| Name | qgs JSON |
| Version |
0.2.8
JSON |
| download |
| home_page | |
| Summary | A 2-layer quasi-geostrophic atmospheric model in Python. Can be coupled to a simple land or shallow-water ocean component. |
| upload_time | 2023-06-17 10:26:32 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | The MIT License (MIT) Copyright (c) 2020-2023 qgs Developers and Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
meteorology
climate
climate-variability
atmospheric-models
ocean-atmosphere-model
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
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| coveralls test coverage |
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|
Quasi-Geostrophic Spectral model (qgs)
======================================
[](https://zenodo.org/badge/latestdoi/246609584)
[](https://doi.org/10.21105/joss.02597)
[](https://qgs.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/Climdyn/qgs/actions/workflows/checks.yml)
[](https://opensource.org/licenses/MIT)
General Information
-------------------
qgs is a Python implementation of an atmospheric model for midlatitudes. It models the dynamics of
a 2-layer [quasi-geostrophic](https://en.wikipedia.org/wiki/Quasi-geostrophic_equations) channel
atmosphere on a [beta-plane](https://en.wikipedia.org/wiki/Beta_plane), coupled to a simple land or
[shallow-water](https://en.wikipedia.org/wiki/Shallow_water_equations) ocean component.
About
-----
(c) 2020-2023 qgs Developers and Contributors
Part of the code originates from the Python [MAOOAM](https://github.com/Climdyn/MAOOAM) implementation by Maxime Tondeur and Jonathan Demaeyer.
See [LICENSE.txt](https://raw.githubusercontent.com/Climdyn/qgs/master/LICENSE.txt) for license information.
**Please cite the code description article if you use (a part of) this software for a publication:**
* Demaeyer J., De Cruz, L. and Vannitsem, S. , (2020). qgs: A flexible Python framework of reduced-order multiscale climate models.
*Journal of Open Source Software*, **5**(56), 2597, [https://doi.org/10.21105/joss.02597](https://doi.org/10.21105/joss.02597).
Please consult the qgs [code repository](http://www.github.com/Climdyn/qgs) for updates.
Installation
------------
#### With pip
The easiest way to install and run qgs is to use [pip](https://pypi.org/)
pip install qgs
and you are set!
Additionally, you can clone the repository
git clone https://github.com/Climdyn/qgs.git
and perform a test by running the script
python qgs/qgs_rp.py
to see if everything runs smoothly (this should take less than a minute).
#### With Anaconda
The easiest way to install and run qgs is to use an appropriate environment created through [Anaconda](https://www.anaconda.com/).
First install Anaconda and clone the repository:
git clone https://github.com/Climdyn/qgs.git
Then install and activate the Python3 Anaconda environment:
conda env create -f environment.yml
conda activate qgs
You can then perform a test by running the script
python qgs_rp.py
to see if everything runs smoothly (this should take less than a minute).
#### Note for Windows and MacOS users
Presently, qgs is compatible with Windows and MacOS but users wanting to use qgs inside their Python scripts must guard the main script with a
```
if __name__ == "__main__":
```
clause and add the following lines below
```
from multiprocessing import freeze_support
freeze_support()
```
About this usage, see for example the main scripts `qgs_rp.py` and `qgs_maooam.py` in the root folder.
Note that the Jupyter notebooks are not concerned by this recommendation and work perfectly well on both operating systems.
> **Why?** These lines are required to make the multiprocessing library works with these operating systems. See [here](https://docs.python.org/3.8/library/multiprocessing.html) for more details,
> and in particular [this section](https://docs.python.org/3.8/library/multiprocessing.html#the-spawn-and-forkserver-start-methods).
#### Activating DifferentialEquations.jl optional support
In addition to the qgs builtin Runge-Kutta integrator, the qgs model can alternatively be integrated with a package called [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl) written in [Julia](https://julialang.org/), and available through the
[diffeqpy](https://github.com/SciML/diffeqpy) python package.
The diffeqpy package first installation step is done by Anaconda in the qgs environment but then you must [install Julia](https://julialang.org/downloads/) and follow the final manual installation instruction found in the [diffeqpy README](https://github.com/SciML/diffeqpy).
These can be summed up as opening a terminal and doing:
```
conda activate qgs
python
```
and then inside the Python command line interface do:
```
>>> import diffeqpy
>>> diffeqpy.install()
```
which will then finalize the installation. An example of a notebook using this package is available in the documentation and on [readthedocs](https://qgs.readthedocs.io/en/latest/files/examples/diffeq.html).
Documentation
-------------
To build the documentation, please run (with the conda environment activated):
cd documentation
make html
You may need to install [make](https://www.gnu.org/software/make/) if it is not already present on your system.
Once built, the documentation is available [here](./documentation/build/html/index.html).
The documentation is also available online on read the docs: [https://qgs.readthedocs.io/](https://qgs.readthedocs.io/)
Usage
-----
qgs can be used by editing and running the script `qgs_rp.py` and `qgs_maooam.py` found in the main folder.
For more advanced usages, please read the [User Guides](https://qgs.readthedocs.io/en/latest/files/user_guide.html).
Examples
--------
Another nice way to run the model is through the use of Jupyter notebooks.
Simple examples can be found in the [notebooks folder](./notebooks).
For instance, running
conda activate qgs
cd notebooks
jupyter-notebook
will lead you to your favorite browser where you can load and run the examples.
Dependencies
------------
qgs needs mainly:
* [Numpy](https://numpy.org/) for numeric support
* [sparse](https://sparse.pydata.org/) for sparse multidimensional arrays support
* [Numba](https://numba.pydata.org/) for code acceleration
* [Sympy](https://www.sympy.org/) for symbolic manipulation of inner products
Check the yaml file [environment.yml](https://raw.githubusercontent.com/Climdyn/qgs/master/environment.yml) for the dependencies.
Forthcoming developments
------------------------
* Scientific development (short-to-mid-term developments)
+ Non-autonomous equation (seasonality, etc...)
+ Energy diagnostics
* Technical mid-term developments
+ Dimensionally robust Parameter class operation
+ Vectorization of the tensor computation
* Long-term development track
+ Active advection
+ True quasi-geostrophic ocean when using ocean model version
+ Salinity in the ocean
+ Symbolic PDE equation specification
+ Numerical basis of function
Contributing to qgs
-------------------
If you want to contribute actively to the roadmap detailed above, please contact the main authors.
In addition, if you have made changes that you think will be useful to others, please feel free to suggest these as a pull request on the [qgs Github repository](https://github.com/Climdyn/qgs).
More information and guidance about how to do a pull request for qgs can be found in the documentation [here](https://qgs.readthedocs.io/en/latest/files/general_information.html#contributing-to-qgs).
Other atmospheric models in Python
----------------------------------
Non-exhaustive list:
* [Q-GCM](http://q-gcm.org/): A mid-latitude grid based ocean-atmosphere model like MAOOAM. Code in Fortran,
interface is in Python.
* [pyqg](https://github.com/pyqg/pyqg): A pseudo-spectral python solver for quasi-geostrophic systems.
* [Isca](https://execlim.github.io/IscaWebsite/index.html): Research GCM written in Fortran and largely
configured with Python scripts, with internal coding changes required for non-standard cases.
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"description": "\nQuasi-Geostrophic Spectral model (qgs)\n======================================\n\n[](https://zenodo.org/badge/latestdoi/246609584)\n[](https://doi.org/10.21105/joss.02597)\n[](https://qgs.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/Climdyn/qgs/actions/workflows/checks.yml)\n[](https://opensource.org/licenses/MIT)\n\nGeneral Information\n-------------------\n\nqgs is a Python implementation of an atmospheric model for midlatitudes. It models the dynamics of\na 2-layer [quasi-geostrophic](https://en.wikipedia.org/wiki/Quasi-geostrophic_equations) channel\natmosphere on a [beta-plane](https://en.wikipedia.org/wiki/Beta_plane), coupled to a simple land or\n[shallow-water](https://en.wikipedia.org/wiki/Shallow_water_equations) ocean component. \n\n\n\nAbout\n-----\n\n(c) 2020-2023 qgs Developers and Contributors\n\nPart of the code originates from the Python [MAOOAM](https://github.com/Climdyn/MAOOAM) implementation by Maxime Tondeur and Jonathan Demaeyer.\n\nSee [LICENSE.txt](https://raw.githubusercontent.com/Climdyn/qgs/master/LICENSE.txt) for license information.\n\n**Please cite the code description article if you use (a part of) this software for a publication:**\n\n* Demaeyer J., De Cruz, L. and Vannitsem, S. , (2020). qgs: A flexible Python framework of reduced-order multiscale climate models. \n *Journal of Open Source Software*, **5**(56), 2597, [https://doi.org/10.21105/joss.02597](https://doi.org/10.21105/joss.02597).\n\nPlease consult the qgs [code repository](http://www.github.com/Climdyn/qgs) for updates.\n\n\nInstallation\n------------\n\n#### With pip\n\nThe easiest way to install and run qgs is to use [pip](https://pypi.org/)\n\n pip install qgs\n\nand you are set!\n\nAdditionally, you can clone the repository\n\n git clone https://github.com/Climdyn/qgs.git\n\nand perform a test by running the script\n\n python qgs/qgs_rp.py\n\nto see if everything runs smoothly (this should take less than a minute).\n\n\n#### With Anaconda\n\nThe easiest way to install and run qgs is to use an appropriate environment created through [Anaconda](https://www.anaconda.com/).\n\nFirst install Anaconda and clone the repository:\n\n git clone https://github.com/Climdyn/qgs.git\n\nThen install and activate the Python3 Anaconda environment:\n\n conda env create -f environment.yml\n conda activate qgs\n\nYou can then perform a test by running the script\n\n python qgs_rp.py\n \nto see if everything runs smoothly (this should take less than a minute).\n\n#### Note for Windows and MacOS users\n\nPresently, qgs is compatible with Windows and MacOS but users wanting to use qgs inside their Python scripts must guard the main script with a\n\n```\nif __name__ == \"__main__\":\n```\n\nclause and add the following lines below\n\n```\n from multiprocessing import freeze_support\n freeze_support()\n```\n\nAbout this usage, see for example the main scripts `qgs_rp.py` and `qgs_maooam.py` in the root folder.\nNote that the Jupyter notebooks are not concerned by this recommendation and work perfectly well on both operating systems.\n\n> **Why?** These lines are required to make the multiprocessing library works with these operating systems. See [here](https://docs.python.org/3.8/library/multiprocessing.html) for more details, \n> and in particular [this section](https://docs.python.org/3.8/library/multiprocessing.html#the-spawn-and-forkserver-start-methods).\n\n\n#### Activating DifferentialEquations.jl optional support\n\nIn addition to the qgs builtin Runge-Kutta integrator, the qgs model can alternatively be integrated with a package called [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl) written in [Julia](https://julialang.org/), and available through the\n[diffeqpy](https://github.com/SciML/diffeqpy) python package.\nThe diffeqpy package first installation step is done by Anaconda in the qgs environment but then you must [install Julia](https://julialang.org/downloads/) and follow the final manual installation instruction found in the [diffeqpy README](https://github.com/SciML/diffeqpy).\n\nThese can be summed up as opening a terminal and doing:\n```\nconda activate qgs\npython\n```\nand then inside the Python command line interface do:\n\n```\n>>> import diffeqpy\n>>> diffeqpy.install()\n```\nwhich will then finalize the installation. An example of a notebook using this package is available in the documentation and on [readthedocs](https://qgs.readthedocs.io/en/latest/files/examples/diffeq.html).\n\nDocumentation\n-------------\n\nTo build the documentation, please run (with the conda environment activated):\n\n cd documentation\n make html\n\nYou may need to install [make](https://www.gnu.org/software/make/) if it is not already present on your system.\nOnce built, the documentation is available [here](./documentation/build/html/index.html).\n\nThe documentation is also available online on read the docs: [https://qgs.readthedocs.io/](https://qgs.readthedocs.io/)\n\nUsage\n-----\n\nqgs can be used by editing and running the script `qgs_rp.py` and `qgs_maooam.py` found in the main folder.\n\nFor more advanced usages, please read the [User Guides](https://qgs.readthedocs.io/en/latest/files/user_guide.html).\n\nExamples\n--------\n\nAnother nice way to run the model is through the use of Jupyter notebooks. \nSimple examples can be found in the [notebooks folder](./notebooks).\nFor instance, running \n\n conda activate qgs\n cd notebooks\n jupyter-notebook\n \nwill lead you to your favorite browser where you can load and run the examples.\n\nDependencies\n------------\n\nqgs needs mainly:\n\n * [Numpy](https://numpy.org/) for numeric support\n * [sparse](https://sparse.pydata.org/) for sparse multidimensional arrays support\n * [Numba](https://numba.pydata.org/) for code acceleration\n * [Sympy](https://www.sympy.org/) for symbolic manipulation of inner products\n \nCheck the yaml file [environment.yml](https://raw.githubusercontent.com/Climdyn/qgs/master/environment.yml) for the dependencies.\n\nForthcoming developments\n------------------------\n\n* Scientific development (short-to-mid-term developments)\n + Non-autonomous equation (seasonality, etc...)\n + Energy diagnostics\n* Technical mid-term developments\n + Dimensionally robust Parameter class operation\n + Vectorization of the tensor computation\n* Long-term development track\n + Active advection\n + True quasi-geostrophic ocean when using ocean model version\n + Salinity in the ocean \n + Symbolic PDE equation specification\n + Numerical basis of function\n \nContributing to qgs\n-------------------\n\nIf you want to contribute actively to the roadmap detailed above, please contact the main authors.\n\nIn addition, if you have made changes that you think will be useful to others, please feel free to suggest these as a pull request on the [qgs Github repository](https://github.com/Climdyn/qgs).\n\nMore information and guidance about how to do a pull request for qgs can be found in the documentation [here](https://qgs.readthedocs.io/en/latest/files/general_information.html#contributing-to-qgs).\n\nOther atmospheric models in Python\n----------------------------------\n\nNon-exhaustive list:\n\n* [Q-GCM](http://q-gcm.org/): A mid-latitude grid based ocean-atmosphere model like MAOOAM. Code in Fortran,\n interface is in Python.\n* [pyqg](https://github.com/pyqg/pyqg): A pseudo-spectral python solver for quasi-geostrophic systems.\n* [Isca](https://execlim.github.io/IscaWebsite/index.html): Research GCM written in Fortran and largely\n configured with Python scripts, with internal coding changes required for non-standard cases.\n \n \n",
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