https://github.com/computationalphysiology/automatic-motion-estimation

Source code for reproducing results in the paper "Automatic motion estimation with applications to hiPSC-CMs"
https://github.com/computationalphysiology/automatic-motion-estimation

Last synced: 7 days ago
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Source code for reproducing results in the paper "Automatic motion estimation with applications to hiPSC-CMs"

• Host: GitHub
• URL: https://github.com/computationalphysiology/automatic-motion-estimation
• Owner: ComputationalPhysiology
• License: mit
• Created: 2023-10-01T18:53:58.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-14T14:28:12.000Z (4 months ago)
• Last Synced: 2024-12-01T19:23:19.856Z (2 months ago)
• Language: Jupyter Notebook
• Homepage: https://computationalphysiology.github.io/automatic-motion-estimation/
• Size: 58.6 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
[![DOI](https://zenodo.org/badge/699045305.svg)](https://zenodo.org/doi/10.5281/zenodo.13379848)

# Supplementary code for the paper Automatic motion estimation with applications to hiPSC-CMs

This repository contains supplementary code for reproducing results in the paper

>

Output of rendered notebooks are found at .

The code is heavily based on the library [`mps-motion`](https://github.com/ComputationalPhysiology/mps_motion). Please check out the [documentation](https://computationalphysiology.github.io/mps_motion/) in that library for more details.

## Running with binder

If you want to run the examples without installing anything you can launch an interactive environment with Binder, by clicking the following button [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ComputationalPhysiology/automatic-motion-estimation/HEAD)

## Running locally

### Python virtual environment

To run the notebooks locally, you first need to install the necessary requirements. There are two options. Either, you can create a virtual environment with python

```

python3 -m venv venv

```

and activate it. On Unix (MacOSX and Linux) you do

```

. \venv/bin/activate

```

and on Windows you do

```

.\venv\Scripts\activate

```

Next you install the dependencies

```

python3 -m pip install -r requirements.txt

```

### Docker

We also provide two different docker images for you. In order to start a container you can use the docker run command. For example the command

```

docker run --rm -v $(pwd):/home/shared -w /home/shared -ti ghcr.io/computationalphysiology/automatic-motion-estimation:latest

```

will run the latest version and share your current working directory with the container. The source code of the repository is located at `/repo` in the docker container.

To run the notebooks, one can use `ghcr.io/computationalphysiology/automatic-motion-estimation-lab:latest`, i.e

```

docker run -ti -p 8888:8888 --rm ghcr.io/computationalphysiology/automatic-motion-estimation-lab:latest

```

to run interactively with Jupyter lab in browser.

## Citing

If you use this code in your research, please cite the following paper

```

@article{10.1088/2057-1976/ad7268,

	author={Finsberg, Henrik Nicolay Topnes and Charwat, Verena and Healy, Kevin E and Wall, Samuel},

	title={Automatic motion estimation with applications to hiPSC-CMs},

	journal={Biomedical Physics & Engineering Express},

	url={http://iopscience.iop.org/article/10.1088/2057-1976/ad7268},

	year={2024},

}

```