Proceedings of SciPy 2020

SciPy 2020, the 19th annual Python in Science Conference, was held July 6-12, virtually via the conference platform Crowdcast. Due to the COVID-19 pandemic, the SciPy conference was held online. The SciPy Conference brings together a community of researchers, engineers, and programmers dedicated to the advancement of scientific computing through open source Python software.

Motile cilia are a highly conserved organelle found on the exterior of many human cells. Cilia beat in rhythmic patterns to transport substances or generate signaling gradients. Disruption of these patterns is often indicative of diseases known as ciliopathies, whose consequences can include dysfunction of macroscopic structures within the lungs, kidneys, brain, and other organs.

Where traditional example-based tests check software using manually-specified input-output pairs, property-based tests exploit a general description of valid inputs and program behaviour to automatically search for falsifying examples.

While general purpose scientific software has enjoyed great success in industry and academia, domain specific scientific software has not yet become well-established in many disciplines where it has potential.

As the scale of science projects increase, so does the demand on computing infrastructures. The complexity of science processing pipelines, and the heterogeneity of the environments on which they are run, continues to increase; in order to deal with this, the algorithmic approaches to executing these applications must also be adapted and improved to deal with this increased complexity.

We present Delta, a Python framework that connects magnetic fusion experiments to high-performance computing (HPC) facilities in order leverage advanced data analysis for near real-time decisions. Using the ADIOS I/O framework, Delta streams measurement data with over 300 MByte/sec from a remote experimental site in Korea to Cori, a Cray XC-40 supercomputer at the National Energy Energy Research Scientific Computing Centre in California.

This paper presents a new lightweight dataflow engine written in Python: Pydra. Pydra is developed as an open-source project in the neuroimaging community, but it is designed as a general-purpose dataflow engine to support any scientific domain.

A framework for combining physics-based and data-driven models to improve well construction is presented in this study. Additionally, the proposed approach provides a more robust and accurate model that mitigates the disadvantages of using purely physics-based or data-driven models.

pandas is an essential tool in the data scientist’s toolkit for modern data engineering, analysis, and modeling in the Python ecosystem. However, dataframes can often be difficult to reason about in terms of their data types and statistical properties as data is reshaped from its raw form to one that’s ready for analysis.

Micro-core architectures combine many simple, low memory, low power computing cores together in a single package. These can be used as a co-processor or standalone but due to limited on-chip memory and esoteric nature of the hardware, writing efficient parallel codes for these chips is challenging.

The focus of this paper is the bit error probability (BEP) performance degradation when the transmit and receive pulse shaping filters are mismatched. The modulation schemes considered are MPSK and MQAM.

Perturbations of organellar structures within a cell are useful indicators of the cell’s response to viral or bacterial invaders. Of the various organelles, mitochondria are meaningful to model because they show distinct migration patterns in the presence of potentially fatal infections, such as tuberculosis.

libCEED is a new lightweight, open-source library for high-performance matrix-free Finite Element computations. libCEED offers a portable interface to high-performance implementations, selectable at runtime, tuned for a variety of current and emerging computational architectures, including CPUs and GPUs.

NumPy simplifies and accelerates mathematical calculations in Python, but only for rectilinear arrays of numbers. Awkward Array provides a similar interface for JSON-like data: slicing, masking, broadcasting, and performing vectorized math on the attributes of objects, unequal-length nested lists (i.

Ubiquitous data poses challenges on current machine learning systems to store, handle and analyze data at scale. Traditionally, this task is tackled by dividing the data into (large) batches. Models are trained on a data batch and then used to obtain predictions.

Unlike arrays and tables, histograms in Python have usually been denied their own object, and have been represented as a single operation producing several arrays. Boost-histogram is a new Python library that provides histograms that can be filled, manipulated, sliced, and projected as objects.

Pyvis is a Python module that enables visualizing and interactively manipulating network graphs in the Jupyter notebook, or as a standalone web application. Pyvis is built on top of the powerful and mature VisJS JavaScript library, which allows for fast and responsive interactions while also abstracting away the low-level JavaScript and HTML.

There is a growing interest in leveraging differential geometry in the machine learning community. Yet, the adoption of the associated geometric computations has been inhibited by the lack of a reference implementation.

Digital hardware circuits (i.e., for application specific integrated circuits or field programmable gate array circuits) can contain a large number of discrete components and connections. These connections are defined by a data structure called a \textquotedbl{}netlist\textquotedbl{}.

Compyle allows users to execute a restricted subset of Python on a variety of HPC platforms. It is an embedded domain-specific language (eDSL) for parallel computing. It currently supports multi-core execution using Cython, and OpenCL and CUDA for GPU devices.

HOOMD-blue is a library for running molecular dynamics and hard particle Monte Carlo simulations that uses pybind11 to provide a Python interface to fast C++ internals. The package is designed to scale from a single CPU core to thousands of NVIDIA or AMD GPUs.

The Linac Coherent Light Source (LCLS) at the SLAC National Accelerator Laboratory is an X-ray Free Electron Laser (X-FEL) facility enabling scientists to take snapshots of single macromolecules to study their structure and dynamics.

Most machine learning models, especially artificial neural networks, require numerical, not categorical data. We briefly describe the advantages and disadvantages of common encoding schemes. For example, one-hot encoding is commonly used for attributes with a few unrelated categories and word embeddings for attributes with many related categories (e.

Jupyter has become the go-to platform for developing data applications but data and security concerns, especially when dealing with healthcare, have become paramount for many institutions and applications dealing with sensitive information.