| #!/usr/bin/env python | |
| #-*- coding:utf-8 -*- | |
| """Test different hollow Matplotlib markers for scatter plots. | |
| Imitate Gnuplot a bit. | |
| """ | |
| #import matplotlib | |
| #matplotlib.use("module://backend_pgf") |
| """Sample random numbers according to a uniformly sampled 2d probability | |
| density function. | |
| The method used here probably is similar to rejection sampling | |
| (http://en.wikipedia.org/wiki/Rejection_sampling). | |
| """ | |
| import numpy as np | |
| import sys | |
| from scipy.interpolate import RectBivariateSpline |
| from hailstoneCython import hailstone | |
| if __name__ == '__main__': | |
| h = hailstone(27) | |
| assert len(h)==112 and h[:4]==[27, 82, 41, 124] and h[-4:]==[8, 4, 2, 1] | |
| print("Maximum length %i was found for hailstone(%i) for numbers <100,000" % | |
| max((len(hailstone(i)), i) for i in range(1,100000))) |
| """Hailstone sequence. | |
| """ | |
| # compile with | |
| # cython hailstoneCython.pyx | |
| # -O2 -shared -fPIC -march=native -IPATH_TO_PYTHON.H -o hailstoneCython.so hailstoneCython.c | |
| cpdef list hailstone(int n): | |
| cdef list seq | |
| seq = [n] |
The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.
The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy