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Diffstat (limited to 'node_modules/chroma-js/src/limits.coffee')
| -rw-r--r-- | node_modules/chroma-js/src/limits.coffee | 202 |
1 files changed, 202 insertions, 0 deletions
diff --git a/node_modules/chroma-js/src/limits.coffee b/node_modules/chroma-js/src/limits.coffee new file mode 100644 index 0000000000..44dd061aa0 --- /dev/null +++ b/node_modules/chroma-js/src/limits.coffee @@ -0,0 +1,202 @@ +### + 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 + |