根据列表中的元素扩展矩阵
Expanding a matrix according to elements in lists
我目前创建了一个接受两个参数的函数。
function p = matr(x, phi)
x_dir = linspace(0, x, 1);
r = linspace(0, phi, 1);
p = zeros(800, 2);
p(1:400, 1) = p(1:400, 1) + x_dir.';
p(401:800, 2) = p(401:800, 2) + r.';
end
给定输入的returns这个矩阵：
path = trajectory(10, pi/2)
path =
0 0
0.0251 0
0.0501 0
0.0752 0
0.1003 0
0.1253 0
0.1504 0
0.1754 0
0.2005 0
0.2256 0
0.2506 0
0.2757 0
0.3008 0
0.3258 0
0.3509 0
0.3759 0
0.4010 0
0.4261 0
0.4511 0
0.4762 0
0.5013 0
0.5263 0
0.5514 0
0.5764 0
0.6015 0
0.6266 0
0.6516 0
0.6767 0
0.7018 0
0.7268 0
0.7519 0
0.7769 0
0.8020 0
0.8271 0
0.8521 0
0.8772 0
0.9023 0
0.9273 0
0.9524 0
0.9774 0
1.0025 0
1.0276 0
1.0526 0
1.0777 0
1.1028 0
1.1278 0
1.1529 0
1.1779 0
1.2030 0
1.2281 0
1.2531 0
1.2782 0
1.3033 0
1.3283 0
1.3534 0
1.3784 0
1.4035 0
1.4286 0
1.4536 0
1.4787 0
1.5038 0
1.5288 0
1.5539 0
1.5789 0
1.6040 0
1.6291 0
1.6541 0
1.6792 0
1.7043 0
1.7293 0
1.7544 0
1.7794 0
1.8045 0
1.8296 0
1.8546 0
1.8797 0
1.9048 0
1.9298 0
1.9549 0
1.9799 0
2.0050 0
2.0301 0
2.0551 0
2.0802 0
2.1053 0
2.1303 0
2.1554 0
2.1805 0
2.2055 0
2.2306 0
2.2556 0
2.2807 0
2.3058 0
2.3308 0
2.3559 0
2.3810 0
2.4060 0
2.4311 0
2.4561 0
2.4812 0
2.5063 0
2.5313 0
2.5564 0
2.5815 0
2.6065 0
2.6316 0
2.6566 0
2.6817 0
2.7068 0
2.7318 0
2.7569 0
2.7820 0
2.8070 0
2.8321 0
2.8571 0
2.8822 0
2.9073 0
2.9323 0
2.9574 0
2.9825 0
3.0075 0
3.0326 0
3.0576 0
3.0827 0
3.1078 0
3.1328 0
3.1579 0
3.1830 0
3.2080 0
3.2331 0
3.2581 0
3.2832 0
3.3083 0
3.3333 0
3.3584 0
3.3835 0
3.4085 0
3.4336 0
3.4586 0
3.4837 0
3.5088 0
3.5338 0
3.5589 0
3.5840 0
3.6090 0
3.6341 0
3.6591 0
3.6842 0
3.7093 0
3.7343 0
3.7594 0
3.7845 0
3.8095 0
3.8346 0
3.8596 0
3.8847 0
3.9098 0
3.9348 0
3.9599 0
3.9850 0
4.0100 0
4.0351 0
4.0602 0
4.0852 0
4.1103 0
4.1353 0
4.1604 0
4.1855 0
4.2105 0
4.2356 0
4.2607 0
4.2857 0
4.3108 0
4.3358 0
4.3609 0
4.3860 0
4.4110 0
4.4361 0
4.4612 0
4.4862 0
4.5113 0
4.5363 0
4.5614 0
4.5865 0
4.6115 0
4.6366 0
4.6617 0
4.6867 0
4.7118 0
4.7368 0
4.7619 0
4.7870 0
4.8120 0
4.8371 0
4.8622 0
4.8872 0
4.9123 0
4.9373 0
4.9624 0
4.9875 0
5.0125 0
5.0376 0
5.0627 0
5.0877 0
5.1128 0
5.1378 0
5.1629 0
5.1880 0
5.2130 0
5.2381 0
5.2632 0
5.2882 0
5.3133 0
5.3383 0
5.3634 0
5.3885 0
5.4135 0
5.4386 0
5.4637 0
5.4887 0
5.5138 0
5.5388 0
5.5639 0
5.5890 0
5.6140 0
5.6391 0
5.6642 0
5.6892 0
5.7143 0
5.7393 0
5.7644 0
5.7895 0
5.8145 0
5.8396 0
5.8647 0
5.8897 0
5.9148 0
5.9398 0
5.9649 0
5.9900 0
6.0150 0
6.0401 0
6.0652 0
6.0902 0
6.1153 0
6.1404 0
6.1654 0
6.1905 0
6.2155 0
6.2406 0
6.2657 0
6.2907 0
6.3158 0
6.3409 0
6.3659 0
6.3910 0
6.4160 0
6.4411 0
6.4662 0
6.4912 0
6.5163 0
6.5414 0
6.5664 0
6.5915 0
6.6165 0
6.6416 0
6.6667 0
6.6917 0
6.7168 0
6.7419 0
6.7669 0
6.7920 0
6.8170 0
6.8421 0
6.8672 0
6.8922 0
6.9173 0
6.9424 0
6.9674 0
6.9925 0
7.0175 0
7.0426 0
7.0677 0
7.0927 0
7.1178 0
7.1429 0
7.1679 0
7.1930 0
7.2180 0
7.2431 0
7.2682 0
7.2932 0
7.3183 0
7.3434 0
7.3684 0
7.3935 0
7.4185 0
7.4436 0
7.4687 0
7.4937 0
7.5188 0
7.5439 0
7.5689 0
7.5940 0
7.6190 0
7.6441 0
7.6692 0
7.6942 0
7.7193 0
7.7444 0
7.7694 0
7.7945 0
7.8195 0
7.8446 0
7.8697 0
7.8947 0
7.9198 0
7.9449 0
7.9699 0
7.9950 0
8.0201 0
8.0451 0
8.0702 0
8.0952 0
8.1203 0
8.1454 0
8.1704 0
8.1955 0
8.2206 0
8.2456 0
8.2707 0
8.2957 0
8.3208 0
8.3459 0
8.3709 0
8.3960 0
8.4211 0
8.4461 0
8.4712 0
8.4962 0
8.5213 0
8.5464 0
8.5714 0
8.5965 0
8.6216 0
8.6466 0
8.6717 0
8.6967 0
8.7218 0
8.7469 0
8.7719 0
8.7970 0
8.8221 0
8.8471 0
8.8722 0
8.8972 0
8.9223 0
8.9474 0
8.9724 0
8.9975 0
9.0226 0
9.0476 0
9.0727 0
9.0977 0
9.1228 0
9.1479 0
9.1729 0
9.1980 0
9.2231 0
9.2481 0
9.2732 0
9.2982 0
9.3233 0
9.3484 0
9.3734 0
9.3985 0
9.4236 0
9.4486 0
9.4737 0
9.4987 0
9.5238 0
9.5489 0
9.5739 0
9.5990 0
9.6241 0
9.6491 0
9.6742 0
9.6992 0
9.7243 0
9.7494 0
9.7744 0
9.7995 0
9.8246 0
9.8496 0
9.8747 0
9.8997 0
9.9248 0
9.9499 0
9.9749 0
10.000 0
0 0
0 0.0039
0 0.0079
0 0.0118
0 0.0157
0 0.0197
0 0.0236
0 0.0276
0 0.0315
0 0.0354
0 0.0394
0 0.0433
0 0.0472
0 0.0512
0 0.0551
0 0.0591
0 0.0630
0 0.0669
0 0.0709
0 0.0748
0 0.0787
0 0.0827
0 0.0866
0 0.0905
0 0.0945
0 0.0984
0 0.1024
0 0.1063
0 0.1102
0 0.1142
0 0.1181
0 0.1220
0 0.1260
0 0.1299
0 0.1339
0 0.1378
0 0.1417
0 0.1457
0 0.1496
0 0.1535
0 0.1575
0 0.1614
0 0.1653
0 0.1693
0 0.1732
0 0.1772
0 0.1811
0 0.1850
0 0.1890
0 0.1929
0 0.1968
0 0.2008
0 0.2047
0 0.2087
0 0.2126
0 0.2165
0 0.2205
0 0.2244
0 0.2283
0 0.2323
0 0.2362
0 0.2401
0 0.2441
0 0.2480
0 0.2520
0 0.2559
0 0.2598
0 0.2638
0 0.2677
0 0.2716
0 0.2756
0 0.2795
0 0.2835
0 0.2874
0 0.2913
0 0.2953
0 0.2992
0 0.3031
0 0.3071
0 0.3110
0 0.3149
0 0.3189
0 0.3228
0 0.3268
0 0.3307
0 0.3346
0 0.3386
0 0.3425
0 0.3464
0 0.3504
0 0.3543
0 0.3583
0 0.3622
0 0.3661
0 0.3701
0 0.3740
0 0.3779
0 0.3819
0 0.3858
0 0.3897
0 0.3937
0 0.3976
0 0.4016
0 0.4055
0 0.4094
0 0.4134
0 0.4173
0 0.4212
0 0.4252
0 0.4291
0 0.4331
0 0.4370
0 0.4409
0 0.4449
0 0.4488
0 0.4527
0 0.4567
0 0.4606
0 0.4645
0 0.4685
0 0.4724
0 0.4764
0 0.4803
0 0.4842
0 0.4882
0 0.4921
0 0.4960
0 0.5000
0 0.5039
0 0.5079
0 0.5118
0 0.5157
0 0.5197
0 0.5236
0 0.5275
0 0.5315
0 0.5354
0 0.5393
0 0.5433
0 0.5472
0 0.5512
0 0.5551
0 0.5590
0 0.5630
0 0.5669
0 0.5708
0 0.5748
0 0.5787
0 0.5827
0 0.5866
0 0.5905
0 0.5945
0 0.5984
0 0.6023
0 0.6063
0 0.6102
0 0.6141
0 0.6181
0 0.6220
0 0.6260
0 0.6299
0 0.6338
0 0.6378
0 0.6417
0 0.6456
0 0.6496
0 0.6535
0 0.6575
0 0.6614
0 0.6653
0 0.6693
0 0.6732
0 0.6771
0 0.6811
0 0.6850
0 0.6889
0 0.6929
0 0.6968
0 0.7008
0 0.7047
0 0.7086
0 0.7126
0 0.7165
0 0.7204
0 0.7244
0 0.7283
0 0.7323
0 0.7362
0 0.7401
0 0.7441
0 0.7480
0 0.7519
0 0.7559
0 0.7598
0 0.7637
0 0.7677
0 0.7716
0 0.7756
0 0.7795
0 0.7834
0 0.7874
0 0.7913
0 0.7952
0 0.7992
0 0.8031
0 0.8071
0 0.8110
0 0.8149
0 0.8189
0 0.8228
0 0.8267
0 0.8307
0 0.8346
0 0.8385
0 0.8425
0 0.8464
0 0.8504
0 0.8543
0 0.8582
0 0.8622
0 0.8661
0 0.8700
0 0.8740
0 0.8779
0 0.8819
0 0.8858
0 0.8897
0 0.8937
0 0.8976
0 0.9015
0 0.9055
0 0.9094
0 0.9133
0 0.9173
0 0.9212
0 0.9252
0 0.9291
0 0.9330
0 0.9370
0 0.9409
0 0.9448
0 0.9488
0 0.9527
0 0.9567
0 0.9606
0 0.9645
0 0.9685
0 0.9724
0 0.9763
0 0.9803
0 0.9842
0 0.9881
0 0.9921
0 0.9960
0 1.0000
0 1.0039
0 1.0078
0 1.0118
0 1.0157
0 1.0196
0 1.0236
0 1.0275
0 1.0315
0 1.0354
0 1.0393
0 1.0433
0 1.0472
0 1.0511
0 1.0551
0 1.0590
0 1.0629
0 1.0669
0 1.0708
0 1.0748
0 1.0787
0 1.0826
0 1.0866
0 1.0905
0 1.0944
0 1.0984
0 1.1023
0 1.1063
0 1.1102
0 1.1141
0 1.1181
0 1.1220
0 1.1259
0 1.1299
0 1.1338
0 1.1377
0 1.1417
0 1.1456
0 1.1496
0 1.1535
0 1.1574
0 1.1614
0 1.1653
0 1.1692
0 1.1732
0 1.1771
0 1.1810
0 1.1850
0 1.1889
0 1.1929
0 1.1968
0 1.2007
0 1.2047
0 1.2086
0 1.2125
0 1.2165
0 1.2204
0 1.2244
0 1.2283
0 1.2322
0 1.2362
0 1.2401
0 1.2440
0 1.2480
0 1.2519
0 1.2558
0 1.2598
0 1.2637
0 1.2677
0 1.2716
0 1.2755
0 1.2795
0 1.2834
0 1.2873
0 1.2913
0 1.2952
0 1.2992
0 1.3031
0 1.3070
0 1.3110
0 1.3149
0 1.3188
0 1.3228
0 1.3267
0 1.3306
0 1.3346
0 1.3385
0 1.3425
0 1.3464
0 1.3503
0 1.3543
0 1.3582
0 1.3621
0 1.3661
0 1.3700
0 1.3740
0 1.3779
0 1.3818
0 1.3858
0 1.3897
0 1.3936
0 1.3976
0 1.4015
0 1.4054
0 1.4094
0 1.4133
0 1.4173
0 1.4212
0 1.4251
0 1.4291
0 1.4330
0 1.4369
0 1.4409
0 1.4448
0 1.4488
0 1.4527
0 1.4566
0 1.4606
0 1.4645
0 1.4684
0 1.4724
0 1.4763
0 1.4802
0 1.4842
0 1.4881
0 1.4921
0 1.4960
0 1.4999
0 1.5039
0 1.5078
0 1.5117
0 1.5157
0 1.5196
0 1.5236
0 1.5275
0 1.5314
0 1.5354
0 1.5393
0 1.5432
0 1.5472
0 1.5511
0 1.5550
0 1.5590
0 1.5629
0 1.5669
0 1.5708
但我想修改此函数，使其可以包含两个列表，例如：
trajectory([x1, x2, x3, ... xn], [phi1, phi2, phi3, ..., phin])
并像这样创建一个矩阵：
trajectory =
x1 0
x1+k 0
. 0
. 0
x1_n 0
0 phi1
0 phi1+k
0 .
0 .
0 phi1_n
x2 0
x2+k 0
. 0
. 0
x2_n 0
0 phi2
0 phi2+k
0 .
0 .
0 phi2_n
等等。所以我在想是否有一种更自动的方法来扩展矩阵，这样可以提供两个列表作为输入参数，并根据列表的元素扩展矩阵。
function p = matr(x, phi)
p = zeros(800*length(x), 2);
for ii = 1:length(x)
x_dir = linspace(0, x(ii), 400);
r = linspace(0, phi(ii), 400);
p((800*(ii-1)+1):(800*(ii-1)+400), 1) = x_dir;
p((800*ii-399):(800*ii), 2) = r;
end
end
或
function p2 = matr_compound(x, phi)
p2 = [];
for ii = 1:length(x)
p2 = [p2; matr(x(ii), phi(ii))];
end
end
我目前创建了一个接受两个参数的函数。
function p = matr(x, phi)
x_dir = linspace(0, x, 1);
r = linspace(0, phi, 1);
p = zeros(800, 2);
p(1:400, 1) = p(1:400, 1) + x_dir.';
p(401:800, 2) = p(401:800, 2) + r.';
end
给定输入的returns这个矩阵：
path = trajectory(10, pi/2)
path =
0 0
0.0251 0
0.0501 0
0.0752 0
0.1003 0
0.1253 0
0.1504 0
0.1754 0
0.2005 0
0.2256 0
0.2506 0
0.2757 0
0.3008 0
0.3258 0
0.3509 0
0.3759 0
0.4010 0
0.4261 0
0.4511 0
0.4762 0
0.5013 0
0.5263 0
0.5514 0
0.5764 0
0.6015 0
0.6266 0
0.6516 0
0.6767 0
0.7018 0
0.7268 0
0.7519 0
0.7769 0
0.8020 0
0.8271 0
0.8521 0
0.8772 0
0.9023 0
0.9273 0
0.9524 0
0.9774 0
1.0025 0
1.0276 0
1.0526 0
1.0777 0
1.1028 0
1.1278 0
1.1529 0
1.1779 0
1.2030 0
1.2281 0
1.2531 0
1.2782 0
1.3033 0
1.3283 0
1.3534 0
1.3784 0
1.4035 0
1.4286 0
1.4536 0
1.4787 0
1.5038 0
1.5288 0
1.5539 0
1.5789 0
1.6040 0
1.6291 0
1.6541 0
1.6792 0
1.7043 0
1.7293 0
1.7544 0
1.7794 0
1.8045 0
1.8296 0
1.8546 0
1.8797 0
1.9048 0
1.9298 0
1.9549 0
1.9799 0
2.0050 0
2.0301 0
2.0551 0
2.0802 0
2.1053 0
2.1303 0
2.1554 0
2.1805 0
2.2055 0
2.2306 0
2.2556 0
2.2807 0
2.3058 0
2.3308 0
2.3559 0
2.3810 0
2.4060 0
2.4311 0
2.4561 0
2.4812 0
2.5063 0
2.5313 0
2.5564 0
2.5815 0
2.6065 0
2.6316 0
2.6566 0
2.6817 0
2.7068 0
2.7318 0
2.7569 0
2.7820 0
2.8070 0
2.8321 0
2.8571 0
2.8822 0
2.9073 0
2.9323 0
2.9574 0
2.9825 0
3.0075 0
3.0326 0
3.0576 0
3.0827 0
3.1078 0
3.1328 0
3.1579 0
3.1830 0
3.2080 0
3.2331 0
3.2581 0
3.2832 0
3.3083 0
3.3333 0
3.3584 0
3.3835 0
3.4085 0
3.4336 0
3.4586 0
3.4837 0
3.5088 0
3.5338 0
3.5589 0
3.5840 0
3.6090 0
3.6341 0
3.6591 0
3.6842 0
3.7093 0
3.7343 0
3.7594 0
3.7845 0
3.8095 0
3.8346 0
3.8596 0
3.8847 0
3.9098 0
3.9348 0
3.9599 0
3.9850 0
4.0100 0
4.0351 0
4.0602 0
4.0852 0
4.1103 0
4.1353 0
4.1604 0
4.1855 0
4.2105 0
4.2356 0
4.2607 0
4.2857 0
4.3108 0
4.3358 0
4.3609 0
4.3860 0
4.4110 0
4.4361 0
4.4612 0
4.4862 0
4.5113 0
4.5363 0
4.5614 0
4.5865 0
4.6115 0
4.6366 0
4.6617 0
4.6867 0
4.7118 0
4.7368 0
4.7619 0
4.7870 0
4.8120 0
4.8371 0
4.8622 0
4.8872 0
4.9123 0
4.9373 0
4.9624 0
4.9875 0
5.0125 0
5.0376 0
5.0627 0
5.0877 0
5.1128 0
5.1378 0
5.1629 0
5.1880 0
5.2130 0
5.2381 0
5.2632 0
5.2882 0
5.3133 0
5.3383 0
5.3634 0
5.3885 0
5.4135 0
5.4386 0
5.4637 0
5.4887 0
5.5138 0
5.5388 0
5.5639 0
5.5890 0
5.6140 0
5.6391 0
5.6642 0
5.6892 0
5.7143 0
5.7393 0
5.7644 0
5.7895 0
5.8145 0
5.8396 0
5.8647 0
5.8897 0
5.9148 0
5.9398 0
5.9649 0
5.9900 0
6.0150 0
6.0401 0
6.0652 0
6.0902 0
6.1153 0
6.1404 0
6.1654 0
6.1905 0
6.2155 0
6.2406 0
6.2657 0
6.2907 0
6.3158 0
6.3409 0
6.3659 0
6.3910 0
6.4160 0
6.4411 0
6.4662 0
6.4912 0
6.5163 0
6.5414 0
6.5664 0
6.5915 0
6.6165 0
6.6416 0
6.6667 0
6.6917 0
6.7168 0
6.7419 0
6.7669 0
6.7920 0
6.8170 0
6.8421 0
6.8672 0
6.8922 0
6.9173 0
6.9424 0
6.9674 0
6.9925 0
7.0175 0
7.0426 0
7.0677 0
7.0927 0
7.1178 0
7.1429 0
7.1679 0
7.1930 0
7.2180 0
7.2431 0
7.2682 0
7.2932 0
7.3183 0
7.3434 0
7.3684 0
7.3935 0
7.4185 0
7.4436 0
7.4687 0
7.4937 0
7.5188 0
7.5439 0
7.5689 0
7.5940 0
7.6190 0
7.6441 0
7.6692 0
7.6942 0
7.7193 0
7.7444 0
7.7694 0
7.7945 0
7.8195 0
7.8446 0
7.8697 0
7.8947 0
7.9198 0
7.9449 0
7.9699 0
7.9950 0
8.0201 0
8.0451 0
8.0702 0
8.0952 0
8.1203 0
8.1454 0
8.1704 0
8.1955 0
8.2206 0
8.2456 0
8.2707 0
8.2957 0
8.3208 0
8.3459 0
8.3709 0
8.3960 0
8.4211 0
8.4461 0
8.4712 0
8.4962 0
8.5213 0
8.5464 0
8.5714 0
8.5965 0
8.6216 0
8.6466 0
8.6717 0
8.6967 0
8.7218 0
8.7469 0
8.7719 0
8.7970 0
8.8221 0
8.8471 0
8.8722 0
8.8972 0
8.9223 0
8.9474 0
8.9724 0
8.9975 0
9.0226 0
9.0476 0
9.0727 0
9.0977 0
9.1228 0
9.1479 0
9.1729 0
9.1980 0
9.2231 0
9.2481 0
9.2732 0
9.2982 0
9.3233 0
9.3484 0
9.3734 0
9.3985 0
9.4236 0
9.4486 0
9.4737 0
9.4987 0
9.5238 0
9.5489 0
9.5739 0
9.5990 0
9.6241 0
9.6491 0
9.6742 0
9.6992 0
9.7243 0
9.7494 0
9.7744 0
9.7995 0
9.8246 0
9.8496 0
9.8747 0
9.8997 0
9.9248 0
9.9499 0
9.9749 0
10.000 0
0 0
0 0.0039
0 0.0079
0 0.0118
0 0.0157
0 0.0197
0 0.0236
0 0.0276
0 0.0315
0 0.0354
0 0.0394
0 0.0433
0 0.0472
0 0.0512
0 0.0551
0 0.0591
0 0.0630
0 0.0669
0 0.0709
0 0.0748
0 0.0787
0 0.0827
0 0.0866
0 0.0905
0 0.0945
0 0.0984
0 0.1024
0 0.1063
0 0.1102
0 0.1142
0 0.1181
0 0.1220
0 0.1260
0 0.1299
0 0.1339
0 0.1378
0 0.1417
0 0.1457
0 0.1496
0 0.1535
0 0.1575
0 0.1614
0 0.1653
0 0.1693
0 0.1732
0 0.1772
0 0.1811
0 0.1850
0 0.1890
0 0.1929
0 0.1968
0 0.2008
0 0.2047
0 0.2087
0 0.2126
0 0.2165
0 0.2205
0 0.2244
0 0.2283
0 0.2323
0 0.2362
0 0.2401
0 0.2441
0 0.2480
0 0.2520
0 0.2559
0 0.2598
0 0.2638
0 0.2677
0 0.2716
0 0.2756
0 0.2795
0 0.2835
0 0.2874
0 0.2913
0 0.2953
0 0.2992
0 0.3031
0 0.3071
0 0.3110
0 0.3149
0 0.3189
0 0.3228
0 0.3268
0 0.3307
0 0.3346
0 0.3386
0 0.3425
0 0.3464
0 0.3504
0 0.3543
0 0.3583
0 0.3622
0 0.3661
0 0.3701
0 0.3740
0 0.3779
0 0.3819
0 0.3858
0 0.3897
0 0.3937
0 0.3976
0 0.4016
0 0.4055
0 0.4094
0 0.4134
0 0.4173
0 0.4212
0 0.4252
0 0.4291
0 0.4331
0 0.4370
0 0.4409
0 0.4449
0 0.4488
0 0.4527
0 0.4567
0 0.4606
0 0.4645
0 0.4685
0 0.4724
0 0.4764
0 0.4803
0 0.4842
0 0.4882
0 0.4921
0 0.4960
0 0.5000
0 0.5039
0 0.5079
0 0.5118
0 0.5157
0 0.5197
0 0.5236
0 0.5275
0 0.5315
0 0.5354
0 0.5393
0 0.5433
0 0.5472
0 0.5512
0 0.5551
0 0.5590
0 0.5630
0 0.5669
0 0.5708
0 0.5748
0 0.5787
0 0.5827
0 0.5866
0 0.5905
0 0.5945
0 0.5984
0 0.6023
0 0.6063
0 0.6102
0 0.6141
0 0.6181
0 0.6220
0 0.6260
0 0.6299
0 0.6338
0 0.6378
0 0.6417
0 0.6456
0 0.6496
0 0.6535
0 0.6575
0 0.6614
0 0.6653
0 0.6693
0 0.6732
0 0.6771
0 0.6811
0 0.6850
0 0.6889
0 0.6929
0 0.6968
0 0.7008
0 0.7047
0 0.7086
0 0.7126
0 0.7165
0 0.7204
0 0.7244
0 0.7283
0 0.7323
0 0.7362
0 0.7401
0 0.7441
0 0.7480
0 0.7519
0 0.7559
0 0.7598
0 0.7637
0 0.7677
0 0.7716
0 0.7756
0 0.7795
0 0.7834
0 0.7874
0 0.7913
0 0.7952
0 0.7992
0 0.8031
0 0.8071
0 0.8110
0 0.8149
0 0.8189
0 0.8228
0 0.8267
0 0.8307
0 0.8346
0 0.8385
0 0.8425
0 0.8464
0 0.8504
0 0.8543
0 0.8582
0 0.8622
0 0.8661
0 0.8700
0 0.8740
0 0.8779
0 0.8819
0 0.8858
0 0.8897
0 0.8937
0 0.8976
0 0.9015
0 0.9055
0 0.9094
0 0.9133
0 0.9173
0 0.9212
0 0.9252
0 0.9291
0 0.9330
0 0.9370
0 0.9409
0 0.9448
0 0.9488
0 0.9527
0 0.9567
0 0.9606
0 0.9645
0 0.9685
0 0.9724
0 0.9763
0 0.9803
0 0.9842
0 0.9881
0 0.9921
0 0.9960
0 1.0000
0 1.0039
0 1.0078
0 1.0118
0 1.0157
0 1.0196
0 1.0236
0 1.0275
0 1.0315
0 1.0354
0 1.0393
0 1.0433
0 1.0472
0 1.0511
0 1.0551
0 1.0590
0 1.0629
0 1.0669
0 1.0708
0 1.0748
0 1.0787
0 1.0826
0 1.0866
0 1.0905
0 1.0944
0 1.0984
0 1.1023
0 1.1063
0 1.1102
0 1.1141
0 1.1181
0 1.1220
0 1.1259
0 1.1299
0 1.1338
0 1.1377
0 1.1417
0 1.1456
0 1.1496
0 1.1535
0 1.1574
0 1.1614
0 1.1653
0 1.1692
0 1.1732
0 1.1771
0 1.1810
0 1.1850
0 1.1889
0 1.1929
0 1.1968
0 1.2007
0 1.2047
0 1.2086
0 1.2125
0 1.2165
0 1.2204
0 1.2244
0 1.2283
0 1.2322
0 1.2362
0 1.2401
0 1.2440
0 1.2480
0 1.2519
0 1.2558
0 1.2598
0 1.2637
0 1.2677
0 1.2716
0 1.2755
0 1.2795
0 1.2834
0 1.2873
0 1.2913
0 1.2952
0 1.2992
0 1.3031
0 1.3070
0 1.3110
0 1.3149
0 1.3188
0 1.3228
0 1.3267
0 1.3306
0 1.3346
0 1.3385
0 1.3425
0 1.3464
0 1.3503
0 1.3543
0 1.3582
0 1.3621
0 1.3661
0 1.3700
0 1.3740
0 1.3779
0 1.3818
0 1.3858
0 1.3897
0 1.3936
0 1.3976
0 1.4015
0 1.4054
0 1.4094
0 1.4133
0 1.4173
0 1.4212
0 1.4251
0 1.4291
0 1.4330
0 1.4369
0 1.4409
0 1.4448
0 1.4488
0 1.4527
0 1.4566
0 1.4606
0 1.4645
0 1.4684
0 1.4724
0 1.4763
0 1.4802
0 1.4842
0 1.4881
0 1.4921
0 1.4960
0 1.4999
0 1.5039
0 1.5078
0 1.5117
0 1.5157
0 1.5196
0 1.5236
0 1.5275
0 1.5314
0 1.5354
0 1.5393
0 1.5432
0 1.5472
0 1.5511
0 1.5550
0 1.5590
0 1.5629
0 1.5669
0 1.5708
但我想修改此函数，使其可以包含两个列表，例如：
trajectory([x1, x2, x3, ... xn], [phi1, phi2, phi3, ..., phin])
并像这样创建一个矩阵：
trajectory =
x1 0
x1+k 0
. 0
. 0
x1_n 0
0 phi1
0 phi1+k
0 .
0 .
0 phi1_n
x2 0
x2+k 0
. 0
. 0
x2_n 0
0 phi2
0 phi2+k
0 .
0 .
0 phi2_n
等等。所以我在想是否有一种更自动的方法来扩展矩阵，这样可以提供两个列表作为输入参数，并根据列表的元素扩展矩阵。
function p = matr(x, phi)
p = zeros(800*length(x), 2);
for ii = 1:length(x)
x_dir = linspace(0, x(ii), 400);
r = linspace(0, phi(ii), 400);
p((800*(ii-1)+1):(800*(ii-1)+400), 1) = x_dir;
p((800*ii-399):(800*ii), 2) = r;
end
end
或
function p2 = matr_compound(x, phi)
p2 = [];
for ii = 1:length(x)
p2 = [p2; matr(x(ii), phi(ii))];
end
end