Interesting Things I Learned at SciPy 2016

Learn about some interesting projects featured at SciPy 2016, brought to you by an attendee who put in the work to bring you this great list of projects.

By Qingkai Kong, UC Berkeley.

Editor's note: This post was written during SciPy 2016, and so rest assured that the author is currently no longer in attendance, as the tone and language may suggest.

SciPy 2016

I am currently at Scipy 2016 conference, so this week's blog I will try to list some of the interesting things I learned from Scipy conference. The conference is from July 12th to July 17th at Austin TX. Here are the interesting things I learned:

July 13th 2016

Jupyter dashbord - Extension for Jupyter Notebook that enables the layout and presentation of dashboards from notebooks.

Jupyterlab - A computational environment. This is a very early preview, and is not suitable for general usage yet. But nice to checkout now.

altair - This is a declarative statistical visualization library for Python.

geopandas - GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types.

yt - A python package for analyzing and visualizing volumetric, multi-resolution data from astrophysical simulations, radio telescopes, and a burgeoning interdisciplinary community.

Glumpy - A python library for scientific visualization that is both fast, scalable and beautiful.

tpot - A python tool that automatically creates and optimizes machine learning pipelines using genetic programming.

The best part of today's conference is that I talked a lot with Sebastian Raschka about Machine learning, and he gave me many great suggestions ^)^ Check out his book if you want to do machine learning with python - Python Machine Learning, which is a very nice book if you want to do machine learning quick.

July 14th 2016

HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise.

lightning - A framework for data visualization providing API-based access to reproducible, web-based, interactive visualizations.

binder - Turn a GitHub repo into a collection of interactive notebooks.

cesium - Open-Source Machine Learning for Time Series Analysis.

dask - A flexible parallel computing library for analytics, it can 'deal with' big data even on a single machine.

simpeg - An open source python package for simulation and gradient based parameter estimation in geophysical applications.

steno3d - Visualize 3d data, but it is not free if data size over certain limit.

Intel Python distribution - It is said to boost the performance of the packages.

HPX-5 - A distributed programming model allowing programs to run unmodified on systems from a single SMP to large clusters and supercomputers with thousands of nodes.

loopy - A code generator for array-based code in the OpenCL/CUDA execution model.

mplstereonet - Provides lower-hemisphere equal-area and equal-angle stereonets for matplotlib.

Sensor Data Management System - Public sensor data.

resampy - Efficient resampling for the audio data.

datalore - An intelligent, cloud-based computational workbook with collaborative support. (require sign up).

sharedmem - A different flavor of multiprocessing in Python.

July 15th 2016

datashader - A graphics pipeline system for creating meaningful representations of large amounts of data.

canopy-geo - Python-based analysis environment for geophysics.

ipywidgets - Interactive HTML widgets for Jupyter notebooks and the IPython kernel.

nbflow - A tool that supports one-button reproducible workflows with the Jupyter Notebook and Scons.

auto-sklearn - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

hyperopt-sklearn - Hyperopt based model selection among machine learning algorithms in scikit-learn.

spearmint - A software package to perform Bayesian optimization.

scikit-optimize - A simple and efficient library for model-based optimization, accessible to everybody and reusable in various contexts.

symengine - A fast symbolic manipulation library, written in C++.

nbdime - Tools for diffing and merging of Jupyter notebooks.

Qingkai KongBio: Qingkai Kong is a PhD candidate at Berkeley Seismological Laboratory, he is interested in data science and earthquakes!

Original. Reposted with permission.