Python

Using cProfile to Generate Profile Data

1. Run Your Script with cProfile: You can profile your Python script using the cProfile module from the command line. Here is an example:

   python -m cProfile -o oil_project.prof path/to/your/script.py arg1 arg2
   

   This command will run your script and save the profiling data to a file named oil_project.prof.

2. Profiling Within the Script: If you prefer to profile specific parts of your script, you can use cProfile within your code:

   import cProfile
   
   pr = cProfile.Profile()
   pr.enable()
   
   # Your main function or code here
   main_function()
   
   pr.disable()
   pr.dump_stats('oil_project.prof')
   

   This will also generate a profile file named oil_project.prof.

Visualizing the Profile Data

1. Using SnakeViz: SnakeViz is a browser-based graphical viewer for cProfile output. You can install it using pip:

   pip install snakeviz
   

   Then, you can visualize the profile data with:

   snakeviz oil_project.prof
   

   This will open an interactive web interface where you can explore the profiling data, including sunburst charts and detailed statistics tables.

2. Using pstats: If you prefer a text-based output, you can use the pstats module:

   import pstats
   
   p = pstats.Stats("oil_project.prof")
   p.sort_stats("cumulative").print_stats(30)  # Print top 30 cumulative
   

   This will print a table showing the top 30 functions by cumulative time.

Additional Profiling Tools

1. Line Profiler: For line-by-line profiling of specific functions, you can use the line_profiler tool. Install it using pip:

   pip install line_profiler
   

   Decorate the functions you want to profile with @profile:

   from line_profiler import LineProfiler
   
   @profile
   def my_function(arg1, arg2):
       # Your function code here
       pass
   
   profiler = LineProfiler()
   profiler.add_function(my_function)
   profiler.run('my_function(arg1, arg2)')
   profiler.print_stats()
   

   Alternatively, you can use kernprof to run the script:

   kernprof -l script_to_profile.py
   python -m line_profiler script_to_profile.lprof > line_profiler_out.txt
   

   This will give you detailed line-by-line performance data.

Statistical Profiling

If you need to work around circular call graphs or require a different profiling approach, you can use statistical profiling tools like pyinstrument. Here’s how you can use it:

PIPETASKCMD=`which your_script`
pyinstrument -r speedscope -o report.speedscope ${PIPETASKCMD} run ...

This will generate a file that can be visualized using web-based tools like Speedscope.

By combining these tools, you can gain a comprehensive understanding of the performance bottlenecks in your Python oil project.