saugatucket-fish-passage

Processing and analysis of fish passage data along the Saugatucket River, RI.

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`.
│
├── 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.readthedocs.io

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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Cloning the GitHub Repository

To clone this GitHub repository to your local machine on Windows, follow these steps:

1. Open Command Prompt or PowerShell.
2. Navigate to the directory where you want to clone the repository using the cd command. For example:

cd path\to\desired\directory

3. Use the following command to clone the repository:

git clone https://github.com/gtdang/saugatucket-fish-passage.git

4. Once the cloning process is complete, navigate into the cloned repository directory:

cd repository-name

Creating a Python Environment

This repository contains a .tool-versions file and a requirements.txt file to manage the Python environment using pyenv and pip. Follow these steps to create a Python environment on Windows:

1. Ensure you have pyenv-win installed on your system. If not, follow installation instructions pyenv-win GitHub. 2. If you have any problems running pyenv commands, follow the Add System Settings directions to update your environment and system variables.

2. Once pyenv-win is installed, navigate to the cloned repository directory in Command Prompt or PowerShell.

3. Use the following command to set up the Python version specified in the .tool-versions file:

pyenv install $(type .tool-versions)

This will install the Python version specified in the .tool-versions file if it's not already installed on your system.

4. After installing the required Python version, use the following command to create a virtual environment and activate it:

pyenv virtualenv $(type .tool-versions) <environment-name>

Replace <environment-name> with the desired name for your virtual environment.

5. Activate the virtual environment by running:

pyenv local <environment-name>

6. Finally, install the Python dependencies listed in the requirements.txt file using pip:

pip install -r requirements.txt

Now you have successfully created a Python environment and installed the required dependencies for this repository on Windows.

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