How to fill a matrix of known size with data from a row vector

I have a row vector ( dimension: 1x171072000) representing per second data for 66 months.

I want to build a matrix with dimension 2592000x66, so that each colums contains data for each month. For example the first column of the matrix must contains vector(1:2592000) and the second column vector(2592001:5184000.

How can I implement this? thank you in advance

Answer 1

[Fangjun Jiang]

reshape()

[Nicolò Monaco]

Thanks for the quick answer

Answer 2

[Voss]

A = randn(1,171072000)

A = 1×171072000

0.3875 -1.9867 -0.2379 0.1738 0.0990 1.0573 -0.8788 1.4036 -3.6949 0.1900 -0.0567 1.6167 1.6784 -0.1925 -0.4005 -0.3892 0.8510 -0.4445 0.4273 -0.8284 0.8122 -1.1188 1.5828 0.0162 -1.6376 1.4737 0.7082 0.6214 -0.6716 0.6999

B = reshape(A,[],66)

B = 2592000×66

0.3875 -1.3692 0.9710 1.1184 -0.1400 0.4886 1.8350 -0.5145 -2.4965 -0.0625 3.0628 0.4617 -0.0233 1.3546 -2.1346 0.5320 -1.7412 0.8743 -0.7809 0.1577 0.2596 -1.1410 0.5543 0.3521 -1.3561 2.9233 0.1950 -0.6069 -0.9593 0.6087 -1.9867 0.2935 0.5990 1.3330 -0.2093 -0.0355 0.8335 -0.0923 0.9512 0.2460 0.1450 -1.4380 -0.7361 0.3305 -0.0382 -0.8544 0.0036 -0.3293 0.6408 0.1290 0.1475 -0.3567 -0.8266 -0.7657 0.3540 -0.8802 0.3913 1.2270 0.0518 -0.8527 -0.2379 -0.1303 0.6242 -0.9432 -0.2440 -0.4866 0.7084 0.6374 -0.0030 -0.4931 -1.2137 1.7080 0.3712 -0.3124 0.2305 1.2967 -0.2044 -0.8888 -0.6738 2.5090 0.2927 -0.4236 1.8914 -0.6045 -0.9274 -0.9079 2.4966 1.5355 -0.4163 0.7406 0.1738 1.1187 -0.9046 -0.9690 0.7033 0.6652 -1.6156 -0.5211 0.1894 -0.4359 -1.0656 1.5449 -2.1343 -0.2652 0.3721 -0.3095 0.4569 0.0577 0.2565 -0.3711 0.1692 -0.8408 -0.1927 -2.0263 -0.0819 0.1040 -1.0257 0.2481 0.9915 -1.6916 0.0990 -1.1458 1.3158 -0.5638 1.6851 -2.1849 -0.7489 0.0538 0.6014 0.3645 1.3252 0.5119 -0.5372 -1.1119 0.3421 -0.4771 0.3793 0.4533 -0.6377 1.6360 -0.5688 -0.7498 -1.3135 -2.3985 -0.5606 -1.7849 -0.3119 -0.3053 -0.0892 -0.5111 1.0573 1.6427 -0.6530 0.2691 1.2518 0.5644 0.0371 1.1183 1.7318 2.1239 0.0241 0.6996 0.5446 -0.6511 -1.6128 -0.2210 -1.7022 0.2040 0.8570 -1.8349 1.6881 -0.0667 0.5122 -2.2710 -0.2053 1.7887 -2.5509 -0.5626 -1.1287 -0.2476 -0.8788 -0.0270 -1.3102 1.8098 1.8844 -0.9971 -0.2106 2.1487 0.4754 -0.4267 -0.8447 0.8141 0.9215 0.1069 0.1939 -0.0554 1.1703 -1.3961 -0.8005 0.4885 -0.5591 -0.1350 0.1337 0.4191 0.8678 0.3670 0.9646 -0.6997 0.0361 -0.8307 1.4036 -0.5129 1.2829 -0.6057 -1.0464 -0.5065 -1.4943 1.2725 -0.9102 -0.5495 -1.4496 0.0917 -0.9924 -0.7929 -0.7865 -0.0007 -0.7678 -0.7087 -0.1772 -1.3529 -1.7970 -0.0122 1.8184 -0.1958 -1.3763 -0.1554 0.0724 -3.0187 -1.1723 -1.3708 -3.6949 0.5822 0.0433 0.6614 0.5026 -0.7977 -0.3218 1.4018 1.0860 -1.1523 0.9896 -0.0124 -1.0040 -2.7534 0.2100 0.8065 0.7002 1.5609 0.0379 -0.2185 0.0230 0.5448 0.9784 0.8731 0.5252 -1.4438 -0.0815 0.1840 1.0748 -0.4470 0.1900 0.8321 -0.7244 0.9139 0.6005 -0.4086 -0.1653 -0.6962 -1.5189 -1.8276 1.1408 0.4139 0.1925 1.3744 0.3097 0.1423 -1.8553 -0.2173 -0.8898 -1.2154 -0.1432 0.0743 2.3276 0.3756 -0.3507 0.8325 -0.6457 1.9939 -1.2789 -0.4749

[Nicolò Monaco]

thanks