让我的JavaScript（假定我的CSV是二维的）转而用于一维CSV

如何使用JavaScript解析此CSV

1363085391,42.890000000000,5.432200000000
1363088879,47.570000000000,4.981800000000
1363120475,56.560000000000,1.768000000000
1363132522,53.000000000000,1.000000000000
1363214378,48.630000000000,4.000000000000
[...]

它显示了加拿大元的汇率。然而，这份名单太多了；它显示了曾经做过的每一笔交易。所以我试图将它从每天数百个数据点减少到每周一个。基本上，将数据点“单一化”为时间间隔，方法是将数量相加并平均价格。通过这种方式，使用更简单的数据，预计输入的行看起来会更好

不幸的是，脚本无法工作；它假设CSV是二维数组，但实际上我认为它只是一维数组？如何更改它以使其正确解析CSV

函数数据（数据集）{
间隔长度=3600；//每小时间隔
最后价格=0；
idx=0；
while（idx=数据集长度）
打破
}
//平均价格
价格=计数>0？价格总和/计数：最后一个价格；
最后价格=价格；
//向单一化数据数组添加新行
monotized_data.append([
时间戳：时间戳，
卷：卷，
价格：价格
]);
}
}
//格式：时间（UNIX时间戳）、价格、交易金额
// http://api.bitcoincharts.com/v1/csv/localbtcCAD.csv.gz
var complexCadCsv=“1363085391,42.890000000000,5.432200000000
1363088879,47.570000000000,4.981800000000
1363120475,56.560000000000,1.768000000000
1363132522,53.000000000000,1.000000000000
1363214378,48.630000000000,4.000000000000
1363217281,48.770000000000,2.000200000000
1363223157,48.860000000000,2.046500000000
1363232051,49.110000000000,4.235500000000
1363272551,54.250000000000,1.000000000000
1363283662,49.780000000000,5.925600000000
1363293072,55.500000000000,1.027000000000
1363321440,56.000000000000,5.357100000000
1363346950,55.220000000000,7.016900000000
1363379555,55.600000000000,4.945900000000
1363379607,55.740000000000,1.000000000000
1363381362,49.220000000000,0.101600000000
1363382662,49.220000000000,4.896100000000
1363391161,55.380000000000,2.000000000000
1363401704,56.060000000000,1.000000000000
1363467393,56.000000000000,0.892900000000
1363496639,56.700000000000,1.500100000000
1363524530,56.930000000000,6.000100000000
1363527377,56.900000000000,6.497900000000
1363542700,56.000000000000,2.142900000000
1363547113,55.000000000000,3.000000000000
1363564084,57.040000000000,2.156400000000
1363638453,57.880000000000,0.331700000000
1363729323,70.000000000000,0.857100000000
1363740718,73.070000000000,0.136800000000
1363795449,63.450000000000,1.000000000000
1363795494,63.860000000000,1.000100000000
1363795603,63.430000000000,0.157700000000
1363798700,68.390000000000,1.462200000000
1363800835,68.180000000000,1.991300000000
1363803497,67.940000000000,1.014600000000
1363803790,68.160000000000,1.027100000000
1363814790,69.580000000000,1.050000000000
1363814810,68.270000000000,0.929400000000
1363825583,68.250000000000,5.230600000000
1363829358,78.000000000000,1.050000000000
1363836583,83.300000000000,2.999800000000
1363837642,84.000000000000,2.000000000000
1363895966,75.410000000000,0.663000000000
1363944788,75.000000000000,4.000000000000
1363984884,90.000000000000,1.111100000000
1363987472,90.000000000000,1.111100000000
1363988438,89.350000000000,0.074000000000
1363989586,85.090000000000,1.999900000000
1364000191,88.000000000000,1.000000000000
1364002717,85.230000000000,1.490100000000
1364010104,70.730000000000,1.000000000000
1364013267,86.000000000000,1.162800000000
1364073182,78.000000000000,3.900000000000
1364089933,80.000000000000,3.025000000000
1364249509,74.360000000000,1.485600000000
1364262262,89.550000000000,1.116700000000
1364265293,90.040000000000,1.055000000000
1364310351,92.450000000000,1.081700000000
1364334487,81.210000000000,1.994800000000
1364355951,94.630000000000,1.420000000000
1364357864,95.380000000000,1.048400000000
1364358542,94.800000000000,1.054900000000
1364364067,82.820000000000,13.219800000000
1364395451,99.100000000000,0.100900000000
1364400184,102.700000000000,1.000000000000
1364401183,100.570000000000,1.093800000000
1364403945,101.420000000000,1.000000000000
1364411110,101.720000000000,0.498800000000
1364436263,106.740000000000,1.799900000000
1364436873,94.960000000000,1.000000000000
1364437451,94.520000000000,0.999900000000
1364440483,104.190000000000,0.499900000000
1364489123,109.760000000000,1.047800000000
1364490688,109.730000000000,1.000000000000
1364494732,100.230000000000,1.000000000000
1364498537,95.620000000000,1.950000000000
1364502332,95.780000000000,1.200700000000
1364505883,99.490000000000,0.251300000000
1364513250,103.900000000000,0.517100000000
1364516343,83.470000000000,1.018300000000
1364573738,97.140000000000,0.257400000000
1364580938,95.700000000000,1.000000000000
1364598407,102.000000000000,1.000000000000
1364600233,102.000000000000,1.000000000000
1364601641,102.000000000000,1.010000000000
1364605133,105.000000000000,1.000000000000
1364709921,99.880000000000,1.000000000000
1364712798,99.990000000000,1.000100000000
1364748894,101.470000000000,1.000000000000
1364755340,100.590000000000,3.000000000000
1364792969,106.000000000000,1.000000000000
1364799933,102.400000000000,1.000000000000
1364800923,101.560000000000,1.000000000000
1364828813,112.000000000000,2.000000000000
1364832014,115.000000000000,5.000000000000
1364832308,115.000000000000,3.000000000000
1364834249,112.720000000000,5.855000000000
1364838578,115.240000000000,5.727400000000
1364841672,104.360000000000,12.457200000000
1364923361,120.900000000000,2.999900000000
1364936087,120.710000000000,4.970600000000
1364948998,124.810000000000,4.999800000000
1364959661,12
data_set = data_set.split('\n').map(line => {
    var linearray = line.split(',');
    return {
        timestamp: parseInt(linearray[0], 10),
        price: parseFloat(linearray[1]),
        volume: parseFloat(linearray[2])
    };
});

    monotized_data.push({
      timestamp: timestamp,
      volume: volume,
      price: price
    });