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Commit 2ba8f80

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etpinardetpinard
committed
split up histogram index
1 parent 7f1801e commit 2ba8f80
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6 files changed

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‎src/traces/histogram/average.js

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/**
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* Copyright 2012-2015, Plotly, Inc.
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* All rights reserved.
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*
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* This source code is licensed under the MIT license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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'use strict';
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module.exports = function doAvg(size, counts) {
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var nMax = size.length,
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total = 0;
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for(var i=0; i<nMax; i++) {
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if(counts[i]) {
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size[i] /= counts[i];
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total += size[i];
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}
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else size[i] = null;
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}
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return total;
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};

‎src/traces/histogram/bin_functions.js

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/**
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* Copyright 2012-2015, Plotly, Inc.
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* All rights reserved.
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*
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* This source code is licensed under the MIT license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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'use strict';
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var isNumeric = require('fast-isnumeric');
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module.exports = {
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count: function(n, i, size) {
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size[n]++;
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return 1;
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},
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sum: function(n, i, size, counterData) {
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var v = counterData[i];
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if(isNumeric(v)) {
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v = Number(v);
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size[n] += v;
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return v;
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}
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return 0;
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},
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avg: function(n, i, size, counterData, counts) {
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var v = counterData[i];
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if(isNumeric(v)) {
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v = Number(v);
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size[n] += v;
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counts[n]++;
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}
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return 0;
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},
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min: function(n, i, size, counterData) {
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var v = counterData[i];
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if(isNumeric(v)) {
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v = Number(v);
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if(!isNumeric(size[n])) {
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size[n] = v;
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return v;
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}
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else if(size[n]>v) {
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size[n] = v;
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return v-size[n];
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}
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}
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return 0;
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},
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max: function(n, i, size, counterData) {
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var v = counterData[i];
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if(isNumeric(v)) {
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v = Number(v);
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if(!isNumeric(size[n])) {
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size[n] = v;
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return v;
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}
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else if(size[n]<v) {
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size[n] = v;
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return v-size[n];
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}
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}
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return 0;
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}
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};

‎src/traces/histogram/calc.js

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/**
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* Copyright 2012-2015, Plotly, Inc.
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* All rights reserved.
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*
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* This source code is licensed under the MIT license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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'use strict';
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var isNumeric = require('fast-isnumeric');
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var Plotly = require('../../plotly');
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var Lib = require('../../lib');
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var binFunctions = require('./bin_functions');
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var normFunctions = require('./norm_functions');
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var doAvg = require('./average');
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module.exports = function calc(gd, trace) {
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// ignore as much processing as possible (and including in autorange) if bar is not visible
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if(trace.visible !== true) return;
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// depending on orientation, set position and size axes and data ranges
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// note: this logic for choosing orientation is duplicated in graph_obj->setstyles
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var pos = [],
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size = [],
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i,
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pa = Plotly.Axes.getFromId(gd,
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trace.orientation==='h' ? (trace.yaxis || 'y') : (trace.xaxis || 'x')),
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maindata = trace.orientation==='h' ? 'y' : 'x',
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counterdata = {x: 'y', y: 'x'}[maindata];
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// prepare the raw data
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var pos0 = pa.makeCalcdata(trace, maindata);
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// calculate the bins
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if((trace['autobin' + maindata]!==false) || !(maindata + 'bins' in trace)) {
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trace[maindata + 'bins'] = Plotly.Axes.autoBin(pos0, pa, trace['nbins' + maindata]);
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// copy bin info back to the source data.
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trace._input[maindata + 'bins'] = trace[maindata + 'bins'];
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}
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var binspec = trace[maindata + 'bins'],
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allbins = typeof binspec.size === 'string',
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bins = allbins ? [] : binspec,
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// make the empty bin array
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i2,
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binend,
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n,
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inc = [],
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counts = [],
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total = 0,
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norm = trace.histnorm,
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func = trace.histfunc,
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densitynorm = norm.indexOf('density')!==-1,
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extremefunc = func==='max' || func==='min',
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sizeinit = extremefunc ? null : 0,
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binfunc = binFunctions.count,
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normfunc = normFunctions[norm],
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doavg = false,
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rawCounterData;
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if(Array.isArray(trace[counterdata]) && func!=='count') {
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rawCounterData = trace[counterdata];
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doavg = func==='avg';
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binfunc = binFunctions[func];
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}
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// create the bins (and any extra arrays needed)
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// assume more than 5000 bins is an error, so we don't crash the browser
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i = binspec.start;
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// decrease end a little in case of rounding errors
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binend = binspec.end +
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(binspec.start - Plotly.Axes.tickIncrement(binspec.start, binspec.size)) / 1e6;
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while(i<binend && pos.length<5000) {
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i2 = Plotly.Axes.tickIncrement(i, binspec.size);
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pos.push((i + i2) / 2);
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size.push(sizeinit);
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// nonuniform bins (like months) we need to search,
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// rather than straight calculate the bin we're in
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if(allbins) bins.push(i);
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// nonuniform bins also need nonuniform normalization factors
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if(densitynorm) inc.push(1 / (i2 - i));
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if(doavg) counts.push(0);
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i = i2;
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}
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var nMax = size.length;
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// bin the data
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for(i=0; i<pos0.length; i++) {
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n = Lib.findBin(pos0[i], bins);
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if(n>=0 && n<nMax) total += binfunc(n, i, size, rawCounterData, counts);
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}
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// average and/or normalize the data, if needed
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if(doavg) total = doAvg(size, counts);
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if(normfunc) normfunc(size, total, inc);
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var serieslen = Math.min(pos.length, size.length),
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cd = [],
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firstNonzero = 0,
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lastNonzero = serieslen-1;
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// look for empty bins at the ends to remove, so autoscale omits them
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for(i=0; i<serieslen; i++) {
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if(size[i]) {
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firstNonzero = i;
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break;
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}
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}
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for(i=serieslen-1; i>firstNonzero; i--) {
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if(size[i]) {
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lastNonzero = i;
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break;
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}
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}
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// create the "calculated data" to plot
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for(i=firstNonzero; i<=lastNonzero; i++) {
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if((isNumeric(pos[i]) && isNumeric(size[i]))) {
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cd.push({p: pos[i], s: size[i], b: 0});
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}
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}
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return cd;
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};

‎src/traces/histogram/defaults.js

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/**
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* Copyright 2012-2015, Plotly, Inc.
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* All rights reserved.
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*
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* This source code is licensed under the MIT license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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'use strict';
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var Plotly = require('../../plotly');
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var Lib = require('../../lib');
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var attributes = require('./attributes');
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module.exports = function(traceIn, traceOut) {
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function coerce(attr, dflt) {
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return Lib.coerce(traceIn, traceOut, attributes, attr, dflt);
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}
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var binDirections = ['x'],
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hasAggregationData,
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x = coerce('x'),
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y = coerce('y');
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if(Plotly.Plots.traceIs(traceOut, '2dMap')) {
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// we could try to accept x0 and dx, etc...
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// but that's a pretty weird use case.
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// for now require both x and y explicitly specified.
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if(!(x && x.length && y && y.length)) {
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traceOut.visible = false;
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return;
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}
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// if marker.color is an array, we can use it in aggregation instead of z
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hasAggregationData = coerce('z') || coerce('marker.color');
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binDirections = ['x','y'];
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} else {
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var orientation = coerce('orientation', (y && !x) ? 'h' : 'v'),
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sample = traceOut[orientation==='v' ? 'x' : 'y'];
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if(!(sample && sample.length)) {
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traceOut.visible = false;
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return;
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}
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if(orientation==='h') binDirections = ['y'];
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hasAggregationData = traceOut[orientation==='h' ? 'x' : 'y'];
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}
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if(hasAggregationData) coerce('histfunc');
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coerce('histnorm');
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binDirections.forEach(function(binDirection) {
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// data being binned - note that even though it's a little weird,
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// it's possible to have bins without data, if there's inferred data
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var binstrt = coerce(binDirection + 'bins.start'),
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binend = coerce(binDirection + 'bins.end'),
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autobin = coerce('autobin' + binDirection, !(binstrt && binend));
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if(autobin) coerce('nbins' + binDirection);
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else coerce(binDirection + 'bins.size');
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});
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};

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