

chroma.analyze = (data, key, filter) ->
    r =
        min: Number.MAX_VALUE
        max: Number.MAX_VALUE*-1
        sum: 0
        values: []
        count: 0

    if not filter?
        filter = ->
            true

    add = (val) ->
        if val? and not isNaN val
            r.values.push val
            r.sum += val
            r.min = val if val < r.min
            r.max = val if val > r.max
            r.count += 1
        return

    visit = (val, k) ->
        if filter val, k
            if key? and type(key) == 'function'
                add key val
            else if key? and type(key) == 'string' or type(key) == 'number'
                add val[key]
            else
                add val

    if type(data) == 'array'
        for val in data
            visit val
    else
        for k, val of data
            visit val, k
    r.domain = [r.min, r.max]
    r.limits = (mode, num) ->
        chroma.limits r, mode, num
    r



chroma.limits = (data, mode='equal', num=7) ->
    if type(data) == 'array'
        data = chroma.analyze data
    min = data.min
    max = data.max
    sum = data.sum
    values = data.values.sort (a,b)->
        a-b

    limits = []

    if mode.substr(0,1) == 'c' # continuous
        limits.push min
        limits.push max

    if mode.substr(0,1) == 'e' # equal interval
        limits.push min
        for i in [1..num-1]
            limits.push min+(i/num)*(max-min)
        limits.push max

    else if mode.substr(0,1) == 'l' # log scale
        if min <= 0
            throw 'Logarithmic scales are only possible for values > 0'
        min_log = Math.LOG10E * log min
        max_log = Math.LOG10E * log max
        limits.push min
        for i in [1..num-1]
            limits.push pow 10, min_log + (i/num) * (max_log - min_log)
        limits.push max

    else if mode.substr(0,1) == 'q' # quantile scale
        limits.push min
        for i in [1..num-1]
            p = values.length * i/num
            pb = floor p
            if pb == p
                limits.push values[pb]
            else # p > pb
                pr = p - pb
                limits.push values[pb]*pr + values[pb+1]*(1-pr)
        limits.push max

    else if mode.substr(0,1) == 'k' # k-means clustering
        ###
        implementation based on
        http://code.google.com/p/figue/source/browse/trunk/figue.js#336
        simplified for 1-d input values
        ###
        n = values.length
        assignments = new Array n
        clusterSizes = new Array num
        repeat = true
        nb_iters = 0
        centroids = null

        # get seed values
        centroids = []
        centroids.push min
        for i in [1..num-1]
            centroids.push min + (i/num) * (max-min)
        centroids.push max

        while repeat
            # assignment step
            for j in [0..num-1]
                clusterSizes[j] = 0
            for i in [0..n-1]
                value = values[i]
                mindist = Number.MAX_VALUE
                for j in [0..num-1]
                    dist = abs centroids[j]-value
                    if dist < mindist
                        mindist = dist
                        best = j
                clusterSizes[best]++
                assignments[i] = best

            # update centroids step
            newCentroids = new Array num
            for j in [0..num-1]
                newCentroids[j] = null
            for i in [0..n-1]
                cluster = assignments[i]
                if newCentroids[cluster] == null
                    newCentroids[cluster] = values[i]
                else
                    newCentroids[cluster] += values[i]
            for j in [0..num-1]
                newCentroids[j] *= 1/clusterSizes[j]

            # check convergence
            repeat = false
            for j in [0..num-1]
                if newCentroids[j] != centroids[i]
                    repeat = true
                    break

            centroids = newCentroids
            nb_iters++

            if nb_iters > 200
                repeat = false

        # finished k-means clustering
        # the next part is borrowed from gabrielflor.it
        kClusters = {}
        for j in [0..num-1]
            kClusters[j] = []
        for i in [0..n-1]
            cluster = assignments[i]
            kClusters[cluster].push values[i]
        tmpKMeansBreaks = []
        for j in [0..num-1]
            tmpKMeansBreaks.push kClusters[j][0]
            tmpKMeansBreaks.push kClusters[j][kClusters[j].length-1]
        tmpKMeansBreaks = tmpKMeansBreaks.sort (a,b)->
            a-b
        limits.push tmpKMeansBreaks[0]
        for i in [1..tmpKMeansBreaks.length-1] by 2
            if not isNaN(tmpKMeansBreaks[i])
                limits.push tmpKMeansBreaks[i]
    limits

