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    <p>You can certainly use least squares without knowing the actual formula. If all of your plots are measured at the same x value, then this is easy -- you simply compute the sum in the normal way:</p> <p><img src="https://i.stack.imgur.com/NFp9R.gif" alt="enter image description here"></p> <p>where y_i is a point in your 8-node plot, sigma_i is the error on the point and Y(x_i) is the value of the plot from the database at the same x position as y_i. You can see why this is trivial if all your plots are measured at the same x value.</p> <p>If they're not, you can get Y(x_i) either by fitting the plot from the database with some function (if you know it) or by interpolating between the points (if you don't know it). The simplest interpolation is just to connect the points with straight lines and find the value of the straight lines at the x_i that you want. <a href="https://en.wikipedia.org/wiki/Spline_interpolation" rel="nofollow noreferrer">Other interpolations</a> might do better.</p> <p>In my field, we use <a href="http://root.cern.ch/" rel="nofollow noreferrer">ROOT</a> for these kind of things. However, <a href="http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html" rel="nofollow noreferrer">scipy</a> has a great collections of functions, and it might be easier to get started with -- if you don't mind using Python.</p> <p>One major problem you could have would be that the two plots are not independent. <a href="https://en.wikipedia.org/wiki/McNemar%27s_test" rel="nofollow noreferrer">Wikipedia suggests McNemar's test in this case.</a></p> <p>Another problem you could have is that you don't have much information in your test plot, so your results will be affected greatly by statistical fluctuations. In other words, if you only have 8 test points and two plots match, how will you know if the underlying functions are really the same, or if the 8 points simply jumped around (inside their error bars) in such a way that it looks like the plot from the database -- purely by chance! ... I'm afraid you won't really know. So the plots that test well will include false positives (low purity), and some of the plots that don't happen to test well were probably actually good matches (low efficiency).</p> <p>To solve that, you would need to either use a test plot with more points or else bring in other information. If you can throw away plots from the database that you know can't match for other reasons, that will help a lot.</p>
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