SnakeViz VS tuna

From the same guy, use snakeviz to diagnose code. Video: [9:57] https://www.youtube.com/watch?v=m_a0fN48Alw

Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.

I'm looking for a Rust equivalent Python's cProfile https://docs.python.org/3/library/profile.html if possible with visualizations like in SnakeViz https://jiffyclub.github.io/snakeviz/

If you want stats for only your list of functions, you can do that with pstats, or you could use some third-party tool like https://jiffyclub.github.io/snakeviz/ or myriad other options.

I would make a list of tuples where each tuple is (2,0),(-2,0),(0,2),etc... and put those in a list. Then you can use random.choice to pick a random one out of the list. How often are you going to be doing this next to the edge of the grid? If its not that much then I would just try to have it re-draw a new random choice from the list in those cases. This isn't an elegant solution, since it could take an unknown amount of time to draw something valid, but it might be fast enough in practice. Then if that ends up causing issues on the edge cases you could have some logic to select a list to draw from that only contains the valid choices. Also remember, don't assume that some part of your code is the slow part. Always profile it to get some actual information on how long each section is taking so you can optimize the parts of the code that actually need it. I use snakeviz for profiling python code.

Official site: http://jiffyclub.github.io/snakeviz/

I've always used snakeviz with the stdlib profiler https://jiffyclub.github.io/snakeviz/

In prod, the pyinstrument profiler has worked well for me https://pyinstrument.readthedocs.io/en/latest/guide.html#pro...

its going to be very hard to give advice on this without seeing the code. could you share the relevant code? if you're not sure what the slow part is, i recommend https://jiffyclub.github.io/snakeviz/ it should allow you to see what function is taking long

Every minor detail matters and adds to the total import time as part of the cold start. We need to optimize our code and imports. If you use Python, you can analyze your code with a tool like Tuna and optimize your libraries (perhaps replace slower ones) and your imports.

I started to examine the output of python -X importtime -m gefyra 2> import.log just to check the imports. There is an awesome tool to analyze the Python imports: tuna (see: https://github.com/nschloe/tuna). tuna allows analyzing the import times from the log. Run it like so tuna import.log. It opens a browser window and visualizes the import times. With that I was able to manually move all imports to the functions in which they are needed (and bring in some other optimizations). This greatly violates PEP 8 (https://peps.python.org/pep-0008/#imports) but leads to very fast startup times.