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I am currently working on a 2D top down rogue-like game using Python. The map is a dungeon containing many open rectangular rooms (image), each with around 2-4 enemies inside. I am currently looking to implement a path-finding system where the enemies will move around each other and attempt to swarm the player.

So far, I have implemented an A* algorithm that does allow the enemies to navigate and swarm the player in this way. However, my approach is causing very low frame rates: generally around 15 FPS but it will go as low as under 1 FPS when an enemy has no path to the player. I feel it is very inefficient, since path-finding is being done for every enemy on each frame. Currently, other enemies are seen as obstacles for the A* algorithm, and the only optimization is that an enemy will move directly towards the player if there are no obstacles in its way. Here's the code:

I'm working on a script to parse txt files and store them into a pandas dataframe that I can export to a CSV.

My script works easily when I was using <100 of my files - but now when trying to run it on the full sample, I'm running into a lot of issues.

Im dealing with ~8000 .txt files with an average size of 300 KB, so in total about 2.5 GB in size.

I was wondering if I could get tips on how to make my code more efficient.

for opening and reading files, I use:

I implemented a support vector machine in python using the cvxopt qp solver where I need to compute a gram matrix of two vectors with a kernel function at each element. I implemented it correctly using for loops but this strategy is computationally intensive. I would like to vectorize the code.

Example:

Here is what I have written:

I have 3 huge CSV files containing climate data, each about 5GB. The first cell in each line is the meteorological station's number (from 0 to about 100,000) each station contains from 1 to 800 lines in each file, which is not necessarily equal in all files. For example, Station 11 has 600, 500, and 200 lines in file1, file2, and file3 respectively. I want to read all the lines of each station, do some operations on them, then write results to another file, then the next station, etc. The files are too large to load at once in memory, so I tried some solutions to read them with minimal memory load, like this post and this post which include this method:

I am trying to use output of external program run using the run function. this program regularly throws a row of data which i need to use in mine script I have found a subprocess library and used its run()/check_output()

Example:
def usual_process(): # some code here for i in subprocess.check_output(['foo','$$']): some_function(i)

Now assuming that foo is already in a PATH variable and it outputs a string in semi-random periods.
I want the program to do its own things, and run some_function(i)every time foo sends new row to its output.

which boiles to two problems. piping the output into a for loop and running this as a background subprocess Thank you

Update: I have managed to get the foo output onto some_function using This