There are missing data, however they are all at the end of the first -time-pressure values. Filling them would require extrapolation, and that is to be strictly avoided here because that would require creating data where not previously existed, rather than interpolating intermediate missing values.
One approach —
% C1 = readcell('DENSITY_BASED_SOLVER.xlsx')
T1 = readtable('DENSITY_BASED_SOLVER.xlsx', 'VariableNamingRule','preserve', 'HeaderLines',1)
VN = T1.Properties.VariableNames;
t{1} = T1{:,1};
p{1} = T1{:,2};
t{2} = T1{:,6};
p{2} = T1{:,8};
% Q1 = nnz(isnan([p{1}]))
% Q11 = nnz(isnan(t{1}))
% Q12 = find(isnan(p{1}))
% t{1}(Q12-1)
% find(diff(Q12)>1)
% Q2 = nnz(isnan([t{2},p{2}]))
figure
plot(t{1}, p{1}, 'DisplayName','First Set')
hold on
xline(t{1}(484), '--r', 'DisplayName','Finite Time Limit')
plot(t{2}, p{2}, 'DisplayName','Second Set')
hold off
grid
xlabel('Time (ms)')
ylabel('Pressure (MPa)')
title('Original')
legend('Location','best')
[min_time_ends,idx] = min([t{1}(end) t{2}(end)]) % Shortest Time Vector & Set Number ('idx')
tq{1} = t{1}(~isnan(t{1})); % Eliminiate the 'NaN' Values So The Interpolation Will Work
[tq{1},idx] = unique(tq{1}); % Eliminate Duplicate Time Values, Return Unique Time Values & Associated Index
pq{1} = p{1}(idx); % Pressure Values At Unique Time Values
pq{1} = interp1(tq{1}, pq{1}, t{2}); % Interpolate Longer Pressure (p{2}) To Shorter Time Vector (t{1})
pdif = p{2} - pq{1}; % Pressure Difference
figure
plot(t{2}, pq{1}, 'DisplayName','First Set (Interpolated)')
hold on
plot(t{2}, p{2}, 'DisplayName','Second Set')
plot(t{2}, pdif, ':k', 'DisplayName','Pressure Difference')
hold off
grid
xlabel('Time (ms)')
ylabel('Pressure (MPa)')
title('Processed')
legend('Location','best')
Processing these data are not straightforward.
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