To fit a smooth curve to your data sets, you can use interpolation or curve fitting techniques. One common approach is to use the interp1 function in MATLAB/Octave, which performs linear interpolation by default. Here's an example of how you can modify your code to include curve fitting:
In this code, the interp1 function is used to interpolate the data points onto a finer grid (cy_fit) using spline interpolation. The resulting interpolated data is then plotted to obtain smooth curves. Feel free to adjust the interpolation method or the number of points in the cy_fit grid to suit your needs.
p_pro_100_ = [5.8045; 5.2853; 5.0573; 4.4518; 3.1702; 1.7467];
p_pro_4_ = [6.1060; 4.5033; 5.4187; 4.8306; 4.5205; 4.0070; 3.4725];
p_pro_50um_ = [6.0051; 4.4763; 4.1868; 2.0373];
cy_50 = [4 300 400 450]';
cy = [4 100 200 300 400 500 600]';
cy_100 = [4 100 200 300 350 370]';
% Perform curve fitting
cy_fit = linspace(min(cy), max(cy), 1000); % Generate a finer grid for smooth curve
p_pro_4_fit = interp1(cy, p_pro_4_, cy_fit, 'spline');
p_pro_50um_fit = interp1(cy_50, p_pro_50um_, cy_fit, 'spline');
p_pro_100_fit = interp1(cy_100, p_pro_100_, cy_fit, 'spline');
figure
hold on
ax = gca;
ax.XTick = 100:100:600;
plot(cy_fit, p_pro_4_fit, '-o', 'LineWidth', 0.2)
plot(cy_fit, p_pro_50um_fit, '-*', 'LineWidth', 1)
plot(cy_fit, p_pro_100_fit, '-^', 'LineWidth', 1)
legend('20', '50', '100')
Do let me know if it helps.
