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Can I used Unique to find unique x,y,z coordinates in table data?
8 ビュー (過去 30 日間)
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Jorge
2024 年 9 月 4 日
I have x,y,z positional data collected and arranged into a table, under two different conditions. For example, I have entries like
x
x1,y1,z1, value1, value 2, ...,0
x2,y1,z1, value3, value4, ...,0
...
x1,y1,z1,value9,value10, ...,1
x2,y1,z1,value11,value 12, ...,1
The last variable in the table is the "different' condition under which the same variables were measured at the same locations.
I want to find the unique x,y,z coordinates in the table and use these to create a summary table that shows the values under the two conditions under which the data was collected. i could use "find" and a vector of coordinates to find these on the table, but I think it would be more elegant if I obtained the unique coordinates from the table, and uses these to find the "other" entry in the table at the same x,y,z location that has the same data collected under the different condition. I've attached some of my data.
for example, I'd like to find the two entries with 1775,575,125 coordinates, and create another table that lists the coordinates, the meanZM and meanGAIpred value from the clamped=1 entry, and the meanInvCurrentfrom the clamped=0 entry.
I'll get sometihng working using "find" in the meantime....
Thank you.
採用された回答
その他の回答 (1 件)
Voss
2024 年 9 月 4 日
load('matlab.mat')
T = summaryTable1
T = 223x9 table
X-pos Y-pos Z-pos meanInvCurrent meanVaACcurrent meanVaDCcurrent meanZm meanGAIpred clamped
_____ _____ _____ ______________ _______________ _______________ _______ ___________ _______
1775 575 125 56.138 56.95 0.26575 0.70237 569.5 1
1775 600 125 57.294 38.9 0.16425 1.0283 389 1
1775 625 125 57.556 26.544 0.12891 1.507 265.44 1
1775 650 125 58.437 22.581 0.12894 1.7714 225.81 1
1775 675 125 59.781 27.619 0.0625 1.4483 276.19 1
1775 700 125 61.306 41.875 0.082031 0.95522 418.75 1
1775 725 125 59.975 58.737 0.093781 0.70204 587.38 1
1775 575 125 57.163 0.1875 0.089875 Inf 1.875 0
1775 600 125 55.969 0.097656 0.13303 Inf 0.97656 0
1775 625 125 55.419 0 0.10941 Inf 0 0
1775 650 125 54.812 0.019563 0.14475 Inf 0.19562 0
1775 675 125 53.869 0 0.12903 Inf 0 0
1775 700 125 52.644 0.49703 0.50881 Inf 4.9703 0
1775 725 125 51.175 0 0.13287 Inf 0 0
1800 575 125 52.869 10.794 0.31688 3.7059 107.94 1
1800 600 125 58.263 34.294 0.19556 1.1664 342.94 1
[g_idx,result] = findgroups(T(:,[1 2 3]));
vars_1 = {'meanZm','meanGAIpred'};
vars_0 = {'meanInvCurrent'};
N = size(result,1);
vars = [vars_1 vars_0];
result(:,vars) = array2table(NaN(N,numel(vars)));
for ii = 1:N
tmp = T(g_idx == ii,:);
idx = tmp{:,'clamped'} == 1;
if any(idx)
result(ii,vars_1) = tmp(idx,vars_1);
end
if any(~idx)
result(ii,vars_0) = tmp(~idx,vars_0);
end
end
disp(result)
X-pos Y-pos Z-pos meanZm meanGAIpred meanInvCurrent
_____ _____ _____ _______ ___________ ______________
1775 575 125 0.70237 569.5 57.163
1775 600 125 1.0283 389 55.969
1775 625 125 1.507 265.44 55.419
1775 650 125 1.7714 225.81 54.812
1775 675 125 1.4483 276.19 53.869
1775 700 125 0.95522 418.75 52.644
1775 725 125 0.70204 587.38 51.175
1800 575 125 3.7059 107.94 58.744
1800 600 125 1.1664 342.94 57.569
1800 625 125 0.75641 528.81 57.144
1800 650 125 0.67298 594.38 56.594
1800 675 125 0.72984 548.06 55.6
1800 700 125 1.0615 376.81 54.144
1800 725 125 3.3197 120.5 52.525
1825 575 125 0.5805 689.06 59.138
1825 600 125 0.70828 564.75 58.256
1825 625 125 0.82443 485.19 58.063
1825 650 125 0.87146 459 57.606
1825 675 125 0.83399 479.62 56.544
1825 700 125 0.76786 529.38 54.85
1825 725 125 0.59651 670.56 52.881
1850 575 125 1.0767 371.5 58.931
1850 600 125 1.3196 303.12 58.538
1850 625 125 1.4849 269.38 58.625
1850 650 125 1.5444 259 58.256
1850 675 125 1.4877 268.88 57.075
1850 700 125 1.3295 300.88 55.075
1850 725 125 1.0953 365.19 52.738
1875 575 125 1.7482 228.81 58.919
1875 600 125 2.0343 196.62 59.031
1875 625 125 2.2285 179.5 59.431
1875 650 125 2.2989 174 59.138
1875 675 125 2.1342 190.81 57.775
1875 700 125 2.0408 196 55.431
1875 725 125 1.7544 228 52.7
1900 575 125 2.4797 161.31 59.594
1900 600 125 2.8009 142.81 60.362
1900 625 125 3.0075 133 61.169
1900 650 125 3.0963 129.19 60.962
1900 675 125 3.0075 133 59.406
1900 700 125 2.7972 143 56.619
1900 725 125 2.4683 162.06 53.231
1925 575 125 3.2258 124 61.681
1925 600 125 3.5976 111.19 63.2
1925 625 125 3.8462 104 64.487
1925 650 125 3.819 105.38 64.462
1925 675 125 3.821 104.69 62.544
1925 700 125 3.5875 111.5 59.131
1925 725 125 3.2258 124 54.925
1950 575 125 3.9653 100.88 64.794
1950 600 125 4.3896 91.125 61.688
1950 625 125 4.6658 85.731 59.325
1950 650 125 4.7478 84.25 58.437
1950 675 125 4.6539 85.95 59.031
1950 700 125 4.3686 91.562 60.856
1950 725 125 3.9555 101.12 58.106
1975 575 125 4.5661 88 59.031
1975 600 125 5.1489 77.688 54.738
1975 625 125 5.4201 73.8 52.2
1975 650 125 5.5316 72.312 51.35
1975 675 125 5.4201 73.8 52.188
1975 700 125 5.1216 78.1 54.706
1975 725 125 4.6682 85.687 58.031
2000 575 125 5.3468 74.812 53.138
2000 600 125 5.814 68.8 49.063
2000 625 125 6.0976 65.6 46.688
2000 650 125 6.1825 64.7 45.95
2000 675 125 6.0976 65.6 46.763
2000 700 125 5.8045 68.912 49.244
2000 725 125 5.2819 75.731 53.256
2025 575 125 5.8332 68.575 48.906
2025 600 125 6.3634 62.894 45.106
2025 625 125 6.6667 60 42.712
2025 650 125 6.734 59.4 42.006
2025 675 125 6.6667 60 42.95
2025 700 125 6.2696 63.8 45.381
2025 725 125 5.7513 69.55 49.256
2050 575 125 6.1538 65 46.381
2050 600 125 6.6667 60 42.575
2050 625 125 6.9565 57.5 40.306
2050 650 125 7.1048 56.3 39.681
2050 675 125 6.9565 57.5 40.575
2050 700 125 6.6129 60.513 43.094
2050 725 125 6.0895 65.688 46.862
2075 575 125 6.2902 63.594 45.456
2075 600 125 6.8847 58.1 41.719
2075 625 125 7.1272 56.125 39.5
2075 650 125 7.2727 55 38.906
2075 675 125 7.1048 56.3 39.837
2075 700 125 6.734 59.4 42.356
2075 725 125 6.1538 65 46.163
2100 575 125 6.1753 64.775 46.131
2100 600 125 6.6584 60.075 42.506
2100 625 125 6.9703 57.387 40.256
2100 650 125 7.1048 56.3 39.706
2100 675 125 6.9843 57.275 40.688
2100 700 125 6.6667 60 43.169
2100 725 125 6.0608 66 46.937
2125 575 125 5.9428 67.312 48.4
2125 600 125 6.4155 62.35 44.831
2125 625 125 6.6667 60 42.625
2125 650 125 6.788 58.931 42.038
2125 675 125 6.6667 60 43.006
2125 700 125 6.2696 63.8 45.394
2125 725 125 5.7143 70 49.294
2150 575 125 5.4201 73.8 52.231
2150 600 125 5.8802 68.025 48.5
2150 625 125 6.1538 65 46.312
2150 650 125 6.3813 62.769 45.688
2150 675 125 6.0976 65.6 46.65
2150 700 125 5.7982 68.987 49.156
2150 725 125 5.291 75.6 NaN
10 件のコメント
Voss
2024 年 9 月 4 日
編集済み: Voss
2024 年 9 月 4 日
Anyway, to answer the question: Yes, you can use unique.
One way to do that is to replace this line in my answer
[g_idx,result] = findgroups(T(:,[1 2 3]));
with
[result,~,g_idx] = unique(T(:,[1 2 3]),'rows','stable');
which produces this result:
load('matlab.mat')
T = summaryTable1;
[result,~,g_idx] = unique(T(:,[1 2 3]),'rows','stable');
vars_1 = {'meanZm','meanGAIpred'};
vars_0 = {'meanInvCurrent'};
N = size(result,1);
vars = [vars_1 vars_0];
result(:,vars) = array2table(NaN(N,numel(vars)));
for ii = 1:N
tmp = T(g_idx == ii,:);
idx = tmp{:,'clamped'} == 1;
if any(idx)
result(ii,vars_1) = tmp(idx,vars_1);
end
if any(~idx)
result(ii,vars_0) = tmp(~idx,vars_0);
end
end
disp(result)
X-pos Y-pos Z-pos meanZm meanGAIpred meanInvCurrent
_____ _____ _____ _______ ___________ ______________
1775 575 125 0.70237 569.5 57.163
1775 600 125 1.0283 389 55.969
1775 625 125 1.507 265.44 55.419
1775 650 125 1.7714 225.81 54.812
1775 675 125 1.4483 276.19 53.869
1775 700 125 0.95522 418.75 52.644
1775 725 125 0.70204 587.38 51.175
1800 575 125 3.7059 107.94 58.744
1800 600 125 1.1664 342.94 57.569
1800 625 125 0.75641 528.81 57.144
1800 650 125 0.67298 594.38 56.594
1800 675 125 0.72984 548.06 55.6
1800 700 125 1.0615 376.81 54.144
1800 725 125 3.3197 120.5 52.525
1825 575 125 0.5805 689.06 59.138
1825 600 125 0.70828 564.75 58.256
1825 625 125 0.82443 485.19 58.063
1825 650 125 0.87146 459 57.606
1825 675 125 0.83399 479.62 56.544
1825 700 125 0.76786 529.38 54.85
1825 725 125 0.59651 670.56 52.881
1850 575 125 1.0767 371.5 58.931
1850 600 125 1.3196 303.12 58.538
1850 625 125 1.4849 269.38 58.625
1850 650 125 1.5444 259 58.256
1850 675 125 1.4877 268.88 57.075
1850 700 125 1.3295 300.88 55.075
1850 725 125 1.0953 365.19 52.738
1875 575 125 1.7482 228.81 58.919
1875 600 125 2.0343 196.62 59.031
1875 625 125 2.2285 179.5 59.431
1875 650 125 2.2989 174 59.138
1875 675 125 2.1342 190.81 57.775
1875 700 125 2.0408 196 55.431
1875 725 125 1.7544 228 52.7
1900 575 125 2.4797 161.31 59.594
1900 600 125 2.8009 142.81 60.362
1900 625 125 3.0075 133 61.169
1900 650 125 3.0963 129.19 60.962
1900 675 125 3.0075 133 59.406
1900 700 125 2.7972 143 56.619
1900 725 125 2.4683 162.06 53.231
1925 575 125 3.2258 124 61.681
1925 600 125 3.5976 111.19 63.2
1925 625 125 3.8462 104 64.487
1925 650 125 3.819 105.38 64.462
1925 675 125 3.821 104.69 62.544
1925 700 125 3.5875 111.5 59.131
1925 725 125 3.2258 124 54.925
1950 575 125 3.9653 100.88 64.794
1950 600 125 4.3896 91.125 61.688
1950 625 125 4.6658 85.731 59.325
1950 650 125 4.7478 84.25 58.437
1950 675 125 4.6539 85.95 59.031
1950 700 125 4.3686 91.562 60.856
1950 725 125 3.9555 101.12 58.106
1975 575 125 4.5661 88 59.031
1975 600 125 5.1489 77.688 54.738
1975 625 125 5.4201 73.8 52.2
1975 650 125 5.5316 72.312 51.35
1975 675 125 5.4201 73.8 52.188
1975 700 125 5.1216 78.1 54.706
1975 725 125 4.6682 85.687 58.031
2000 575 125 5.3468 74.812 53.138
2000 600 125 5.814 68.8 49.063
2000 625 125 6.0976 65.6 46.688
2000 650 125 6.1825 64.7 45.95
2000 675 125 6.0976 65.6 46.763
2000 700 125 5.8045 68.912 49.244
2000 725 125 5.2819 75.731 53.256
2025 575 125 5.8332 68.575 48.906
2025 600 125 6.3634 62.894 45.106
2025 625 125 6.6667 60 42.712
2025 650 125 6.734 59.4 42.006
2025 675 125 6.6667 60 42.95
2025 700 125 6.2696 63.8 45.381
2025 725 125 5.7513 69.55 49.256
2050 575 125 6.1538 65 46.381
2050 600 125 6.6667 60 42.575
2050 625 125 6.9565 57.5 40.306
2050 650 125 7.1048 56.3 39.681
2050 675 125 6.9565 57.5 40.575
2050 700 125 6.6129 60.513 43.094
2050 725 125 6.0895 65.688 46.862
2075 575 125 6.2902 63.594 45.456
2075 600 125 6.8847 58.1 41.719
2075 625 125 7.1272 56.125 39.5
2075 650 125 7.2727 55 38.906
2075 675 125 7.1048 56.3 39.837
2075 700 125 6.734 59.4 42.356
2075 725 125 6.1538 65 46.163
2100 575 125 6.1753 64.775 46.131
2100 600 125 6.6584 60.075 42.506
2100 625 125 6.9703 57.387 40.256
2100 650 125 7.1048 56.3 39.706
2100 675 125 6.9843 57.275 40.688
2100 700 125 6.6667 60 43.169
2100 725 125 6.0608 66 46.937
2125 575 125 5.9428 67.312 48.4
2125 600 125 6.4155 62.35 44.831
2125 625 125 6.6667 60 42.625
2125 650 125 6.788 58.931 42.038
2125 675 125 6.6667 60 43.006
2125 700 125 6.2696 63.8 45.394
2125 725 125 5.7143 70 49.294
2150 575 125 5.4201 73.8 52.231
2150 600 125 5.8802 68.025 48.5
2150 625 125 6.1538 65 46.312
2150 650 125 6.3813 62.769 45.688
2150 675 125 6.0976 65.6 46.65
2150 700 125 5.7982 68.987 49.156
2150 725 125 5.291 75.6 NaN
Another way is to use splitapply and a separate function (called fun for lack of a better term):
[result,~,idx] = unique(T(:,[1 2 3]),'rows','stable');
result = [result array2table(splitapply(@fun,T(:,[7 8 4 9]),idx), ...
'VariableNames',T.Properties.VariableNames([7 8 4]))];
disp(result)
X-pos Y-pos Z-pos meanZm meanGAIpred meanInvCurrent
_____ _____ _____ _______ ___________ ______________
1775 575 125 0.70237 569.5 57.163
1775 600 125 1.0283 389 55.969
1775 625 125 1.507 265.44 55.419
1775 650 125 1.7714 225.81 54.812
1775 675 125 1.4483 276.19 53.869
1775 700 125 0.95522 418.75 52.644
1775 725 125 0.70204 587.38 51.175
1800 575 125 3.7059 107.94 58.744
1800 600 125 1.1664 342.94 57.569
1800 625 125 0.75641 528.81 57.144
1800 650 125 0.67298 594.38 56.594
1800 675 125 0.72984 548.06 55.6
1800 700 125 1.0615 376.81 54.144
1800 725 125 3.3197 120.5 52.525
1825 575 125 0.5805 689.06 59.138
1825 600 125 0.70828 564.75 58.256
1825 625 125 0.82443 485.19 58.063
1825 650 125 0.87146 459 57.606
1825 675 125 0.83399 479.62 56.544
1825 700 125 0.76786 529.38 54.85
1825 725 125 0.59651 670.56 52.881
1850 575 125 1.0767 371.5 58.931
1850 600 125 1.3196 303.12 58.538
1850 625 125 1.4849 269.38 58.625
1850 650 125 1.5444 259 58.256
1850 675 125 1.4877 268.88 57.075
1850 700 125 1.3295 300.88 55.075
1850 725 125 1.0953 365.19 52.738
1875 575 125 1.7482 228.81 58.919
1875 600 125 2.0343 196.62 59.031
1875 625 125 2.2285 179.5 59.431
1875 650 125 2.2989 174 59.138
1875 675 125 2.1342 190.81 57.775
1875 700 125 2.0408 196 55.431
1875 725 125 1.7544 228 52.7
1900 575 125 2.4797 161.31 59.594
1900 600 125 2.8009 142.81 60.362
1900 625 125 3.0075 133 61.169
1900 650 125 3.0963 129.19 60.962
1900 675 125 3.0075 133 59.406
1900 700 125 2.7972 143 56.619
1900 725 125 2.4683 162.06 53.231
1925 575 125 3.2258 124 61.681
1925 600 125 3.5976 111.19 63.2
1925 625 125 3.8462 104 64.487
1925 650 125 3.819 105.38 64.462
1925 675 125 3.821 104.69 62.544
1925 700 125 3.5875 111.5 59.131
1925 725 125 3.2258 124 54.925
1950 575 125 3.9653 100.88 64.794
1950 600 125 4.3896 91.125 61.688
1950 625 125 4.6658 85.731 59.325
1950 650 125 4.7478 84.25 58.437
1950 675 125 4.6539 85.95 59.031
1950 700 125 4.3686 91.562 60.856
1950 725 125 3.9555 101.12 58.106
1975 575 125 4.5661 88 59.031
1975 600 125 5.1489 77.688 54.738
1975 625 125 5.4201 73.8 52.2
1975 650 125 5.5316 72.312 51.35
1975 675 125 5.4201 73.8 52.188
1975 700 125 5.1216 78.1 54.706
1975 725 125 4.6682 85.687 58.031
2000 575 125 5.3468 74.812 53.138
2000 600 125 5.814 68.8 49.063
2000 625 125 6.0976 65.6 46.688
2000 650 125 6.1825 64.7 45.95
2000 675 125 6.0976 65.6 46.763
2000 700 125 5.8045 68.912 49.244
2000 725 125 5.2819 75.731 53.256
2025 575 125 5.8332 68.575 48.906
2025 600 125 6.3634 62.894 45.106
2025 625 125 6.6667 60 42.712
2025 650 125 6.734 59.4 42.006
2025 675 125 6.6667 60 42.95
2025 700 125 6.2696 63.8 45.381
2025 725 125 5.7513 69.55 49.256
2050 575 125 6.1538 65 46.381
2050 600 125 6.6667 60 42.575
2050 625 125 6.9565 57.5 40.306
2050 650 125 7.1048 56.3 39.681
2050 675 125 6.9565 57.5 40.575
2050 700 125 6.6129 60.513 43.094
2050 725 125 6.0895 65.688 46.862
2075 575 125 6.2902 63.594 45.456
2075 600 125 6.8847 58.1 41.719
2075 625 125 7.1272 56.125 39.5
2075 650 125 7.2727 55 38.906
2075 675 125 7.1048 56.3 39.837
2075 700 125 6.734 59.4 42.356
2075 725 125 6.1538 65 46.163
2100 575 125 6.1753 64.775 46.131
2100 600 125 6.6584 60.075 42.506
2100 625 125 6.9703 57.387 40.256
2100 650 125 7.1048 56.3 39.706
2100 675 125 6.9843 57.275 40.688
2100 700 125 6.6667 60 43.169
2100 725 125 6.0608 66 46.937
2125 575 125 5.9428 67.312 48.4
2125 600 125 6.4155 62.35 44.831
2125 625 125 6.6667 60 42.625
2125 650 125 6.788 58.931 42.038
2125 675 125 6.6667 60 43.006
2125 700 125 6.2696 63.8 45.394
2125 725 125 5.7143 70 49.294
2150 575 125 5.4201 73.8 52.231
2150 600 125 5.8802 68.025 48.5
2150 625 125 6.1538 65 46.312
2150 650 125 6.3813 62.769 45.688
2150 675 125 6.0976 65.6 46.65
2150 700 125 5.7982 68.987 49.156
2150 725 125 5.291 75.6 NaN
which works the same as when using findgroups for determining the groups:
[g_idx,result] = findgroups(T(:,[1 2 3]));
result = [result array2table(splitapply(@fun,T(:,[7 8 4 9]),idx), ...
'VariableNames',T.Properties.VariableNames([7 8 4]))];
disp(result)
X-pos Y-pos Z-pos meanZm meanGAIpred meanInvCurrent
_____ _____ _____ _______ ___________ ______________
1775 575 125 0.70237 569.5 57.163
1775 600 125 1.0283 389 55.969
1775 625 125 1.507 265.44 55.419
1775 650 125 1.7714 225.81 54.812
1775 675 125 1.4483 276.19 53.869
1775 700 125 0.95522 418.75 52.644
1775 725 125 0.70204 587.38 51.175
1800 575 125 3.7059 107.94 58.744
1800 600 125 1.1664 342.94 57.569
1800 625 125 0.75641 528.81 57.144
1800 650 125 0.67298 594.38 56.594
1800 675 125 0.72984 548.06 55.6
1800 700 125 1.0615 376.81 54.144
1800 725 125 3.3197 120.5 52.525
1825 575 125 0.5805 689.06 59.138
1825 600 125 0.70828 564.75 58.256
1825 625 125 0.82443 485.19 58.063
1825 650 125 0.87146 459 57.606
1825 675 125 0.83399 479.62 56.544
1825 700 125 0.76786 529.38 54.85
1825 725 125 0.59651 670.56 52.881
1850 575 125 1.0767 371.5 58.931
1850 600 125 1.3196 303.12 58.538
1850 625 125 1.4849 269.38 58.625
1850 650 125 1.5444 259 58.256
1850 675 125 1.4877 268.88 57.075
1850 700 125 1.3295 300.88 55.075
1850 725 125 1.0953 365.19 52.738
1875 575 125 1.7482 228.81 58.919
1875 600 125 2.0343 196.62 59.031
1875 625 125 2.2285 179.5 59.431
1875 650 125 2.2989 174 59.138
1875 675 125 2.1342 190.81 57.775
1875 700 125 2.0408 196 55.431
1875 725 125 1.7544 228 52.7
1900 575 125 2.4797 161.31 59.594
1900 600 125 2.8009 142.81 60.362
1900 625 125 3.0075 133 61.169
1900 650 125 3.0963 129.19 60.962
1900 675 125 3.0075 133 59.406
1900 700 125 2.7972 143 56.619
1900 725 125 2.4683 162.06 53.231
1925 575 125 3.2258 124 61.681
1925 600 125 3.5976 111.19 63.2
1925 625 125 3.8462 104 64.487
1925 650 125 3.819 105.38 64.462
1925 675 125 3.821 104.69 62.544
1925 700 125 3.5875 111.5 59.131
1925 725 125 3.2258 124 54.925
1950 575 125 3.9653 100.88 64.794
1950 600 125 4.3896 91.125 61.688
1950 625 125 4.6658 85.731 59.325
1950 650 125 4.7478 84.25 58.437
1950 675 125 4.6539 85.95 59.031
1950 700 125 4.3686 91.562 60.856
1950 725 125 3.9555 101.12 58.106
1975 575 125 4.5661 88 59.031
1975 600 125 5.1489 77.688 54.738
1975 625 125 5.4201 73.8 52.2
1975 650 125 5.5316 72.312 51.35
1975 675 125 5.4201 73.8 52.188
1975 700 125 5.1216 78.1 54.706
1975 725 125 4.6682 85.687 58.031
2000 575 125 5.3468 74.812 53.138
2000 600 125 5.814 68.8 49.063
2000 625 125 6.0976 65.6 46.688
2000 650 125 6.1825 64.7 45.95
2000 675 125 6.0976 65.6 46.763
2000 700 125 5.8045 68.912 49.244
2000 725 125 5.2819 75.731 53.256
2025 575 125 5.8332 68.575 48.906
2025 600 125 6.3634 62.894 45.106
2025 625 125 6.6667 60 42.712
2025 650 125 6.734 59.4 42.006
2025 675 125 6.6667 60 42.95
2025 700 125 6.2696 63.8 45.381
2025 725 125 5.7513 69.55 49.256
2050 575 125 6.1538 65 46.381
2050 600 125 6.6667 60 42.575
2050 625 125 6.9565 57.5 40.306
2050 650 125 7.1048 56.3 39.681
2050 675 125 6.9565 57.5 40.575
2050 700 125 6.6129 60.513 43.094
2050 725 125 6.0895 65.688 46.862
2075 575 125 6.2902 63.594 45.456
2075 600 125 6.8847 58.1 41.719
2075 625 125 7.1272 56.125 39.5
2075 650 125 7.2727 55 38.906
2075 675 125 7.1048 56.3 39.837
2075 700 125 6.734 59.4 42.356
2075 725 125 6.1538 65 46.163
2100 575 125 6.1753 64.775 46.131
2100 600 125 6.6584 60.075 42.506
2100 625 125 6.9703 57.387 40.256
2100 650 125 7.1048 56.3 39.706
2100 675 125 6.9843 57.275 40.688
2100 700 125 6.6667 60 43.169
2100 725 125 6.0608 66 46.937
2125 575 125 5.9428 67.312 48.4
2125 600 125 6.4155 62.35 44.831
2125 625 125 6.6667 60 42.625
2125 650 125 6.788 58.931 42.038
2125 675 125 6.6667 60 43.006
2125 700 125 6.2696 63.8 45.394
2125 725 125 5.7143 70 49.294
2150 575 125 5.4201 73.8 52.231
2150 600 125 5.8802 68.025 48.5
2150 625 125 6.1538 65 46.312
2150 650 125 6.3813 62.769 45.688
2150 675 125 6.0976 65.6 46.65
2150 700 125 5.7982 68.987 49.156
2150 725 125 5.291 75.6 NaN
function out = fun(mZ,mG,mI,c)
out = NaN(1,3);
idx = c == 1;
if any(idx)
out([1 2]) = [mZ(idx) mG(idx)];
end
if any(~idx)
out(3) = mI(~idx);
end
end
Jorge
2024 年 9 月 5 日
wow...this is great stuff....I need to play with it to understand it all, but this is great!. Thank you.
dpb
2024 年 9 月 5 日
編集済み: dpb
2024 年 9 月 5 日
load matlab summaryTable1
tS=summaryTable1; clear summaryTable1
tS.Properties.VariableNames=strrep(tS.Properties.VariableNames,'-pos','');
head(tS,3)
X Y Z meanInvCurrent meanVaACcurrent meanVaDCcurrent meanZm meanGAIpred clamped
____ ___ ___ ______________ _______________ _______________ _______ ___________ _______
1775 575 125 56.138 56.95 0.26575 0.70237 569.5 1
1775 600 125 57.294 38.9 0.16425 1.0283 389 1
1775 625 125 57.556 26.544 0.12891 1.507 265.44 1
tM=varfun(@mean,tS,'GroupingVariables',{'X','Y','Z'},'InputVariables',{'meanZm','meanGAIpred'});
head(tM,5)
X Y Z GroupCount mean_meanZm mean_meanGAIpred
____ ___ ___ __________ ___________ ________________
1775 575 125 2 Inf 285.69
1775 600 125 2 Inf 194.99
1775 625 125 2 Inf 132.72
1775 650 125 2 Inf 113
1775 675 125 2 Inf 138.09
Ewww...what's up w/ that???
[nnz(isfinite(tS.meanZm)) height(tS)]
ans = 1x2
171 223
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
[g,ig]=findgroups(tS.X,tS.Y,tS.Z);
tS(g==1,:)
ans = 2x9 table
X Y Z meanInvCurrent meanVaACcurrent meanVaDCcurrent meanZm meanGAIpred clamped
____ ___ ___ ______________ _______________ _______________ _______ ___________ _______
1775 575 125 56.138 56.95 0.26575 0.70237 569.5 1
1775 575 125 57.163 0.1875 0.089875 Inf 1.875 0
Indeed, for the first group one of the meanZm values is, indeed, inf.
For convenience, since mean() has an 'omitnan' flag builtin, but not one for 'omitinf', let's fixup the data first and try again...this time we use an anonymous function to be able to set the flag in mean()...
tS.meanZm(isinf(tS.meanZm))=nan;
tM=varfun(@(x)mean(x,'omitnan'),tS,'GroupingVariables',{'X','Y','Z'},'InputVariables',{'meanZm','meanGAIpred'});
tM.Properties.VariableNames=strrep(tM.Properties.VariableNames,'Fun_','Mean_');
head(tM,5)
X Y Z GroupCount Mean_meanZm Mean_meanGAIpred
____ ___ ___ __________ ___________ ________________
1775 575 125 2 0.70237 285.69
1775 600 125 2 1.0283 194.99
1775 625 125 2 1.507 132.72
1775 650 125 2 1.7714 113
1775 675 125 2 1.4483 138.09
Voss
2024 年 9 月 5 日
I want to find the unique x,y,z coordinates in the table and use these to create a summary table that shows the values under the two conditions under which the data was collected... [obtain] the unique coordinates from the table, and uses these to find the "other" entry in the table at the same x,y,z location that has the same data collected under the different condition... for example, I'd like to find the two entries with 1775,575,125 coordinates, and create another table that lists the coordinates, the meanZM and meanGAIpred value from the clamped=1 entry, and the meanInvCurrentfrom the clamped=0 entry.'
?
dpb
2024 年 9 月 5 日
@Voss - probably, but it might not be very pretty. You can write functions that can output a different number of rows/variables but it can get somewhat messy with varfun when start getting complicated. Sometimes you have to introduce a helper variable in the original table to get to something else indirectly.
Like the other, I'll have to try to find another time to try to do something that involved; I'll have to first study the request more thoroughly because I'm not sure I even have the requirements down yet...that's why I stopped on the original with findgroups and just did the more-or-less trivial exercise above...they're easy! :)
Jorge
2024 年 9 月 5 日
again...very great stuff! very informative and educational. Thank you both for taking the time.
dpb
2024 年 9 月 5 日
I just love playing with varfun!!! <vbg> Sorry I do have other pressing commitments that I can only just take quick relief breaks at the moment...
dpb
2024 年 9 月 6 日
編集済み: dpb
2024 年 9 月 6 日
No, it can't be done with varfun, but can be with rowfun. I had forgotten that varfun operates by passing each column of the group one at a time; rowfun passes the groups of rows; all requested columns.
You can do it with your function as
tM=rowfun(@fun,tS,'GroupingVariables',{'X','Y','Z'}, ...
'InputVariables',{'meanZm','meanGAIpred','meanInvCurrent','clamped'});
I rewrote the function a little, but it does the same thing...
function out = jorge(mZ,mG,mI,c) % rowfun passes the variables as separate variables
M=[mZ mG mI c]; % combine into an array
isc=(c==1); % logical addressing for the clamped row
out=[M(isc,1:2) M(~isc,3)]; % pick from the array based on column, clamped
try
assert(width(out)==3); % make sure have three elements
catch
out=[out nan]; % fixup if not...
end
end
The above like @Voss's, assumes there are at most two cases, clamped and unclamped for each position; the fixup wouldn't be necessary if that were universally true; the very last case in the sample file is missing the unclamped case; one presumes that's probably an artifact of the file and not a general case, but if it is real, this gets what is there. It wouldn't work as written if it were the clamped record.
Jorge
2024 年 9 月 6 日
good stuff....thank you. Like I said earlier, all this discussion and examples have been exxtremely useful and instructional. Thank you!
dpb
2024 年 9 月 6 日
You're welcome, as noted, I think varfun and rowfun and related functionality with splitapply and/or groupsummary is exceedingly powerful and often not or underappreciated as to just what/how much can be done with them.
And, I think teaching/extending users' acquaintance with the features available is a valuable function of the forum, not just answering a given question...
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