1 ACCEPTED SOLUTION
Accepted Solutions
I added spline interpolation to give you an idea what it looks like and why I suggested to stay with linear interpolation.
You could change "cspline" to "lspline" for a less oscillating curve, but I guess that there is no benefit in using spline interpolation.
Remark: You talked about "polynomial fit" and even though I called my function (wrongly) "fit" it is NOT a fit but rather an interpolation, no matter if linear or spline. A polynomial fit could be obtained using polyfit, but the resulting curve normally would not run through a single single one of the 15 given data points (unless you chose a polynomial of order 14) but will be a good overall fit. Here an example to fit the data with a simple quadratic parabola:
One additional remark: Luckily the x values were the same for both data sets (vx=vx65). If the data would have been sampled at different x values, an additional step would have been necessary.
Modified P6 worksheet attached
Here is an utility function you might find useful.
It uses linear interpolation only and I think thats all you need as you don't gain any value by spline interpolation. But of course it would be easy to replace the linear interpolation by a spline interpolation. Only the first one, as for the temperature you would have to stay with linear interpolation as you only have two values.
P6 worksheet attached
I added spline interpolation to give you an idea what it looks like and why I suggested to stay with linear interpolation.
You could change "cspline" to "lspline" for a less oscillating curve, but I guess that there is no benefit in using spline interpolation.
Remark: You talked about "polynomial fit" and even though I called my function (wrongly) "fit" it is NOT a fit but rather an interpolation, no matter if linear or spline. A polynomial fit could be obtained using polyfit, but the resulting curve normally would not run through a single single one of the 15 given data points (unless you chose a polynomial of order 14) but will be a good overall fit. Here an example to fit the data with a simple quadratic parabola:
One additional remark: Luckily the x values were the same for both data sets (vx=vx65). If the data would have been sampled at different x values, an additional step would have been necessary.
Modified P6 worksheet attached
All that should be necessary is to change the definition of "T" and "yData".
Because Primes interpolation expects the values in ascending order, the order in both vectors must be exchanged.
Of course you would also change the values used in the plots
