle == "Alcohol Frequency"){
					return "AlcoholFrequency"
				}
				if (attributeTitle == "Tobacco Frequency"){
					return "TobaccoFrequency"
				}
				if (attributeTitle == "Cannabis Frequency"){
					return "CannabisFrequency"
				}
				if (attributeTitle == "Pets Rating"){
					return "PetsRating"
				}
				if (attributeTitle == "Dogs Rating"){
					return "DogsRating"
				}
				if (attributeTitle == "Cats Rating"){
					return "CatsRating"
				}

				return attributeTitle
			}

			attributeToGetAverageFor := getAttributeToGetAverageFor()

			totalAnalyzedUsers := 0

			//Map structure: X-Axis attribute -> Number of users with this x-axis attribute who have a y-axis attribute response
			responseCountsMap := make(map[string]int)
			//Map structure: X-axis attribute response -> All Y-axis attribute responses summed for users with this x-axis attribute
			responseSumsMap := make(map[string]float64)

			// This stores a count of the number of users whose X axis value is unknown
			// For example, if X axis is Age, this is the number of users who did not respond
			numberOfUsersWithUnknownXAxisValue := 0
			// This stores the total sum of all y axis values for users who have an unknown X axis value
			// For example, if the X axis attribute is age, and the Y axis attribute is average height,
			//		this stores the sum of heights of all users who have no age on their profile.
			usersWithUnknownXAxisValueYAxisValuesSum := float64(0)

			for _, userIdentityHash := range userIdentityHashesToAnalyzeList{

				profileFound, getAnyUserAttributeValueFunction, err := getRetrieveAnyAttributeFromUserNewestProfileFunction(userIdentityHash, networkType)
				if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }
				if (profileFound == false){
					continue
				}

				userIsDisabled, _, _, err := getAnyUserAttributeValueFunction("Disabled")
				if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }
				if (userIsDisabled == true){
					continue
				}

				attributeExists, _, userAttributeToGetAverageForValue, err := getAnyUserAttributeValueFunction(attributeToGetAverageFor)
				if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }
				if (attributeExists == false){
					// This user did not respond to the attribute we are getting the average for
					// We will not add them to the statistics maps
					continue
				}

				totalAnalyzedUsers += 1

				userAttributeToGetAverageForValueFloat64, err := helpers.ConvertStringToFloat64(userAttributeToGetAverageForValue)
				if (err != nil) {
					return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, errors.New("Database corrupt: Contains invalid " + userAttributeToGetAverageForValue + " value: " + userAttributeToGetAverageForValue)
				}

				attributeFound, _, userXAxisAttributeValue, err := getAnyUserAttributeValueFunction(xAxisAttribute)
				if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }
				if (attributeFound == false){
					// This user did not respond to the X axis attribute
					// We calculate the average for users who do not respond and put it in its own category

					numberOfUsersWithUnknownXAxisValue += 1
					usersWithUnknownXAxisValueYAxisValuesSum += userAttributeToGetAverageForValueFloat64
					continue
				}

				responseCountsMap[userXAxisAttributeValue] += 1

				responseSumsMap[userXAxisAttributeValue] += userAttributeToGetAverageForValueFloat64
			}

			_, _, formatYAxisValuesFunction, _, _, err := attributeDisplay.GetProfileAttributeDisplayInfo(attributeToGetAverageFor)
			if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }

			statisticsItemsList := make([]StatisticsItem, 0, len(responseCountsMap))

			for attributeResponse, responsesCount := range responseCountsMap{

				attributeResponseFormatted, err := formatXAxisValuesFunction(attributeResponse)
				if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }

				allResponsesSum, exists := responseSumsMap[attributeResponse]
				if (exists == false){
					return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, errors.New("Response sums map missing attribute value")	
				}

				averageValue := allResponsesSum/float64(responsesCount)

				averageValueString := helpers.ConvertFloat64ToStringRounded(averageValue, yAxisRoundingPrecision)

				averageValueFormatted, err := formatYAxisValuesFunction(averageValueString)
				if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }

				newStatisticsItem := StatisticsItem{
					Label: attributeResponse,
					LabelFormatted: attributeResponseFormatted,
					Value: averageValue,
					ValueFormatted: averageValueFormatted,
				}

				statisticsItemsList = append(statisticsItemsList, newStatisticsItem)
			}

			// We use this function to format values after grouping, if grouping is needed
			formatValuesFunction := func(input float64)(string, error){

				inputString := helpers.ConvertFloat64ToStringRounded(input, yAxisRoundingPrecision)

				valueFormatted, err := formatYAxisValuesFunction(inputString)
				if (err != nil) { return "", err }

				return valueFormatted, nil
			}

			if (numberOfUsersWithUnknownXAxisValue == 0){
				return totalAnalyzedUsers, statisticsItemsList, responseCountsMap, true, responseSumsMap, formatValuesFunction, false, StatisticsItem{}, nil
			}

			unknownResponsesAverage := usersWithUnknownXAxisValueYAxisValuesSum/float64(numberOfUsersWithUnknownXAxisValue)

			unknownResponsesValueFormatted, err := formatValuesFunction(unknownResponsesAverage)
			if (err != nil) { return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, err }

			// This item represents the average value for the yAxisAttribute for users who did not respond.
			// For example, if the xAxisAttribute is Height, and the yAxisAttribute is AverageWealth, this value
			//		will represent the average wealth for users who did not provide Height on their profile.
			unknownStatisticsItem := StatisticsItem{
				Label: xAxisUnknownLabel,
				LabelFormatted: xAxisUnknownLabel,
				Value: unknownResponsesAverage,
				ValueFormatted: unknownResponsesValueFormatted,
			}

			return totalAnalyzedUsers, statisticsItemsList, responseCountsMap, true, responseSumsMap, formatValuesFunction, true, unknownStatisticsItem, nil
		}

		return 0, nil, nil, false, nil, nil, false, StatisticsItem{}, errors.New("Invalid y-axis attribute: " + yAxisAttribute)
	}

	totalAnalyzedUsers, statisticsItemsList, responseCountsMap, yAxisIsAverage, responseSumsMap, formatValuesFunction, includeUnknownItem, unknownValueItem, err := getStatisticsItemsList()
	if (err != nil) { return 0, nil, false, nil, nil, err }	

	sortStatisticsItemsList(statisticsItemsList, xAxisIsNumerical)

	// We now see if we need to group the items in the list together
	// We do this if there are more than 10 categories

	if (len(statisticsItemsList) <= 10){

		// No grouping needed. We are done.

		if (includeUnknownItem == true){
			statisticsItemsList = append(statisticsItemsList, unknownValueItem)
		}

		return totalAnalyzedUsers, statisticsItemsList, false, nil, formatValuesFunction, nil
	}

	groupedStatisticsItemsList, err := getStatisticsItemsListGrouped(10, statisticsItemsList, xAxisIsNumerical, responseCountsMap, yAxisIsAverage, responseSumsMap, formatValuesFunction)
	if (err != nil) { return 0, nil, false, nil, nil, err }

	if (includeUnknownItem == true){
		statisticsItemsList = append(statisticsItemsList, unknownValueItem)
		groupedStatisticsItemsList = append(groupedStatisticsItemsList, unknownValueItem)
	}

	return totalAnalyzedUsers, statisticsItemsList, true, groupedStatisticsItemsList, formatValuesFunction, nil	
}


//Outputs:
//	-int: Number of users analyzed in statistics
//	-[]StatisticsItem: Statistics items list (sorted, not grouped)
//	-bool: Grouping performed
//	-[]StatisticsItem: Grouped items list
//	-error
func GetUserStatisticsItemsLists_DonutChart(identityType string,
											networkType byte,
											attributeToAnalyze string,
											attributeIsNumerical bool,
											formatAttributeLabelsFunction func(string)(string, error),
											unknownLabelTranslated string)(int, []StatisticsItem, bool, []StatisticsItem, error){

	isValid := helpers.VerifyNetworkType(networkType)
	if (isValid == false){
		networkTypeString := helpers.ConvertByteToString(networkType)
		return 0, nil, false, nil, errors.New("GetUserStatisticsItemsLists_DonutChart called with invalid networkType: " + networkTypeString)
	}

	totalAnalyzedUsers, statisticsItemsList, attributeCountsMap, numberOfUnknownResponders, err := getProfileAttributeCountStatisticsItemsList(identityType, networkType, attributeToAnalyze, formatAttributeLabelsFunction)
	if (err != nil) { return 0, nil, false, nil, err }

	sortStatisticsItemsList(statisticsItemsList, attributeIsNumerical)

	getUnknownValueItem := func()StatisticsItem{

		numberOfUnknownRespondersString := helpers.ConvertIntToString(numberOfUnknownResponders)

		unknownValueItem := StatisticsItem{
			Label: unknownLabelTranslated,
			LabelFormatted: unknownLabelTranslated,
			Value: float64(numberOfUnknownResponders),
			ValueFormatted: numberOfUnknownRespondersString,
		}

		return unknownValueItem
	}

	if (len(statisticsItemsList) <= 8){

		// No grouping needed.

		if (numberOfUnknownResponders != 0){

			unknownValueItem := getUnknownValueItem()

			statisticsItemsList = append(statisticsItemsList, unknownValueItem)
		}

		return totalAnalyzedUsers, statisticsItemsList, false, nil, nil
	}

	formatValuesFunction := func(input float64)(string, error){

		// input will always be the number of users who responded with the value
		// Thus, input will always be an integer

		result := helpers.ConvertFloat64ToStringRounded(input, 0)

		return result, nil
	}

	groupedStatisticsItemsList, err := getStatisticsItemsListGrouped(8, statisticsItemsList, attributeIsNumerical, attributeCountsMap, false, nil, formatValuesFunction)
	if (err != nil) { return 0, nil, false, nil, err }

	if (numberOfUnknownResponders != 0){

		unknownValueItem := getUnknownValueItem()

		statisticsItemsList = append(statisticsItemsList, unknownValueItem)
		groupedStatisticsItemsList = append(groupedStatisticsItemsList, unknownValueItem)
	}

	return totalAnalyzedUsers, statisticsItemsList, true, groupedStatisticsItemsList, nil
}


// This function will return a statistics items list of the following format:
//	"Label": Attribute name (Example: "Male")
//	"Value": The number of users who responded with the attribute (in this example: "Male")
//
// All users of provided identityType who are not disabled will be analyzed
//	-int: Number of analyzed users
//	-[]StatisticsItem: Statistics items list (not sorted or grouped)
//	-map[string]int: Response counts map (Response -> Number of responders)
//	-int: Number of No Response/Unknown value responders
//	-error
func getProfileAttributeCountStatisticsItemsList(identityType string,
											networkType byte,
											attributeName string,
											formatLabelsFunction func(string)(string, error))(int, []StatisticsItem, map[string]int, int, error){

	userIdentityHashesToAnalyzeList, err := getUserIdentityHashesToAnalyzeList(identityType)
	if (err != nil) { return 0, nil, nil, 0, err }

	totalAnalyzedUsers := 0

	// Map structure: User Attribute response -> Number of users who responded with the response
	responseCountsMap := make(map[string]int)

	// This stores the number of users for whom we do not know their value
	numberOfUnknownValueUsers := 0

	for _, userIdentityHash := range userIdentityHashesToAnalyzeList{

		profileFound, getAnyUserAttributeValueFunction, err := getRetrieveAnyAttributeFromUserNewestProfileFunction(userIdentityHash, networkType)
		if (err != nil) { return 0, nil, nil, 0, err }
		if (profileFound == false){
			continue
		}
		userIsDisabled, _, _, err := getAnyUserAttributeValueFunction("Disabled")
		if (err != nil) { return 0, nil, nil, 0, err }
		if (userIsDisabled == true){
			continue
		}

		totalAnalyzedUsers += 1

		attributeFound, _, attributeValue, err := getAnyUserAttributeValueFunction(attributeName)
		if (err != nil) { return 0, nil, nil, 0, err }
		if (attributeFound == false){
			numberOfUnknownValueUsers += 1
			continue
		}

		responseCountsMap[attributeValue] += 1
	}

	statisticsItemsList := make([]StatisticsItem, 0, len(responseCountsMap))

	for attributeResponse, numberOfUsers := range responseCountsMap{

		attributeResponseFormatted, err := formatLabelsFunction(attributeResponse)
		if (err != nil) { return 0, nil, nil, 0, err }

		attributeNumberOfUsersString := helpers.ConvertIntToString(numberOfUsers)

		newStatisticsItem := StatisticsItem{
			Label: attributeResponse,
			LabelFormatted: attributeResponseFormatted,
			Value: float64(numberOfUsers),
			ValueFormatted: attributeNumberOfUsersString,
		}

		statisticsItemsList = append(statisticsItemsList, newStatisticsItem)
	}

	return totalAnalyzedUsers, statisticsItemsList, responseCountsMap, numberOfUnknownValueUsers, nil
}

func sortStatisticsItemsList(inputStatisticsItemsList []StatisticsItem, labelIsNumerical bool){

	if (len(inputStatisticsItemsList) <= 1){
		return
	}

	if (labelIsNumerical == true){

		// We sort the items by label values in ascending order
		// Example: Bar chart columns are ages, in order of youngest to oldest

		compareItemsFunction := func(itemA StatisticsItem, itemB StatisticsItem)int{

			itemALabel := itemA.Label
			itemBLabel := itemB.Label

			itemAFloat64, err := helpers.ConvertStringToFloat64(itemALabel)
			if (err != nil) {
				panic("Invalid statistics item: Item Label is not float: " + itemALabel)
			}

			itemBFloat64, err := helpers.ConvertStringToFloat64(itemBLabel)
			if (err != nil) {
				panic("Invalid statistics item: Item Label is not float: " + itemBLabel)
			}

			if (itemAFloat64 == itemBFloat64){
				return 0
			}

			if (itemAFloat64 < itemBFloat64){
				return -1
			}

			return 1
		}

		slices.SortFunc(inputStatisticsItemsList, compareItemsFunction)

		return
	}

	// We sort the items by their values in descending order

	compareItemsFunction := func(itemA StatisticsItem, itemB StatisticsItem)int{

		itemAValue := itemA.Value
		itemBValue := itemB.Value

		if (itemAValue == itemBValue){
			return 0
		}

		if (itemAValue < itemBValue){
			return 1
		}

		return -1
	}

	slices.SortFunc(inputStatisticsItemsList, compareItemsFunction)
}

// This function will group a statistics items list.
// It will group Labels and their values to fit into a specified number of groups
// Example: "1","2","3","4" -> "1-2", "3-4"
//Inputs:
//	-int: Maximum groups to create
//	-[]StatisticsItem: Statistics items list to group
//	-bool: Label is numerical
//		-If it is, we will group labels into groups of numbers.
//		-Otherwise, we will group all categories after the first maximumGroupsToCreate into a group called Other
//	-map[string]int
//		-Response Counts map (Attribute response -> Number of responders with that response)
//	-bool: Value is average
//		-If value is average, we will combine the values of each group and find their average
//		-Otherwise, we will find their sum
//	-map[string]float64
//		-Response Sums map (X-axis attribute response -> all Y-axis responses summed) (If yAxisIsAverage == true)
//	-func(float64)(string, error): This is the function we use to format the values
//Outputs:
//	-[]StatisticsItem: Grouped statistics items list
//	-error
func getStatisticsItemsListGrouped(maximumGroupsToCreate int,
								inputStatisticsItemsList []StatisticsItem,
								labelIsNumerical bool,
								responseCountsMap map[string]int,
								valueIsAverage bool,
								responseSumsMap map[string]float64,
								formatValuesFunction func(float64)(string, error))([]StatisticsItem, error){

	if (len(inputStatisticsItemsList) <= maximumGroupsToCreate){
		return nil, errors.New("maximumGroupsToCreate is <= length of input statistics items list")
	}

	// We deep copy the statistics items list to retain the sorted version
	// We need to retain both versions because the user can view the raw or grouped data in the GUI

	statisticsItemsList := slices.Clone(inputStatisticsItemsList)

	// We use this function to get the new value for a group of labels
	getGroupValue := func(itemsToCombineList []StatisticsItem)(float64, error){

		// This will count the total number of users who responded with the responses within this group
		// Example: Labels are "Blue", "Green", this variable will store the number of users who responded with either Blue or Green
		totalRespondersCount := float64(0)

		// This will store the sum for all response values within the group
		// We only need to add to this sum if valueIsAverage == true
		allReponsesSummed := float64(0)

		for _, statisticsItem := range itemsToCombineList{

			itemLabel := statisticsItem.Label

			responderCount, exists := responseCountsMap[itemLabel]
			if (exists == false){
				return 0, errors.New("responseCountsMap missing label: " + itemLabel)
			}

			totalRespondersCount += float64(responderCount)

			if (valueIsAverage == true){

				yAxisAttributeResponsesSum, exists := responseSumsMap[itemLabel]
				if (exists == false){
					return 0, errors.New("responseSumsMap missing label: " + itemLabel)
				}
				allReponsesSummed += yAxisAttributeResponsesSum
			}
		}

		if (valueIsAverage == false){

			return totalRespondersCount, nil
		}

		// The value is an average
		// We need to find the average for all of the user responses for the labels in the input list
		// The Values in the inputStatisticsItemsList are averages
		// We can't average out the averages, because that will not give us the true average
		// We have to use the original sums for all group items and average them

		if (totalRespondersCount == 0){
			return 0, errors.New("totalRespondersCount is 0.")
		}

		value := allReponsesSummed/float64(totalRespondersCount)

		return value, nil
	}

	if (labelIsNumerical == true){

		maximumItemsPerCategory := int(math.Ceil(float64(len(statisticsItemsList))/float64(maximumGroupsToCreate)))

		statisticsItemsListSublists, err := helpers.SplitListIntoSublists(statisticsItemsList, maximumItemsPerCategory)
		if (err != nil) { return nil, err }

		groupedItemsList := make([]StatisticsItem, 0, len(statisticsItemsListSublists))

		for _, groupItemsListSublist := range statisticsItemsListSublists{

			if (len(groupItemsListSublist) == 1){
				// Sometimes, a group with 1 item will be created
				// This happens if the groups cannot be evenly divided, so there is a remainder of 1.
				// Example: 10->4 groups = 3, 3, 3, 1.
				//TODO: Prevent this from happening so groups always have more than 1 subitem

				groupItem := groupItemsListSublist[0]

				groupedItemsList = append(groupedItemsList, groupItem)
				continue
			}

			finalIndex := len(groupItemsListSublist)-1

			initialItem := groupItemsListSublist[0]
			finalItem := groupItemsListSublist[finalIndex]

			initialLabel := initialItem.Label
			initialLabelFormatted := initialItem.LabelFormatted

			finalLabel := finalItem.Label
			finalLabelFormatted := finalItem.LabelFormatted

			groupValue, err := getGroupValue(groupItemsListSublist)
			if (err != nil) { return nil, err }

			groupValueFormatted, err := formatValuesFunction(groupValue)
			if (err != nil) { return nil, err }

			newGroupStatisticsItem := StatisticsItem{
				Label: initialLabel + "-" + finalLabel,
				LabelFormatted: initialLabelFormatted + "-" + finalLabelFormatted,
				Value: groupValue,
				ValueFormatted: groupValueFormatted,
			}

			groupedItemsList = append(groupedItemsList, newGroupStatisticsItem)
		}

		return groupedItemsList, nil
	}

	// Label is not numerical
	// We combine all categories after the first maximumGroupsToCreate into a category called Other

	itemsToKeep := statisticsItemsList[:maximumGroupsToCreate]

	itemsToCombine := statisticsItemsList[maximumGroupsToCreate:]

	otherTranslated := translation.TranslateTextFromEnglishToMyLanguage("Other")

	otherGroupValue, err := getGroupValue(itemsToCombine)
	if (err != nil) { return nil, err }

	otherGroupValueFormatted, err := formatValuesFunction(otherGroupValue)
	if (err != nil) { return nil, err }

	otherGroupItem := StatisticsItem{
		Label: "Other",
		LabelFormatted: otherTranslated,
		Value: otherGroupValue,
		ValueFormatted: otherGroupValueFormatted,
	}

	groupedStatisticsItemsList := append(itemsToKeep, otherGroupItem)

	return groupedStatisticsItemsList, nil
}


func getUserIdentityHashesToAnalyzeList(identityType string)([][16]byte, error){

	allUserIdentityHashesList, err := badgerDatabase.GetAllProfileIdentityHashes(identityType)
	if (err != nil) { return nil, err }

	if (len(allUserIdentityHashesList) < 10000){
		return allUserIdentityHashesList, nil
	}

	helpers.RandomizeListOrder(allUserIdentityHashesList)

	identityHashesToAnalyzeList := allUserIdentityHashesList[:10000]

	return identityHashesToAnalyzeList, nil
}

//Outputs:
//	-bool: Profile Exists
//	-func(string)(bool, int, string, error)
//	-error
func getRetrieveAnyAttributeFromUserNewestProfileFunction(identityHash [16]byte, networkType byte)(bool, func(string)(bool, int, string, error), error){

	newestProfileExists, profileVersion, _, _, _, newestProfileMap, err := profileStorage.GetNewestUserProfile(identityHash, networkType)
	if (err != nil) { return false, nil, err }
	if (newestProfileExists == false){
		return false, nil, nil
	}

	getAnyAttributeFromUserNewestProfileFunction, err := calculatedAttributes.GetRetrieveAnyProfileAttributeIncludingCalculatedFunction(profileVersion, newestProfileMap)
	if (err != nil) { return false, nil, err }

	return true, getAnyAttributeFromUserNewestProfileFunction, nil
}