Movmedian with non-uniform sample points

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Robert Watson
Robert Watson on 21 Nov 2025 at 11:38
Commented: Star Strider on 21 Nov 2025 at 13:56
Hi,
I'm trying to use the movmedian function to denoise and down-sample a large 3D matrix.
This matrix is structured such that each colum represents a signal trace, captured in a specific location. There are a total of 3 repeated signal traces for each location, stored as adjacent columns in dimension 2. Neighbors in dimension 2 outside these groups correspond to repeats from spatially adjacent locations.
I want to use the movmedian function to capture the median from each repeat group and its neighbors, using the spatial correlation to help reject noise, as below:
% Set matrix dimensions
sz1 = 1024;
sz2 = 5;
sz3 = 10;
% Set number of repeats for each signal
n_repeat = 3;
% Make noisey signal matrix
th = linspace(0, 2*pi, sz1)';
signal = sin(th);
data_clean = repmat(signal, 1, n_repeat*sz2, sz3);
data_noise = data_clean + 0.1*rand(sz1, n_repeat*sz2, sz3);
% Apply movmedian de-noise & downsampling
SamplePoints = 1:n_repeat:sz2;
data_denoise = movmedian(data_noise,[n_repeat, (2*n_repeat) - 1], ...
2, "SamplePoints", SamplePoints);
However, running this code, I receive the error message below:
%Error using movmedian
%The sample points vector length must be 15, the same as the size of the input data in the operating dimension.
This seems to be a bug in the input validation logic.
The documentation states that movmedian() is capable of running with non-uniform sample points, provided they are sorted and unique.
However, any such vector of sample points would by definition have length less than the dimension along which the filter is applied.
Am I misunderstanding something, or are there any workarounds that I can use to achieve what I want without having to run unnecessary compute operations across the full matrix and discard results or revert to a nested for loop?
Appreciate any help offered,
Robert.
  1 Comment
Robert Watson
Robert Watson on 21 Nov 2025 at 13:38
Based on comments below, it seems I misunderstood the purpose of the SamplePoints input.
It seems this is intended to define the location of each sample in a coordinate system other than linearly incrementing sample indices (i.e. more complex than [1 2 3 4 ...]) and not to select the step size with which the moving median is applied, as I had assumed.

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Accepted Answer

Star Strider
Star Strider on 21 Nov 2025 at 13:21
Ran in:
Your movmedian call is operating along the second dimension, that being 15.
The documentation could definitely be improved, since it is ambigouus to me with respect to the SamplePoints name-value pair. Looking through the extended documentation does not provide any further enlightenment. MathWorks definitely needs to explain how it works and how it is supposed to be used, in detail and with examples that are actually meaningful.
If used as appparently it is supposed to be used, on matrices it appears to produce column-wise duplicates of the same values.
Example --
% Set matrix dimensions
sz1 = 1024;
sz2 = 5;
sz3 = 10;
% Set number of repeats for each signal
n_repeat = 3;
% Make noisey signal matrix
th = linspace(0, 2*pi, sz1)';
signal = sin(th);
data_clean = repmat(signal, 1, n_repeat*sz2, sz3);
data_noise = data_clean + 0.1*rand(sz1, n_repeat*sz2, sz3)
data_noise =
data_noise(:,:,1) = 0.0855 0.0804 0.0805 0.0593 0.0353 0.0926 0.0425 0.0590 0.0483 0.0659 0.0451 0.0897 0.0786 0.0581 0.0139 0.0991 0.0702 0.0160 0.0628 0.0759 0.0314 0.0826 0.0361 0.0371 0.0504 0.0434 0.0171 0.0238 0.0360 0.0217 0.0890 0.0839 0.1090 0.0386 0.0626 0.0259 0.0659 0.0858 0.1001 0.1052 0.0314 0.0441 0.0855 0.0712 0.0714 0.0590 0.1123 0.0427 0.0862 0.0981 0.0728 0.0984 0.0733 0.0376 0.0677 0.0734 0.0834 0.0895 0.1100 0.0380 0.1182 0.0312 0.1065 0.1125 0.0406 0.0404 0.1172 0.0572 0.1200 0.0823 0.0325 0.0592 0.1128 0.1074 0.0404 0.1134 0.1214 0.0873 0.0960 0.1009 0.0816 0.1076 0.0324 0.0537 0.1122 0.0375 0.0621 0.0497 0.0840 0.0709 0.1257 0.0505 0.1215 0.0755 0.0828 0.0817 0.0882 0.0683 0.1257 0.1327 0.1284 0.0414 0.0617 0.1097 0.0489 0.1295 0.0720 0.0925 0.1413 0.0599 0.0697 0.0707 0.1337 0.1080 0.1258 0.0528 0.0675 0.0772 0.0523 0.0448 0.0674 0.0535 0.0974 0.1424 0.0571 0.0685 0.0569 0.0666 0.0807 0.0720 0.0965 0.1201 0.0847 0.1430 0.0614 0.1080 0.1004 0.0712 0.1453 0.0876 0.0622 0.0691 0.1225 0.1388 0.0764 0.1242 0.0922 0.1146 0.1539 0.1457 0.1318 0.1357 0.0710 0.1201 0.0653 0.0621 0.1404 0.0904 0.1061 0.1435 0.1056 0.1023 0.0666 0.1064 0.1428 0.0676 0.1039 0.1586 0.1022 0.1180 0.1214 0.1267 0.1324 0.1259 0.0933 0.1386 0.1362 0.1152 0.1299 0.0695 0.1319 0.1626 0.0971 0.1104 0.0896 0.1517 0.1718 0.1033 0.1431 0.1063 0.1502 0.0977 0.1085 0.1174 0.0765 0.0917 0.1109 0.1579 0.1465 0.1241 0.1680 0.1247 0.0998 0.1513 0.0866 0.0874 0.1518 0.0803 0.1778 0.0977 0.1344 0.1732 0.1853 0.1710 0.1580 0.1688 0.1354 0.1778 0.1853 0.1280 0.1216 0.1395 0.1770 0.1439 0.0876 0.1411 0.1420 0.1311 0.1336 0.1825 0.1281 0.1678 0.1771 0.1086 0.1899 0.1649 0.1746 0.0933 0.1534 0.0977 0.1484 0.1874 0.1766 0.1415 0.1419 0.1376 0.1137 0.1769 0.1879 0.1738 0.1392 0.1899 0.1349 0.1952 0.1602 0.1763 0.1207 0.1330 0.1947 0.1929 0.1986 0.2035 0.1602 0.1772 0.1133 0.1398 0.1244 0.1283 0.2008 0.1756 0.1333 0.1711 0.1732 0.1446 0.1466 0.1589 0.1786 0.1990 0.1882 0.1859 0.1232 0.1350 0.1879 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0.9378 0.9437 0.9520 1.0111 0.9671 0.9430 0.9720 0.9685 1.0031 0.9200 0.9312 0.9248 0.9543 0.9973 0.9919 0.9670 0.9729 0.9349 0.9713 0.9691 0.9135 0.9632 0.9670 0.9665 0.9412 0.9219 0.9909 0.9122 0.9283 0.9293 0.9498 0.9351 0.9774 0.9164 0.9086 0.9136 0.9197 0.9516 0.9604 0.9411 0.9264 0.9232 0.9432 0.9910 0.9871 0.9875 0.9190 0.9622 0.9177 0.9414 0.9074 0.9161 0.9599 0.9110 0.9412 0.9316 0.9531 0.9598 0.9520 1.0021 0.9068 0.9656 0.9286 0.9916 0.9875 0.9801 0.9068 0.9208 0.9145 0.9366 0.9932 0.9663 0.9380 0.9207 0.9202 0.9332 0.9059 0.9252 0.9569 0.9604 0.9661 0.9233 0.9271 0.9913 0.9549 0.9362 0.9446 0.9109 0.9858 0.9852 0.9146 0.9553 0.9954 0.9767 0.9654 0.8997 0.9825 0.9303 0.9602 0.9576 0.9012 0.9894 0.9645 0.9935 0.9359 0.9010 0.9575 0.8964 0.9474 0.9472 0.9301 0.9694 0.9871 0.9547 0.9806 0.9544 0.9737 0.9580 0.9287 0.9791 0.9513 0.9881 0.9150 0.9678 0.9834 0.9881 0.9259 0.9013 0.9359 0.9445 0.9094 0.9756 0.9862 0.9263 0.9176 0.9591 0.9058 0.9831 0.9284 0.9531 0.9693 0.9128 0.8967 0.9221 0.9343 0.9871 0.9850 0.9217 0.9064 0.9706 0.9232 0.9233 0.9172 0.9520 0.9380 0.9183 0.9440 0.9066 0.9117 0.9347 0.9438 0.9805 0.9075 0.9856 0.9465 0.9782 0.9120 0.8910 0.9343 0.9741 0.9465 0.9120 0.9303 0.9331 0.8931 0.9833 0.8890 0.9006 0.9475 0.8958 0.9344 0.9720 0.9736 0.9043 0.9776 0.9374 0.9327 0.8975 0.9009 0.9624 0.9759 0.9482 0.9360 0.9459 0.8865 0.9440 0.9186 0.9745 0.8826 0.9220 0.9303 0.9549 0.9585 0.9311 0.9284 0.9495 0.9738 0.9339 0.8990 0.9673 0.9719 0.9400 0.8866 0.9749 0.9203 0.9146 0.9565 0.8941 0.9004 0.8824 0.9004 0.8773 0.9587 0.9226 0.8968 0.8838 0.9172 0.9585 0.9139 0.9304 0.9565 0.9078 0.8777 0.9399 0.9479 0.8803 0.8729 0.9360 0.9673 0.9574 0.9064 0.9279 0.8888 0.9232 0.8886 0.9025 0.9385 0.9121 0.8903 0.9332 0.8998 0.9667 0.8887 0.9664 0.9367 0.9398 0.9625 0.9003 0.9388 0.8766 0.9134 0.9142 0.8834 0.9650 0.8990 0.9136 0.9635 0.9087 0.8917 0.9600 0.8839 0.8955 0.9239 0.8974 0.8844 0.8941 0.9298 0.8913 0.9483 0.8719 0.9314 0.8762 0.9591 0.8735 0.8761 0.9565 0.9085 0.9429 0.9221 0.9226 0.8986 0.8932 0.8917 0.8626 0.8999 0.8739 0.9108 0.9295 0.8850 0.9471 0.8946 0.9199 0.9302 0.9178 0.8712 0.9235 0.9213 0.8875 0.9270 0.9379 0.9520 0.8925 0.9109 0.9358 0.9463 0.9056 0.8612 0.9472 0.8557 0.8535 0.8680 0.9420 0.9206 0.9260 0.8725 0.9155 0.9083 0.8667 0.8917 0.9371 0.8878 0.9032 0.9248 0.8768 0.8942 0.8554 0.8528 0.9073 0.9205 0.8854 0.8592 0.9455 0.8539 0.8545 0.9082 0.9200 0.9248 0.8488 0.8865 0.8824 0.9392 0.8577 0.8666 0.9093 0.9146 0.9403 0.9353 0.9269 0.9157 0.8532 0.8935 0.8464 0.8676 0.8455 0.8621 0.9172 0.9229 0.8723 0.8507 0.8647 0.8837 0.8635 0.8694 0.9411 0.8700 0.8883 0.9076 0.9341 0.8868 0.8643 0.9387 0.8702 0.9353 0.9333 0.9083 0.8860 0.9308 0.8572 0.9183 0.8522 0.9373 0.8847 0.8374 0.8406 0.8603 0.8818 0.8833 0.8388 0.9268 0.8390 0.8703 0.8875 0.8582 0.8720 0.8398 0.8893 0.9129 0.8436 0.9098 0.8485 0.8490 0.9153 0.8363 0.8726 0.8742 0.8774 0.9044 0.8517 0.8971 0.8788 0.9191 0.8782 0.8923 0.9163 0.8689 0.8702 0.9063 0.8556 0.9219 0.8997 0.8893 0.8370 0.8614 0.8665 0.9229 0.9044 0.8628 0.9152 0.8776 0.8419 0.9245 0.8537 0.8686 0.8746 0.8714 0.8996 0.8343 0.8680 0.8351 0.9165 0.8839 0.8549 0.8325 0.9054 0.8611 0.9093 0.8560 0.8459 0.8801 0.8517 0.9211 0.8819 0.8795 0.8676 0.9153 0.9125 0.8804 0.9085 0.8657 0.8599 0.9142 0.8995 0.9196 0.8761 0.9147 0.8428 0.9036 0.8447 0.8642 0.8982 0.9016 0.8545 0.8687 0.8929 0.8664 0.9148 0.8178 0.9027 0.8835 0.8729 0.8751 0.9013 0.9145 0.8777 0.8560 0.8873 0.8355 0.9003 0.8697 0.8424 0.8825 0.8548 0.9035 0.8174 0.8813 0.8407 0.8372 0.8838 0.9047 0.8952 0.8934 0.8734 0.8707 0.8505 0.8988 0.8774 0.8414 0.8135 0.8664 0.8429 0.8478 0.8387 0.8915 0.8848 0.8102 0.8560 0.8506 0.8408 0.8446 0.8415 0.8441 0.8357 0.8652 0.8145 0.8274 0.8151 0.8778 0.8194 0.8840 0.8209 0.8787 0.8720 0.8456 0.8989 0.8050 0.8976 0.9001 0.8681 0.8029 0.8622 0.8077 0.8239 0.8779 0.8424 0.8852 0.8647 0.7989 0.8877 0.8273 0.8565 0.8741 0.8336 0.8324 0.8127 0.8679 0.8309 0.8821 0.8405 0.8794 0.8283 0.8050 0.8762 0.8850 0.8049 0.8169 0.8285 0.8854 0.8634 0.7956 0.8113 0.8484 0.8176 0.8704 0.8223 0.8680 0.8650 0.8718 0.8604 0.8555 0.8538 0.8355 0.8711 0.8850 0.8181 0.7951 0.8601 0.8081 0.8503 0.8833 0.7924 0.8558 0.8274 0.7903 0.8569 0.8079 0.8777 0.8387 0.8449 0.8847 0.8847 0.8039 0.8242 0.8645 0.8174 0.8553 0.8158 0.8176 0.8186 0.8555 0.8418 0.8690 0.8635 0.8262 0.7944 0.8403 0.8565 0.8688 0.8571 0.8235 0.7864 0.7841 0.8119 0.8300 0.7994 0.8411 0.8223 0.8436 0.7916 0.8505 0.8353 0.7802 0.7935 0.8352 0.8146 0.8497 0.7981 0.8366 0.8486 0.8227 0.8023 0.8702 0.7765 0.8262 0.8703 0.8283 0.7809 0.8583 0.8679 0.7796 0.7802 0.8677 0.8222 0.8151 0.8632 0.8655 0.8385 0.8617 0.8554 0.8103 0.8481 0.7913 0.8093 0.8576 0.8671 0.8264 0.8312 0.7812 0.8587 0.7819 0.8520 0.8458 0.8070 0.8185 0.7880 0.8025 0.8007 0.8078 0.7895 0.7977 0.8359 0.7944 0.8416 0.7676 0.8084 0.8459 0.8409 0.7804 0.8634 0.8102 0.7939 0.7906 0.7989 0.8002 0.7867 0.8306 0.8389 0.7768 0.8190 0.8521 0.8030 0.7811 0.7800 0.8148 0.7950 0.8269 0.7789 0.8467 0.8498 0.7798 0.8337 0.7707 0.7966 0.8060 0.8067 0.8284 0.8490 0.7625 0.8515 0.8006 0.7570 0.8502 0.7908 0.7688 0.8372 0.7619 0.8236 0.7614 0.7619 0.7827 0.8514 0.7816 0.7889 0.8474 0.7769 0.8342 0.8483 0.7795 0.7995 0.7542 0.7772 0.7797 0.7632 0.7876 0.7587 0.8349 0.8285 0.8382 0.8380 0.8014 0.8197 0.8240 0.7548 0.7651 0.7485 0.8230 0.7958 0.7979 0.7709 0.7560 0.7766 0.8107 0.7766 0.7898 0.8403 0.7570 0.8330 0.7724 0.7456 0.7828 0.7713 0.7952 0.8223 0.8124 0.8373 0.8313 0.8004 0.8263 0.7735 0.8222 0.7781 0.7522 0.7654 0.8003 0.7474 0.7842 0.7623 0.7385 0.8286 0.7605 0.7728 0.7463 0.7873 0.7921 0.7969 0.7566 0.7605 0.8229 0.8295 0.8314 0.8040 0.7426 0.7856 0.8204 0.7637 0.7868 0.8212 0.8149 0.7918 0.8291 0.8101 0.7490 0.7905 0.8178 0.8016 0.7402 0.7325 0.7622 0.7676 0.7985 0.8189 0.7581 0.7566 0.8239 0.7280 0.7348 0.7998 0.7614 0.7873 0.7761 0.7779 0.7607 0.7745 0.7264 0.7330 0.7676 0.7648 0.7762 0.8007 0.7367 0.8114 0.8171 0.8177 0.8197 0.8200 0.7670 0.7762 0.7678 0.7863 0.7617 0.7338 0.7323 0.7615 0.8171 0.7948 0.7524 0.8031 0.7917 0.7379 0.7490 0.7432 0.7516 0.7420 0.7226 0.7178 0.7204 0.7277 0.7435 0.8106 0.7448 0.8131 0.8032 0.7237 0.7668 0.7354 0.7647 0.7310 0.8004 0.7974 0.8027 0.7900 0.7909 0.7517 0.7749 0.7578 0.7640 0.7784 0.7805 0.7222 0.7677 0.7860 0.7911 0.7868 0.7996 0.7824 0.7184 0.7513 0.7888 0.7786 0.7284 0.7958 0.7373 0.7446 0.7774 0.7406 0.7765 0.7180 0.7032 0.7162 0.7656 0.7280 0.7191 0.7849 0.7340 0.7331 0.7959 0.7221 0.7866 0.7022 0.7228 0.7188 0.7227 0.7033 0.7787 0.7937 0.7277 0.6983 0.7217 0.7560 0.7028 0.7198 0.7523 0.7265 0.7456 0.7316 0.7815 0.7935 0.7367 0.7578 0.7335 0.7131 0.7191 0.7576 0.7014 0.6987 0.7082 0.7705 0.7785 0.7603 0.7903 0.7861 0.6967 0.7215 0.7657 0.7567 0.7301 0.7077 0.7740 0.6980 0.7577 0.7808 0.7195 0.7266 0.7040 0.7071 0.7608 0.7004 0.7697 0.7385 0.7152 0.7406 0.7527 0.7637 0.7450 0.7220 0.7743 0.7456 0.7173 0.6943 0.7387 0.6848 0.7806 0.7497 0.7603 0.6834 0.7169 0.7605 0.7569 0.6861 0.7606 0.7710 0.7155 0.7254 0.7373 0.7274 0.6871 0.6987 0.6939 0.6772 0.7666 0.7027 0.7230 0.6911 0.7383 0.7019 0.7289 0.7572 0.7690 0.7651 0.7239 0.7509 0.7277 0.7335 0.7199 0.7248 0.7233 0.7234 0.7122 0.7358 0.7242 0.7067 0.7085 0.7626 0.7239 0.7059 0.6833 0.7540 0.7593 0.6662 0.6682 0.6675 0.7009 0.6661 0.7260 0.7068 0.7439 0.7607 0.7517 0.6878 0.7029 0.7282 0.7007 0.6690 0.7112 0.7516 0.7366 0.7440 0.7267 0.6739 0.7199 0.6774 0.6956 0.6665 0.7472 0.7541 0.6655 0.6953 0.6897 0.7174 0.6924 0.6725 0.7282 0.7143 0.6671 0.7383 0.6796 0.7448 0.7299 0.6619 0.7515 0.6858 0.6976 0.6709 0.7475 0.6879 0.7151 0.7356 0.6850 0.6801 0.6726 0.6972 0.6917 0.6848 0.7396 0.7197 0.7379 0.6842
SzDN = size(data_noise)
SzDN = 1×3
1024 15 10
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
% Apply movmedian de-noise & downsampling
% SamplePoints = 1:n_repeat:sz2;
SamplePoints = sort(rand(1, 15));
% data_denoise = movmedian(data_noise,[n_repeat, (2*n_repeat) - 1], ...
% 2, "SamplePoints", SamplePoints);
data_denoise = movmedian(data_noise,[n_repeat, (2*n_repeat) - 1], ...
2, "SamplePoints", SamplePoints)
data_denoise =
data_denoise(:,:,1) = 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0593 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0371 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0714 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0734 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0823 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0840 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0828 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.0720 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1080 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1061 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1214 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1104 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1241 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1580 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1420 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1602 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1756 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1711 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1760 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1722 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1922 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1840 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1826 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.1896 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2068 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2164 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2158 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2309 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2267 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2635 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2425 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2568 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2572 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2331 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2709 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2686 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 0.2970 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1.0399 1.0399 1.0399 1.0399 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0275 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0041 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0190 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0273 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 1.0140 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 0.9993 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0126 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0406 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0180 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0438 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0127 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0212 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0111 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0292 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0187 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0238 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0110 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0123 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0179 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 1.0070 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 1.0155 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9899 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 0.9990 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 1.0077 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9995 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9819 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9951 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9808 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9783 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 0.9749 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 1.0034 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9773 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9628 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9905 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9693 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9897 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9855 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9670 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9498 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9411 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9412 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9366 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9446 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9602 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9547 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9445 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9284 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9380 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9303 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9440 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9339 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9004 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9304 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9232 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.9134 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8955 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.8999 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.9213 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8917 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.8942 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.9093 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.8694 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.9083 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8703 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8774 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8893 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8714 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8795 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8982 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8777 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8813 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8560 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8415 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8647 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8336 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8484 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8555 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8387 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8403 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8223 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8283 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8385 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8025 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8084 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8030 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.8060 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.7816 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.8014 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.7766 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.8003 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7873 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7918 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7676 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7762 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7615 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7420 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7805 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7765 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7228 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7316 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7367 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7266 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7406 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7274 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7277 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7234 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7029 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.7112 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6976 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917 0.6917
There are likely more efficient (and understandable) methods to do what you want to do. I suggest that you explore the Signal Processing Toolbox, since all the 'mov...' functions are essentially lowpass filters anyway.
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  2 Comments
Robert Watson
Robert Watson on 21 Nov 2025 at 13:48
Thanks, appreciate your help!
I agree the documentation could be improved to clarify the intended function of the sampling points.
I wound up running the movmedian across all of the data and discarding the unused columns as below. This includes a large amount of extra compute operations, but was still about 2x faster than the nested loop I had been using to compute only the desired output prior to considering the movmedian approach above.
% Set matrix dimensions
sz1 = 1024;
sz2 = 5;
sz3 = 10;
% Set number of repeats for each signal
n_repeat = 3;
% Make noisey signal matrix
th = linspace(0, 2*pi, sz1)';
signal = sin(th);
data_clean = repmat(signal, 1, n_repeat*sz2, sz3);
data_noise = data_clean + 0.1*rand(sz1, n_repeat*sz2, sz3);
% Apply movmedian de-noise & downsampling
SamplePoints = 1:n_repeat:sz2;
data_denoise = movmedian(data_noise,[n_repeat, (2*n_repeat) - 1], 2);
data_denoise = data_denoise(:,SamplePoints,:);
Star Strider
Star Strider on 21 Nov 2025 at 13:56
As always, my pleasure!
I am happy that you got it working o produce the result you want.

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More Answers (1)

Chuguang Pan
Chuguang Pan on 21 Nov 2025 at 12:29
Edited: Chuguang Pan on 21 Nov 2025 at 12:30
@Robert Watson.The SamplePoints argument of movmedian function represents the location of data, it must has the same length with input data. For downsampling the input, I think it is better to use downsample function.
% Set matrix dimensions
sz1 = 1024;
sz2 = 5;
sz3 = 10;
% Set number of repeats for each signal
n_repeat = 3;
% Make noisey signal matrix
th = linspace(0, 2*pi, sz1)';
signal = sin(th);
data_clean = repmat(signal, 1, n_repeat*sz2, sz3);
data_noise = data_clean + 0.1*rand(sz1, n_repeat*sz2, sz3);
% downsamples the data_denoise with downsample function
data_denoise = movmedian(data_noise,[n_repeat,2*n_repeat-1],2);
data_denoise_ds = cell2mat(cellfun(@(sig) downsample(sig,n_repeat),mat2cell(data_denoise,sz1,ones(1,n_repeat*sz2),ones(1,sz3)),"UniformOutput",false));
  1 Comment
Robert Watson
Robert Watson on 21 Nov 2025 at 13:41
Thanks for your help. I seem to have misunderstood the purpose of the SamplePoints input.

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