Constrained form-finding meets automatic differentiation.
Project description
COMPAS CEM
Constrained form-finding meets automatic differentiation.
Introduction
COMPAS CEM is a structural design tool that generates efficient form for spatial bar structures subjected to combinations of tension and compression forces. Examples of such structures are gridshells, bridges, stadiums, tensegrities and multistory buildings.
The generated forms can be steered to meet force and geometrical constraints, such as limiting the length of a selection of elements in the structure, best-fitting an arbitrary target surface, or restraining the magnitude of the reactions forces at the supports of a structure.
This constrained form-finding process is solved under the hood using numerical optimization and automatic differentiation -- two commonplace techniques in the world of machine learning that COMPAS CEM makes readily accesible to designers around the world.
COMPAS CEM is a COMPAS extension written in pure Python. It runs on Windows, MacOS and Linux and it does not depend on any CAD software to work.
Are you a Grasshopper person though? Worry not. CAD-independence doesn't mean CAD-incompatibility: we ship COMPAS CEM as a Grasshopper plugin so that you can readily integrate our constrained form-finding engine into your next parametric pipeline.
Feel free to check the examples and the docs to get a glimpse of what COMPAS CEM can do for you. If you are further interested in learning more about the underpinnings of the CEM framework, the constrained form-finding method that COMPAS CEM implements, we refer you to this journal paper.
Authors
COMPAS CEM is developed by Rafael Pastrana at the CREATE Laboratory at the Princeton University School of Architecture in collaboration with Patrick Ole Ohlbrock from the Chair of Structural Design at ETH Zürich and Pierluigi D'Acunto from the Professorship of Structural Design at the Technical University of Munich.
Installation
Install compas_cem
in only three simple steps.
We assume you have Anaconda installed in your machine. If not, please download and install it before continuing.
First, create a new anaconda environment from your command line. The only dependency is compas
.
Here we chose the name of the environment to be cem
, but you can call it spacecowboy
if you prefer.
conda create -n cem COMPAS
Next, activate the cem
environment. Anaconda environments are like bubbles that keep installations and dependencies isolated from other parts in your machine. In other words, what happens in cem
stays in cem
! 🕺🏻
conda activate cem
Finally, install compas_cem
with a one-liner:
pip install compas-cem
To double-check that everything is up and running, while still in the command line, type the following and hit enter:
python -c "import compas_cem"
If no errors show up, celebrate 🎉! You have a working installation of compas_cem
.
Grasshopper Plugin
There will be times when modeling a complex structure is easier to do with a few mouse-clicks instead of a hundred lines of code.
The grasshopper version of compas_cem
allows you to use all the important bits of our constrained form-finding engine in a (familiar) visual programming environment.
To additionally install compas_cem
as a grasshopper plugin, close Rhino, go to the command line and activate the anaconda environment where compas_cem
lives. Note that you should have installed compas_cem
from your command line before installing the grasshopper plugin.
conda activate cem
Let's connect compas_cem
, compas_rhino
and compas_ghpython
to Rhino from te command line.
If you were wondering, the last two are installed by default by compas
.
python -m compas_rhino.install -v 7.0
Note that the flag -v 7.0
indicates that we will be installing compas_cem
and company to Rhino 7.
If you are working with Rhino 6, replace that last bit with -v 6.0
.
Finally, launch grasshopper and start dropping compas_cem
components onto the canvas! Send pictures! 🏖
Caveat
The compas_cem
plugin for grasshopper is a collection of .ghuser
objects. As such, they have one important limitation: once used in a document, they forget who they are. The don't know they were created out of a ghuser component, they will be simple GHPython components. This has an important consequence: if you update compas_cem
, those components already in use will NOT be automatically updated. More information here.
Contributing
Pull requests are welcome!
Make sure to read our contribution guide.
Please don't forget to run invoke test
in your command line before making a pull request.
Issue tracker
If you find a bug or want to suggest a potential enhancement, please help us tackle it by filing a report.
License
MIT.
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 Distribution
File details
Details for the file compas_cem-0.1.12.tar.gz
.
File metadata
- Download URL: compas_cem-0.1.12.tar.gz
- Upload date:
- Size: 150.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | dccb2e87928e132b6c6c684d501630ef8a924d8ef80a904b432f1baa522b4f23 |
|
MD5 | 19965d8149b6947fe6b4ee63f6ce357d |
|
BLAKE2b-256 | 31bdb343a4534ee8cc93f3f85a82e57e464d3c5e2f95ad983ae73493ab0c5c6d |
File details
Details for the file compas_cem-0.1.12-py2.py3-none-any.whl
.
File metadata
- Download URL: compas_cem-0.1.12-py2.py3-none-any.whl
- Upload date:
- Size: 252.3 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 27354f123467fab267f5052effeb6b6759634ede6e9c437c16838891813875c3 |
|
MD5 | d2452a310a461e2c1a075892fc2bb0cd |
|
BLAKE2b-256 | c78c30995ec72ca1a8d70b43fd04c6e12a7f9cd8785417dd108d23ad4caef8be |