chroma-js

### 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 * 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