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| /** | |
| * Copyright 2012-2017, Plotly, Inc. | |
| * All rights reserved. | |
| * | |
| * This source code is licensed under the MIT license found in the | |
| * LICENSE file in the root directory of this source tree. | |
| */ | |
| ; | |
| var BADNUM = require('../../constants/numerical').BADNUM; | |
| module.exports = function linePoints(d, opts) { | |
| var xa = opts.xaxis, | |
| ya = opts.yaxis, | |
| simplify = opts.simplify, | |
| connectGaps = opts.connectGaps, | |
| baseTolerance = opts.baseTolerance, | |
| linear = opts.linear, | |
| segments = [], | |
| minTolerance = 0.2, // fraction of tolerance "so close we don't even consider it a new point" | |
| pts = new Array(d.length), | |
| pti = 0, | |
| i, | |
| // pt variables are pixel coordinates [x,y] of one point | |
| clusterStartPt, // these four are the outputs of clustering on a line | |
| clusterEndPt, | |
| clusterHighPt, | |
| clusterLowPt, | |
| thisPt, // "this" is the next point we're considering adding to the cluster | |
| clusterRefDist, | |
| clusterHighFirst, // did we encounter the high point first, then a low point, or vice versa? | |
| clusterUnitVector, // the first two points in the cluster determine its unit vector | |
| // so the second is always in the "High" direction | |
| thisVector, // the pixel delta from clusterStartPt | |
| // val variables are (signed) pixel distances along the cluster vector | |
| clusterHighVal, | |
| clusterLowVal, | |
| thisVal, | |
| // deviation variables are (signed) pixel distances normal to the cluster vector | |
| clusterMinDeviation, | |
| clusterMaxDeviation, | |
| thisDeviation; | |
| if(!simplify) { | |
| baseTolerance = minTolerance = -1; | |
| } | |
| // turn one calcdata point into pixel coordinates | |
| function getPt(index) { | |
| var x = xa.c2p(d[index].x), | |
| y = ya.c2p(d[index].y); | |
| if(x === BADNUM || y === BADNUM) return false; | |
| return [x, y]; | |
| } | |
| // if we're off-screen, increase tolerance over baseTolerance | |
| function getTolerance(pt) { | |
| var xFrac = pt[0] / xa._length, | |
| yFrac = pt[1] / ya._length; | |
| return (1 + 10 * Math.max(0, -xFrac, xFrac - 1, -yFrac, yFrac - 1)) * baseTolerance; | |
| } | |
| function ptDist(pt1, pt2) { | |
| var dx = pt1[0] - pt2[0], | |
| dy = pt1[1] - pt2[1]; | |
| return Math.sqrt(dx * dx + dy * dy); | |
| } | |
| // loop over ALL points in this trace | |
| for(i = 0; i < d.length; i++) { | |
| clusterStartPt = getPt(i); | |
| if(!clusterStartPt) continue; | |
| pti = 0; | |
| pts[pti++] = clusterStartPt; | |
| // loop over one segment of the trace | |
| for(i++; i < d.length; i++) { | |
| clusterHighPt = getPt(i); | |
| if(!clusterHighPt) { | |
| if(connectGaps) continue; | |
| else break; | |
| } | |
| // can't decimate if nonlinear line shape | |
| // TODO: we *could* decimate [hv]{2,3} shapes if we restricted clusters to horz or vert again | |
| // but spline would be verrry awkward to decimate | |
| if(!linear) { | |
| pts[pti++] = clusterHighPt; | |
| continue; | |
| } | |
| clusterRefDist = ptDist(clusterHighPt, clusterStartPt); | |
| if(clusterRefDist < getTolerance(clusterHighPt) * minTolerance) continue; | |
| clusterUnitVector = [ | |
| (clusterHighPt[0] - clusterStartPt[0]) / clusterRefDist, | |
| (clusterHighPt[1] - clusterStartPt[1]) / clusterRefDist | |
| ]; | |
| clusterLowPt = clusterStartPt; | |
| clusterHighVal = clusterRefDist; | |
| clusterLowVal = clusterMinDeviation = clusterMaxDeviation = 0; | |
| clusterHighFirst = false; | |
| clusterEndPt = clusterHighPt; | |
| // loop over one cluster of points that collapse onto one line | |
| for(i++; i < d.length; i++) { | |
| thisPt = getPt(i); | |
| if(!thisPt) { | |
| if(connectGaps) continue; | |
| else break; | |
| } | |
| thisVector = [ | |
| thisPt[0] - clusterStartPt[0], | |
| thisPt[1] - clusterStartPt[1] | |
| ]; | |
| // cross product (or dot with normal to the cluster vector) | |
| thisDeviation = thisVector[0] * clusterUnitVector[1] - thisVector[1] * clusterUnitVector[0]; | |
| clusterMinDeviation = Math.min(clusterMinDeviation, thisDeviation); | |
| clusterMaxDeviation = Math.max(clusterMaxDeviation, thisDeviation); | |
| if(clusterMaxDeviation - clusterMinDeviation > getTolerance(thisPt)) break; | |
| clusterEndPt = thisPt; | |
| thisVal = thisVector[0] * clusterUnitVector[0] + thisVector[1] * clusterUnitVector[1]; | |
| if(thisVal > clusterHighVal) { | |
| clusterHighVal = thisVal; | |
| clusterHighPt = thisPt; | |
| clusterHighFirst = false; | |
| } else if(thisVal < clusterLowVal) { | |
| clusterLowVal = thisVal; | |
| clusterLowPt = thisPt; | |
| clusterHighFirst = true; | |
| } | |
| } | |
| // insert this cluster into pts | |
| // we've already inserted the start pt, now check if we have high and low pts | |
| if(clusterHighFirst) { | |
| pts[pti++] = clusterHighPt; | |
| if(clusterEndPt !== clusterLowPt) pts[pti++] = clusterLowPt; | |
| } else { | |
| if(clusterLowPt !== clusterStartPt) pts[pti++] = clusterLowPt; | |
| if(clusterEndPt !== clusterHighPt) pts[pti++] = clusterHighPt; | |
| } | |
| // and finally insert the end pt | |
| pts[pti++] = clusterEndPt; | |
| // have we reached the end of this segment? | |
| if(i >= d.length || !thisPt) break; | |
| // otherwise we have an out-of-cluster point to insert as next clusterStartPt | |
| pts[pti++] = thisPt; | |
| clusterStartPt = thisPt; | |
| } | |
| segments.push(pts.slice(0, pti)); | |
| } | |
| return segments; | |
| }; | |