How do I decode encrypted data and plot it??

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I have some data that is encrypted so that values <1.2V are considered logic 0 and values >2.2V are considered logic 1. The true signal is either 0V (for logic 0) or 3.3V (for logic 1), and any value above or below this threshold will include noise. I am stuck on how to get the values to stay within the threshold and how to plot the new data once it does so.
I have attached the data and my code so far but I am unsure of how to progess. Can I get any help?
time = xlsread('data.csv', 'A2:A201');
pair_1 = xlsread('data.csv', 'B2:B201');
pair_2 = xlsread('data.csv', 'C2:C201');
pair_3 = xlsread('data.csv', 'D2:D201');
pair_4 = xlsread('data.csv', 'E2:E201');
if pair_1 < 1.2
pair_1 = 0
if pair_1 > 2.2
pair_1 = 3.3
plot(time, pair_1)

Accepted Answer

Mitch Lautigar
Mitch Lautigar on 10 May 2022
You have a couple different ways to do this. I've listed two below to try and help.
Both ways I did it start with the following.
% Predeclarations %
data = readtable('data.csv'); %read in csv file as a table. xlsread is being/has been phased out.
data2 = table2array(data); %gets csv file into an array for easy checking.
%method 1 -- for loops
%this method works because data2 is an array of set size.
% Advantages: Easy to debug, and results are quite viewable.
% Disadvantages: If you are looking at 10k data points, this can become memory intensive.
data_check_array = []; %empty array to stack in checks.
[a,b] = size(data2);
for i = 1:length(data2) %handles each row
stack = [];
for j = 2:b %handles each column ignoring the time value.
if (data2(i,j) >= 1.2) && (data2(i,j) <= 2.2) %this means neither true or false
stack = [stack,0.5];
elseif (data2(i,j) < 1.2) %means false
stack = [stack,0];
else %means true
stack = [stack,1];
end %end j for loop
data_check_array = [data_check_array;stack];
end %end i for loop
% method 2 -- Hide and Seek
%This method works because you are only seeking specific numerical values in the table
% Advantages: This method is quick on computations and leads to solid end results
% Disadvantages: This method is much harder to debug.
true_indx = find(data2(:,2:end) > 2.2);
false_indx = find(data2(:,2:end) < 1.2);
[a,b] = size(data2);
check_array(1:a,2:b) = 0.5; %assume all values are not checked to not have to have a null index.
check_array(true_indx) = 1;
check_array(false_indx) = 0;
check_array(:,1) = data2(:,1);
I sadly don't have the capabilities to test this code, but I am confident the logic is good. Hope it helps!

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