Bevan,

You can estimate the intercept and slope using least-squares (scipy.optimize.leastsq). Make sure though that errors in X are small compared to errors in Y, otherwise, your slope will be underestimated.

Using the slope, you can write a function lower(b,a, X,Y) that will compute y=aX+b and return True if Y < y. Computing the ratio of true elements will give you the percentage of points below the curve. You can then find b such that the ratio is .5 and .95 using scipy.optimize.fmin.

There are other ways to do this;

Make a 2D histogram of the data (normed), compute the cumulative sum along Y and find the histogram bins (along x) such that the cumulative histogram is approximately equal to .5 and .95.

Partition the data in N sets along the x-axis, fit a normal distribution to each set and compute the quantile corresponding to .5 and .95 cumulative probability density.


David

By the way, anonymous mails from newcomers don't get as much attention as those that are signed. Call it mailing list etiquette.