# Normalize to scale

I have an 2-D array of data (C), where C(:,1) has values corresponding to C(:,2). C(:,2) varies from 0.0001:0.0001:1, i.e. 10,000 values. I need to calculate the d(log(C(i,1))) / d(log(C(i,2))), which I do by simply calculating log(C(i,1)) / log(C(i,2)). But as C(i,2), approaches 1, the denominator approaches zero, and the quotient shoots up. One way to keep this in check would be to normalize it using a parameter, but I'm not sure how to do that. I actually want to plot this quotient, i.e. the slope d(log(C(i,1))) / d(log(C(i,2))) vs log(C(:,2)), and as C(i,2) approaches 1, the slope should converge to zero, to be able to study it. Does anyone have an idea about this?

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