Computational Calcification Analysis

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

Requirements

This project requires CUDA 11.7 to be installed. Requirements

To run this project, the following requirements need to be met:

CUDA 11.7 Python 3.10.x

Creating a Virtual Environment

To ensure that the project runs smoothly and without conflicts, it is recommended to create a new virtual environment using either Conda or Micromamba. Here are the steps to create a new virtual environment using both methods:

Conda

Activate the virtual environment by running the following command:

conda activate myenvironment

Micromamba

Install Micromamba by following the instructions on the official website.

Once Micromamba is installed, open a new terminal window and navigate to the project.directory

Create a new virtual environment by running the following command:

micromamba env create -f environment.yml -p /path/to/your/environment

Replace /path/to/your/environment with the name you want to give your virtual environment.

Activate the virtual environment by running the following command:

conda activate myenvironment

Running the Project

To run the project, make sure you have activated the virtual environment and then run the following command:

python main.py

Install Conda by following the instructions on the official website.

Once Conda is installed, open a new terminal window and navigate to the project directory.

Create a new virtual environment by running the following command:

conda env create -f environment.yml -p /path/to/your/environment

Replace /path/to/your/environment with the name you want to give your virtual environment.

Activate the virtual environment by running the following command:

conda activate myenvironment