Month: June 2024
Project for the Web in Teams
Hi everyone,
We are currently using Project for the Web in Teams and usually we attach some files on tasks. Our problem is that we were told that our file storage capacity within the Microsoft Dataverse is 12.95GB out of 26GB.
My questions are:
1. Does the 26GB file storage capacity for one project created in Project for the Web, or for all the projects?
2. What files does the file storage capacity constitute?
Hi everyone, We are currently using Project for the Web in Teams and usually we attach some files on tasks. Our problem is that we were told that our file storage capacity within the Microsoft Dataverse is 12.95GB out of 26GB. My questions are:1. Does the 26GB file storage capacity for one project created in Project for the Web, or for all the projects?2. What files does the file storage capacity constitute? Read More
Excel, how to add a coloured cell as only “1” and ignore the cell value, w/ multiple colours?
Hey, ive got a attendance report with up to 10 weeks per person. Right now i have 2 sepertae formulas, 1 which uses cell value to determine colour (so 1=red (absent), 2=green (present) etc.) And another formula that uses the “total weeks present” (‘4/10) to determine a percentage (=40%). Is there any way i can get the “total weeks present” to increase by 1 (from’4/10 to ‘5/10) when i fill in the cell with present (2) and not increase the “total weeks present” by 2 (almost ignoring the cell value, but detecting the cell is full or something like that)?
Also is there any way to make it so 2 different numbers add up as well, so that 1=absent(doesnt add up) 2=present(adds up) and 3=late(adds up)?
=COUNTIF(range, 2) & “/10”
=COUNTIF(B2:K2, 2) & “/” & COLUMNS($B$1:$K$1)
Hey, ive got a attendance report with up to 10 weeks per person. Right now i have 2 sepertae formulas, 1 which uses cell value to determine colour (so 1=red (absent), 2=green (present) etc.) And another formula that uses the “total weeks present” (‘4/10) to determine a percentage (=40%). Is there any way i can get the “total weeks present” to increase by 1 (from’4/10 to ‘5/10) when i fill in the cell with present (2) and not increase the “total weeks present” by 2 (almost ignoring the cell value, but detecting the cell is full or something like that)?Also is there any way to make it so 2 different numbers add up as well, so that 1=absent(doesnt add up) 2=present(adds up) and 3=late(adds up)?=COUNTIF(range, 2) & “/10”
=COUNTIF(B2:K2, 2) & “/” & COLUMNS($B$1:$K$1) Read More
How to make a ResNet network with more than 3 channels?
This is in relation to the move from LayerGraphs, trainNetwork, and resnetLayers to dlnetworks, trainnet, and resnetNetwork.
I have a dataset consisting of 125×125 images, with 8 pixel channels instead of the usual 3 for RGB. With the older resnetLayers (which creates a ResNet LayerGraph) and trainNetwork, I was able to store this data as cells in a table and train with that. However, resnetLayers and trainNetwork are no longer recommended by MATLAB, and they recommend using resnetNetwork (which creates a ResNet dlnetwork) and trainnet instead. I want to keep up with the more modern and supported implementations, so I tried rewriting my code to use these, but I run into a problem where it seems trainnet won’t accept this format of data anymore, and I haven’t yet found a way to make it work. How should I go about passing this data to trainnet?
%Load the training data
load("dataset.mat", "traindata");
traindata
%Train a ResNet LayerGraph using the old functions
imageSize = [125 125 8];
numClasses = 2;
layers = resnetLayers(imageSize,numClasses);
opts = trainingOptions("adam",…
"ExecutionEnvironment","cpu",…
"InitialLearnRate",0.0001,…
"MaxEpochs",1);
trainedNetwork = trainNetwork(traindata,layers,opts)
%Try to train a ResNet dlnetwork with the new functions, get an error
dlresnet = resnetNetwork(imageSize,numClasses);
dlresnet = initialize(dlresnet);
trainedDlnetwork = trainnet(traindata,dlresnet,"crossentropy",opts)This is in relation to the move from LayerGraphs, trainNetwork, and resnetLayers to dlnetworks, trainnet, and resnetNetwork.
I have a dataset consisting of 125×125 images, with 8 pixel channels instead of the usual 3 for RGB. With the older resnetLayers (which creates a ResNet LayerGraph) and trainNetwork, I was able to store this data as cells in a table and train with that. However, resnetLayers and trainNetwork are no longer recommended by MATLAB, and they recommend using resnetNetwork (which creates a ResNet dlnetwork) and trainnet instead. I want to keep up with the more modern and supported implementations, so I tried rewriting my code to use these, but I run into a problem where it seems trainnet won’t accept this format of data anymore, and I haven’t yet found a way to make it work. How should I go about passing this data to trainnet?
%Load the training data
load("dataset.mat", "traindata");
traindata
%Train a ResNet LayerGraph using the old functions
imageSize = [125 125 8];
numClasses = 2;
layers = resnetLayers(imageSize,numClasses);
opts = trainingOptions("adam",…
"ExecutionEnvironment","cpu",…
"InitialLearnRate",0.0001,…
"MaxEpochs",1);
trainedNetwork = trainNetwork(traindata,layers,opts)
%Try to train a ResNet dlnetwork with the new functions, get an error
dlresnet = resnetNetwork(imageSize,numClasses);
dlresnet = initialize(dlresnet);
trainedDlnetwork = trainnet(traindata,dlresnet,"crossentropy",opts) This is in relation to the move from LayerGraphs, trainNetwork, and resnetLayers to dlnetworks, trainnet, and resnetNetwork.
I have a dataset consisting of 125×125 images, with 8 pixel channels instead of the usual 3 for RGB. With the older resnetLayers (which creates a ResNet LayerGraph) and trainNetwork, I was able to store this data as cells in a table and train with that. However, resnetLayers and trainNetwork are no longer recommended by MATLAB, and they recommend using resnetNetwork (which creates a ResNet dlnetwork) and trainnet instead. I want to keep up with the more modern and supported implementations, so I tried rewriting my code to use these, but I run into a problem where it seems trainnet won’t accept this format of data anymore, and I haven’t yet found a way to make it work. How should I go about passing this data to trainnet?
%Load the training data
load("dataset.mat", "traindata");
traindata
%Train a ResNet LayerGraph using the old functions
imageSize = [125 125 8];
numClasses = 2;
layers = resnetLayers(imageSize,numClasses);
opts = trainingOptions("adam",…
"ExecutionEnvironment","cpu",…
"InitialLearnRate",0.0001,…
"MaxEpochs",1);
trainedNetwork = trainNetwork(traindata,layers,opts)
%Try to train a ResNet dlnetwork with the new functions, get an error
dlresnet = resnetNetwork(imageSize,numClasses);
dlresnet = initialize(dlresnet);
trainedDlnetwork = trainnet(traindata,dlresnet,"crossentropy",opts) machine learning, resnet, computer vision MATLAB Answers — New Questions
linkprop does not work with multidimensional array of objects
Bug report: If given an array (not a vector) of objects, then linkprop doesn’t link properties for all the objects it is given, but rather a subset of them:
fig=figure;
nrow=3;
ncol=2;
tl=tiledlayout(nrow,ncol);%,’tileindexing’,’columnmajor’);
ax=gobjects(nrow,ncol);
for cnt1=1:nrow*ncol
ax(cnt1)=nexttile;
plot(1:2,3:4)
end
ln=linkprop(ax,{‘xlim’,’ylim’});
ax(1).XLim=[1.5,2.5];
disp(‘ln.Targets(:).XLim’)
ln.Targets(:).XLim
disp(‘ax(:).XLim’)
ax(:).XLim
Which objects do get linked depends on whether ‘rowmajor’ or ‘colummajor’ tile indexing are used. It sort-of operates on just the first row/column of objects given in the array, for instance try changing nrow and ncol to be equal to each other. I have the problem in 2023a, seems it’s also present in the latest release (2024a) based on the results above.Bug report: If given an array (not a vector) of objects, then linkprop doesn’t link properties for all the objects it is given, but rather a subset of them:
fig=figure;
nrow=3;
ncol=2;
tl=tiledlayout(nrow,ncol);%,’tileindexing’,’columnmajor’);
ax=gobjects(nrow,ncol);
for cnt1=1:nrow*ncol
ax(cnt1)=nexttile;
plot(1:2,3:4)
end
ln=linkprop(ax,{‘xlim’,’ylim’});
ax(1).XLim=[1.5,2.5];
disp(‘ln.Targets(:).XLim’)
ln.Targets(:).XLim
disp(‘ax(:).XLim’)
ax(:).XLim
Which objects do get linked depends on whether ‘rowmajor’ or ‘colummajor’ tile indexing are used. It sort-of operates on just the first row/column of objects given in the array, for instance try changing nrow and ncol to be equal to each other. I have the problem in 2023a, seems it’s also present in the latest release (2024a) based on the results above. Bug report: If given an array (not a vector) of objects, then linkprop doesn’t link properties for all the objects it is given, but rather a subset of them:
fig=figure;
nrow=3;
ncol=2;
tl=tiledlayout(nrow,ncol);%,’tileindexing’,’columnmajor’);
ax=gobjects(nrow,ncol);
for cnt1=1:nrow*ncol
ax(cnt1)=nexttile;
plot(1:2,3:4)
end
ln=linkprop(ax,{‘xlim’,’ylim’});
ax(1).XLim=[1.5,2.5];
disp(‘ln.Targets(:).XLim’)
ln.Targets(:).XLim
disp(‘ax(:).XLim’)
ax(:).XLim
Which objects do get linked depends on whether ‘rowmajor’ or ‘colummajor’ tile indexing are used. It sort-of operates on just the first row/column of objects given in the array, for instance try changing nrow and ncol to be equal to each other. I have the problem in 2023a, seems it’s also present in the latest release (2024a) based on the results above. linkprop MATLAB Answers — New Questions
Making an Xray image out of x, y, z coordinates ERROR
I am trying to make an xray image out of given x, y, z coordinates. Here is my code below:
fileID = fopen(‘Chest_Xray_Raw_Data.txt’, ‘r’);
data = textscan(fileID, ‘%f,%f,%f,%f’, ‘Delimiter’, ‘,’);
fclose(fileID);
% Extract x, y, z coordinates and grayscale values
x = data{1};
y = data{2};
z = data{3}; % If needed, otherwise ignore
grayscale = data{4};
% Determine the size of the image
maxX = int64(max(x));
maxY = int64(max(y));
% Normalize coordinates to fit into the pixel grid
x = round(x – min(x) + 1);
y = round(y – min(y) + 1);
% Initialize the image matrix
imageMatrix = zeros(maxY, maxX);
% Populate the image matrix with grayscale values
for i = 1:length(x)
imageMatrix(y(i), x(i)) = grayscale(i);
end
% Display the image
figure;
imshow(imageMatrix, []);
title(‘Reconstructed Image from Raw Data’);
colormap(gray); % Use grayscale colormap
colorbar;
An example of the data I am given is this, but obviously larger:
+4.23789300E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23619400E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23449400E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23279500E+002, +0.00000000E+000, +0.00000000E+000, +420
When I run it, I get this message
"Index exceeds the number of array elements. Index must not exceed 0.
Error in untitled (line 24)
imageMatrix(y(i), x(i)) = grayscale(i);"
How can I get rid of this error message? Thanks!I am trying to make an xray image out of given x, y, z coordinates. Here is my code below:
fileID = fopen(‘Chest_Xray_Raw_Data.txt’, ‘r’);
data = textscan(fileID, ‘%f,%f,%f,%f’, ‘Delimiter’, ‘,’);
fclose(fileID);
% Extract x, y, z coordinates and grayscale values
x = data{1};
y = data{2};
z = data{3}; % If needed, otherwise ignore
grayscale = data{4};
% Determine the size of the image
maxX = int64(max(x));
maxY = int64(max(y));
% Normalize coordinates to fit into the pixel grid
x = round(x – min(x) + 1);
y = round(y – min(y) + 1);
% Initialize the image matrix
imageMatrix = zeros(maxY, maxX);
% Populate the image matrix with grayscale values
for i = 1:length(x)
imageMatrix(y(i), x(i)) = grayscale(i);
end
% Display the image
figure;
imshow(imageMatrix, []);
title(‘Reconstructed Image from Raw Data’);
colormap(gray); % Use grayscale colormap
colorbar;
An example of the data I am given is this, but obviously larger:
+4.23789300E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23619400E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23449400E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23279500E+002, +0.00000000E+000, +0.00000000E+000, +420
When I run it, I get this message
"Index exceeds the number of array elements. Index must not exceed 0.
Error in untitled (line 24)
imageMatrix(y(i), x(i)) = grayscale(i);"
How can I get rid of this error message? Thanks! I am trying to make an xray image out of given x, y, z coordinates. Here is my code below:
fileID = fopen(‘Chest_Xray_Raw_Data.txt’, ‘r’);
data = textscan(fileID, ‘%f,%f,%f,%f’, ‘Delimiter’, ‘,’);
fclose(fileID);
% Extract x, y, z coordinates and grayscale values
x = data{1};
y = data{2};
z = data{3}; % If needed, otherwise ignore
grayscale = data{4};
% Determine the size of the image
maxX = int64(max(x));
maxY = int64(max(y));
% Normalize coordinates to fit into the pixel grid
x = round(x – min(x) + 1);
y = round(y – min(y) + 1);
% Initialize the image matrix
imageMatrix = zeros(maxY, maxX);
% Populate the image matrix with grayscale values
for i = 1:length(x)
imageMatrix(y(i), x(i)) = grayscale(i);
end
% Display the image
figure;
imshow(imageMatrix, []);
title(‘Reconstructed Image from Raw Data’);
colormap(gray); % Use grayscale colormap
colorbar;
An example of the data I am given is this, but obviously larger:
+4.23789300E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23619400E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23449400E+002, +0.00000000E+000, +0.00000000E+000, +420
+4.23279500E+002, +0.00000000E+000, +0.00000000E+000, +420
When I run it, I get this message
"Index exceeds the number of array elements. Index must not exceed 0.
Error in untitled (line 24)
imageMatrix(y(i), x(i)) = grayscale(i);"
How can I get rid of this error message? Thanks! error, grayscale MATLAB Answers — New Questions
Why is indexing a sparse matrix not behaving how I would expect?
I am trying to understand someone else’s code so I don’t entirely understand what is happening (it is trying to fill in missing values) but this is what I am observing. I have the following sparse matrix:
val =
(1,41) 1
(1,42) -2
(2,42) 1
(1,43) 1
(2,43) -2
(3,43) 1
(2,44) 1
(3,44) -2
(3,45) 1
(4,45) 1
(4,46) -2
(5,46) 1
(4,47) 1
(5,47) -2
(6,47) 1
(5,48) 1
(6,48) -2
(6,49) 1
(7,53) 1
(7,54) -2
(8,54) 1
(7,55) 1
(8,55) -2
(9,55) 1
(8,56) 1
(9,56) -2
(10,56) 1
(9,57) 1
(10,57) -2
(10,58) 1
I also have a kown list of indexes that is almost continuous from 1:100 but excludes index numbers 43, 47, 55, 56.
When I try to index
sparse(:,known_list);
I would expect to get a list excluding all the unknown values 43, 47, 55, 56. So that I would have something like this:
val =
(1,41) 1
(1,42) -2
(2,42) 1
(2,44) 1
(3,44) -2
(3,45) 1
(4,45) 1
(4,46) -2
(5,46) 1
(5,48) 1
(6,48) -2
(6,49) 1
(7,53) 1
(7,54) -2
(8,54) 1
(9,57) 1
(10,57) -2
(10,58) 1
But instead I get:
ans =
(1,41) 1
(1,42) -2
(2,42) 1
(2,43) 1
(3,43) -2
(3,44) 1
(4,44) 1
(4,45) -2
(5,45) 1
(5,46) 1
(6,46) -2
(6,47) 1
(7,51) 1
(7,52) -2
(8,52) 1
(9,53) 1
(10,53) -2
(10,54) 1
I can’t make head or tails of the logic Matlab is using!!! I thought I understood sparse matrixes, but maybe there is something I am not getting!?!?I am trying to understand someone else’s code so I don’t entirely understand what is happening (it is trying to fill in missing values) but this is what I am observing. I have the following sparse matrix:
val =
(1,41) 1
(1,42) -2
(2,42) 1
(1,43) 1
(2,43) -2
(3,43) 1
(2,44) 1
(3,44) -2
(3,45) 1
(4,45) 1
(4,46) -2
(5,46) 1
(4,47) 1
(5,47) -2
(6,47) 1
(5,48) 1
(6,48) -2
(6,49) 1
(7,53) 1
(7,54) -2
(8,54) 1
(7,55) 1
(8,55) -2
(9,55) 1
(8,56) 1
(9,56) -2
(10,56) 1
(9,57) 1
(10,57) -2
(10,58) 1
I also have a kown list of indexes that is almost continuous from 1:100 but excludes index numbers 43, 47, 55, 56.
When I try to index
sparse(:,known_list);
I would expect to get a list excluding all the unknown values 43, 47, 55, 56. So that I would have something like this:
val =
(1,41) 1
(1,42) -2
(2,42) 1
(2,44) 1
(3,44) -2
(3,45) 1
(4,45) 1
(4,46) -2
(5,46) 1
(5,48) 1
(6,48) -2
(6,49) 1
(7,53) 1
(7,54) -2
(8,54) 1
(9,57) 1
(10,57) -2
(10,58) 1
But instead I get:
ans =
(1,41) 1
(1,42) -2
(2,42) 1
(2,43) 1
(3,43) -2
(3,44) 1
(4,44) 1
(4,45) -2
(5,45) 1
(5,46) 1
(6,46) -2
(6,47) 1
(7,51) 1
(7,52) -2
(8,52) 1
(9,53) 1
(10,53) -2
(10,54) 1
I can’t make head or tails of the logic Matlab is using!!! I thought I understood sparse matrixes, but maybe there is something I am not getting!?!? I am trying to understand someone else’s code so I don’t entirely understand what is happening (it is trying to fill in missing values) but this is what I am observing. I have the following sparse matrix:
val =
(1,41) 1
(1,42) -2
(2,42) 1
(1,43) 1
(2,43) -2
(3,43) 1
(2,44) 1
(3,44) -2
(3,45) 1
(4,45) 1
(4,46) -2
(5,46) 1
(4,47) 1
(5,47) -2
(6,47) 1
(5,48) 1
(6,48) -2
(6,49) 1
(7,53) 1
(7,54) -2
(8,54) 1
(7,55) 1
(8,55) -2
(9,55) 1
(8,56) 1
(9,56) -2
(10,56) 1
(9,57) 1
(10,57) -2
(10,58) 1
I also have a kown list of indexes that is almost continuous from 1:100 but excludes index numbers 43, 47, 55, 56.
When I try to index
sparse(:,known_list);
I would expect to get a list excluding all the unknown values 43, 47, 55, 56. So that I would have something like this:
val =
(1,41) 1
(1,42) -2
(2,42) 1
(2,44) 1
(3,44) -2
(3,45) 1
(4,45) 1
(4,46) -2
(5,46) 1
(5,48) 1
(6,48) -2
(6,49) 1
(7,53) 1
(7,54) -2
(8,54) 1
(9,57) 1
(10,57) -2
(10,58) 1
But instead I get:
ans =
(1,41) 1
(1,42) -2
(2,42) 1
(2,43) 1
(3,43) -2
(3,44) 1
(4,44) 1
(4,45) -2
(5,45) 1
(5,46) 1
(6,46) -2
(6,47) 1
(7,51) 1
(7,52) -2
(8,52) 1
(9,53) 1
(10,53) -2
(10,54) 1
I can’t make head or tails of the logic Matlab is using!!! I thought I understood sparse matrixes, but maybe there is something I am not getting!?!? sparse, interpolation, nan MATLAB Answers — New Questions
Ranking 3 columns
Hi all, I have a tracker that I’m trying to create a ranking for.
So far I’ve been using:
=RANK.EQ($S3,$S$3:$S$15)+COUNTIFS($S$3:$S$15,$S3,$T$3:$T$15,”>”&$T3)
Which ranks Total AOV against Orders, if I’m correct.
What I want to do is also rank orders per hour, but I’m struggling to see how to add that in.
TOTAL AOVORDERSORDER PER HOURRANKING£33.26391111£23.3629582£22.9017683
Hi all, I have a tracker that I’m trying to create a ranking for. So far I’ve been using: =RANK.EQ($S3,$S$3:$S$15)+COUNTIFS($S$3:$S$15,$S3,$T$3:$T$15,”>”&$T3) Which ranks Total AOV against Orders, if I’m correct. What I want to do is also rank orders per hour, but I’m struggling to see how to add that in. TOTAL AOVORDERSORDER PER HOURRANKING£33.26391111£23.3629582£22.9017683 Read More
T-SQL Bit Field in View
Hi,
I’m not sure if this is the proper place for a T-SQL question. If not, I’d appreciate a pointer to the correct forum.
I have a view, and I would like a field that shows whether or not a specific value is null.
If I use this
“IsNull” = CASE WHEN MyValue IS NULL THEN 1 ELSE 0 END
it ends up with exactly what I would want. It’s a non-nullable field, except it’s an integer.
Now, if I wrap that entire CASE in either a CAST or CONVERT to BIT, the resulting bit field is nullable.
How do I tell SQL Server that the field is not nullable?
Thanks in advance for any help,
Chris
Hi, I’m not sure if this is the proper place for a T-SQL question. If not, I’d appreciate a pointer to the correct forum. I have a view, and I would like a field that shows whether or not a specific value is null. If I use this “IsNull” = CASE WHEN MyValue IS NULL THEN 1 ELSE 0 ENDit ends up with exactly what I would want. It’s a non-nullable field, except it’s an integer. Now, if I wrap that entire CASE in either a CAST or CONVERT to BIT, the resulting bit field is nullable. How do I tell SQL Server that the field is not nullable? Thanks in advance for any help,Chris Read More
Key features of Copilot that differentiate it from other AI-assisted development tools,
What are the key features of Copilot that differentiate it from other AI-assisted development tools, and how can developers leverage these features to enhance their productivity and code quality?
What are the key features of Copilot that differentiate it from other AI-assisted development tools, and how can developers leverage these features to enhance their productivity and code quality? Read More
How Copilot handles sensitive information
How does Copilot handle sensitive information and ensure the security of code snippets generated during development?
How does Copilot handle sensitive information and ensure the security of code snippets generated during development? Read More
Note: We’ll remove classic Teams in the next few weeks
Teams has a message on it that it will be removing teams from my system. I don’t want it removed. The New Teams doesn’t work on my computer. How do I stop this?
Teams has a message on it that it will be removing teams from my system. I don’t want it removed. The New Teams doesn’t work on my computer. How do I stop this? Read More
Curve Fitter Polynomial Plots Strangely In Script
Hello,
I am trying to analyze some doppler shifts on a spectrogram. I am using the Matlab Curve Fitter Tool to fit a curve to very specific data. The curve fitter works well. It generates polynomial parameters but when I try to graph this polynomial in a normal script, the output looks nothing like the graph in the curve fitter.
Here is the curve fitter:
Here is the curve fitter polynomial output:
And when I try taking these values (p1, … p10) and plotting the polynomial in a regular script, the output looks like this:
Which looks nothing like the curve in the curve fitter app. Additionally, the y-axis for the script graph is way off. I have tried playing around with the scaling outputs that the polyfit() function gives me but I haven’t made any progress. I have also tried to use the "center and scale" option but haven’t gotten that to work either. I clearly don’t understand something fundamental with this tool.
Thanks in advance for any help!
Here is my script’s code: (p values pulled straight from the curve fitter app)
p1 = 0.0067;
p2 = -1.7909;
p3 = 155.6940;
p4 = 1.1965e+03;
p5 = -1.2672e+06;
p6 = 1.1048e+08;
p7 = -4.9186e+09;
p8 = 1.258e+11;
p9 = -1.7629e+12;
p10 = 1.0551e+13;
x = 40:0.01:50;
p = [p1 p2 p3 p4 p5 p6 p7 p8 p9 p10];
plot(x, polyval(p, x));Hello,
I am trying to analyze some doppler shifts on a spectrogram. I am using the Matlab Curve Fitter Tool to fit a curve to very specific data. The curve fitter works well. It generates polynomial parameters but when I try to graph this polynomial in a normal script, the output looks nothing like the graph in the curve fitter.
Here is the curve fitter:
Here is the curve fitter polynomial output:
And when I try taking these values (p1, … p10) and plotting the polynomial in a regular script, the output looks like this:
Which looks nothing like the curve in the curve fitter app. Additionally, the y-axis for the script graph is way off. I have tried playing around with the scaling outputs that the polyfit() function gives me but I haven’t made any progress. I have also tried to use the "center and scale" option but haven’t gotten that to work either. I clearly don’t understand something fundamental with this tool.
Thanks in advance for any help!
Here is my script’s code: (p values pulled straight from the curve fitter app)
p1 = 0.0067;
p2 = -1.7909;
p3 = 155.6940;
p4 = 1.1965e+03;
p5 = -1.2672e+06;
p6 = 1.1048e+08;
p7 = -4.9186e+09;
p8 = 1.258e+11;
p9 = -1.7629e+12;
p10 = 1.0551e+13;
x = 40:0.01:50;
p = [p1 p2 p3 p4 p5 p6 p7 p8 p9 p10];
plot(x, polyval(p, x)); Hello,
I am trying to analyze some doppler shifts on a spectrogram. I am using the Matlab Curve Fitter Tool to fit a curve to very specific data. The curve fitter works well. It generates polynomial parameters but when I try to graph this polynomial in a normal script, the output looks nothing like the graph in the curve fitter.
Here is the curve fitter:
Here is the curve fitter polynomial output:
And when I try taking these values (p1, … p10) and plotting the polynomial in a regular script, the output looks like this:
Which looks nothing like the curve in the curve fitter app. Additionally, the y-axis for the script graph is way off. I have tried playing around with the scaling outputs that the polyfit() function gives me but I haven’t made any progress. I have also tried to use the "center and scale" option but haven’t gotten that to work either. I clearly don’t understand something fundamental with this tool.
Thanks in advance for any help!
Here is my script’s code: (p values pulled straight from the curve fitter app)
p1 = 0.0067;
p2 = -1.7909;
p3 = 155.6940;
p4 = 1.1965e+03;
p5 = -1.2672e+06;
p6 = 1.1048e+08;
p7 = -4.9186e+09;
p8 = 1.258e+11;
p9 = -1.7629e+12;
p10 = 1.0551e+13;
x = 40:0.01:50;
p = [p1 p2 p3 p4 p5 p6 p7 p8 p9 p10];
plot(x, polyval(p, x)); curve fitter, line of best fit, polynomial, matlab, doppler, doppler shift, spectrogram, curve fitting MATLAB Answers — New Questions
Using copyfile in standalone matlab app give error
Using copyfile in standalone matlab app.
[SUCCESS,MSG] = copyfile(fromPath,toPath, ‘f’);
where fromPath is s3 path
and toPath is local folder in windows.
When app is run from within matlab, copyfile copies data from S3 to destination folder correctly with SUCCESS=1
When standalone app is run it return following text in MSG "s3 contains an unsupported provider" and SUCCESS=0
Verified that AWS environment varibles are set correctly while running app in standlone mode.
Let me know any hints to debug.Using copyfile in standalone matlab app.
[SUCCESS,MSG] = copyfile(fromPath,toPath, ‘f’);
where fromPath is s3 path
and toPath is local folder in windows.
When app is run from within matlab, copyfile copies data from S3 to destination folder correctly with SUCCESS=1
When standalone app is run it return following text in MSG "s3 contains an unsupported provider" and SUCCESS=0
Verified that AWS environment varibles are set correctly while running app in standlone mode.
Let me know any hints to debug. Using copyfile in standalone matlab app.
[SUCCESS,MSG] = copyfile(fromPath,toPath, ‘f’);
where fromPath is s3 path
and toPath is local folder in windows.
When app is run from within matlab, copyfile copies data from S3 to destination folder correctly with SUCCESS=1
When standalone app is run it return following text in MSG "s3 contains an unsupported provider" and SUCCESS=0
Verified that AWS environment varibles are set correctly while running app in standlone mode.
Let me know any hints to debug. matlab, appdesigner, s3, copyfile MATLAB Answers — New Questions
How do I turn off Copilot in Word documents?
I don’t need Copilot in my word documents and I don’t need the copilot icon stalking me on every page when I begin to type. Its very distracting. How do I disable Copilot?
I don’t need Copilot in my word documents and I don’t need the copilot icon stalking me on every page when I begin to type. Its very distracting. How do I disable Copilot? Read More
Script to copy selected filtered Excel pivot table columns to another excel tab?
Hello,
I have been filtering different views of my base Pivot table (based on the 4 different Age categories present in my dataset) and copying selected columns over to a new Excel tab to show the result in a small table for that age group. I do this workflow for each of the Age categories, so the new Excel tab ultimately has 4 tables (one for each Age category). This is a highly manual process prone to error.
Once I’ve refreshed my pivot table with the next Age category filter, is there a script that would then select the 3 columns I’m interested in and copy them over into consecutive column (so as to form a table)? THEN – I want to go back to the Pivot table, (manually) refresh the results for the next Age Category, use the script to select the same 3 columns and copy it over to the Excel tab that has the previous table(s) I’ve already copied over, and create the new table next to the previously-created table (with one blank column in-between). Here’s a screenshot of what the output results would look like:
So, once the Pivot table has been manually refreshed to show the Age Category I want, my thought is to execute the script to select the same 3 columns and copy it over to the Excel tab in adjacent tables as shown above. Can this be done to take some of the manual parts of the process and put them in a script as described? Thank you!
Hello, I have been filtering different views of my base Pivot table (based on the 4 different Age categories present in my dataset) and copying selected columns over to a new Excel tab to show the result in a small table for that age group. I do this workflow for each of the Age categories, so the new Excel tab ultimately has 4 tables (one for each Age category). This is a highly manual process prone to error. Once I’ve refreshed my pivot table with the next Age category filter, is there a script that would then select the 3 columns I’m interested in and copy them over into consecutive column (so as to form a table)? THEN – I want to go back to the Pivot table, (manually) refresh the results for the next Age Category, use the script to select the same 3 columns and copy it over to the Excel tab that has the previous table(s) I’ve already copied over, and create the new table next to the previously-created table (with one blank column in-between). Here’s a screenshot of what the output results would look like: So, once the Pivot table has been manually refreshed to show the Age Category I want, my thought is to execute the script to select the same 3 columns and copy it over to the Excel tab in adjacent tables as shown above. Can this be done to take some of the manual parts of the process and put them in a script as described? Thank you! Read More
Copilot for Security TI Embedded Experience in Defender XDR is now GA
The Microsoft Defender Threat Intelligence (MDTI) and Defender XDR teams are pleased to announce that the Copilot for Security threat intelligence embedded experience in the Defender XDR portal is now generally available. As of today, Defender XDR customers will see a handy AI-powered sidecar in the Threat Analytics, intel profiles, intel explorer, and intel projects tabs in the threat intelligence blade (in brackets below), which returns, contextualizes, and summarizes intelligence from across MDTI and Threat Analytics about threat actors, threat tooling, and indicators of compromise (IoCs) related to their vulnerabilities and security incidents.
The embedded experience on the right hand side of the Defender XDR portal has an open prompt bar as well as a guided experience with three pre-populated prompts.
How Copilot Enhances Microsoft Threat Intelligence
Microsoft Copilot for Security enables customers to access, operate on, and integrate Microsoft’s raw and finished threat intelligence via natural language with simple requests known as prompts, which ask important questions about MDTI’s data and content, such as “Tell me more about the threat actor Silk Typhoon.” The answers returned from prompts are always up to date with the latest threat intelligence information, including IoCs, data from mass collection and analysis, intelligence articles, intel profiles (vulnerabilities, threat actors, threat tooling, techniques), and guidance. This critical information delivered instantly and in-context, up-levels and enables different security personas to defend at machine speed and scale.
Key Capabilities of the Threat Intelligence Embedded Experience
Think of the Copilot threat intelligence embedded experience in Defender XDR as a helpful research assistant that can pull, contextualize, and summarize relevant intelligence at machine speed to drive an optimal security plan for your organization. Customers can evaluate artifacts and correlate MDTI and Threat Analytics content and data with other security information from Defender XDR, such as incidents and hunting activities, to help them assess their vulnerabilities and quickly understand the broader scope of an attack. With helpful pre-populated prompts or through typing your own, the Copilot sidecar helps you quickly understand threats and assess vulnerabilities faster and more efficiently than before in several exciting ways:
Summarize threat intelligence: By clicking on the pre-populated prompt ‘Give me an overview of the latest threats to my organization,’ Copilot returns the latest Intel Profiles and Activity Snapshots that contain mentions of your vulnerabilities, TTPs (tactics, techniques, and procedures) that include the infrastructure your organization runs, and other relevant factors such as intelligence that mentions your industry and region:
The first pre-populated prompt returns and summarizes intelligence most relevant to your organization.
Customers can also ask Copilot to summarize other relevant intelligence via open prompts, such as “Tell me about the threat actor Mango Sandstorm” or “Summarize the latest threat activity involving Cobalt Strike.”
Prioritize threats: By clicking the pre-populated prompt “Which threats should I focus on based on their exposure score,” Copilot queries Threat Analytics and MDTI to deliver the intelligence most relevant to an organization based on the exposures and vulnerabilities they have across their attack surface. Customers can also quickly retrieve information on indicators, including IP addresses and domains, to enrich artifacts with content such as threat articles and intel profiles to understand the risk they pose to their organization’s unique attack surface.
The second pre-populated prompt returns the most critical threat intelligence.
Understand your risk: Copilot can reason over vulnerability intelligence in MDTI and Threat Analytics to deliver a customized, prioritized list based on a customer organization’s unique security posture. By clicking on the pre-populated prompt “Which threat actors are targeting infrastructure in my industry?” Copilot returns summaries of the top threat actors implicated in attacks involving your industry. This information provides an excellent starting point for threat research and building out a robust defense strategy.
The third pre-populated prompts offers intelligence relevant to your organization based on industry and other factors.
New to MDTI? Here’s where to start
Learn more about getting started with MDTI here, and read everything you need to know about MDTI as a Copilot for Security customer here. Also, be sure to join our fast-growing community of security pros and experts to provide product feedback and suggestions and start conversations about how MDTI is helping your team stay on top of threats.
Microsoft Tech Community – Latest Blogs –Read More
Get more device control flexibility with BitLocker settings in Defender for Endpoint
With hybrid work here to stay and data-centric cyberattacks on the rise, safeguarding sensitive information is critical to every security strategy. While data loss prevention (DLP) is often considered for cloud storage locations, the management of removable storage devices such as USBs is equally important, to help ensure that data-at-rest is encrypted and integrity and confidentiality of sensitive information is maintained.
We’re excited to announce that Defender for Endpoint device control support for BitLocker is now in public preview. This new feature provides security admins with more granular control through policy exceptions for BitLocker encrypted devices.
Comprehensive management of removable storage devices
BitLocker encryption has long been recognized for its ability to protect data on devices by encrypting the entire drive, ensuring that data remains inaccessible to unauthorized users. With the integration of BitLocker device control, organizations can now seamlessly integrate their Defender for Endpoint policies with BitLocker’s best-in-class encryption for a comprehensive method to manage access to removable storage based on the BitLocker encryption state.
This flexibility allows administrators to require BitLocker encryption, and then manage exceptions for other trusted devices and users.
Figure 1: Encryption state device control
Figure 1 shows device control with a new descriptor Id called DeviceEncryptionStateId that includes or excludes devices in rules by encryption state (BitlockerEncrypted or Plain). This descriptorId can be added to groups that are managed via Intune (OMA-URI) or Group Policy
Setting up device control
Setting up device control with an approved list can be configured with 3 rules:
Allow unencrypted removable media devices read only access – which applies to all removable media devices except BitLocker encrypted and unencrypted devices that are specifically added
Allow unencrypted removable media devices with an exception full access – which applies to all allowed BitLocker unencrypted devices
Allow BitLocker encrypted removable media full device access – which applies to all the BitLocker encrypted devices
Figure 2: Approved devices configuration
The policy can be tested by using three-different removable media devices:
Green USB (BitLocker encrypted)
Blue USB (unencrypted, but granted full access)
Red USB (read-only)
Figure 3: Approved devices configuration
Figures 3 and 4 show that when device control blocks access, and there is an audit rule defined, a ReusableStorageAcessTrigger event gets created—visible in Advanced Hunting.
Figure 4: Results when a Green USB, Blue USB, and Red USB are inserted
End user experience
A notification is also sent to the end-user to provide awareness.
Figure 5: Notification to the device-owner
Comprehensive endpoint security
The release of BitLocker device control combines the policy enforcement capabilities of Defender for Endpoint with the robust encryption of BitLocker and gives admins new flexibility in device control to use BitLocker encrypted devices at scale.
Get more information:
Check out the documentation here.
Learn more about
Not a Defender for Endpoint customer? Start a free trial today.
Microsoft Tech Community – Latest Blogs –Read More
Build error: C++ compiler produced errors. See the Build Log for further details.
Hello, I wanted to create a MEX file in MATLAB, but I couldn’t do that because of the following message in the "build log":
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_data.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_data.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_initialize.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_initialize.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_terminate.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_terminate.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_info.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_info.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_api.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_api.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_mex.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_mex.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "MWCudaDimUtility.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
MWCudaDimUtility.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "cpp_mexapi_version.cpp"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
cpp_mexapi_version.cpp
nvcc -m64 -Wl,–no-undefined -shared -static -L"C:Program FilesMATLABR2018bexternlibwin64mingw64" -llibmx -llibmex -llibmat -lm -llibmwlapack -llibmwblas -llibMatlabDataArray -llibMatlabEngine -Xnvlink -w -Wno-deprecated-gpu-targets -Xlinker /NODEFAULTLIB:libcmt.lib -o BPmimo2C_mex.mexw64 -arch sm_35 BPmimo2C_data.obj BPmimo2C_initialize.obj BPmimo2C_terminate.obj BPmimo2C.obj _coder_BPmimo2C_info.obj _coder_BPmimo2C_api.obj _coder_BPmimo2C_mex.obj MWCudaDimUtility.obj cpp_mexapi_version.obj -L"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.2libx64" cuda.lib cudart.lib cublas.lib cusolver.lib cufft.lib -Xlinker libemlrt.lib,libcovrt.lib,libut.lib,libmwmathutil.lib,/export:mexFunction,/export:emlrtMexFcnProperties
nvcc fatal : Unknown option ‘-Wl,–no-undefined’
gmake: *** [BPmimo2C_mex.mexw64] Error 1
Warning: Selected MEX compiler ‘MinGW64 Compiler (C++)’ is not supported for GPU code generation. Refer to the GPU Coder documentation for a list of supported GPU MEX compilers.
> In coder.gpu.getDefaultGpuToolchain
In emlckernel
In emlckernel
In emlcprivate
In coder.internal.gui.codegenWrapper
In codeUnifiedProject
In codeUnifiedProject
In emlcprivate
??? Build error: C++ compiler produced errors. See the Build Log for further details.
Code generation failed: View Error Report
I have previously installed visual studio 2019, and added the "cl.exe" path to my environmental variable. Moreover, Microsoft visual C++ redistributable x64 2015-2019 is currently available on my system.
Here is the added path:
What do you think about where my problem come from?
Any help would be appreciated.
Thank youHello, I wanted to create a MEX file in MATLAB, but I couldn’t do that because of the following message in the "build log":
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_data.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_data.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_initialize.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_initialize.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_terminate.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_terminate.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_info.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_info.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_api.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_api.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_mex.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_mex.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "MWCudaDimUtility.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
MWCudaDimUtility.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "cpp_mexapi_version.cpp"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
cpp_mexapi_version.cpp
nvcc -m64 -Wl,–no-undefined -shared -static -L"C:Program FilesMATLABR2018bexternlibwin64mingw64" -llibmx -llibmex -llibmat -lm -llibmwlapack -llibmwblas -llibMatlabDataArray -llibMatlabEngine -Xnvlink -w -Wno-deprecated-gpu-targets -Xlinker /NODEFAULTLIB:libcmt.lib -o BPmimo2C_mex.mexw64 -arch sm_35 BPmimo2C_data.obj BPmimo2C_initialize.obj BPmimo2C_terminate.obj BPmimo2C.obj _coder_BPmimo2C_info.obj _coder_BPmimo2C_api.obj _coder_BPmimo2C_mex.obj MWCudaDimUtility.obj cpp_mexapi_version.obj -L"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.2libx64" cuda.lib cudart.lib cublas.lib cusolver.lib cufft.lib -Xlinker libemlrt.lib,libcovrt.lib,libut.lib,libmwmathutil.lib,/export:mexFunction,/export:emlrtMexFcnProperties
nvcc fatal : Unknown option ‘-Wl,–no-undefined’
gmake: *** [BPmimo2C_mex.mexw64] Error 1
Warning: Selected MEX compiler ‘MinGW64 Compiler (C++)’ is not supported for GPU code generation. Refer to the GPU Coder documentation for a list of supported GPU MEX compilers.
> In coder.gpu.getDefaultGpuToolchain
In emlckernel
In emlckernel
In emlcprivate
In coder.internal.gui.codegenWrapper
In codeUnifiedProject
In codeUnifiedProject
In emlcprivate
??? Build error: C++ compiler produced errors. See the Build Log for further details.
Code generation failed: View Error Report
I have previously installed visual studio 2019, and added the "cl.exe" path to my environmental variable. Moreover, Microsoft visual C++ redistributable x64 2015-2019 is currently available on my system.
Here is the added path:
What do you think about where my problem come from?
Any help would be appreciated.
Thank you Hello, I wanted to create a MEX file in MATLAB, but I couldn’t do that because of the following message in the "build log":
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_data.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_data.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_initialize.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_initialize.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C_terminate.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C_terminate.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "BPmimo2C.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
BPmimo2C.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_info.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_info.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_api.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_api.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "interface/_coder_BPmimo2C_mex.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
_coder_BPmimo2C_mex.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "MWCudaDimUtility.cu"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
MWCudaDimUtility.cu
nvcc -c -rdc=true -Xcompiler "/wd 4819" -Xcompiler "/MD" -Xcudafe "–diag_suppress=unsigned_compare_with_zero –diag_suppress=useless_type_qualifier_on_return_type" -D_GNU_SOURCE -DMATLAB_MEX_FILE -Wno-deprecated-declarations -arch sm_35 -I "C:PROGRA~1MATLABR2018bsimulinkinclude" -I "C:PROGRA~1MATLABR2018btoolboxsharedsimtargets" -I "D:fekomultiple ring arraycodegenmexBPmimo2C" -I "D:fekomultiple ring array" -I ".interface" -I "C:PROGRA~1MATLABR2018bexterninclude" -I "." "cpp_mexapi_version.cpp"
nvcc warning : The ‘compute_35’, ‘compute_37’, ‘compute_50’, ‘sm_35’, ‘sm_37’ and ‘sm_50’ architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
cpp_mexapi_version.cpp
nvcc -m64 -Wl,–no-undefined -shared -static -L"C:Program FilesMATLABR2018bexternlibwin64mingw64" -llibmx -llibmex -llibmat -lm -llibmwlapack -llibmwblas -llibMatlabDataArray -llibMatlabEngine -Xnvlink -w -Wno-deprecated-gpu-targets -Xlinker /NODEFAULTLIB:libcmt.lib -o BPmimo2C_mex.mexw64 -arch sm_35 BPmimo2C_data.obj BPmimo2C_initialize.obj BPmimo2C_terminate.obj BPmimo2C.obj _coder_BPmimo2C_info.obj _coder_BPmimo2C_api.obj _coder_BPmimo2C_mex.obj MWCudaDimUtility.obj cpp_mexapi_version.obj -L"C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.2libx64" cuda.lib cudart.lib cublas.lib cusolver.lib cufft.lib -Xlinker libemlrt.lib,libcovrt.lib,libut.lib,libmwmathutil.lib,/export:mexFunction,/export:emlrtMexFcnProperties
nvcc fatal : Unknown option ‘-Wl,–no-undefined’
gmake: *** [BPmimo2C_mex.mexw64] Error 1
Warning: Selected MEX compiler ‘MinGW64 Compiler (C++)’ is not supported for GPU code generation. Refer to the GPU Coder documentation for a list of supported GPU MEX compilers.
> In coder.gpu.getDefaultGpuToolchain
In emlckernel
In emlckernel
In emlcprivate
In coder.internal.gui.codegenWrapper
In codeUnifiedProject
In codeUnifiedProject
In emlcprivate
??? Build error: C++ compiler produced errors. See the Build Log for further details.
Code generation failed: View Error Report
I have previously installed visual studio 2019, and added the "cl.exe" path to my environmental variable. Moreover, Microsoft visual C++ redistributable x64 2015-2019 is currently available on my system.
Here is the added path:
What do you think about where my problem come from?
Any help would be appreciated.
Thank you mex compiler, mex, gpu coder, matlab MATLAB Answers — New Questions
readmatrix() returning “NaN” for all data cells
I have run into some trouble using the "readmatrix" function and was wondering if anyone has any insights. I am trying to plot a comparison between a 2D airfoil’s Cp distribution as a function of %chord and my aircraft’s 3D Cp as a %chord (at a given span position) from XFLR5’s .txt files.
When my 3D aircraft does not have wing flaps, the readmatrix function works perfectly; however, when I use the file with flaps I get a column of NaNs as my matrix output. The two files are almost identical spare a few lines close to the start, leading me to be very confused why the second txt file cannot be properly interpreted with the readmatrix() function. Additionally, I don’t need any of the data before the y-span row. I have tried other methods such as "readcell" and "readtable" but those either don’t work or intrepret the data imporperly. Does anyone have any suggestions?
Thanks!
Here is how I call each of these (pardon my variable naming convention)
"Aoa3MainWing…" is the file which is working correctly and "MainWing…" is the one with errors
Below Screenshots the working file (Aoa3MainWing…)
Above: txt file for no flaps
Above: datafile after readmatrix()
Below Screenshots of the non-working file (MainWing…)
Above: txt file for flaps (only difference ,aside from data values, is "Main WingFlap…" and "Flap 2 Moment…" lines being added)
Above: datafile after readmatrix() producing only a column of NaNsI have run into some trouble using the "readmatrix" function and was wondering if anyone has any insights. I am trying to plot a comparison between a 2D airfoil’s Cp distribution as a function of %chord and my aircraft’s 3D Cp as a %chord (at a given span position) from XFLR5’s .txt files.
When my 3D aircraft does not have wing flaps, the readmatrix function works perfectly; however, when I use the file with flaps I get a column of NaNs as my matrix output. The two files are almost identical spare a few lines close to the start, leading me to be very confused why the second txt file cannot be properly interpreted with the readmatrix() function. Additionally, I don’t need any of the data before the y-span row. I have tried other methods such as "readcell" and "readtable" but those either don’t work or intrepret the data imporperly. Does anyone have any suggestions?
Thanks!
Here is how I call each of these (pardon my variable naming convention)
"Aoa3MainWing…" is the file which is working correctly and "MainWing…" is the one with errors
Below Screenshots the working file (Aoa3MainWing…)
Above: txt file for no flaps
Above: datafile after readmatrix()
Below Screenshots of the non-working file (MainWing…)
Above: txt file for flaps (only difference ,aside from data values, is "Main WingFlap…" and "Flap 2 Moment…" lines being added)
Above: datafile after readmatrix() producing only a column of NaNs I have run into some trouble using the "readmatrix" function and was wondering if anyone has any insights. I am trying to plot a comparison between a 2D airfoil’s Cp distribution as a function of %chord and my aircraft’s 3D Cp as a %chord (at a given span position) from XFLR5’s .txt files.
When my 3D aircraft does not have wing flaps, the readmatrix function works perfectly; however, when I use the file with flaps I get a column of NaNs as my matrix output. The two files are almost identical spare a few lines close to the start, leading me to be very confused why the second txt file cannot be properly interpreted with the readmatrix() function. Additionally, I don’t need any of the data before the y-span row. I have tried other methods such as "readcell" and "readtable" but those either don’t work or intrepret the data imporperly. Does anyone have any suggestions?
Thanks!
Here is how I call each of these (pardon my variable naming convention)
"Aoa3MainWing…" is the file which is working correctly and "MainWing…" is the one with errors
Below Screenshots the working file (Aoa3MainWing…)
Above: txt file for no flaps
Above: datafile after readmatrix()
Below Screenshots of the non-working file (MainWing…)
Above: txt file for flaps (only difference ,aside from data values, is "Main WingFlap…" and "Flap 2 Moment…" lines being added)
Above: datafile after readmatrix() producing only a column of NaNs readmatrix, data import MATLAB Answers — New Questions
SPO Customize a List’s Form to add a Logo and Column order through JSON
Hello All,
I’m trying to customize a form on a SharePoint list with JSON. I’m able to add sections and organize some of the columns but I have two issues.
One I’m trying to add a logo to the form but all I’m getting is a broken image on the form. More details are below. How do you add a logo to a SharePoint List’s form?
Below is the JSON code that I tried to get a logo on the list’s form. Some background information, I’ve uploaded a logo to the “site assets” library on the same sites.
The file in the format of “.png”. The following lines of code only show a broken image on the form.
Second, some columns are not showing in the order that I’ve set them in the JSON code. Is there a way to set the order of the columns in the form through JSON? See below for more detail. Also, the column “Type of Purchase” is showing at the bottom of the form section even though I’ve coded to top of the section.
Any help is greatly appreciated.
“elmType”:”img”,
“attributes”:{
“scr”: “=@currentweb + ‘/SiteAssets/Small_logo.png'”,
“title”:”Logo”
},
“style”:{
“flex”: “none”,
“padding”:”0px”,
“padding-left”:”10px”,
“height”:”80px”,
“color”:”white”
}
// THIS IS THE SECOND SNIPET I TRIED
“elmType”:”img”,
“attributes”:{ “scr”:=”https://TenantName.sharepoint.com/sites/SPOTestSite/SiteAssets/Small_Logo.png”,
“title”:”Logo”
},
“style”:{
“flex”: “none”,
“padding”:”0px”,
“padding-left”:”10px”,
“height”:”80px”,
“color”:”white”
}
}
Hello All,I’m trying to customize a form on a SharePoint list with JSON. I’m able to add sections and organize some of the columns but I have two issues.One I’m trying to add a logo to the form but all I’m getting is a broken image on the form. More details are below. How do you add a logo to a SharePoint List’s form?Below is the JSON code that I tried to get a logo on the list’s form. Some background information, I’ve uploaded a logo to the “site assets” library on the same sites.The file in the format of “.png”. The following lines of code only show a broken image on the form. Second, some columns are not showing in the order that I’ve set them in the JSON code. Is there a way to set the order of the columns in the form through JSON? See below for more detail. Also, the column “Type of Purchase” is showing at the bottom of the form section even though I’ve coded to top of the section. Any help is greatly appreciated. “elmType”:”img”,
“attributes”:{
“scr”: “=@currentweb + ‘/SiteAssets/Small_logo.png'”,
“title”:”Logo”
},
“style”:{
“flex”: “none”,
“padding”:”0px”,
“padding-left”:”10px”,
“height”:”80px”,
“color”:”white”
}
// THIS IS THE SECOND SNIPET I TRIED
“elmType”:”img”,
“attributes”:{ “scr”:=”https://TenantName.sharepoint.com/sites/SPOTestSite/SiteAssets/Small_Logo.png”,
“title”:”Logo”
},
“style”:{
“flex”: “none”,
“padding”:”0px”,
“padding-left”:”10px”,
“height”:”80px”,
“color”:”white”
}
} Read More
