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There are several measures you can take to enhance the performance of your Fortran code while integrating it with Python:

1. Use optimized compilers: Use a Fortran compiler that optimizes the code for the target architecture. Popular compilers include Intel Fortran Compiler, GFortran, and PGI Fortran.

2. Use optimized libraries: Use optimized numerical libraries such as BLAS, LAPACK or Intel MKL to speed up matrix computations.

3. Use Fortran wrapper libraries: There are several Fortran wrapper libraries such as f2py and PyCall that allow calling Fortran functions from Python without modifying the original Fortran code.

4. Use multithreading: Fortran code can be parallelized using OpenMP or MPI. This can lead to significant speedups in computation time.

5. Use profiling tools: Use profiling tools to identify bottlenecks and optimize the code. Profiling tools such as gprof and VTune can help identify sections of code that take up most of the execution time.

6. Optimize memory usage: Keep the memory usage of your code optimized by avoiding unnecessary declares and minimizing costly memory transfer between Python and Fortran.

7. Keep the code simple: Simplify the code as much as possible to make it more efficient. Remove unnecessary branches, loops, and other constructs that slow down the execution time.

8. Use modern versions of Fortran: Use the latest versions of Fortran such as Fortran 90, 95 or 2003, which provide better optimization and stricter data typing.