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I have 16 binary images of logic type, and I want to put those images (variable names) into an array and iterate through them in a for loop doing image processing.
Below is an example of my binary image names, and my current for loop (does not work).
bin_RD1 = imbinarize(rightDam1, T_RD1); %these are my binary images
bin_RD2 = imbinarize(rightDam2, T_RD2);
bin_RD3 = imbinarize(rightDam3, T_RD3);
bin_RD4 = imbinarize(rightDam4, T_RD4);
i = who('bin*'); %says of type 16x1 cell
for j = 1:length(i) %j is listed as just a number
k = i{j}; %char type: 'bin_RD1'
% logical k; did not work
roi = bwareaopen(k, 25);
graindata = regionprops('table',roi,'Area','EquivDiameter','MajorAxisLength','MinorAxisLength','Centroid','Orientation');
end
Your wording is confusing and I don't know if this is what you're looking for. Try assigning your variables in matrix form (please keep in mind I've never worked with image manipulation so my indexing could be very wrong):
for i=1:16
bin_RD(:,:,i) = imbinarize(rightDam(:,:,i),T_RD(i));
end
You can use an operation like this to process your bin_RD variable as well. You wouldn't even have to leave the loop.
for i=1:16
bin_RD(:,:,i) = imbinarize(rightDam(:,:,i),T_RD(i));
roi(:,:,i) = bwareaopen(bin_RD(:,:,i), 25);
graindata = regionprops('table',roi(:,:,i),'Area','EquivDiameter','MajorAxisLength','MinorAxisLength','Centroid','Orientation');
end
One last piece of advice: I used i=1:16, but if you ever want to use this code again on a situation where you might have 5, 22, 100, etc images, use for i=1:length(T_RD) or something like that and you won't have to change it every time.
Using Matlab R2019a, is there any way to avoid the for-loop in the following code in spite of the dimensions containing different element so that each element has to be checked? M is a vector with indices, and Inpts.payout is a 5D array with numerical data.
for m = 1:length(M)-1
for power = 1:noScenarios
for production = 1:noScenarios
for inflation = 1:noScenarios
for interest = 1:noScenarios
if Inpts.payout(M(m),power,production,inflation,interest)<0
Inpts.payout(M(m+1),power,production,inflation,interest)=...
Inpts.payout(M(m+1),power,production,inflation,interest)...
+Inpts.payout(M(m),power,production,inflation,interest);
Inpts.payout(M(m),power,production,inflation,interest)=0;
end
end
end
end
end
end
It is quite simple to remove the inner 4 loops. This will be more efficient unless you have a huge matrix Inpts.payout, as a new indexing matrix must be generated.
The following code extracts the two relevant 'planes' from the input data, does the logic on them, then writes them back:
for m = 1:length(M)-1
payout_m = Inpts.payout(M(m),:,:,:,:);
payout_m1 = Inpts.payout(M(m+1),:,:,:,:);
indx = payout_m < 0;
payout_m1(indx) = payout_m1(indx) + payout_m(indx);
payout_m(indx) = 0;
Inpts.payout(M(m),:,:,:,:) = payout_m;
Inpts.payout(M(m+1),:,:,:,:) = payout_m1;
end
It is possible to avoid extracting the 'planes' and writing them back by working directly with the input data matrix. However, this yields more complex code.
However, we can easily avoid some indexing operations this way:
payout_m = Inpts.payout(M(1),:,:,:,:);
for m = 1:length(M)-1
payout_m1 = Inpts.payout(M(m+1),:,:,:,:);
indx = payout_m < 0;
payout_m1(indx) = payout_m1(indx) + payout_m(indx);
payout_m(indx) = 0;
Inpts.payout(M(m),:,:,:,:) = payout_m;
payout_m = payout_m1;
end
Inpts.payout(M(m+1),:,:,:,:) = payout_m1;
It seems like there is not a way to avoid this. I am assuming that each for lop independently changes a variable parameter used in the main calculation. Thus, it is required to have this many for loops. My only suggestion is to turn your nested loops into a function if you're concerned about appearance. Not sure if this will help run-time.
I am writing a code to do some very simple descriptive statistics, but I found myself being very repetitive with my syntax.
I know there's a way to shorten this code and make it more elegant and time efficient with something like a for-loop, but I am not quite keen enough in coding (yet) to know how to do this...
I have three variables, or groups (All data, condition 1, and condition 2). I also have 8 matlab functions that I need to perform on each of the three groups (e.g mean, median). I am saving all of the data in a table where each column corresponds to one of the functions (e.g. mean) and each row is that function performed on the correspnding group (e.g. (1,1) is mean of 'all data', (2,1) is mean of 'cond 1', and (3,1) is mean of 'cond 2'). It is important to preserve this structure as I am outputting to a csv file that I can open in excel. The columns, again, are labeled according the function, and the rows are ordered by 1) all data 2) cond 1, and 3) cond 2.
The data I am working with is in the second column of these matrices, by the way.
So here is the tedious way I am accomplishing this:
x = cell(3,8);
x{1,1} = mean(alldata(:,2));
x{2,1} = mean(cond1data(:,2));
x{3,1} = mean(cond2data(:,2));
x{1,2} = median(alldata(:,2));
x{2,2} = median(cond1data(:,2));
x{3,2} = median(cond2data(:,2));
x{1,3} = std(alldata(:,2));
x{2,3} = std(cond1data(:,2));
x{3,3} = std(cond2data(:,2));
x{1,4} = var(alldata(:,2)); % variance
x{2,4} = var(cond1data(:,2));
x{3,4} = var(cond2data(:,2));
x{1,5} = range(alldata(:,2));
x{2,5} = range(cond1data(:,2));
x{3,5} = range(cond2data(:,2));
x{1,6} = iqr(alldata(:,2)); % inter quartile range
x{2,6} = iqr(cond1data(:,2));
x{3,6} = iqr(cond2data(:,2));
x{1,7} = skewness(alldata(:,2));
x{2,7} = skewness(cond1data(:,2));
x{3,7} = skewness(cond2data(:,2));
x{1,8} = kurtosis(alldata(:,2));
x{2,8} = kurtosis(cond1data(:,2));
x{3,8} = kurtosis(cond2data(:,2));
% write output to .csv file using cell to table conversion
T = cell2table(x, 'VariableNames',{'mean', 'median', 'stddev', 'variance', 'range', 'IQR', 'skewness', 'kurtosis'});
writetable(T,'descriptivestats.csv')
I know there is a way to loop through this stuff and get the same output in a much shorter code. I tried to write a for-loop but I am just confusing myself and not sure how to do this. I'll include it anyway so maybe you can get an idea of what I'm trying to do.
x = cell(3,8);
data = [alldata, cond2data, cond2data];
dfunction = ['mean', 'median', 'std', 'var', 'range', 'iqr', 'skewness', 'kurtosis'];
for i = 1:8,
for y = 1:3
x{y,i} = dfucntion(i)(data(1)(:,2));
x{y+1,i} = dfunction(i)(data(2)(:,2));
x{y+2,i} = dfunction(i)(data(3)(:,2));
end
end
T = cell2table(x, 'VariableNames',{'mean', 'median', 'stddev', 'variance', 'range', 'IQR', 'skewness', 'kurtosis'});
writetable(T,'descriptivestats.csv')
Any ideas on how to make this work??
You want to use a cell array of function handles. The easiest way to do that is to use the # operator, as in
dfunctions = {#mean, #median, #std, #var, #range, #iqr, #skewness, #kurtosis};
Also, you want to combine your three data variables into one variable, to make it easier to iterate over them. There are two choices I can see. If your data variables are all M-by-2 in dimension, you could concatenate them into a M-by-2-by-3 three-dimensional array. You could do that with
data = cat(3, alldata, cond1data, cond2data);
The indexing expression into data that retrieves the values you want would be data(:, 2, y). That said, I think this approach would have to copy a lot of data around and probably isn't the best for performance. The other way to combine data together is in 1-by-3 cell array, like this:
data = {alldata, cond1data, cond2data};
The indexing expression into data that retrieves the values you want in this case would be data{y}(:, 2).
Since you are looping from y == 1 to y == 3, you only need one line in your inner loop body, not three.
for y = 1:3
x{y, i} = dfunctions{i}(data{y}(:,2));
end
Finally, to get the cell array of strings containing function names to pass to cell2table, you can use cellfun to apply func2str to each element of dfunctions:
funcnames = cellfun(#func2str, dfunctions, 'UniformOutput', false);
The final version looks like this:
dfunctions = {#mean, #median, #std, #var, #range, #iqr, #skewness, #kurtosis};
data = {alldata, cond1data, cond2data};
x = cell(length(data), length(dfunctions));
for i = 1:length(dfunctions)
for y = 1:length(data)
x{y, i} = dfunctions{i}(data{y}(:,2));
end
end
funcnames = cellfun(#func2str, dfunctions, 'UniformOutput', false);
T = cell2table(x, 'VariableNames', funcnames);
writetable(T,'descriptivestats.csv');
You can create a cell array of functions using str2func :
function_string = {'mean', 'median', 'std', 'var', 'range', 'iqr', 'skewness', 'kurtosis'};
dfunction = {};
for ii = 1:length(function_string)
fun{ii} = str2func(function_string{ii})
end
Then you can use it on your data as you'd like to :
for ii = 1:8,
for y = 1:3
x{y,i} = dfucntion{ii}(data(1)(:,2));
x{y+1,i} = dfunction{ii}(data(2)(:,2));
x{y+2,i} = dfunction{ii}(data(3)(:,2));
end
end
Say I have a for loop in MATLAB:
scales = 5:5:95;
for scale = scales
do stuff
end
How can I get the iteration number inside a MATLAB for loop as concisely as possible?
In Python for example I would use:
for idx, item in enumerate(scales):
where idx is the iteration number.
I know that in MATLAB (like in any other language) I could create a count variable:
scales = 5:5:95;
scale_count = 0;
for scale = scales
scale_count = scale_count + 1;
do stuff
end
I could otherwise use find:
scales = 5:5:95;
for scale = scales
scale_count = find(scales == scale);
do stuff
end
But I'm curious to know whether there exists a more concise way to do it, e.g. like in the Python example.
Maybe you can use the following:
scales = 5:5:95;
for iter = 1:length(scales)
scale=scales(iter); % "iter" is the iteration number.
do stuff
end
Since for iterates over the columns of whatever you give it, another way of approximating multiple loop variables would be to use an appropriately constructed matrix:
for scale=[5:5:95; 1:19]
% do stuff with scale(1) or scale(2) as appropriate
end
(my personal preference is to loop over the indices as per Parag's answer and just refer to data(index) directly within the loop, without an intermediate. Matlab's syntax isn't very concise at the best of times - you just get used to it)
The MATLAB way is probably doing it with vectors.
For example suppose you want to find in a vector if there is a value that is equal to its position. You would generally do this:
a = [10 20 1 3 5];
found = 0;
for index = 1:length(a)
if a(index) == index
found = 1;
break;
end
end
Instead you can do:
found = any(a == 1:length(a));
In general
for i=1:length(a)
dostuff(a(i), i);
end
can be replaced with:
dostuff(a(i), 1:length(a))
it dostuff can be vectorized or
arrayfun(#dostuff, a, 1:length(a))
otherwise.
I need to calculate the mean, standard deviation, and other values for a number of variables and I was wondering how to use a loop to my advantage. I have 5 electrodes of data. So to calculate the mean of each I do this:
mean_ch1 = mean(ch1);
mean_ch2 = mean(ch2);
mean_ch3 = mean(ch3);
mean_ch4 = mean(ch4);
mean_ch5 = mean(ch5);
What I want is to be able to condense that code into a line or so. The code I tried does not work:
for i = 1:5
mean_ch(i) = mean(ch(i));
end
I know this code is wrong but it conveys the idea of what I'm trying to accomplish. I want to end up with 5 separate variables that are named by the loop or a cell array with all 5 variables within it allowing for easy recall. I know there must be a way to write this code I'm just not sure how to accomplish it.
You have a few options for how you can do this:
You can put all your channel data into one large matrix first, then compute the mean of the rows or columns using the function MEAN. For example, if each chX variable is an N-by-1 array, you can do the following:
chArray = [ch1 ch2 ch3 ch4 ch5]; %# Make an N-by-5 matrix
meanArray = mean(chArray); %# Take the mean of each column
You can put all your channel data into a cell array first, then compute the mean of each cell using the function CELLFUN:
meanArray = cellfun(#mean,{ch1,ch2,ch3,ch4,ch5});
This would work even if each chX array is a different length from one another.
You can use EVAL to generate the separate variables for each channel mean:
for iChannel = 1:5
varName = ['ch' int2str(iChannel)]; %# Create the name string
eval(['mean_' varName ' = mean(' varName ');']);
end
If it's always exactly 5 channels, you can do
ch = {ch1, ch2, ch3, ch4, ch5}
for j = 1:5
mean_ch(j) = mean(ch{j});
end
A more complicated way would be
for j = 1:nchannels
mean_ch(j) = eval(['mean(ch' num2str(j) ')']);
end
Apart from gnovice's answer. You could use structures and dynamic field names to accomplish your task. First I assume that your channel data variables are all in the format ch* and are the only variables in your MATLAB workspace. The you could do something like the following
%# Move the channel data into a structure with fields ch1, ch2, ....
%# This could be done by saving and reloading the workspace
save('channelData.mat','ch*');
chanData = load('channelData.mat');
%# Next you can then loop through the structure calculating the mean for each channel
flds = fieldnames(chanData); %# get the fieldnames stored in the structure
for i=1:length(flds)
mean_ch(i) = mean(chanData.(flds{i});
end