Detecting Different Distributions in data
It's not really feasible to label points as coming from distribution A or B if there is be overlap amongst the distributions. You can't know whether the point fell near the mean of A or the tail of B. In the simplest case, consider two normally distributed datasets with overlap - how can you know which generated it?

You could estimate the probability of each point falling under A or B, but that doesn't really solve the problem. 

A different approach, but one I'm not sure will help, would be to fit a new distribution defined as the convolution of A and B. The ex-gaussian distribution is one such example. But you'd have to do the maths to figure out the PDF equation, and that still won't necessarily label your points.