python 切片 vs matlab 切片
python slicing vs matlab slicing
我正在尝试将此行转换为 python:
xx=wqtran((nn-1)*N*2/K+nN,(mm-1)*N*2/K+nN,kk-1);
(nn-1)*N*2/K+nN 和 (mm-1)*N*2/K+nN 都是 [[1..128]] 表示二维数组的数组，形状为 (1,128)
我已将最后一行翻译为：
firstAxis = round(dot(dot((nn - 1), N), 2) / K + nN)
secondAxis = round(dot(dot((mm - 1), N), 2) / K + nN)
xx=wqtran[firstAxis-1, secondAxis-1, kk - 2]
因为 python 从 0 而不是 1 开始计数，并且它不支持浮点数索引（自动转换）。
xx与2个代码不同的问题：在matlab中xx是(128,128)矩阵。
而在 python 中它是 (1,128) 矩阵。
有谁知道xx在翻译中丢失的地方吗？
Python xx
array([[-452.58030867-676.77261453j, -195.12401501-661.388484j ,
-174.14069268-609.99929489j, -132.25284621-580.72939701j,
-148.73799066-547.11073351j, -159.38166597-546.81537669j,
-161.87484594-539.42802218j, -169.35077334-545.3686585j ,
-180.50911641-544.03823644j, -197.29690441-557.23079163j,
-196.51746545-567.5090211j , -213.84197505-581.57152941j,
-226.74628312-623.65644362j, -200.66624603-651.50540145j,
-177.38731357-655.67501859j, -180.69705033-675.17325716j,
-155.81407249-676.37138946j, -157.96308479-695.13212395j,
-108.8650474 -736.25936469j, -87.578376 -700.20224727j,
-61.02036179-666.00742621j, -64.97015 -638.36503754j,
-101.50993294-656.93949503j, -93.54775041-647.98566233j,
-107.68397683-697.7795849j , -75.53171122-657.14821992j,
-85.23769943-688.35238679j, -92.83159059-686.44693563j,
-71.43133014-697.60140338j, -81.08785253-711.97445565j,
-44.87630158-720.17033913j, -52.44268065-722.02504979j,
-5.25277497-740.89853418j, -17.6212513 -686.37476794j,
-29.2393824 -726.59904826j, 7.16364259-719.91709074j,
5.90517312-717.18410487j, 22.8522649 -699.23464182j,
18.63531638-715.0072984j , 36.32674701-693.05814042j,
26.7880449 -694.37681242j, 27.16655687-689.18079609j,
27.85147304-711.44759484j, 61.06491269-720.79713687j,
52.0941088 -706.34408308j, 75.35650028-725.46873301j,
98.57388416-721.28197269j, 145.06602719-711.59314229j,
141.991647 -664.74456743j, 150.71281574-653.53592807j,
125.90978132-630.55654499j, 132.47112733-629.17946652j,
125.51837461-616.7203905j , 122.58396678-619.14956992j,
142.70895126-614.49204458j, 159.16589703-593.52244992j,
138.31128655-582.53972214j, 153.12963358-569.54241982j,
165.29233736-545.01929937j, 154.13129768-471.78284923j,
43.56773616-487.8220431j , 54.25437947-524.72605456j,
42.79114751-545.50768074j, 53.15219565-559.27877631j,
43.76155234-560.12707983j, 54.6208528 -568.90568531j,
56.97801932-575.17701242j, 65.2143702 -583.69413932j,
73.23340404-580.67369009j, 79.07999037-613.44166576j,
143.69183821-585.69001336j, 117.20653662-526.52470153j,
100.57804234-536.03686963j, 108.44277397-538.21328614j,
90.05264023-528.34900258j, 78.72868304-505.73274985j,
69.97859571-513.46003129j, 66.93475688-526.93461531j,
48.55950859-530.81706511j, 48.90034874-555.12529778j,
96.22558483-565.6267038j , 102.4548864 -523.24947975j,
79.69038208-529.98772291j, 87.95211951-510.26008958j,
89.69574996-535.75298725j, 90.254945 -499.1893208j ,
78.94021112-511.87269866j, 81.67490639-504.04487668j,
68.79809877-503.89951716j, 77.24724759-514.19868479j,
58.04074534-515.98187084j, 98.08396247-507.92887948j,
100.20824611-495.01481833j, 72.69918626-489.42866776j,
76.7185985 -482.68643198j, 83.92155433-498.9687003j ,
82.67280296-451.74266885j, 75.86185607-490.23454708j,
57.94986269-458.47283805j, 41.1146784 -479.67628776j,
78.93166632-478.31596167j, 51.50738234-477.3396975j ,
78.20463698-494.72210057j, 85.75196528-469.34299269j,
61.02017345-467.76114977j, 106.63119269-448.17563021j,
84.6209639 -460.80005868j, 132.04063304-453.84619813j,
130.40574008-287.10743659j, -6.2580409 -359.45507084j,
-5.28309533-400.4593048j , 8.32020675-420.38420677j,
12.90232396-425.91811269j, 55.04695631-409.77248592j,
48.69647754-392.02448157j, 45.78594751-370.02692246j,
50.11015329-338.79344838j, -8.11827553-322.21361504j,
3.22840585-285.08547853j, -69.03273845-292.52545131j,
-92.47239304-306.54784527j, -110.46527378-324.81171773j,
-118.13552928-282.0133424j , -156.30293279-316.89599142j,
-205.25063008-284.99760596j, -262.9615674 -320.56291071j,
-297.34446645-364.17111847j, -388.19490322-330.83950936j]])
matlab xx
对于 post 第一行来说太大了：
Columns 1 through 5
-4.5258 - 6.7677i -2.8199 - 6.5171i -2.5924 - 6.3501i -2.2652 - 6.1454i -2.1380 - 6.0565i
-3.3526 - 7.0387i -1.9512 - 6.6139i -1.8210 - 6.3541i -1.5917 - 6.1802i -1.5068 - 6.0629i
-2.9576 - 6.8487i -1.7324 - 6.2677i -1.7414 - 6.1000i -1.5113 - 5.9629i -1.4118 - 5.8737i
-2.5942 - 6.5328i -1.6652 - 6.0255i -1.5915 - 6.0017i -1.3225 - 5.8073i -1.2911 - 5.6604i
-2.7038 - 6.1171i -1.8519 - 5.7833i -1.6694 - 5.7016i -1.5398 - 5.4927i -1.4874 - 5.4711i
-3.0414 - 6.0944i -2.0874 - 5.8841i -1.9274 - 5.7351i -1.7284 - 5.6416i -1.6399 - 5.4976i
-3.1291 - 6.2552i -2.1179 - 5.9566i -1.9873 - 5.8116i -1.7679 - 5.6209i -1.7752 - 5.5574i
-3.1846 - 6.2824i -2.2147 - 5.9888i -2.0517 - 5.8205i -1.9089 - 5.6169i -1.8547 - 5.5742i
-3.3634 - 6.3435i -2.3585 - 6.0483i -2.2724 - 5.8377i -2.1611 - 5.7308i -2.0719 - 5.6881i
-3.4930 - 6.5714i -2.4824 - 6.2474i -2.4096 - 6.1046i -2.1738 - 6.0056i -2.0842 - 5.8660i
-3.4977 - 6.6898i -2.4959 - 6.3652i -2.3771 - 6.2161i -2.1896 - 5.9859i -2.2244 - 5.9183i
-3.7146 - 6.8227i -2.6272 - 6.4702i -2.5624 - 6.2801i -2.4266 - 6.1418i -2.3639 - 6.1235i
-3.8749 - 7.2053i -2.8709 - 6.7784i -2.7552 - 6.6938i -2.5841 - 6.5032i -2.4996 - 6.4839i
-3.7870 - 7.6706i -2.6680 - 7.3165i -2.4975 - 7.0619i -2.3808 - 6.9107i -2.2599 - 6.8245i
-3.5470 - 7.9192i -2.3884 - 7.3498i -2.3778 - 7.1098i -2.2237 - 6.9752i -2.1495 - 6.8788i
-3.3774 - 8.1407i -2.3445 - 7.4918i -2.3308 - 7.3440i -2.1527 - 7.2085i -2.0745 - 7.1216i
-3.1525 - 8.2276i -2.2111 - 7.6250i -2.1091 - 7.5007i -1.9231 - 7.3094i -1.8284 - 7.2038i
-3.0647 - 8.3645i -2.0377 - 7.7732i -1.9548 - 7.5233i -1.8111 - 7.3576i -1.7689 - 7.2205i
-2.9077 - 8.4493i -1.9463 - 7.7723i -1.9293 - 7.6172i -1.7293 - 7.4210i -1.6972 - 7.3599i
-2.8770 - 8.5876i -1.8656 - 7.9865i -1.7569 - 7.7734i -1.6328 - 7.5554i -1.5008 - 7.4696i
-2.7261 - 8.8312i -1.6047 - 8.0594i -1.5835 - 7.8066i -1.4221 - 7.5995i -1.3657 - 7.4299i
-2.5481 - 8.9706i -1.4817 - 8.0861i -1.4902 - 7.8014i -1.4058 - 7.6098i -1.3197 - 7.4890i
-2.3348 - 9.2287i -1.2996 - 8.1146i -1.4435 - 7.9271i -1.2240 - 7.7699i -1.1770 - 7.5609i
-1.9191 - 9.3370i -1.1828 - 8.1255i -1.2125 - 8.0338i -1.0260 - 7.7334i -1.0170 - 7.5347i
-1.7057 - 9.2125i -1.0760 - 8.1519i -1.0698 - 7.9991i -0.9242 - 7.6857i -1.0191 - 7.4748i
-1.5766 - 9.3942i -0.9115 - 8.2014i -0.9481 - 8.0558i -0.8732 - 7.6749i -0.9802 - 7.6137i
-1.1969 - 9.5142i -0.7600 - 8.2883i -0.7354 - 8.0880i -0.8064 - 7.7545i -0.8295 - 7.6697i
-0.6769 - 9.4567i -0.4273 - 8.1623i -0.5976 - 8.0069i -0.6075 - 7.7987i -0.6769 - 7.6913i
-0.3850 - 9.0560i -0.4248 - 8.0788i -0.4050 - 8.0236i -0.4633 - 7.7094i -0.5513 - 7.7039i
-0.4366 - 8.8908i -0.2136 - 8.0561i -0.2837 - 7.8554i -0.3773 - 7.6979i -0.3598 - 7.6238i
-0.1777 - 8.9291i -0.0988 - 7.8777i -0.2033 - 7.8333i -0.2561 - 7.5843i -0.3953 - 7.5606i
-0.0161 - 8.6390i -0.1203 - 7.8495i -0.1071 - 7.7569i -0.2803 - 7.6176i -0.2740 - 7.7102i
0.1264 - 8.6460i 0.1332 - 7.8396i 0.0593 - 7.6877i -0.0735 - 7.6183i 0.0476 - 7.6904i
0.5057 - 8.2794i 0.2117 - 7.5596i 0.0900 - 7.5168i 0.0649 - 7.5802i 0.2925 - 7.4029i
0.2960 - 7.7818i 0.1738 - 7.4925i 0.1958 - 7.4376i 0.2700 - 7.3344i 0.2059 - 7.0959i
0.1925 - 7.8667i 0.3320 - 7.3270i 0.1611 - 7.1907i 0.1782 - 7.1215i 0.0658 - 7.0173i
0.2259 - 7.5840i 0.1145 - 7.0688i 0.0032 - 7.1909i 0.0513 - 7.1091i 0.0311 - 7.0961i
0.0885 - 7.5782i 0.0182 - 7.1834i 0.0570 - 7.2710i 0.2009 - 7.1118i 0.0902 - 6.9441i
0.0255 - 7.4985i 0.0030 - 7.1903i 0.1157 - 7.2326i 0.0663 - 6.9297i -0.0887 - 7.0950i
0.1082 - 7.5894i 0.1059 - 7.2266i 0.2692 - 7.1582i 0.0889 - 7.0127i 0.2412 - 7.0495i
0.1297 - 7.4225i 0.0920 - 7.1568i 0.1424 - 7.0073i 0.0280 - 7.0163i 0.0716 - 6.9444i
这是 numpy 中的 advanced indexing:
In [30]: arr = np.arange(9).reshape(3,3)
In [31]: arr
Out[31]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
2个索引数组（为了显示方便，相同）：
In [32]: i, j = np.array([0,1,2]), np.array([0,1,2])
In [33]: arr[i,j]
Out[33]: array([0, 4, 8])
我们有2个形状为(3,)的数组，结果也是(3,)，数组的'diagonal'。要在 MATLAB 中做同样的事情，我们必须使用 sub2ind 或类似的东西将索引对转换为平面索引。
但是如果第一个索引是(3,1)，那么我们得到一个(3,3)的结果，一个块。 （这是 MATLAB 语法的默认设置）：
In [34]: i1 = i.reshape(3,1)
In [35]: arr[i1,j]
Out[35]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
In [36]: i1,j
Out[36]:
(array([[0],
[1],
[2]]),
array([0, 1, 2]))
ix_ 是生成列向量的便捷工具 i1:
In [37]: arr[np.ix_(i,j)]
Out[37]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
在numpy中获取对角线和块在做同样的事情，broadcasting索引数组相互对抗。
相当于 broadcasting 加上：
In [38]: 10*i+j
Out[38]: array([ 0, 11, 22])
In [39]: 10*i1+j
Out[39]:
array([[ 0, 1, 2],
[10, 11, 12],
[20, 21, 22]])
对于您的 (1,128) 个数组，转置可以解决问题：
In [40]: I=i.reshape(1,3); J=j.reshape(1,3)
In [41]: I
Out[41]: array([[0, 1, 2]])
In [42]: arr[I,J]
Out[42]: array([[0, 4, 8]])
In [43]: _.shape
Out[43]: (1, 3)
In [44]: arr[I.T,J]
Out[44]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
我没有对 i 使用转置，因为它是 1d。事实上，MATLAB 矩阵总是 2d（或更高）并且 numpy 可以是 0 或 1d，这是任性的 MATLAB 用户最难掌握的事情之一。
如果你这样做
array[rows, cols]
其中 rows 和 cols 是 lists/np.arrays，numpy 将 select 处的元素
[[rows[0], cols[0]], [rows[1], cols[1]],..., [rows[n], cols[n]]]
如果您使用的是 numpy 数组，下面的内容应该可以满足您的需求
xx=wqtran[(firstAxis-1)[:, np.newaxis], secondAxis-1, kk - 2]
已经发布了使用 np.ix_ 的更简洁的解决方案 here
我正在尝试将此行转换为 python:
xx=wqtran((nn-1)*N*2/K+nN,(mm-1)*N*2/K+nN,kk-1);
(nn-1)*N*2/K+nN 和 (mm-1)*N*2/K+nN 都是 [[1..128]] 表示二维数组的数组，形状为 (1,128)
我已将最后一行翻译为：
firstAxis = round(dot(dot((nn - 1), N), 2) / K + nN)
secondAxis = round(dot(dot((mm - 1), N), 2) / K + nN)
xx=wqtran[firstAxis-1, secondAxis-1, kk - 2]
因为 python 从 0 而不是 1 开始计数，并且它不支持浮点数索引（自动转换）。
xx与2个代码不同的问题：在matlab中xx是(128,128)矩阵。 而在 python 中它是 (1,128) 矩阵。 有谁知道xx在翻译中丢失的地方吗？
Python xx
array([[-452.58030867-676.77261453j, -195.12401501-661.388484j ,
-174.14069268-609.99929489j, -132.25284621-580.72939701j,
-148.73799066-547.11073351j, -159.38166597-546.81537669j,
-161.87484594-539.42802218j, -169.35077334-545.3686585j ,
-180.50911641-544.03823644j, -197.29690441-557.23079163j,
-196.51746545-567.5090211j , -213.84197505-581.57152941j,
-226.74628312-623.65644362j, -200.66624603-651.50540145j,
-177.38731357-655.67501859j, -180.69705033-675.17325716j,
-155.81407249-676.37138946j, -157.96308479-695.13212395j,
-108.8650474 -736.25936469j, -87.578376 -700.20224727j,
-61.02036179-666.00742621j, -64.97015 -638.36503754j,
-101.50993294-656.93949503j, -93.54775041-647.98566233j,
-107.68397683-697.7795849j , -75.53171122-657.14821992j,
-85.23769943-688.35238679j, -92.83159059-686.44693563j,
-71.43133014-697.60140338j, -81.08785253-711.97445565j,
-44.87630158-720.17033913j, -52.44268065-722.02504979j,
-5.25277497-740.89853418j, -17.6212513 -686.37476794j,
-29.2393824 -726.59904826j, 7.16364259-719.91709074j,
5.90517312-717.18410487j, 22.8522649 -699.23464182j,
18.63531638-715.0072984j , 36.32674701-693.05814042j,
26.7880449 -694.37681242j, 27.16655687-689.18079609j,
27.85147304-711.44759484j, 61.06491269-720.79713687j,
52.0941088 -706.34408308j, 75.35650028-725.46873301j,
98.57388416-721.28197269j, 145.06602719-711.59314229j,
141.991647 -664.74456743j, 150.71281574-653.53592807j,
125.90978132-630.55654499j, 132.47112733-629.17946652j,
125.51837461-616.7203905j , 122.58396678-619.14956992j,
142.70895126-614.49204458j, 159.16589703-593.52244992j,
138.31128655-582.53972214j, 153.12963358-569.54241982j,
165.29233736-545.01929937j, 154.13129768-471.78284923j,
43.56773616-487.8220431j , 54.25437947-524.72605456j,
42.79114751-545.50768074j, 53.15219565-559.27877631j,
43.76155234-560.12707983j, 54.6208528 -568.90568531j,
56.97801932-575.17701242j, 65.2143702 -583.69413932j,
73.23340404-580.67369009j, 79.07999037-613.44166576j,
143.69183821-585.69001336j, 117.20653662-526.52470153j,
100.57804234-536.03686963j, 108.44277397-538.21328614j,
90.05264023-528.34900258j, 78.72868304-505.73274985j,
69.97859571-513.46003129j, 66.93475688-526.93461531j,
48.55950859-530.81706511j, 48.90034874-555.12529778j,
96.22558483-565.6267038j , 102.4548864 -523.24947975j,
79.69038208-529.98772291j, 87.95211951-510.26008958j,
89.69574996-535.75298725j, 90.254945 -499.1893208j ,
78.94021112-511.87269866j, 81.67490639-504.04487668j,
68.79809877-503.89951716j, 77.24724759-514.19868479j,
58.04074534-515.98187084j, 98.08396247-507.92887948j,
100.20824611-495.01481833j, 72.69918626-489.42866776j,
76.7185985 -482.68643198j, 83.92155433-498.9687003j ,
82.67280296-451.74266885j, 75.86185607-490.23454708j,
57.94986269-458.47283805j, 41.1146784 -479.67628776j,
78.93166632-478.31596167j, 51.50738234-477.3396975j ,
78.20463698-494.72210057j, 85.75196528-469.34299269j,
61.02017345-467.76114977j, 106.63119269-448.17563021j,
84.6209639 -460.80005868j, 132.04063304-453.84619813j,
130.40574008-287.10743659j, -6.2580409 -359.45507084j,
-5.28309533-400.4593048j , 8.32020675-420.38420677j,
12.90232396-425.91811269j, 55.04695631-409.77248592j,
48.69647754-392.02448157j, 45.78594751-370.02692246j,
50.11015329-338.79344838j, -8.11827553-322.21361504j,
3.22840585-285.08547853j, -69.03273845-292.52545131j,
-92.47239304-306.54784527j, -110.46527378-324.81171773j,
-118.13552928-282.0133424j , -156.30293279-316.89599142j,
-205.25063008-284.99760596j, -262.9615674 -320.56291071j,
-297.34446645-364.17111847j, -388.19490322-330.83950936j]])
matlab xx 对于 post 第一行来说太大了：
Columns 1 through 5
-4.5258 - 6.7677i -2.8199 - 6.5171i -2.5924 - 6.3501i -2.2652 - 6.1454i -2.1380 - 6.0565i
-3.3526 - 7.0387i -1.9512 - 6.6139i -1.8210 - 6.3541i -1.5917 - 6.1802i -1.5068 - 6.0629i
-2.9576 - 6.8487i -1.7324 - 6.2677i -1.7414 - 6.1000i -1.5113 - 5.9629i -1.4118 - 5.8737i
-2.5942 - 6.5328i -1.6652 - 6.0255i -1.5915 - 6.0017i -1.3225 - 5.8073i -1.2911 - 5.6604i
-2.7038 - 6.1171i -1.8519 - 5.7833i -1.6694 - 5.7016i -1.5398 - 5.4927i -1.4874 - 5.4711i
-3.0414 - 6.0944i -2.0874 - 5.8841i -1.9274 - 5.7351i -1.7284 - 5.6416i -1.6399 - 5.4976i
-3.1291 - 6.2552i -2.1179 - 5.9566i -1.9873 - 5.8116i -1.7679 - 5.6209i -1.7752 - 5.5574i
-3.1846 - 6.2824i -2.2147 - 5.9888i -2.0517 - 5.8205i -1.9089 - 5.6169i -1.8547 - 5.5742i
-3.3634 - 6.3435i -2.3585 - 6.0483i -2.2724 - 5.8377i -2.1611 - 5.7308i -2.0719 - 5.6881i
-3.4930 - 6.5714i -2.4824 - 6.2474i -2.4096 - 6.1046i -2.1738 - 6.0056i -2.0842 - 5.8660i
-3.4977 - 6.6898i -2.4959 - 6.3652i -2.3771 - 6.2161i -2.1896 - 5.9859i -2.2244 - 5.9183i
-3.7146 - 6.8227i -2.6272 - 6.4702i -2.5624 - 6.2801i -2.4266 - 6.1418i -2.3639 - 6.1235i
-3.8749 - 7.2053i -2.8709 - 6.7784i -2.7552 - 6.6938i -2.5841 - 6.5032i -2.4996 - 6.4839i
-3.7870 - 7.6706i -2.6680 - 7.3165i -2.4975 - 7.0619i -2.3808 - 6.9107i -2.2599 - 6.8245i
-3.5470 - 7.9192i -2.3884 - 7.3498i -2.3778 - 7.1098i -2.2237 - 6.9752i -2.1495 - 6.8788i
-3.3774 - 8.1407i -2.3445 - 7.4918i -2.3308 - 7.3440i -2.1527 - 7.2085i -2.0745 - 7.1216i
-3.1525 - 8.2276i -2.2111 - 7.6250i -2.1091 - 7.5007i -1.9231 - 7.3094i -1.8284 - 7.2038i
-3.0647 - 8.3645i -2.0377 - 7.7732i -1.9548 - 7.5233i -1.8111 - 7.3576i -1.7689 - 7.2205i
-2.9077 - 8.4493i -1.9463 - 7.7723i -1.9293 - 7.6172i -1.7293 - 7.4210i -1.6972 - 7.3599i
-2.8770 - 8.5876i -1.8656 - 7.9865i -1.7569 - 7.7734i -1.6328 - 7.5554i -1.5008 - 7.4696i
-2.7261 - 8.8312i -1.6047 - 8.0594i -1.5835 - 7.8066i -1.4221 - 7.5995i -1.3657 - 7.4299i
-2.5481 - 8.9706i -1.4817 - 8.0861i -1.4902 - 7.8014i -1.4058 - 7.6098i -1.3197 - 7.4890i
-2.3348 - 9.2287i -1.2996 - 8.1146i -1.4435 - 7.9271i -1.2240 - 7.7699i -1.1770 - 7.5609i
-1.9191 - 9.3370i -1.1828 - 8.1255i -1.2125 - 8.0338i -1.0260 - 7.7334i -1.0170 - 7.5347i
-1.7057 - 9.2125i -1.0760 - 8.1519i -1.0698 - 7.9991i -0.9242 - 7.6857i -1.0191 - 7.4748i
-1.5766 - 9.3942i -0.9115 - 8.2014i -0.9481 - 8.0558i -0.8732 - 7.6749i -0.9802 - 7.6137i
-1.1969 - 9.5142i -0.7600 - 8.2883i -0.7354 - 8.0880i -0.8064 - 7.7545i -0.8295 - 7.6697i
-0.6769 - 9.4567i -0.4273 - 8.1623i -0.5976 - 8.0069i -0.6075 - 7.7987i -0.6769 - 7.6913i
-0.3850 - 9.0560i -0.4248 - 8.0788i -0.4050 - 8.0236i -0.4633 - 7.7094i -0.5513 - 7.7039i
-0.4366 - 8.8908i -0.2136 - 8.0561i -0.2837 - 7.8554i -0.3773 - 7.6979i -0.3598 - 7.6238i
-0.1777 - 8.9291i -0.0988 - 7.8777i -0.2033 - 7.8333i -0.2561 - 7.5843i -0.3953 - 7.5606i
-0.0161 - 8.6390i -0.1203 - 7.8495i -0.1071 - 7.7569i -0.2803 - 7.6176i -0.2740 - 7.7102i
0.1264 - 8.6460i 0.1332 - 7.8396i 0.0593 - 7.6877i -0.0735 - 7.6183i 0.0476 - 7.6904i
0.5057 - 8.2794i 0.2117 - 7.5596i 0.0900 - 7.5168i 0.0649 - 7.5802i 0.2925 - 7.4029i
0.2960 - 7.7818i 0.1738 - 7.4925i 0.1958 - 7.4376i 0.2700 - 7.3344i 0.2059 - 7.0959i
0.1925 - 7.8667i 0.3320 - 7.3270i 0.1611 - 7.1907i 0.1782 - 7.1215i 0.0658 - 7.0173i
0.2259 - 7.5840i 0.1145 - 7.0688i 0.0032 - 7.1909i 0.0513 - 7.1091i 0.0311 - 7.0961i
0.0885 - 7.5782i 0.0182 - 7.1834i 0.0570 - 7.2710i 0.2009 - 7.1118i 0.0902 - 6.9441i
0.0255 - 7.4985i 0.0030 - 7.1903i 0.1157 - 7.2326i 0.0663 - 6.9297i -0.0887 - 7.0950i
0.1082 - 7.5894i 0.1059 - 7.2266i 0.2692 - 7.1582i 0.0889 - 7.0127i 0.2412 - 7.0495i
0.1297 - 7.4225i 0.0920 - 7.1568i 0.1424 - 7.0073i 0.0280 - 7.0163i 0.0716 - 6.9444i
这是 numpy 中的 advanced indexing:
In [30]: arr = np.arange(9).reshape(3,3)
In [31]: arr
Out[31]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
2个索引数组（为了显示方便，相同）：
In [32]: i, j = np.array([0,1,2]), np.array([0,1,2])
In [33]: arr[i,j]
Out[33]: array([0, 4, 8])
我们有2个形状为(3,)的数组，结果也是(3,)，数组的'diagonal'。要在 MATLAB 中做同样的事情，我们必须使用 sub2ind 或类似的东西将索引对转换为平面索引。
但是如果第一个索引是(3,1)，那么我们得到一个(3,3)的结果，一个块。 （这是 MATLAB 语法的默认设置）：
In [34]: i1 = i.reshape(3,1)
In [35]: arr[i1,j]
Out[35]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
In [36]: i1,j
Out[36]:
(array([[0],
[1],
[2]]),
array([0, 1, 2]))
ix_ 是生成列向量的便捷工具 i1:
In [37]: arr[np.ix_(i,j)]
Out[37]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
在numpy中获取对角线和块在做同样的事情，broadcasting索引数组相互对抗。
相当于 broadcasting 加上：
In [38]: 10*i+j
Out[38]: array([ 0, 11, 22])
In [39]: 10*i1+j
Out[39]:
array([[ 0, 1, 2],
[10, 11, 12],
[20, 21, 22]])
对于您的 (1,128) 个数组，转置可以解决问题：
In [40]: I=i.reshape(1,3); J=j.reshape(1,3)
In [41]: I
Out[41]: array([[0, 1, 2]])
In [42]: arr[I,J]
Out[42]: array([[0, 4, 8]])
In [43]: _.shape
Out[43]: (1, 3)
In [44]: arr[I.T,J]
Out[44]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
我没有对 i 使用转置，因为它是 1d。事实上，MATLAB 矩阵总是 2d（或更高）并且 numpy 可以是 0 或 1d，这是任性的 MATLAB 用户最难掌握的事情之一。
如果你这样做
array[rows, cols]
其中 rows 和 cols 是 lists/np.arrays，numpy 将 select 处的元素
[[rows[0], cols[0]], [rows[1], cols[1]],..., [rows[n], cols[n]]]
如果您使用的是 numpy 数组，下面的内容应该可以满足您的需求
xx=wqtran[(firstAxis-1)[:, np.newaxis], secondAxis-1, kk - 2]
已经发布了使用 np.ix_ 的更简洁的解决方案 here