Analyzing relationships between bird calls and environmental data. See the blog post here:
https://deepnote.com/@adithya-balaji/Analyzing-Bird-Audio-Q1tEOBxdTR2mZSXq4FhZwQ
Relevant Links
- Repo template based on Cookie Cutter Data
- Data versioning: DVC
- Linting manager: pre-commit
- Custom CLI Tools: click
$ python -m venv venv
$ source ./venv/bin/activate
(venv) $ pip install -r requirements.txt
(venv) $ brew install pre-commit dvc geos proj
(venv) $ pre-commit installIf you want to skip the pre-commit checks, you need to pass the
--no-verify flag.
To pull the latest data from DVC. (this won't work if you haven't manually been added to the Google Drive folder)
(venv) $ dvc pullThis should pull data from Google Drive into the data/ folder.
├── LICENSE
├── 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.
│
├── 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`.
│
├── 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 prac_dsfinal can be imported
└── prac_dsfinal <- Source code for use in this project.
├── __init__.py <- Makes prac_dsfinal 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