maggot_models

Modeling the Drosophila larva connectome

Installation and setup

If you are new to Python development, my recommended practives are here

Currently, the recommended setup is to use conda or miniconda to create a virtual environment: https://docs.conda.io/en/latest/miniconda.html

conda environments are recommended. To create a new conda environment for this project, navigate to this directory in a terminal and run

$ conda create -f environment.yml

a conda virtual environment will be created with the name maggot_models. To verify that the environment was created run

$ conda info --envs

To activate the virtual environment run

$ conda activate maggot_models

Using this package is also possible with pip and a virtual environment manager. If you would like to use pip please contact @bdpedigo and I can make sure the pip requirements.txt is up to date (it isn't right now)

Project Organization

├── 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`.
|   |
|   └── outs           <- figures and intermediate results labeled by notebook that generated them.
│
├── 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
│
├── simulations        <- Synthetic data experiments and outputs
│   └── runs           <- Sacred output for individual experiment runs
│
├── 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