Yves Biener
3414b6c145
Approximation uses simple linear regression to determine whether the _location information_ is significant enough (through the calculated steepness `beta`) which can be used to determine in a faster and more efficient way whether the calculation of the model is necessary and helpful in the first place.
Boundary-aware centrality for Spatial Graphs
University project for boundary-aware node centralities for spatial graphs. For the project details and results see the typst paper. For building the document and the usage of the python library used for this project see the corresponding sections below.
Code usage
Important
The implementation uses gurobi for solving the linear problem for the function fitting which requires a license. Please refer to the gurobi license documentation for details.
Install the requirements into a virtual environment:
# create virtual environment
python -m venv venv
# activate virtual environment
source venv/bin/activate
# install required dependencies
pip install -r requirements.txt
The src directory contains all the python source files that are used for the creation of the diagrams and images found in the doc/figures directory. For the corresponding usages for both datasets used for this project please see merfish.py and mibitof.py.
For running the generation of the diagrams and images you can run one of the two python scripts like so (inside of the virtual environment):
python merfish.py
Document generation
The document is written using typst and can be compiled into a pdf file using:
typst c doc/paper.typ
The generated pdf file will then be generated into the doc directory named paper.pdf.