Welcome to CosmoVis!

We introduce CosmoVis, an open-source web-based astrophysics visualization tool that facilitates the interactive analysis of large-scale hydrodynamic cosmological simulation datasets. CosmoVis enables astrophysicists as well as citizen scientists to share and explore these datasets, which are often comprised of complex, unwieldy data structures greater that 1 TB in size. Our tool visualizes a range of salient gas, dark matter, and stellar attributes extracted from the source simulations, and enables further analysis of the data using observational analogues, specifically absorption line spectroscopy. CosmoVis introduces novel analysis functionality through the use of virtual skewers that define a sightline through the volume to quickly obtain detailed diagnostics about the gaseous medium along the path of the skewer, including synthetic spectra that can be used to make direct comparisons with observational datasets. We identify the main analysis tasks that CosmoVis enables, and evaluate the software by presenting a series of contemporary scientific use cases that utilize CosmoVis. Additionally, we conduct a series of task-based interviews with astrophysicists indicating the usefulness of CosmoVis for a range of data analysis tasks.

A live demo can be found here: CosmoVis

A dev demo with experimental features can be found here: CosmoVis

Here is a video that outlines a scientific use case from CosmoVis: link

Installation Instructions

CosmoVis can be configured to run locally or remotely on a server, but the most simple way is to have it run locally. Hosting has the benefit of being able to access the visualization from other devices, and only takes a few extra steps to configure. CosmoVis has been tested on Windows, Linux and Mac.

To get started, install Python 3.7+ if you do not have it already (CosmoVis was built with version 3.8.5). It is helpful to create a new environment using Conda, but it is not necessary. There are a few specific Python packages that need to be installed:

$ pip install eventlet==0.25.2 Flask==1.1.2 Flask-SocketIO==4.3.0 Frozen-Flask==0.15 python-engineio==3.13.0 python-socketio==5.6.0 mpi4py numpy cython git

Note: in order to install mpi4py, you may need to install a version of MPI on your machine, such as OpenMPI.

In addition, the development versions of yt, trident and yt-astro-analysis must be installed (in that order):

$ git clone https://github.com/yt-project/yt.git yt
$ cd yt
$ pip install -e .
$ cd ..
$ git clone https://github.com/trident-project/trident.git trident
$ cd trident
$ pip install -e .
$ cd ..
$ git clone https://github.com/yt-project/yt_astro_analysis.git yt_astro_analysis
$ cd yt_astro_analysis
$ pip install -e .
$ cd ..

To finish installing Trident, open Python in the terminal and import Trident.

$ python
> import trident

The first time Trident runs an installation dialogue appears. Follow the on screen instructions and verify that is has been installed successfully by typing:

> trident.verify()

Alternatively, one can use the included requirements.txt file to install Python dependencies:

`pip install -r requirements.txt`

Running CosmoVis

• In the terminal, cd into the CosmoVis folder and type python cosmo-serv.py
• Once the application starts, wait for the data to finish loading and in the web browser go to localhost:5000
• If the webpage does not load, try doing a hard refresh of the page (cmd+shft+R on Mac or ctrl+shft+r on Windows) as it may take a moment to first display.

Usage

• CosmoVis enables real time volume rendering in the web browser. Try it out by clicking and dragging within the visualization. Use your mouse or trackpad scrolling to zoom in and out of the simulation.
• On the right, click on the "data selection" to open a panel that allows for switching between simulations, changing the resolution, and slicing the volume.

Available Cosmological Simulation Particle Types and Fields

Example particle types and fields made available in cosmological simulation snapshots that can be retrieved and plotted using CosmoVis. Simulation datasets typically organize their data in terms of differentparticle types (gas, dark matter, stars, and black holes), each expressinga variety of physical quantity fields.

Particle Type	Fields	Illustris (2013)	EAGLE (2017)	IllustrisTNG (2018)
Gas	AGN Radiation (Bolometric intensity)	x		x
	Center Of Mass			x
	Cooling Rate	x		x
	Coordinates	x	x	x
	Density	x	x	x
	Electron Abundance	x		x
	Element/Metal Abundances		x	x
	Energy Dissipation			x
	Entropy		x
	Expansion Factor at Maximum Temperature		x
	Gravitational Potential Energy	x		x
	Host Halo Mass		x	x
	GroupNumber		x
	Host Halo TVir Mass		x
	Internal Energy	x	x	x
	InternalEnergyOld			x
	Iron Mass Frac From SNIa		x
	Mach Number			x
	Magnetic Field			x
	Magnetic Field Divergence			x
	Mass	x	x	x
	Maximum Temperature		x
	Metal Mass Frac From AGB, SNII and SNIa Stars		x
	Metallicity	x	x	x
	Metals			x
	Metals Tagged			x
	Metal Mass Frac From SNII		x
	Metal Mass Frac From SNIa		x
	Neutral Hydrogen Abundance	x		x
	NumTracers	x
	OnEquationOfState		x
	ParticleIDs	x	x	x
	Smoothed Element Abundance		x
	Smoothed Iron Mass Frac From SNIa		x
	Smoothed Metallicity		x
	SmoothingLength	x	x
	Star Formation Rate	x	x	x
	Subfind DM Density			x
	Subfind Density	x		x
	Subfind Hsml	x		x
	Subfind Vel Disp	x		x
	Sub Group Number		x
	Temperature		x
	Total Mass From AGB, SNII and SNIa Stars		x
	Total Mass From SNII		x
	Total Mass From SNIa		x
	Velocity	x	x	x
	Volume	x
	Wind Dark Matter Velocity Dispersion	x		x
Dark matter	Coordinates	x	x	x
	GroupNumber		x
	ParticleIDs	x	x	x
	Potential	x		x
	Subfind DM Density			x
	Subfind Density	x		x
	Subfind Hsml	x		x
	Subfind Vel Disp	x		x
	SubGroupNumber		x
	Velocity	x	x	x
Star particles	Birth Density		x
	Birth Position			x
	Birth Velocity			x
	Coordinates	x	x	x
	Element/Metal Abundances		x	x
	Expansion Factor at Maximum Temperature		x
	Feedback Energy Fraction		x
	GroupNumber		x
	Host Halo TVir Mass		x
	Initial Mass	x	x	x
	Iron Mass Frac From SNIa		x
	Mass	x	x	x
	Maximum Temperature		x
	Metal Mass Frac From AGB, SNII and SNIa Stars		x
	Metal Mass Frac From SNII		x
	Metal Mass Frac From SNIa		x
	Metallicity	x	x	x
	Metals			x
	Metals Tagged			x
	Number of Tracers	x
	ParticleIDs	x		x
	Potential	x		x
	Previous Stellar Enrichment		x
	Smoothed Element Abundance		x
	Smoothed Iron Mass Frac From SNIa		x
	Smoothed Metallicity		x
	Smoothing Length		x
	Stellar Enrichment Counter		x
	Stellar Formation Time	x	x	x
	Stellar Photometrics	x		x
	Stellar Hsml			x
	Subfind DM Density			x
	Subfind Density	x		x
	Subfind Hsml	x		x
	Subfind Vel Disp	x		x
	SubGroupNumber		x
	Total Mass From AGB, SNII and SNIa Stars		x
	Total Mass From SNII		x
	Total Mass From SNIa		x
	Velocity	x	x	x
Black holes	Black Hole Mass	x	x	x
	Black Hole Mass Accretion Rate	x	x	x
	Bondi Accretion Rate			x
	Coordinates	x	x	x
	Cumulative Thermal/Kinetic AGN Energy Injection	x		x
	BH_CumEgyInjection_RM			x
	Cumulative Accreted Mass	x	x	x
	Cumulative Number of BH Seeds Swallowed		x
	BH_CumMassGrowth_QM	x		x
	BH_CumMassGrowth_RM			x
	Density	x	x	x
	Eddington Accretion Rate			x
	Expansion Factor When BH last accreted another BH		x
	Formation Time		x
	Gravitational Potential	x		x
	Host Halo Mass	x	x	x
	BH_Hsml	x		x
	BH_Mass_bubbles	x
	BH_Mass_ini	x
	BH MostMassiveProgenitorID		x
	Mass	x	x	x
	Mean Magnetic Pressure			x
	Number of Black Hole Mergers	x		x
	Number of Tracers	x
	Pressure	x	x	x
	Sound Speed		x
	Surrounding Gas Velocity		x
	GroupNumber		x
	ParticleIDs	x	x	x
	Smoothing Length		x
	Subfind DM Density			x
	Subfind Density	x		x
	Subfind Hsml	x		x
	Subfind Vel Disp	x		x
	Thermal Energy in QSO-Heated Bubbles	x		x
	SubGroupNumber		x
	Velocity	x	x	x