If you use f3ast in any work or publication, we kindly ask you to cite:
The package can be installed with pip:
pip install f3ast
To install the most up-to-date version and get the example notebooks, clone the repository from github:
git clone [email protected]:Skoricius/f3ast.git
pip install -e f3ast --user
Note: ensure you have the most up-to-date version of pip.
For development, it is recommended to work within poetry environment. For this, install poetry. Run poetry install --all-extras to create the environment and install all packages. To activate the environment run poetry shell.
See readthedocs for detailed documentation and examples.
This library was tested with Python 3.9. Earlier versions of Python might not have all the libraries required to make this project work.
On MACs, there might be an issue with numba threading. It has something to do with tbb library. If someone finds out a fix, please let me know.
A simple workaround is to comment out line 9 in slicing.py which might make the slicing slightly slower.
Make sure the project is installed. Also, install pandoc with apt install pandoc. Navigate to ./docs folder of the cloned directory. In Linux or with Git Bash (on Windows) run:
make html
If using Windows without Git Bash, first install make. Easiest is to open Powershell and install chocolatey. Then run:
choco install make
make html
Open ./docs/_build/html/index.html.
Microscope settings are defined in settings.hjson file and contain information about the microscope and basic slicing settings.
In the following example, we load the structure and the settings, define the deposit model we are using, and build the stream.
# load the settings
settings = load_settings()
# get the structure from a file
struct = Structure.from_file('testing/FunktyBall.stl', **settings["structure"])
struct.rescale(2) # make the structure a bit bigger
# define the model
gr = 0.1
k = 1
sigma = 4
model = DDModel(struct, gr, k, sigma, **settings['dd_model'])
# Solve for dwells and build the stream
stream_builder, dwell_solver = StreamBuilder.from_model(model, **settings['stream_builder'])
dwell_solver.print_total_time()
# save the streamfile and the build information
save_path = 'funky_ball'
strm = stream_builder.get_stream()
strm.write(save_path)
save_build(save_path, dwell_solver, stream_builder)For a full step-by-step example, see examples/building.ipynb.
For acquiring calibration parameters, see examples/calibration.ipynb.
- progressive slicing
- filling in the STL (this should only be done in get_eqd function and should be relatively straightforward)
- add black, isort
- add pull request actions
- improved testing and CI