Derivatives

Calculating the time (or spatial) derivative of a 1D dataset is something that may occur quite frequently. Occuring tasks are best written in functions for many reasons (name at least two). Write a function called ForwardDifferencingXY that takes a 2 x n array as input where the independent variable (e.g., time) is stored in the first column, and the dependent variable (e.g., \(CO_2\) ) in the second column. The output should also be a 2 x n array with the independent variable in the first column and the derivative of the dependent variable in the second colum. Here you should do this for the Keeling curve and for the Gausspeak.

Attention

Exercise 4.9:

• Use your function ForwardDifferencingXY to calculate rates of change for the Keeling curve, the sinoid and the 1D Gauss Peak.

• Make your function more robust so that it catches wrong use interactions (e.g., passing on a n x 2 array instead of 2 x n array).

• Make your user function more user friendly by providing an optional figure with sub-panels showing the original data on top, and the time derivative at the bottom.

• Add noise to your input data using numpys randn function, what does that do to your derivatives. Why?