An atmospheric growth and evolution model based on the EVo degassing model and FastChem 2.0

Related tags

Deep LearningEVolve
Overview

EVolve

Linking planetary mantles to atmospheric chemistry through volcanism using EVo and FastChem.

Overview

EVolve is a linked mantle degassing and atmospheric growth code, which models the growth of a rocky planet's secondary atmosphere under the influence of volcanism.

Installation

EVolve is written in Python3, and is incompatible with Python 2.7. Two very useful tools to set up python environments:
Pip - package installer for Python
Anaconda - virtual environment manager

  1. Clone the repository with submodules and enter directory

    git clone --recurse-submodules [email protected]:pipliggins/evolve.git
    

    Note: If you don't clone with submodules you won't get the two modules used to run EVolve, the EVo volcanic degassing model and the FastChem equilibrium chemistry code.

  2. Compile FastChem:

    cd fastchem
    git submodules update --init --recursive
    mkdir build & cd build
    cmake -DUSE_PYTHON==ON ..
    make
    

    This will pull the pybind11 module required for the python bindings, and compile both the C++ code, and the python bindings which are used in EVolve to conect to FastChem.

    Note: FastChem is an external C++ module, used to compute atmospheric equilibrium chemistry. Therefore, to run on Windows, I recommend using WSL (Windows Subsystem for Linux) to make the process of compiling the C code easier. If you encounter installation issues relating to the cmake version, I found the accepted answer here to work for me. A list of the suggested terminal commands can also be found at the bottom of this README file.

  3. Install dependencies using either Pip install or Anaconda. Check requirements.txt for full details. If using Pip, install all dependencies from the main directory of EVolve using

    pip3 install -r requirements.txt
    

    Troubleshoot: The GMPY2 module requires several libraries (MPFR and MPC) which are not pre-loaded in some operating systems, particularly Windows. If the GMPY2 module does not install, or you have other install issues, try

    pip3 install wheel
    sudo apt install libgmp-dev libmpfr-dev libmpc-dev
    pip3 install -r requirements.txt
    

Running EVolve

EVolve can be run either with or without the FastChem equilibrium chemistry in the atmosphere. To run Evolve with FastChem, from the main directory of EVolve run

python evolve.py inputs.yaml --fastchem

The available tags are:

  • --fastchem ).This will use fastchem to run equilibrium chemistry in the atmosphere, producing more chemical species than the magma degassing model uses and enabling the atmospheric equilibrium temperature to be lower than magmatic.

  • --nocrust ).This option stops a crustal reservoir from being formed out of the degassed melt which has been erupted. Instead, the degassed melt and any volatiles remaining in it are re-incorporated back into the mantle. If this tag is NOT used, the mantle mass will gradually reduce as there is no mechanism for re-introducing the crustal material back into the mantle implemented here.

All the input models for EVolve, and the submodules EVo and FastChem are stored in the 'inputs' folder:

Filename Relevant module Properties
atm.yaml EVolve main Sets the pre-existing atmospheric chemistry and surface pressures + temperatures for the planet
mantle.yaml EVolve main Sets the initial planetary mantle/rocky body properties, including temperature, mass, fO2, the mantle volatile concentrations and the volcanic intrusive:extrusive ratio
planet.yaml EVolve main Sets generic planetary properties and important run settings, including planetary mass, radius, the amount of mantle melting occurring at each timestep and the size & number of timesteps the model will run.
chem.yaml EVo Contains the major oxide composition of the magma being input to EVo
env.yaml EVo Contains the majority of the run settings and volatile contents for the EVo run.
output.yaml EVo Stops any graphical input from EVo compared to it's default settings
config.input FastChem Sets the names and locations for input and output files for FastChem, and output settings
parameters.dat FastChem Location of elemental abundance files, and configuration parameters

Files highlighted in bold should be edited by the user; all others are optimied for EVolve and/or will be edited by the code as it is running. Explainations for each parameter setting in the EVolve files can be found at the bottom of this README file.

As EVolve runs, it creates and updates files in the outputs folder as follows:

Filename Data
atmosphere_out.csv Planetary surface pressure and atmospheric composition for tracked molecules in units of volume mixing ratios (actually mo fraction), calculated after each time step
mantle_out.csv Mantle volatile budget and fO2 after each timestep
volc_out.csv The final pressure iteration from the EVo output file in each timestep (storing melt volatile contents, atomic volatile contents, gas speciation in mol & wt fractions, etc)
fc_input.csv Generated if fastchem is selected: The input to FastChem after atmospheric mixing, and hydrogen escape if that is occuring, for each timestep.
fc_out.csv Generated if fastchem is selected: The results from FastChem after each timestep

Installation help for WSL

If you see an error saying that the installed version of cmake is too low to install FastChem, try these commands: Please note this is just a suggestion based on what worked for me, try these workarounds at your own risk!

sudo apt-get update
sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget

wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | sudo apt-key add -

sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ bionic main'
sudo apt-get update

sudo apt-get install cmake
Owner
Pip Liggins
3rd year PhD student studying Earth Sciences. I model volcanic degassing chemistry and its impact on planetary atmospheres.
Pip Liggins
Weakly supervised medical named entity classification

Trove Trove is a research framework for building weakly supervised (bio)medical named entity recognition (NER) and other entity attribute classifiers

60 Nov 18, 2022
BookMyShowPC - Movie Ticket Reservation App made with Tkinter

Book My Show PC What is this? Movie Ticket Reservation App made with Tkinter. Tk

The Nithin Balaji 3 Dec 09, 2022
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

Princeton Natural Language Processing 119 Sep 28, 2022
code associated with ACL 2021 DExperts paper

DExperts Hi! This repository contains code for the paper DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts to appear at

Alisa Liu 68 Dec 15, 2022
The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting

About The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting The demo program was only tested under Conda in a standard

Anh-Dzung Doan 5 Nov 28, 2022
DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time

DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time Introduction This is official implementation for DR-GAN (IEEE TCS

Kang Liao 18 Dec 23, 2022
A Python Package For System Identification Using NARMAX Models

SysIdentPy is a Python module for System Identification using NARMAX models built on top of numpy and is distributed under the 3-Clause BSD license. N

Wilson Rocha 175 Dec 25, 2022
Repository for Driving Style Recognition algorithms for Autonomous Vehicles

Driving Style Recognition Using Interval Type-2 Fuzzy Inference System and Multiple Experts Decision Making Created by Iago Pachêco Gomes at USP - ICM

Iago Gomes 9 Nov 28, 2022
Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework via Self-Supervised Multi-Task Learning. Code will be available soon.

Official-PyTorch-Implementation-of-TransMEF Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fu

117 Dec 27, 2022
pytorch implementation of Attention is all you need

A Pytorch Implementation of the Transformer: Attention Is All You Need Our implementation is largely based on Tensorflow implementation Requirements N

230 Dec 07, 2022
An Open-Source Package for Information Retrieval.

OpenMatch An Open-Source Package for Information Retrieval. 😃 What's New Top Spot on TREC-COVID Challenge (May 2020, Round2) The twin goals of the ch

THUNLP 439 Dec 27, 2022
Implementation of the method described in the Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

4 Mar 11, 2022
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision This is the repository for our Paper/Contribution to the WI2022 in Nürnber

Maximilian Harl 6 Jan 17, 2022
StyleGAN2-ADA-training-jupyter - Training custom datasets in styleGAN2-ADA by NVIDIA using Jupyter

styleGAN2-ADA-training-jupyter Training custom datasets in styleGAN2-ADA on Jupyter Official StyleGAN2-ADA by NIVIDIA Paper Training Generative Advers

Mang Su Hyun 2 Feb 24, 2022
Apache Flink

Apache Flink Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities. Learn more about Flin

The Apache Software Foundation 20.4k Dec 30, 2022
💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Official PyTorch implementation and EmoCause evaluatio

Hyunwoo Kim 51 Jan 06, 2023
PyTorch implementation for Graph Contrastive Learning with Augmentations

Graph Contrastive Learning with Augmentations PyTorch implementation for Graph Contrastive Learning with Augmentations [poster] [appendix] Yuning You*

Shen Lab at Texas A&M University 382 Dec 15, 2022
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation Figure 1: We estimate the 6DoF rigid transformation of a 3D face (rendered in si

Vítor Albiero 519 Dec 29, 2022
YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitraril

Adam Van Etten 161 Jan 06, 2023
PyTorch code for training MM-DistillNet for multimodal knowledge distillation

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge MM-DistillNet is a

51 Dec 20, 2022