The challenge "Simulating a dynamical system" in Episode 16 asks for values at times [0,1,...,t_final] but the solutions provide values at times [0,1,...,t_final - 1].
For example, in part two of the solution, population is initialized with the initial_population, and then the loop runs 9 times (range(1, 10)) to give a total of ten values, the last of which is the value at t = 9. A tenth run is needed to get what was asked for, so the loop should be:
for t in range(t_final):
population.append( logistic_map(population[t], r) )
Happily, this looks even more like the function in part one, where you get the value at t + 1 in terms of the value at t, rather than the value at t in terms of the value at t - 1.
This PR supplies the corrected code for parts two and three, and the value of the population at t = 10, 100, 1000 instead of at t = 9, 99, 999.
The challenge "Simulating a dynamical system" in Episode 16 asks for values at times
[0,1,...,t_final]but the solutions provide values at times[0,1,...,t_final - 1].For example, in part two of the solution,
populationis initialized with theinitial_population, and then the loop runs 9 times (range(1, 10)) to give a total of ten values, the last of which is the value at t = 9. A tenth run is needed to get what was asked for, so the loop should be:Happily, this looks even more like the function in part one, where you get the value at t + 1 in terms of the value at t, rather than the value at t in terms of the value at t - 1.
This PR supplies the corrected code for parts two and three, and the value of the population at t = 10, 100, 1000 instead of at t = 9, 99, 999.