SnakeViz VS plotly

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

Github: https://github.com/plotly/plotly.py

First we need to install the Plotly. To create some very dynamic graphics, this tool helps a lot.

my good dude wake up and smell the plotly. Knowing the ins and outs of matplotlib is helpful but doing interactive stuff with jupyter I always use plotly.

Plotly documentation (https://plotly.com/python/)

I don´t know your workflow, but I use 2 markdown based presentations: obsidian advance slides and Quarto presentations. The former is a plugin for Obsidian, which is the software I use to take all my notes, write my thesis, etc., so It makes it extremely easy to make presentations since all my information is in Obsidian. In the other hand, Quarto is a publishing system (articles, presentations, websites books) that can be easily integrated with python and R. This makes it supper convenient for showing my data to my PI since I can analyze my data and at the same time make a presentation for the data. Besides this, Quarto also integrates with my Zotero library, so I can insert citations. Lastly, one thing that made my Quarto presentations infinitely better that the powerpoints, Is that I can insert interactive graphs with plotly, so when I'm showing my data, my PI is able to explore the data inside the presentation.

Plotly GitHub repository: https://github.com/plotly/plotly.py

I'm using plotly.py now. This is why I love this community.

Depending on the nature of your reporting requirements, you could output a formatted Excel document with Python and a library such as openpyxl, and shove that into your SharePoint environment. This would be less dynamic than PBI reports can be, but may be sufficient. If you want viz as well, you can use something like ggplot or Plotly. Again, less dynamic than PBI for the same effort.