A Python wrapper around the NRLMSIS model.
Project description
pymsis: A python wrapper of the NRLMSIS model
Pymsis is meant to be a minimal and fast Python wrapper of the NRLMSIS models. Documentation to get started quickly can be found on the home page. It includes some examples that demonstrate how to access and plot the data.
NRL Mass Spectrometer, Incoherent Scatter Radar Extended Model (MSIS)
The MSIS model is developed by the Naval Research Laboratory.
Note that the MSIS2 code is not available for commercial use without contacting NRL. See the MSIS2 license file for explicit details. We do not repackage any of the MSIS source code in this repository for that reason. However, we do provide utilities to easily download and extract the original source code. By using that code you agree to their terms and conditions.
References
Please acknowledge the University of Colorado Space Weather Technology, Research and Education Center (SWx TREC) and cite the original papers if you make use of this model in a publication.
Emmert, J. T., Drob, D. P., Picone, J. M., Siskind, D. E., Jones, M., Mlynczak, M. G., et al. (2020). NRLMSIS 2.0: A whole‐atmosphere empirical model of temperature and neutral species densities. Earth and Space Science, 7, e2020EA001321. https://doi.org/10.1029/2020EA001321
The Original NRLMSISE-00 paper
Picone, J. M., Hedin, A. E., Drob, D. P., and Aikin, A. C., NRLMSISE‐00 empirical model of the atmosphere: Statistical comparisons and scientific issues, J. Geophys. Res., 107( A12), 1468, doi:10.1029/2002JA009430, 2002.
Installation
The easiest way to install pymsis is to install from PyPI.
pip install pymsis
For the most up-to-date pymsis, you can install directly from the git repository
pip install git+https://github.com/SWxTREC/pymsis.git
or to work on it locally, you can clone the repository and use an editable install
git clone https://github.com/SWxTREC/pymsis.git
cd pymsis
pip install -e .
Remote installation
The installation is dependent on access to the NRL source code. If the download fails, of you have no internet access you can manually install the Fortran source code as follows.
Download the source code
The source code is hosted on the NRL’s website: https://map.nrl.navy.mil/map/pub/nrl/NRLMSIS/NRLMSIS2.0/ Download the NRLMSIS2.0.tar.gz file to your local system.
Extract the source files
The tar file needs to be extracted to a new msis2 directory in the base of the pymsis package.
mkdir msis2 tar -xvzf NRLMSIS2.0.tar.gz -C msis2/
Install the Python package
pip install .
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for pymsis-0.2.1-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b8cb659e7932d63ed161d0e3a51beb4eff8fa5b7705d616c75b2a76449cec6c2 |
|
MD5 | 32207d7ce351d2326c53f9968893abb8 |
|
BLAKE2b-256 | 7447e78a805cc00f87f37d5b83b1365064d98a85bf230130bdaaf6de61565f14 |
Hashes for pymsis-0.2.1-cp39-cp39-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d0f50ec44b3922f6183bd4575c9ffda2a37b7b836a8e199fdfd05ee43786209d |
|
MD5 | d4ea08df3917c710812a61d451075f7b |
|
BLAKE2b-256 | 0b288ad1bc38a9d958ecae9d8213ecba5ff67474b848a1793afc6c2c9436d5db |
Hashes for pymsis-0.2.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | aa3cf5d58f80d05ca792c4e2729a29735a896f74872eeecd818285bdc60614b7 |
|
MD5 | 7110bfe867e849fd8868e57b0574fab1 |
|
BLAKE2b-256 | 1a5c1b7637ce4b7238d7d1128042a3d78d98b23501359145118490f547168989 |
Hashes for pymsis-0.2.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e6d3ea3be885a4b68a8f84b3cb270ab75be91e84e90656056a500d5d511a5fbf |
|
MD5 | fc11bcc08bde8c723a8a52646fb25f79 |
|
BLAKE2b-256 | 7c411418e0aad5b640aca4d41ad3613bd6404d4c6f383b6468b2531e306235ef |
Hashes for pymsis-0.2.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 98b769938b2b591e345808b4fc7055c1d2c6b72f6b764e71834ba044f2098633 |
|
MD5 | 388aa96faf52aeeb6570ffd74aac0ed2 |
|
BLAKE2b-256 | f2ea5adeed3b6187308f1889acd40028f89a28247912f3647d19c8a2c8689daa |
Hashes for pymsis-0.2.1-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 85639582b9493221670857dfb0451611101e1c3ca42dcf7313098cb89e8f1499 |
|
MD5 | 55363e5e69d37a3620f84e02ec9d42db |
|
BLAKE2b-256 | a03d44011570c4b36cb02d7aab4bf46b8a138de077b9eaf72421c2790c653705 |
Hashes for pymsis-0.2.1-cp38-cp38-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e1a8919a09d547d546bc3d66d3b5f97cfffe3899f217cef0f76e7dcbc14854bc |
|
MD5 | 459f2645657a599442ddd262b5d65851 |
|
BLAKE2b-256 | 4c5afabcab7e4cf5b68ab33199210c3f42a115a246e1b70094b84f04848b95a2 |
Hashes for pymsis-0.2.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a5cc727f928acaba994d061d65094e4850124957accb933635d9174e602094c4 |
|
MD5 | 2e71e5d3fb938a58eded407792ef1b9b |
|
BLAKE2b-256 | 3fe8e803d3973bcb248b35face39bfc2508fae12fb46cd2cde5c1f0ad25c43ab |
Hashes for pymsis-0.2.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3022e6a15b0c3513fbb213fb2fb5ed0e49f8c13f466916543cc9cee73c0dd5d4 |
|
MD5 | 59473a71ad8f602b55997810c63b4d0d |
|
BLAKE2b-256 | 9985bc1a2cbfc2e11d551402a2a0b907ad0d11f22d943863ed6dc55d2b317b66 |
Hashes for pymsis-0.2.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b4f4393321ec153053faa8175b499b76d50afedbed0cfe989708e6f9617b6cb3 |
|
MD5 | d321d905cc5a889101a621d1ed06738c |
|
BLAKE2b-256 | 298b3a18b6f40cc2a52745ce7573ac5962cd6f91854d439bd996f1aa8a7aef2e |
Hashes for pymsis-0.2.1-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 20f083257ffb9b5dc332617545d211c471a85d1401c01426223fe2559933dc3c |
|
MD5 | f8ab9d9fe37f81ce685119d6e1e9e186 |
|
BLAKE2b-256 | af8251e4914f03dc0c7ffb00e201296b01234b5b0bf5cb2fac7d843763384e84 |
Hashes for pymsis-0.2.1-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1af70b0c1c78cb3d31c2ad20af49a504e324585f5581ede56b8c940ad99a2a52 |
|
MD5 | 40d01916fdaa2ad5a7650ed62bcf7c46 |
|
BLAKE2b-256 | 4576e300ada647b3f966854dc0619d50f3d1010e8e51de2e4b1f37ed06e9ba04 |
Hashes for pymsis-0.2.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e674f30f1e96d4fc62064d48d4878f68ffe5814132c033ea9bc2fb3da1831ee3 |
|
MD5 | dc3ddc3808f09602372a907a61570cf6 |
|
BLAKE2b-256 | d325508456faaf579d6c4c5ff899c50528edd616a391ccf143e48beca3fe58b7 |
Hashes for pymsis-0.2.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2ab6996f2a662f2b3d33e8e4d4e165c1a3a4f13cd3640a9d2128b5bb60660794 |
|
MD5 | f01e5dae1a7fa753cbb5901c0941668b |
|
BLAKE2b-256 | 4352b4ab994c886a17657564ebf9f386af63edebd6ba869acf6f35fb5db43d0d |
Hashes for pymsis-0.2.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 33b1de4d1bda5be07284d9c685e0d7cd24b1845a08c9f33a6e02edddff63169a |
|
MD5 | f9fd960a7a2b0287c2f9346403e0a8b2 |
|
BLAKE2b-256 | c819e7651d7b57caecf1a401eac0ff60551d246791d1f113150342e1747720fd |