Chapter 4: Incorrect number of input parameters for demo predict() and update()

[JacobGlennAyers]

Hi there, I have been looking around for examples to compare the performance between regular Kalman Filters and Extended Kalman Filters on univariate functions. I was happy to find your 04-One-Dimensional-Kalman-Filters.ipynb part of this book, but I was disappointed to find that with the provided functions in the "Exercise Nonlinear Systems" portion, the predict() and update() functions in the for loop of the solution have a mismatched number of parameters compared to the provided update() and predict() functions, which leads to a compilation error.

Furthermore, I don't find the demo of the builtin filterpy demo to be all that helpful since it isn't 1-to-1 comparing how it is implemented in the aforementioned for-loop. I will try to work through this ladder problem myself.