These files may be downloaded from https://portal.g-node.org/python-autumnschool/materials:cython
Please do explore beyond the problems given, and feel free to ask questions at any time.
Consider the provided code, fractal.py, that computes pretty pictures. Optimise it, using Cython and the techniques discussed in the lecture. Your Cython code should be able to outperform the NumPy version (also provided)!
Implement a function in Cython that computes sin(x) on each element of a large floating point array. Compare the speed of this function to NumPy's built-in sin (which, by the way, can also perate on an entire array at a time). If it is faster, can you explain why?
Implement an L-system in Cython. Try, for example, to build a Sierpinski Triangle or the Dragon Curve. If you want, you can first implement it in pure Python, and then add the type information later.
Use your Cython program to generate coordinates, then simply "connect the dots" using matplotlib (import matplotlib.pyplot as plt; plt.plot(...)).
Cython can accelerate operations on non-numerical types too. For example, if you store your coordinates in a list, you can use:
cdef list L = []For dictionaries, the type is dict.