Large-scale neural data analysis with Spark
@freeman-lab / (6)
Thunder is a library for analyzing large-scale spatial and temporal neural data. It includes utilities for loading and saving data using a variety of input formats, classes for working with distributed spatial and temporal data, and modular functions for time series analysis, image processing, factorization, and model fitting. Thunder is written against Spark's Python API (PySpark), making extensive use of NumPy and SciPy. It is pip-installable, and requires a working installation of Spark (currently supporting versions 1.0 and 1.1).
This package doesn't have any releases published in the Spark Packages repo, or with maven coordinates supplied. You may have to build this package from source, or it may simply be a script. To use this Spark Package, please follow the instructions in the README.