Metadata-Version: 2.1
Name: data-pipeline-ml-caqtl-visualization
Version: 0.1.4
Summary: ML visualization pipeline for caQTL evaluation
Home-page: https://github.com/mcvickerlab/CaVarCNN/blob/master
License: MIT
Keywords: machine-learning,visualization,caQTL,PyTorch
Author: Jaureguy760
Author-email: jeffpjaureguy@gmail.com
Requires-Python: >=3.10,<3.13
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: click
Requires-Dist: h5py
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: polars
Requires-Dist: scipy
Requires-Dist: statsmodels
Project-URL: Repository, https://github.com/mcvickerlab/CaVarCNN/blob/master
Description-Content-Type: text/markdown

## Data Pipeline

Processes inference models predictions and observed data, exploratory data analysis, data vizualization.

## Configuration

Before running the pipelines, you need to configure them. Configuration files are located in the `/config/` directory. For custom configurations:

    This will create the following files that the user needs to fill out:

    - `pipelines/data_pipeline/configs/direct_input_config.json`
    - `pipelines/data_pipeline/configs/personal_config.json`

2. **Edit Config Files**: Modify the configuration files to match your data and setup. These files contain the necessary parameters and paths required to run the pipelines successfully. Ensure that all paths, model checkpoints, and settings are correctly specified to match your environment.



### Option 1: Default Repository Structure

Use this option if you're following the default setup as structured in the repository:

```bash
python generate_config.py --config_file configs/default_config.json
```

### Option 2: Custom Configuration

Use this option if you need to specify custom paths and settings:

```bash
python generate_config.py --direct_input --config_file configs/direct_input_config.json
```

3. **Usage**: Once the configuration is complete, you can run the pipeline.

## Running the pipeline

### Data Frame Generation

### Exploratory Data Analysis(EDA)

### Data Visualization


## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.

## License
This project is licensed under the MIT License - see the LICENSE file for details


