Python loops over large array structures are known to run slowly. Tests with class Grid2D from Chapter 4.3.5 show that filling a two-dimensional array of size 1100 × 1100 with nested loops in Python may require about 150 times longer execution time than using Fortran 77 for the same purpose. With Numerical Python (NumPy) and vectorized expressions (from Chapter 4.2) one can speed up the code by a factor of about 50, which gives decent performance.
There are cases where vectorization in terms of NumPy functions is demanding or inconvenient: it would be a lot easier to just write straight loops over array structures in Fortran, C, or C++. This is quite easy, and the details of doing this in F77 are explained in the present chapter. Chapter 10 covers the same material in a C and C++ context.
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© 2009 Springer-Verlag Berlin Heidelberg
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(2009). Fortran Programming with NumPy Arrays. In: Langtangen, H.P. (eds) Python Scripting for Computational Science. Texts in Computational Science and Engineering, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73916-6_9
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DOI: https://doi.org/10.1007/978-3-540-73916-6_9
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