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###
chroma.js
Copyright (c) 2011-2013, Gregor Aisch
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* The name Gregor Aisch may not be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL GREGOR AISCH OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
@source: https://github.com/gka/chroma.js
###
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 * Math.log min
max_log = Math.LOG10E * Math.log max
limits.push min
for i in [1..num-1]
limits.push Math.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 = Math.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 = Math.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
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