[](https://doi.org/10.5281/zenodo.8138859)
[](https://anaconda.org/aatmdelissen/pymoto)
[](https://pypi.org/project/pyMOTO/)
# pyMOTO
* [Link to Documentation](https://pymoto.readthedocs.io)
* [Link to GitHub](https://github.com/aatmdelissen/pyMOTO)
Modular topology optimization framework with semi-automatic derivatives. The two main types `Module` and `Signal`
are used to implement a problem formulation to be optimized. The `Module` implements functionality (and design
sensitivity calculations) and `Signal` carries data of both the variables and their derivatives.
Sensitivity calculations are done based on backpropagation. The final value of interest is seeded with sensitivity
value $\frac{\textup{d}f}{\textup{d}f}=1$. Then the modules are executed in reverse order, each applying the chain rule.
As example for a `Module` which maps $x\rightarrow y$, only the following operation needs to be implemented:
$$
\frac{\textup{d}f}{\textup{d}x} = \frac{\textup{d}f}{\textup{d}y}\frac{\textup{d}y}{\textup{d}x}\text{.}
$$
In this way, the order of modules can easily be adapted without having to worry about sensitivities, as these are
automatically calculated.
# Quick start installation
1. Make sure you have Python running in some kind of virtual environment (e.g.
[conda](https://docs.conda.io/projects/conda/en/stable/), [miniconda](https://docs.conda.io/en/latest/miniconda.html),
[venv](https://realpython.com/python-virtual-environments-a-primer/))
2. Install the pymoto Python package (and its dependencies)
- Option A (conda): If you are working with Conda, install by `conda install -c aatmdelissen pymoto`
- Option B (pip): Type `pip install pymoto` into your console to install
3. Download one of the examples found in the repository's example folder
([here](https://github.com/aatmdelissen/pyMOTO/tree/master/examples))
4. Run the example by typing `python ex_....py` in the console
For development, a local installation of `pyMOTO` can be done by first downloading/cloning the entire git repo, and then calling
`pip install -e .` in the `pyMOTO` folder (of course from within your virtual environment).
## Dependencies
* [**numpy**](https://numpy.org/doc/stable/) - Dense linear algebra and solvers
* [**scipy**](https://docs.scipy.org/doc/scipy/) - Sparse linear algebra and solvers
* [**sympy**](https://docs.sympy.org/latest/index.html) - Symbolic differentiation for `MathGeneral` module
* [**Matplotlib**](https://matplotlib.org/stable/) - Plotting and visualisation
* (optional) [**opt_einsum**](https://optimized-einsum.readthedocs.io/en/stable/install.html) - Optimized function for `EinSum` module
For fast linear solvers for sparse matrices:
* (optional) [**pypardiso**](https://github.com/haasad/PyPardisoProject) - Uses the Intel OneAPI PARDISO solver (recommended)
* (optional) [**scikit-umfpack**](https://scikit-umfpack.github.io/scikit-umfpack/install.html) - Fast LU linear solver based on UMFPACK
* (optional) [**scikit-sparse**](https://github.com/scikit-sparse/scikit-sparse) - Fast Cholesky solver based on CHOLMOD
* (optional) [**cvxopt**](https://cvxopt.org/install/index.html) - Another fast Cholesky solver based on CHOLMOD
__Note on linear solvers for sparse matrices:__ Scipy implements a version of LU which is quite slow. To increase the
speed of the optimization, `pypardiso` is recommended as it contains a very robust and flexible solver for symmetric
and asymmetric matrices. An alternative is `scikit-umfpack` which provides a fast LU factorization. For symmetric
matrices a Cholesky factorization is recommended (not provided with Scipy), which can be used by either installing
`scikit-sparse` or `cvxopt`.
# License
pyMOTO is available under te [MIT License](https://opensource.org/licenses/MIT).
Raw data
{
"_id": null,
"home_page": "https://github.com/aatmdelissen/pyMOTO",
"name": "pyMOTO",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "topology optimization, generative design, structural, sensitivities, derivatives, framework, modular, blocks, pipeline",
"author": "Arnoud Delissen",
"author_email": "arnouddelissen+pymoto@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/10/02/99f92445243f9b43514155f5289cfec4b806aae8900c4120f4bbda4fd919/pymoto-1.4.0.tar.gz",
"platform": null,
"description": "[](https://doi.org/10.5281/zenodo.8138859) \n[](https://anaconda.org/aatmdelissen/pymoto)\n[](https://pypi.org/project/pyMOTO/)\n\n# pyMOTO\n\n* [Link to Documentation](https://pymoto.readthedocs.io)\n* [Link to GitHub](https://github.com/aatmdelissen/pyMOTO)\n\nModular topology optimization framework with semi-automatic derivatives. The two main types `Module` and `Signal`\nare used to implement a problem formulation to be optimized. The `Module` implements functionality (and design \nsensitivity calculations) and `Signal` carries data of both the variables and their derivatives. \n\nSensitivity calculations are done based on backpropagation. The final value of interest is seeded with sensitivity\nvalue $\\frac{\\textup{d}f}{\\textup{d}f}=1$. Then the modules are executed in reverse order, each applying the chain rule.\nAs example for a `Module` which maps $x\\rightarrow y$, only the following operation needs to be implemented:\n\n$$\n\\frac{\\textup{d}f}{\\textup{d}x} = \\frac{\\textup{d}f}{\\textup{d}y}\\frac{\\textup{d}y}{\\textup{d}x}\\text{.} \n$$\n\nIn this way, the order of modules can easily be adapted without having to worry about sensitivities, as these are \nautomatically calculated.\n\n# Quick start installation\n1. Make sure you have Python running in some kind of virtual environment (e.g. \n[conda](https://docs.conda.io/projects/conda/en/stable/), [miniconda](https://docs.conda.io/en/latest/miniconda.html),\n[venv](https://realpython.com/python-virtual-environments-a-primer/))\n2. Install the pymoto Python package (and its dependencies)\n - Option A (conda): If you are working with Conda, install by `conda install -c aatmdelissen pymoto`\n - Option B (pip): Type `pip install pymoto` into your console to install\n3. Download one of the examples found in the repository's example folder \n([here](https://github.com/aatmdelissen/pyMOTO/tree/master/examples))\n4. Run the example by typing `python ex_....py` in the console\n\n\nFor development, a local installation of `pyMOTO` can be done by first downloading/cloning the entire git repo, and then calling \n`pip install -e .` in the `pyMOTO` folder (of course from within your virtual environment).\n\n## Dependencies\n* [**numpy**](https://numpy.org/doc/stable/) - Dense linear algebra and solvers\n* [**scipy**](https://docs.scipy.org/doc/scipy/) - Sparse linear algebra and solvers\n* [**sympy**](https://docs.sympy.org/latest/index.html) - Symbolic differentiation for `MathGeneral` module\n* [**Matplotlib**](https://matplotlib.org/stable/) - Plotting and visualisation\n* (optional) [**opt_einsum**](https://optimized-einsum.readthedocs.io/en/stable/install.html) - Optimized function for `EinSum` module\n\nFor fast linear solvers for sparse matrices:\n* (optional) [**pypardiso**](https://github.com/haasad/PyPardisoProject) - Uses the Intel OneAPI PARDISO solver (recommended)\n* (optional) [**scikit-umfpack**](https://scikit-umfpack.github.io/scikit-umfpack/install.html) - Fast LU linear solver based on UMFPACK\n* (optional) [**scikit-sparse**](https://github.com/scikit-sparse/scikit-sparse) - Fast Cholesky solver based on CHOLMOD\n* (optional) [**cvxopt**](https://cvxopt.org/install/index.html) - Another fast Cholesky solver based on CHOLMOD\n\n__Note on linear solvers for sparse matrices:__ Scipy implements a version of LU which is quite slow. To increase the \nspeed of the optimization, `pypardiso` is recommended as it contains a very robust and flexible solver for symmetric \nand asymmetric matrices. An alternative is `scikit-umfpack` which provides a fast LU factorization. For symmetric \nmatrices a Cholesky factorization is recommended (not provided with Scipy), which can be used by either installing \n`scikit-sparse` or `cvxopt`.\n\n# License\npyMOTO is available under te [MIT License](https://opensource.org/licenses/MIT).\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "A modular approach for topology optimization",
"version": "1.4.0",
"project_urls": {
"Homepage": "https://github.com/aatmdelissen/pyMOTO"
},
"split_keywords": [
"topology optimization",
" generative design",
" structural",
" sensitivities",
" derivatives",
" framework",
" modular",
" blocks",
" pipeline"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e4696b3bb8b5da16ffd1d98be3bbe9e980dd391f2081bae82d2c6f624b7b4a8a",
"md5": "1799f7b23bbf678fe01bc9da8bb98ab3",
"sha256": "3fe22e5031ed54c35af14c065ad7ffdc2ea466cb3ad6fbe2c05178522f8cd2e6"
},
"downloads": -1,
"filename": "pyMOTO-1.4.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "1799f7b23bbf678fe01bc9da8bb98ab3",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 80377,
"upload_time": "2024-09-05T20:00:11",
"upload_time_iso_8601": "2024-09-05T20:00:11.764752Z",
"url": "https://files.pythonhosted.org/packages/e4/69/6b3bb8b5da16ffd1d98be3bbe9e980dd391f2081bae82d2c6f624b7b4a8a/pyMOTO-1.4.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "100299f92445243f9b43514155f5289cfec4b806aae8900c4120f4bbda4fd919",
"md5": "06fcfaafc5fb4b68051aef58c77676fc",
"sha256": "c51b7b16decb11725dc3832410a04e3f4a3de8bcb29945b85e89a80ed40947da"
},
"downloads": -1,
"filename": "pymoto-1.4.0.tar.gz",
"has_sig": false,
"md5_digest": "06fcfaafc5fb4b68051aef58c77676fc",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 97453,
"upload_time": "2024-09-05T20:00:14",
"upload_time_iso_8601": "2024-09-05T20:00:14.111942Z",
"url": "https://files.pythonhosted.org/packages/10/02/99f92445243f9b43514155f5289cfec4b806aae8900c4120f4bbda4fd919/pymoto-1.4.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-05 20:00:14",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "aatmdelissen",
"github_project": "pyMOTO",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "pymoto"
}
JSON
