Only one exercise needs to be completed. Choose whichever you prefer and commit the results to the Assignment 3 repository as instructed.
Due to the dynamic typing of Python, loops tend to be the most inefficient part of Python programs. Use cPython and line_profiling to
locate the functions and lines where the most time is being spent in the nbody_iter.py program you wrote for Assignment 5. Use NumPy to
replace the high overhead loops with array operations in order to show a measurable improvement in the performance. Place a comment at the
start of the program indicating the performance improvement you achieved. Call the resulting program nbody_numpy.py and commit it to
the same repository.
A colleage has asked you for help. They have been given a task of developing a calculator program in Python that will be used for a
numerically intensive task. It’s important that it works as fast as possible, or the project results may be delayed. Your colleage
has a pretty good understanding of Python, and so developed the program using NumPy to help improve the performance. Unfortunately
it is not working well enough for the project, and they have run out of ideas on how to make it better. You offer to help, since you
recently found out about the cProfile and line_profiler tools.
Download the source code and tests for the program.
Use cPython and line_profiler to determine where the code is performing poorly and why. Make changes to the code to improve the performance
and add a comment to the top of the cacluator.py file to indicate the speedup you achieve. Commit the resulting program to the repository you
created in Assignment 3. Keep the same name for the program, and only calculator.py needs to be committed.