As I skimmed through several internet postings on different elaborate methods for decimating data (that is, reducing the resolution of a dataset through the application of some function), I realized that the filter function would perform all the work easily.
Consider an array of values:
var a = "4.901712886872069,4.905571030847647,4.909414346738851,4.913242948087607,4.91705694713669,4.92085645484947,4.92464158092928,4.928412433838424,4.932169120816823,4.93591174790032,4.939640419938632,4.94335524061298,4.947056312453386,4.950743736855651,4.954417614098028,4.958078043357581,4.961725122726249,4.965358949226622,4.968979618827425,4.972587226458727,4.976181866026878,4.97976363042917,4.983332611568248,4.986888900366257,4.990432586778738,4.9939637598082856,4.997550054547602,5.001123533723662,5.004684288602818,5.008232409479947,5.011767985692191,5.015291105632452,5.018801856762651,5.022300325626761,5.025786597863601,5.029260758219422,5.032722890560263,5.036173077884096,5.039611402332775,5.043037945203758,5.046452786961651,5.049856007249539,5.053247684900134,5.05662789794673,5.059996723633982,5.063354238428486,5.066700518029206,5.070035637377705,5.07335967066822,5.076672691357564,5.079974772174871,5.083265985131165,5.086546401528796,5.0898160919706985,5.0930751263695155,5.096323573956563,5.099561503290659,5.102788982266802,5.106006078124715,5.109212857457251,5.112469599977525,5.115715770546765,5.1189514375797165,5.122176668829165,5.125391531394446,5.128596091729822,5.131790415652724,5.134974568351865,5.138148614395217,5.141312617737873,5.144466641729777,5.147610749123334,5.150745002080902,5.153869462182165,5.156984190431394,5.160089247264595,5.163184692556541,5.166270585627705,5.169346985251077,5.172413949658882,5.175471536549192,5.178519803092442,5.181558805937842,5.184588601219694,5.187609244563616,5.190620791092663,5.193623295433372,5.1966168117216975,5.199601393608879,5.202577094267201,5.205543966395682,5.208556759340414,5.211560502621755,5.214555250442706,5.21754105652075,5.220517974093625,5.223486055925034,5.2264453543102425,5.229395921081616,5.232337807614062,5.235271064830402,5.238195743206656,5.241111892777258,5.2440195631401885,5.246918803462037,5.249809662482995,5.252692188521762,5.255566429480405,5.258432432849123,5.261290245710962,5.264139914746451,5.266981486238185,5.269815006075326,5.272640519758055,5.275458072401956,5.278267708742335,5.281069473138485,5.283863409577888,5.286649561680356,5.289427972702121,5.292198685539861,5.295011909539312,5.2978172415063565,5.300614725596722,5.303404405596596,5.306186324926734,5.308960526646518,5.311727053457957,5.314485947709622".split(',');
Now let's say I want to decimate that so that a small status chart only ever shows around 10 data points. More or less are fine as long as I'm not continuing to show more data as I add several more batches to this initial set. The following filter function uses the modulo operator to make a simple reduction function.
a.filter(function(elem,index,array){return index % (Math.round(array.length/10)) ==0;});
...yields a set of 10 data points, one for every time the array index of the value divided the dynamic value array.length/10 is equal to zero. Math.round simply makes our dynamic value a whole number, which works well with modulo (%) against the integer index numbers in the array.
"4.901712886872069,4.950743736855651,4.997550054547602,5.043037945203758,5.086546401528796,5.128596091729822,5.169346985251077,5.208556759340414,5.246918803462037,5.283863409577888"
Now any charting we may do with this data will continue to tell the story, sacrificing resolution in exchange for performance.
