| Name | scikit-sundae JSON |
| Version |
1.0.0rc3
JSON |
| download |
| home_page | None |
| Summary | SUNDIALS bindings to differential aglebraic equation solvers. |
| upload_time | 2024-11-12 22:01:09 |
| maintainer | Corey R. Randall |
| docs_url | None |
| author | Corey R. Randall |
| requires_python | <3.14,>=3.9 |
| license | BSD 3-Clause License Copyright (c) 2024, Alliance for Sustainable Energy, LLC Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
sundials
dae
ode
integrator
ivp
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- <img alt='Logo' style='width: 75%; min-width: 250px; max-width: 500px;'
src='https://github.com/NREL/scikit-sundae/blob/main/images/readme_logo.png?raw=true'/> -->
# scikit-SUNDAE
[![CI][ci-b]][ci-l]
![tests][test-b]
![coverage][cov-b]
[![pep8][pep-b]][pep-l]
[ci-b]: https://github.com/NREL/scikit-sundae/actions/workflows/ci.yml/badge.svg
[ci-l]: https://github.com/NREL/scikit-sundae/actions/workflows/ci.yml
[test-b]: https://github.com/NREL/scikit-sundae/blob/main/images/tests.svg?raw=true
[cov-b]: https://github.com/NREL/scikit-sundae/blob/main/images/coverage.svg?raw=true
[pep-b]: https://img.shields.io/badge/code%20style-pep8-orange.svg
[pep-l]: https://www.python.org/dev/peps/pep-0008
## Summary
scikit-SUNDAE provides Python bindings to [SUNDIALS](https://sundials.readthedocs.io/) integrators. The implicit differential algebraic (IDA) solver and C-based variable-coefficient ordinary differential equations (CVODE) solver are both included.
The name SUNDAE combines (SUN)DIALS and DAE, which stands for differential algebraic equations. Solvers specific to DAE problems are not frequently available in Python. An ordinary differential equation (ODE) solver is also included for completeness. ODEs can be categorized as a subset of DAEs (i.e., DAEs with no algebraic constraints).
## Installation
scikit-SUNDAE is installable via either `pip` or `conda`. To install from [PyPI](https://pypi.org/project/scikit-sundae/) use the following command.
```
pip install scikit-sundae
```
If you prefer using the `conda` package manager, you can install scikit-SUNDAE from the `conda-forge` channel using the command below.
```
conda install -c conda-forge scikit-sundae
```
Both sources contain binary installations. If your combination of operating system and CPU architecture is not supported, please submit an [issue](https://github.com/NREL/scikit-sundae/issues/) to let us know. If you'd prefer to build from source, please see the [documentation](https://scikit-sundae.readthedocs.io/en/latest/user_guide/installation.html).
## Get Started
You are now ready to start solving. Run one of the following examples to check your installation. Afterward, check out the [documentation](https://scikit-sundae.readthedocs.io/) for a full list of options (including event functions), detailed examples, and more.
```python
# Use the CVODE integrator to solve the Van der Pol equation
from sksundae.cvode import CVODE
import matplotlib.pyplot as plt
def rhsfn(t, y, yp):
yp[0] = y[1]
yp[1] = 1000*(1 - y[0]**2)*y[1] - y[0]
solver = CVODE(rhsfn)
soln = solver.solve([0, 3000], [2, 0])
plt.plot(soln.t, soln.y[:, 0])
plt.show()
```
The `CVODE` solver demonstrated above is only capable of solving pure ODEs. The constant parameters and time span used above match an example given by [MATLAB](https://www.mathworks.com/help/matlab/ref/ode15s.html) for easy comparison. If you are trying to solve a DAE, you will want to use the `IDA` solver instead. A minimal DAE example is given below for the Robertson problem. As with the CVODE example, the parameters below are chosen to match an online [MATLAB](https://www.mathworks.com/help/matlab/ref/ode15s.html) example for easy comparison.
```python
# Use the IDA integrator to solve the Robertson problem
from sksundae.ida import IDA
import matplotlib.pyplot as plt
def resfn(t, y, yp, res):
res[0] = yp[0] + 0.04*y[0] - 1e4*y[1]*y[2]
res[1] = yp[1] - 0.04*y[0] + 1e4*y[1]*y[2] + 3e7*y[1]**2
res[2] = y[0] + y[1] + y[2] - 1
solver = IDA(resfn, algebraic_idx=[2], calc_initcond='yp0')
soln = solver.solve([4e-6, 4e6], [1, 0, 0], [0, 0, 0])
plt.plot(soln.t, soln.y)
plt.legend(['y0', 'y1', 'y2'])
plt.show()
```
**Notes:**
* If you are new to Python, check out [Spyder IDE](https://www.spyder-ide.org/). Spyder is a powerful interactive development environment (IDE) that can make programming in Python more approachable to new users.
* Check the [solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) documentation from scipy or the [scipy-dae](https://pypi.org/project/scipy-dae/) package repository if you are looking for common examples to test out and compare against. Translating an example from another package can help you learn how to use scikit-SUNDAE before trying to solve more challenging problems.
## Citing this Work
This work was authored by researchers at the National Renewable Energy Laboratory (NREL). The project is tracked in NREL's software records under SWR-24-137 and has a DOI available for citing the work. If you use use this package in your work, please include the following citation:
> Placeholder... waiting for DOI.
For convenience, we also provide the following for your BibTex:
```
@misc{Randall2024,
title = {{scikit-SUNDAE: Python bindings to SUNDIALS DAE solvers}},
author = {Randall, Corey R.},
year = {2024},
doi = {placeholder... waiting for DOI},
url = {https://github.com/NREL/scikit-sundae},
}
```
## Acknowledgements
scikit-SUNDAE was originally inspired by [scikits.odes](https://scikits-odes.readthedocs.io/) which also offers Python bindings to SUNDIALS. The API for scikit-SUNDAE was mostly adopted from scikits.odes; however, all of our source code is original. If you are comparing the two:
1. **scikits.odes:** includes iterative solvers and some optional solvers (e.g., LAPACK). The package only provides source distributions, so users must configure and compile SUNDAILS on their own.
2. **scikit-SUNDAE:** includes more flexible events function capabilities (e.g., direction detection and terminal flags), scipy-like output, and provides both binary and source distributions. Iterative and optional solvers are not available.
Our binary distributions include pre-compiled dynamic SUNDIALS libraries. These are self-contained and will not affect other, existing installations you may already have. To be in compliance with SUNDIALS distribution requirements, all scikit-SUNDAE distributions include a copy of the [SUNDIALS license](https://github.com/LLNL/sundials/blob/main/LICENSE).
## Contributing
If you'd like to contribute to this package, please look through the existing [issues](https://github.com/NREL/scikit-sundae/issues). If the bug you've caught or the feature you'd like to add isn't already reported, please submit a new issue. You should also read through the [developer guidelines](https://scikit-sundae.readthedocs.io/en/latest/development/) if you plan to work on the issue yourself.
## Disclaimer
This work was authored by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE). The views expressed in the repository do not necessarily represent the views of the DOE or the U.S. Government.
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"description": "<!-- <img alt='Logo' style='width: 75%; min-width: 250px; max-width: 500px;'\n src='https://github.com/NREL/scikit-sundae/blob/main/images/readme_logo.png?raw=true'/> -->\n\n # scikit-SUNDAE\n\n[![CI][ci-b]][ci-l] \n![tests][test-b] \n![coverage][cov-b] \n[![pep8][pep-b]][pep-l]\n\n[ci-b]: https://github.com/NREL/scikit-sundae/actions/workflows/ci.yml/badge.svg\n[ci-l]: https://github.com/NREL/scikit-sundae/actions/workflows/ci.yml\n\n[test-b]: https://github.com/NREL/scikit-sundae/blob/main/images/tests.svg?raw=true\n[cov-b]: https://github.com/NREL/scikit-sundae/blob/main/images/coverage.svg?raw=true\n\n[pep-b]: https://img.shields.io/badge/code%20style-pep8-orange.svg\n[pep-l]: https://www.python.org/dev/peps/pep-0008\n\n## Summary\nscikit-SUNDAE provides Python bindings to [SUNDIALS](https://sundials.readthedocs.io/) integrators. The implicit differential algebraic (IDA) solver and C-based variable-coefficient ordinary differential equations (CVODE) solver are both included.\n\nThe name SUNDAE combines (SUN)DIALS and DAE, which stands for differential algebraic equations. Solvers specific to DAE problems are not frequently available in Python. An ordinary differential equation (ODE) solver is also included for completeness. ODEs can be categorized as a subset of DAEs (i.e., DAEs with no algebraic constraints).\n\n## Installation\nscikit-SUNDAE is installable via either `pip` or `conda`. To install from [PyPI](https://pypi.org/project/scikit-sundae/) use the following command.\n\n```\npip install scikit-sundae\n```\n\nIf you prefer using the `conda` package manager, you can install scikit-SUNDAE from the `conda-forge` channel using the command below.\n\n```\nconda install -c conda-forge scikit-sundae\n```\n\nBoth sources contain binary installations. If your combination of operating system and CPU architecture is not supported, please submit an [issue](https://github.com/NREL/scikit-sundae/issues/) to let us know. If you'd prefer to build from source, please see the [documentation](https://scikit-sundae.readthedocs.io/en/latest/user_guide/installation.html).\n\n## Get Started\nYou are now ready to start solving. Run one of the following examples to check your installation. Afterward, check out the [documentation](https://scikit-sundae.readthedocs.io/) for a full list of options (including event functions), detailed examples, and more.\n\n```python\n# Use the CVODE integrator to solve the Van der Pol equation\n\nfrom sksundae.cvode import CVODE\nimport matplotlib.pyplot as plt\n\ndef rhsfn(t, y, yp):\n yp[0] = y[1]\n yp[1] = 1000*(1 - y[0]**2)*y[1] - y[0]\n\nsolver = CVODE(rhsfn)\nsoln = solver.solve([0, 3000], [2, 0])\n\nplt.plot(soln.t, soln.y[:, 0])\nplt.show()\n```\n\nThe `CVODE` solver demonstrated above is only capable of solving pure ODEs. The constant parameters and time span used above match an example given by [MATLAB](https://www.mathworks.com/help/matlab/ref/ode15s.html) for easy comparison. If you are trying to solve a DAE, you will want to use the `IDA` solver instead. A minimal DAE example is given below for the Robertson problem. As with the CVODE example, the parameters below are chosen to match an online [MATLAB](https://www.mathworks.com/help/matlab/ref/ode15s.html) example for easy comparison.\n\n```python\n# Use the IDA integrator to solve the Robertson problem\n\nfrom sksundae.ida import IDA\nimport matplotlib.pyplot as plt\n\ndef resfn(t, y, yp, res):\n res[0] = yp[0] + 0.04*y[0] - 1e4*y[1]*y[2]\n res[1] = yp[1] - 0.04*y[0] + 1e4*y[1]*y[2] + 3e7*y[1]**2\n res[2] = y[0] + y[1] + y[2] - 1\n\nsolver = IDA(resfn, algebraic_idx=[2], calc_initcond='yp0')\nsoln = solver.solve([4e-6, 4e6], [1, 0, 0], [0, 0, 0])\n\nplt.plot(soln.t, soln.y)\nplt.legend(['y0', 'y1', 'y2'])\nplt.show()\n```\n\n**Notes:**\n* If you are new to Python, check out [Spyder IDE](https://www.spyder-ide.org/). Spyder is a powerful interactive development environment (IDE) that can make programming in Python more approachable to new users.\n* Check the [solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) documentation from scipy or the [scipy-dae](https://pypi.org/project/scipy-dae/) package repository if you are looking for common examples to test out and compare against. Translating an example from another package can help you learn how to use scikit-SUNDAE before trying to solve more challenging problems.\n\n## Citing this Work\nThis work was authored by researchers at the National Renewable Energy Laboratory (NREL). The project is tracked in NREL's software records under SWR-24-137 and has a DOI available for citing the work. If you use use this package in your work, please include the following citation:\n\n> Placeholder... waiting for DOI.\n\nFor convenience, we also provide the following for your BibTex:\n\n```\n@misc{Randall2024,\n title = {{scikit-SUNDAE: Python bindings to SUNDIALS DAE solvers}},\n author = {Randall, Corey R.},\n year = {2024},\n doi = {placeholder... waiting for DOI},\n url = {https://github.com/NREL/scikit-sundae},\n}\n```\n\n## Acknowledgements\nscikit-SUNDAE was originally inspired by [scikits.odes](https://scikits-odes.readthedocs.io/) which also offers Python bindings to SUNDIALS. The API for scikit-SUNDAE was mostly adopted from scikits.odes; however, all of our source code is original. If you are comparing the two:\n\n1. **scikits.odes:** includes iterative solvers and some optional solvers (e.g., LAPACK). The package only provides source distributions, so users must configure and compile SUNDAILS on their own.\n2. **scikit-SUNDAE:** includes more flexible events function capabilities (e.g., direction detection and terminal flags), scipy-like output, and provides both binary and source distributions. Iterative and optional solvers are not available.\n\nOur binary distributions include pre-compiled dynamic SUNDIALS libraries. These are self-contained and will not affect other, existing installations you may already have. To be in compliance with SUNDIALS distribution requirements, all scikit-SUNDAE distributions include a copy of the [SUNDIALS license](https://github.com/LLNL/sundials/blob/main/LICENSE).\n\n## Contributing\nIf you'd like to contribute to this package, please look through the existing [issues](https://github.com/NREL/scikit-sundae/issues). If the bug you've caught or the feature you'd like to add isn't already reported, please submit a new issue. You should also read through the [developer guidelines](https://scikit-sundae.readthedocs.io/en/latest/development/) if you plan to work on the issue yourself.\n\n## Disclaimer\nThis work was authored by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE). The views expressed in the repository do not necessarily represent the views of the DOE or the U.S. Government.\n",
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