Bayesian Traction Force Microscopy
Motivation: TFM data is annoying, disperse tool set (all in matlab / ImageJ / javascript)
PyTraction: Modern software and easy to use end-to-end
🕘 Tests for edge cases on input
🕘 Simulation validation
🕘 Add clean docker image
Please try running the following notebooks on google colab. You will need to generate a personal access token.
Example 1: Basic usage with an image stack and reference image in the correct format
Example 2: Basic usage with an ROI
Example 3: Basic usage unexpected image formats
Navigate to folder where you want to install and ensure you have miniconda and git installed and run the following commands.
git clone https://github.com/rg314/pytraction.git
conda create -n pytraction python=3.8
conda activate pytraction
pip install pytraction/
cd pytraction
python scripts/get_example_data.py
pip install notebook
python -m ipykernel install --user --name=pytraction
jupyter notebook scripts/usage.ipynb
Note: if running on Windows you may have problems with the shapely library and some ctypes extensions. Please install shapely via conda install shapely.
For HTTPS
git clone https://github.com/rg314/pytraction.git
For SSH
git clone git@github.com:rg314/pytraction.git
Create conda env
conda create -n pytraction python=3.8
Activate conda env
conda activate pytraction
Install pytraction (for dev install in editable mode - e .)
pip install pytraction/
Change directory to repository
cd pytraction
To get example data run
python scripts/get_example_data.py
Install jupyter notebook
pip install notebook
Install pytraction kernel to use with jupyter notebook
python -m ipykernel install --user --name=pytraction
For basic usage run
jupyter notebook scripts/usage.ipynb
The following code show an basic example. Please make sure you download example data by running the following script python scripts/get_example_data.py. You need to make sure that the data folder is in your working directory when you run the following code. For a more in-depth examples please see scripts/usage.ipynb
For basic usage:
from pytraction import TractionForceConfig
from pytraction import plot, process_stack
import matplotlib.pyplot as plt
pix_per_mu = 1.3 # The number of pixels per micron
E = 100 # Youngs modulus in Pa
img_path = 'data/example1/e01_pos1_axon1.tif'
ref_path = 'data/example1/e01_pos1_axon1_ref.tif'
config = 'config/config.yaml'
traction_config = TractionForceConfig(E=E, scaling_factor=pix_per_mu, config=config)
img, ref, _ = traction_config.load_data(img_path, ref_path)
log = process_stack(img[:1,:,:,:], ref, config=traction_config)
plot(log, frame=0)
plt.show()
Please follow OneDrive link to draft manuscript or OneDrive link to folder. I've chosen OneDrive as it nicely integrates with EndNote and multiple users editing at once.
The draft manuscript has only been sent to a few authors to date. Authors are listed alphabetically. Contributors will be invited to manuscript if a significant contribution is made which can consist of:
- Significantly contributing to design or code of the core python package.
- Demonstrating usage of core python package compared to other analysis techniques.
- Demonstrating usage of core python package with computational techniques.
- Fixing major bugs in core python package.
- Contributed or provided experimental data for the manuscript.
If you believe that you have made a significant contribution (and have not been invited to the manuscript) or would like to make a significant contribution please contact me.
Contributing: Think of Big O notation and Occam's_razor
For commiting big code blocks please relate them to issues and create a new branch. The branch name as the abbriviated issue (issue8 = iss8).
git checkout -b iss8
Commit to current branch and assign a reviewer when merging pull request into main branch from the webapp.
Here is a list of common resources that have been used