Title: Cell Modeling in Jupyter Notebook using CompuCell3D
CompuCell3D (CC3D) is an open-source software framework for building and executing multi-cell biological virtual-tissue models. It represents cells using the Glazier–Graner–Hogeweg model, also known as Cellular Potts model. The primary CC3D application consists of two separate tools, a smart model editor (Twedit++) and a tool for model execution, visualization and steering (Player). The CompuCell3D version 4.x release introduces support for Jupyter Notebooks, an interactive computational environment, which brings the benefits of reproducibility, portability, and self-documentation. Since model specifications in CC3D are written in Python and CC3DML and Jupyter supports Python and other languages, Jupyter can naturally act as an integrated development environment (IDE) for CC3D users as well as a live document with embedded text and simulations. This update follows the trend in software to move away from monolithic freestanding applications to the distribution of methodologies in the form of libraries that can be used in conjunction with other libraries and packages. With these benefits, CC3D deployed inJupyter Notebook is a more natural and efficient platform for scientific publishing and education using CC3D. more »« less
Sego, T. J.; Sluka, James P.; Sauro, Herbert M.; Glazier, James A.(
, PLOS Computational Biology)
Kemp, Melissa L.
(Ed.)
Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.
PmagPy Online: Jupyter Notebooks, the PmagPy Software Package and the Magnetics Information Consortium (MagIC) Database
Lisa Tauxe$^1$, Rupert Minnett$^2$, Nick Jarboe$^1$, Catherine Constable$^1$, Anthony Koppers$^2$, Lori Jonestrask$^1$, Nick Swanson-Hysell$^3$
$^1$Scripps Institution of Oceanography, United States of America; $^2$ Oregon State University; $^3$ University of California, Berkely; ltauxe@ucsd.edu
The Magnetics Information Consortium (MagIC), hosted at http://earthref.org/MagIC is a database that serves as a Findable, Accessible, Interoperable, Reusable (FAIR) archive for paleomagnetic and rock magnetic data. It has a flexible, comprehensive data model that can accomodate most kinds of paleomagnetic data. The PmagPy software package is a cross-platform and open-source set of tools written in Python for the analysis of paleomagnetic data that serves as one interface to MagIC, accommodating various levels of user expertise. It is available through github.com/PmagPy. Because PmagPy requires installation of Python, several non-standard Python modules, and the PmagPy software package, there is a speed bump for many practitioners on beginning to use the software. In order to make the software and MagIC more accessible to the broad spectrum of scientists interested in paleo and rock magnetism, we have prepared a set of Jupyter notebooks, hosted on jupyterhub.earthref.org which serve a set of purposes. 1) There is a complete course in Python for Earth Scientists, 2) a set of notebooks that introduce PmagPy (pulling the software package from the github repository) and illustrate how it can be used to create data products and figures for typical papers, and 3) show how to prepare data from the laboratory to upload into the MagIC database. The latter will satisfy expectations from NSF for data archiving and for example the AGU publication data archiving requirements.
Getting started
To use the PmagPy notebooks online, go to website at https://jupyterhub.earthref.org/. Create an Earthref account using your ORCID and log on. [This allows you to keep files in a private work space.]
Open the PmagPy Online - Setup notebook and execute the two cells. Then click on File = > Open and click on the PmagPy_Online folder. Open the PmagPy_online notebook and work through the examples. There are other notebooks that are useful for the working paleomagnetist.
Alternatively, you can install Python and the PmagPy software package on your computer (see https://earthref.org/PmagPy/cookbook for instructions). Follow the instructions for "Full PmagPy install and update" through section 1.4 (Quickstart with PmagPy notebooks). This notebook is in the collection of PmagPy notebooks.
Overview of MagIC
The Magnetics Information Consortium (MagIC), hosted at http://earthref.org/MagIC is a database that serves as a Findable, Accessible, Interoperable, Reusable (FAIR) archive for paleomagnetic and rock magnetic data. Its datamodel is fully described here: https://www2.earthref.org/MagIC/data-models/3.0. Each contribution is associated with a publication via the DOI. There are nine data tables:
contribution: metadata of the associated publication.
locations: metadata for locations, which are groups of sites (e.g., stratigraphic section, region, etc.)
sites: metadata and derived data at the site level (units with a common expectation)
samples: metadata and derived data at the sample level.
specimens: metadata and derived data at the specimen level.
criteria: criteria by which data are deemed acceptable
ages: ages and metadata for sites/samples/specimens
images: associated images and plots.
Overview of PmagPy
The functionality of PmagPy is demonstrated within notebooks in the PmagPy repository:
PmagPy_online.ipynb: serves as an introdution to PmagPy and MagIC (this conference). It highlights the link between PmagPy and the Findable Accessible Interoperable Reusabe (FAIR) database maintained by the Magnetics Information Consortium (MagIC) at https://earthref.org/MagIC.
Other notebooks of interest are:
PmagPy_calculations.ipynb: demonstrates many of the PmagPy calculation functions such as those that rotate directions, return statistical parameters, and simulate data from specified distributions.
PmagPy_plots_analysis.ipynb: demonstrates PmagPy functions that can be used to visual data as well as those that conduct statistical tests that have associated visualizations.
PmagPy_MagIC.ipynb: demonstrates how PmagPy can be used to read and write data to and from the MagIC database format including conversion from many individual lab measurement file formats.
Please see also our YouTube channel with more presentations from the 2020 MagIC workshop here: https://www.youtube.com/playlist?list=PLirL2unikKCgUkHQ3m8nT29tMCJNBj4kj
Hut, Rolf; Drost, Niels; van de Giesen, Nick; van Werkhoven, Ben; Abdollahi, Banafsheh; Aerts, Jerom; Albers, Thomas; Alidoost, Fakhereh; Andela, Bouwe; Camphuijsen, Jaro; et al(
, Geoscientific Model Development)
Abstract. Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022). With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.
Eaton, Deren A. R.; Matschiner, ed., Michael(
, Methods in Ecology and Evolution)
Abstract
Toytreeis a lightweight Python library for programmatically visualizing and manipulating tree‐based data structures. It implements a minimalist design aesthetic and modern plotting architecture suited for interactive coding in IPython/Jupyter.
Tree drawings are generated in HTML using thetoyplotlibrary backend, and display natively in Jupyter notebooks with interactivity features. Tree drawings can be combined with other plotting functions from thetoyplotlibrary (e.g. scatterplots, histograms) to create composite figures on a shared coordinate grid, and can be exported to additional formats including PNG, PDF and SVG.
To parse and store tree data,toytreeuses a modified fork of theete3TreeNode object, which includes functions for manipulating, annotating and comparing trees.Toytreeintegrates these functions with a plotting layout to allow node values to be extracted from trees in the correct order to style nodes for plotting. In addition,toytreeprovides functions for parsing additional tree formats, generating random trees, inferring consensus trees and drawing grids or clouds from multiple trees to visualize discordance.
The goal oftoytreeis to provide a simple Python equivalent to commonly used tree manipulation and plotting libraries in R, and in doing so, to promote further development of phylogenetic and other tree‐based methods in Python.Toytreeis released under the GPLv3 license. Source code is available on GitHub and documentation is available athttps://toytree.readthedocs.io.
Temen, Story; Jain, Amber; Akimov, Alexey V.(
, International Journal of Quantum Chemistry)
Abstract
We report the implementation of a hierarchical equations of motion (HEOM) module within the open‐source Libra software. It includes the standard and scaled HEOM algorithms for computing the dynamics of open quantum systems interacting with a harmonic bath. The module allows the computing of the evolution of the reduced density matrix, as well as spectral lineshapes. The truncation, filtering, and “update list” schemes, as well as OpenMP parallelization, allow for further computational saving. The package is written in a mix of C++ and Python languages, delivering the best compromise between user friendliness and efficiency. The Python layer of the package takes advantage of standard Python libraries, such as h5py, which allows efficient storage and retrieval of the generated results. The package can be seamlessly used within Jupyter notebooks; its careful design shall provide the maximal convenience and intuitiveness to its users.
Chung, Trinity, Sego, T. J., and Glazier, James A. Cell Modeling in Jupyter Notebook using CompuCell3D. Retrieved from https://par.nsf.gov/biblio/10478035. Journal of advanced technological education . Web. doi:10.5281/zenodo.7600786.
Chung, Trinity, Sego, T. J., & Glazier, James A. Cell Modeling in Jupyter Notebook using CompuCell3D. Journal of advanced technological education, (). Retrieved from https://par.nsf.gov/biblio/10478035. https://doi.org/10.5281/zenodo.7600786
@article{osti_10478035,
place = {Country unknown/Code not available},
title = {Cell Modeling in Jupyter Notebook using CompuCell3D},
url = {https://par.nsf.gov/biblio/10478035},
DOI = {10.5281/zenodo.7600786},
abstractNote = {CompuCell3D (CC3D) is an open-source software framework for building and executing multi-cell biological virtual-tissue models. It represents cells using the Glazier–Graner–Hogeweg model, also known as Cellular Potts model. The primary CC3D application consists of two separate tools, a smart model editor (Twedit++) and a tool for model execution, visualization and steering (Player). The CompuCell3D version 4.x release introduces support for Jupyter Notebooks, an interactive computational environment, which brings the benefits of reproducibility, portability, and self-documentation. Since model specifications in CC3D are written in Python and CC3DML and Jupyter supports Python and other languages, Jupyter can naturally act as an integrated development environment (IDE) for CC3D users as well as a live document with embedded text and simulations. This update follows the trend in software to move away from monolithic freestanding applications to the distribution of methodologies in the form of libraries that can be used in conjunction with other libraries and packages. With these benefits, CC3D deployed inJupyter Notebook is a more natural and efficient platform for scientific publishing and education using CC3D.},
journal = {Journal of advanced technological education},
publisher = {Zenodo},
author = {Chung, Trinity and Sego, T. J. and Glazier, James A.},
}
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