Posts with the tag Python:
I took the day off today to recover from second round of the covid vaccine and spent a little while implementing my own version of Conway’s Game of Life in Python.
Here’s an example of a simulation involving a grid of 500 x 500 cells. When initializing the first generation each cell had a 0.3 probability of starting as a living cell (start-as-living probability). Increasing this value will increase the number of cells the grid is initialized with. I ran the simulation for 100 generations.
and with a 0.1 start-as-living probability.
I was curious how changing the starting probability of a cell starting the simulation as living would effect the game as it progressed.
Polo: an open-source graphical user interface for crystallization screening was just published in the Journal of Applied Crystallography. It is my first academic paper as well as my first first-author paper.
Please check out the article at this link!
Below are a few of the figures from the article, click to be taken to the full descriptions.
Recently I was asked by a friend if they knew about any databases that classified cannabis strains by symptoms people tend to use them to relieve. I didn’t know of the existence of any but had heard about leafly.com which catalogues user reviews of various cannabis strains and compiles data on their characteristics.
I thought this could be a good place for them to start and so I started looking into what it would take to make a webscrapper to pull down all the data leafly has complied on hundreds on cannabis strains.
It turns out it didn’t take that much.
The way academic citations are measured currently is pretty standardized. Authors of article A accrue a citation whenever their article is directly cited in article B. But there is likely a large amount of work that was cited by article A but not by article B. The authors of this work which indirectly contributed to article B by contributing to article A (which B cites) will not see a citation.
What if instead citing one article triggered a recursive call all the way down the network formed by articles and their citations? Would this end up eventually citing almost all articles in a field?
NBI Background The National Bridge Inventory (NBI) is a program of the Federal Highway Administration which is an agency within the U.S Department of Transportation. The NBI makes available records and statistics about all the bridges in the United States which includes information about bridge location, integrity, inspection history and usage.
Potential encoding discrepancy As a side project I have been working on creating a more exhaustive Python package for parsing NBI data. This is mainly focused on decoding the numerical representations present in data files to their semantic meanings specified in the NBI documentation.
I ran into errors when trying to decode the state code fields, which based on the available documentation uses the coding table below.
The past couple days I have been running some ligand docking simulations as part of my current rotation with the Cortopasssi lab using Rosetta. One of these docking simulations involved fitting a small portion of the insulin receptor (IR) the lab is interested in, into a known binding region of the Shc1 protein.
Any Rosetta docking simulation will require hundreds of repetitions, which generate a significant number of pdb files which show the final conformation of the protein and ligand at the end of a given simulation.
While reading about the best way to aggregate and do analyise on these results I spent a bit of time looking for ways to visualize everything Rosetta spits out.
Skribbl.io is a great free quarantine / social distanced game where one person attempts to draw a word while everyone else guesses what they are drawing. When setting up the game you can supply your own list of comma separated words doing the game.
The problem with doing this manually is that one person playing will know all the words.
For an upcoming Zoom party I created a python command line application that takes in subreddit names and a few other parameters and using the Praw library retrieves the most commonly used words from the top comments of posts to a subreddit.