I have contributed a reimplementation of PyMC3's random variable API and automatic transforms on random variables, as well as workflow-related enhancements with others on the dev team.
The GitHub repository can be found here.
I have contributed PRs for documentation on dask-jobqueue, which I have used productively at work to harness spare capacity on our compute cluster.
The GitHub repository can be found here.
I developed pyjanitor, an open source port of the R package janitor, to bring friendly APIs to data cleaning tasks for data scientists. It features an API that helps data scientists express their data cleaning/preprocessing pipeline in a verb-based, fluent fashion.
The GitHub repository can be found here.
I am consolidating my Hive Plot and Circos Plot implementations into a single visualization package. Design goals include:
Contributors include Jon, Leo, and Nelson - very happy to be working with them!
The GitHub repository can be found here.
I have made multiple PRs to PyMC3, which were bug fixes, documentation and small feature additions.
I found a bug in PyMC3's multinomial random variate sampler, related to floating point precision issues while moving numbers from the GPU to the CPU, when working on my Bayesian analysis recipes repository. Specifically, we get probability values that sum to infinitesimally larger than one. I thus submitted a patch that fixed that converts the probabilities to float64 precision and re-normalizes the probabilities to 1, before using them for random sampling.
I contributed small changes to the docs, to make it super clear that precision and standard deviation parameterizations were alternate parameterizations, and only one was needed.
I contributed the definition of a "mode" for the Weibull distribution, so that I could use it in a Mixture Weibull.
In my experiments with new array libraries, I contributed a matrix power implementation to CuPy. My goal was to enable fast network science operations on the GPU.
I contributed to the matplotlib enhancement proposal 12, which proposed reorganizing the examples gallery to make it easier for matplotlib users to find relevant examples.
Part of the problem was that there were old pylab examples, but the new pyplot API was now preferred over the old pylab API (which was really present mostly to convert MATLAB users). I helped fix all the pylab examples by changing
from pylab import *
statements to explicit
import matplotlib.pyplot as plt import numpy as np
Continuing my desire to learn more about open source and to make contributions to data visualization libraries, I contributed an implementation of horizontal (HBar)and vertical (VBar) bar glyphs.
Many thanks for the help of Bryan Van de Ven and Sarah Bird, lead developers on Bokeh, who guided me through the code base line by line.
pyflatten is a package for flattening nested data structures. Developed originally by David Duvenaud, Matt Johnson and Dougal MacLaurin, I packaged it into an independent utility.
The GitHub repository can be found here.