For a piece of software I am writing I need to find the area under a curve that is collected as a list of points that make it up. I am trying to determine the fastest way to get the area.
The only option I see is to approximate a function that represents the list of points and integrate over it, though I was wondering if there were any methods that worked on the points directly? Hopefully being a bit faster and possible more exact?
About the data: The $Y$ values of the points will always be positive, and can possibly be wildly far apart in value. The $X$ values will almost never be evenly spaced and usually be some floating point number.
EDIT: Also. The data is first recorded as a list of points where the $X$ values are spaced evenly. This data is then calibrated based on some defined calibration function. This is what causes the $X$ values to change their spacing.
Knowing this function and the pre-calibration values, could there be some sort of combination of the Trapezoidal Rule and this calibration function to get an even more accurate area than simply using the Trapezoidal Rule on the calibrated data?