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https://github.com/blutjens/e3sm-mmf_baseline

https://github.com/blutjens/e3sm-mmf_baseline

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (13.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: blutjens
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 39 MB
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  • Stars: 0
  • Watchers: 1
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Fork of sungdukyu/E3SM-MMF_baseline
Created about 3 years ago · Last pushed about 3 years ago
Metadata Files
Readme

README.md

E3SM-MMF_baseline

Dataset Information

Variable list can be found here: https://docs.google.com/spreadsheets/d/1ljRfHq6QB36u0TuoxQXcV4_DSQUR0X4UimZ4QHR8f9M/edit#gid=0

1: E3SM-MMF High-Resolution Real Geography - Two files (one for input; the other for output) are produced at each time step for a 10-year long simulation (with timestep = 20 min.), totaling 525,600 files (= 10 year * 365 days/year * 72 steps/day * 2 files/day) - Total data volume: 43 TB - File size: - Input: 102 MB/file - Output: 61 MB/file - File format: netcdf - Dimensions: - ncol (horizontal dimension of an unstructured grid): 21600 - lev (vertical dimension): 60

2: E3SM-MMF Low Resolution Real Geography - All same as above except for file sizes and dimension sizes. - Total data volume: 800GB - File size: - Input: 1.9 MB/file - Output: 1.1 MB/file - File format: netcdf - Dimensions: - ncol (horizontal dimension of an unstructured grid): 384 - lev (vertical dimension): 60

3: E3SM-MMF Low Resolution Aquaplanet - All same as above except for file sizes and dimension sizes. - Total data volume: 800GB - File size: - Input: 1.9 MB/file - Output: 1.1 MB/file - File format: netcdf - Dimensions: - ncol (horizontal dimension of an unstructured grid): 384 - lev (vertical dimension): 60

Installation

(local machine) in your conda base environment do the following to ensure proper channel management: ```

Prefer packages in conda-forge

conda config --system --prepend channels conda-forge

Packages in lower-priority channels not considered if a package with the same

name exists in a higher priority channel. Can dramatically speed up installations.

Conda recommends this as a default

https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-channels.html

conda config --set channelpriority strict conda config --system --set autoupdateconda false conda config --system --set showchannel_urls true ```

Install script

todo: move to clean environment.yml ```

conda deactivate # It's likely not necessary to deactivate base env.

module load anaconda3/2020.11 # Note my supercomputer loads a base env and this install has only been tested with that.

conda create -n e3sm python=3.8.5 conda activate e3sm conda install -c conda-forge numpy conda install -c conda-forge pandas conda install -c conda-forge xarray netCDF4 bottleneck conda install -c conda-forge scikit-learn statsmodels scipy conda install -c conda-forge matplotlib seaborn conda install -c conda-forge jupyterlab tqdm conda install -c conda-forge dask hdf5 h5py conda install -c conda-forge ipython yaml # All packages already installed conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 pip install --upgrade pip # Make sure this is run inside the conda env. pip install tensorflow pip install keras-tuner --upgrade pip install tensorflow-addons pip install qhoptim

Link conda environment to jupyter

python -m ipykernel install --user --name=e3sm ```

Owner

  • Name: Björn Lütjens (he/him)
  • Login: blutjens
  • Kind: user
  • Company: MIT

Postdoctoral Associate in tackling climate change with AI @ MIT. Project overview at https://blutjens.github.io/

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  • Total Commits: 29
  • Total Committers: 3
  • Avg Commits per committer: 9.667
  • Development Distribution Score (DDS): 0.207
Past Year
  • Commits: 29
  • Committers: 3
  • Avg Commits per committer: 9.667
  • Development Distribution Score (DDS): 0.207
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Sungduk Yu s****k@u****u 23
ygaxolotl b****s@g****m 3
ajenney 7****y 3
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uci.edu: 1

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Dependencies

misc/environment.yml pypi
  • tensorflow-addons ==0.19.0
  • typeguard ==3.0.2