将matlab running average转换为Python会产生意想不到的结果

Question

我在Matlab中有一段代码，用于计算运行平均值：

as = movmean(std_new1,PTA);

在matlab中计算时，如下所示：

as = [NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.0311573, 0.03135, 0.0315315, 0.0317018, 0.0318609, 0.0320087, 0.0321454, 0.0322708, 0.0323851, 0.0324881, 0.0325799, 0.0326605, 0.0329592, 0.0334758, 0.0342104, 0.0351631, 0.0363338, 0.0377224, 0.0393291, 0.0411538, 0.0431965, 0.0454572, 0.0473395, 0.0488433, 0.0499687, 0.0507156, 0.051084, 0.051074, 0.0506856, 0.0499187, 0.0487733, 0.0472495, 0.0456993, 0.0441228, 0.0425198, 0.0408905, 0.0392348, 0.0375527, 0.0358443, 0.0341094, 0.0323482, 0.0305606, 0.0290992, 0.0279639, 0.0271548, 0.0266719, 0.0265151, 0.0266844, 0.02718, 0.0280016, 0.0291495, 0.0306235, 0.0319449, 0.0331137, 0.03413, 0.0349937, 0.0357048, 0.0362634, 0.0366693, 0.0369227, 0.0370235, 0.0369717, 0.0369048, 0.0368227, 0.0367255, 0.0366131, 0.0364856, 0.036343, 0.0361852, 0.0360122, 0.0358241, 0.0356209, 0.03539, 0.0351316, 0.0348455, 0.0345318, 0.0341905, 0.0338216, 0.0334251, 0.033001, 0.0325493, 0.0320699, 0.0315601, 0.0310198, 0.030449, 0.0298477, 0.029216, 0.0285537, 0.027861, 0.0271378, 0.0263841, 0.0255999, 0.0248585, 0.02416, 0.0235044, 0.0228916, 0.0223217, 0.0217946, 0.0213104, 0.020869, 0.0204704, 0.0201148, 0.0198367, 0.0196361, 0.0195132, 0.0194679, 0.0195001, 0.0196099, 0.0197973, 0.0200623, 0.0204049, 0.0208251, 0.0211917, 0.0215047, 0.0217641, 0.02197, 0.0221224, 0.0222211, 0.022429, 0.0226666, 0.0229393, 0.0232537, 0.0235459, 0.0238112, 0.0240434, 0.0242341, 0.0243722]

我需要在Python中执行相同的操作。我基本上尝试了这里提出的每一个解决方案Moving average or running mean，但问题是没有一个能对我的数据给出正确的结果

这是std_new1

std_new1 = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.0223287, 0.023921, 0.0255133, 0.0271056, 0.0286979, 0.0302902, 0.0318825, 0.0334747, 0.035067, 0.0366593, 0.0382516, 0.0370447, 0.0358378, 0.0346309, 0.0334241, 0.0322172, 0.0310103, 0.0298034, 0.0285965, 0.0273896, 0.0261827, 0.0275508, 0.028919, 0.0302871, 0.0316552, 0.0330233, 0.0343914, 0.0357595, 0.0371276, 0.0384957, 0.0398638, 0.0430172, 0.0461705, 0.0493238, 0.0524771, 0.0556305, 0.0587838, 0.0619371, 0.0650904, 0.0682438, 0.0713971, 0.0651962, 0.0589952, 0.0527943, 0.0465933, 0.0403924, 0.0341914, 0.0279905, 0.0217896, 0.0155886, 0.00938767, 0.0120134, 0.0146392, 0.017265, 0.0198907, 0.0225165, 0.0251422, 0.027768, 0.0303938, 0.0330195, 0.0356453, 0.0359675, 0.0362898, 0.036612, 0.0369342, 0.0372565, 0.0375787, 0.037901, 0.0382232, 0.0385454, 0.0388677, 0.0384419, 0.0380161, 0.0375903, 0.0371645, 0.0367387, 0.0363129, 0.0358871, 0.0354613, 0.0350355, 0.0346097, 0.034629, 0.0346483, 0.0346676, 0.034687, 0.0347063, 0.0347256, 0.034745, 0.0347643, 0.0347836, 0.034803, 0.033825, 0.032847, 0.0318689, 0.0308909, 0.0299129, 0.0289349, 0.0279569, 0.0269789, 0.0260009, 0.0250229, 0.0244325, 0.0238421, 0.0232518, 0.0226614, 0.022071, 0.0214806, 0.0208903, 0.0202999, 0.0197095, 0.0191191, 0.0189982, 0.0188772, 0.0187562, 0.0186352, 0.0185142, 0.0183932, 0.0182722, 0.0181512, 0.0180302, 0.0179092, 0.0188705, 0.0198318, 0.0207932, 0.0217545, 0.0227158, 0.0236771, 0.0246384, 0.0255997, 0.026561, 0.0275224, 0.02633, 0.0251377, 0.0239454, 0.022753, 0.0215607, 0.0203684])

这是PTA（1x1矩阵）

PTA = np.array([20])

比如下面的一个,

AS = [np.mean(std_new1[x:x + PTA[0]]) for x in range(len(std_new1) - PTA[0] + 1)]

给出了几乎相同的结果，但开头的NA值较少，结尾缺少数值

这是用Python计算的：

[       nan        nan        nan        nan        nan        nan
        nan        nan        nan 0.03115731 0.03135002 0.03153151
 0.03170179 0.03186087 0.03200873 0.03214538 0.03227083 0.03238507
 0.0324881  0.03257992 0.03266053 0.03295915 0.03347579 0.03421044
 0.03516309 0.03633375 0.03772243 0.03932911 0.04115381 0.04319652
 0.04545724 0.04733951 0.04884332 0.04996868 0.05071558 0.05108404
 0.05107404 0.05068559 0.04991868 0.04877333 0.04724952 0.04569933
 0.04412277 0.04251983 0.04089051 0.03923481 0.03755273 0.03584427
 0.03410944 0.03234823 0.03056064 0.0290992  0.02796393 0.02715482
 0.02667186 0.02651507 0.02668443 0.02717996 0.02800164 0.02914948
 0.03062348 0.0319449  0.03311375 0.03413001 0.0349937  0.03570481
 0.03626335 0.0366693  0.03692268 0.03702348 0.0369717  0.03690477
 0.0368227  0.03672548 0.03661312 0.03648561 0.03634296 0.03618516
 0.03601221 0.03582412 0.03562089 0.03539004 0.03513158 0.03484552
 0.03453184 0.03419055 0.03382164 0.03342514 0.03300102 0.03254929
 0.03206995 0.03156012 0.03101981 0.03044902 0.02984774 0.02921597
 0.02855372 0.02786099 0.02713777 0.02638407 0.02559987 0.02485853
 0.02416004 0.02350441 0.02289162 0.02232169 0.0217946  0.02131037
 0.02086898 0.02047045 0.02011476 0.01983666 0.01963615 0.01951322
 0.01946787 0.01950011 0.01960994 0.01979734 0.02006233 0.02040491
 0.02082507 0.02119166 0.02150469 0.02176414 0.02197004 0.02212236
 0.02222112]