Pupilometry using OpenCV to extract salient features, namely pupil diameter over time. This repository comes with a .yml file that can be used with conda to build a virtual environment in which all code will function. The virtual environments uses python2.7
Most important functions can currently be found in:
src/fix/
First, download anaconda to your computer. With conda loaded or in your path, navigate to the head of the repository and run this code from the command line:
conda env create -f .env/pupil27.yml
This will create the proper environment in the folder you have specified previously with conda. Activate the environment with:
conda activate pupil27
Using the interpreter in this environmen, the use of jupyter notebooks is available and all code will run smoothly. To verify the environment was installed correctly you can use:
conda env list
or
conda info --envs
The structural organization of the project is based on the Cookiecutter Data Science package. More information can be found here:
https://drivendata.github.io/cookiecutter-data-science/
Futher tuturial information coming.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience