## 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
Raw data
{
"_id": null,
"home_page": "https://github.com/mcvickerlab/CaVarCNN/blob/master",
"name": "data-pipeline-ml-caqtl-visualization",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.13,>=3.10",
"maintainer_email": null,
"keywords": "machine-learning, visualization, caQTL, PyTorch",
"author": "Jaureguy760",
"author_email": "jeffpjaureguy@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/dc/15/64a08dbca1c0c0916329d7b781dfbafe3e70316ac3f1bfd920242b72fe24/data_pipeline_ml_caqtl_visualization-0.2.1.tar.gz",
"platform": null,
"description": "## Data Pipeline\n\nProcesses inference models predictions and observed data, exploratory data analysis, data vizualization.\n\n## Configuration\n\nBefore running the pipelines, you need to configure them. Configuration files are located in the `/config/` directory. For custom configurations:\n\n This will create the following files that the user needs to fill out:\n\n - `pipelines/data_pipeline/configs/direct_input_config.json`\n - `pipelines/data_pipeline/configs/personal_config.json`\n\n2. **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.\n\n\n\n### Option 1: Default Repository Structure\n\nUse this option if you're following the default setup as structured in the repository:\n\n```bash\npython generate_config.py --config_file configs/default_config.json\n```\n\n### Option 2: Custom Configuration\n\nUse this option if you need to specify custom paths and settings:\n\n```bash\npython generate_config.py --direct_input --config_file configs/direct_input_config.json\n```\n\n3. **Usage**: Once the configuration is complete, you can run the pipeline.\n\n## Running the pipeline\n\n### Data Frame Generation\n\n### Exploratory Data Analysis(EDA)\n\n### Data Visualization\n\n\n## Contributing\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\nThis project is licensed under the MIT License - see the LICENSE file for details\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "ML visualization pipeline for caQTL evaluation",
"version": "0.2.1",
"project_urls": {
"Homepage": "https://github.com/mcvickerlab/CaVarCNN/blob/master",
"Repository": "https://github.com/mcvickerlab/CaVarCNN/blob/master"
},
"split_keywords": [
"machine-learning",
" visualization",
" caqtl",
" pytorch"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "eeae617202e86b637aa21ce302128ce6ee120caf6ecdd59ac692bbc6825d11c9",
"md5": "e88870ff06d25817dbc4acd7af7a3488",
"sha256": "93b8f68f3d015972b321ef582d5405b3eae2e4a63da6a8e4d4e82c5c7a4c74e7"
},
"downloads": -1,
"filename": "data_pipeline_ml_caqtl_visualization-0.2.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e88870ff06d25817dbc4acd7af7a3488",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.13,>=3.10",
"size": 41180,
"upload_time": "2024-10-22T02:37:04",
"upload_time_iso_8601": "2024-10-22T02:37:04.811890Z",
"url": "https://files.pythonhosted.org/packages/ee/ae/617202e86b637aa21ce302128ce6ee120caf6ecdd59ac692bbc6825d11c9/data_pipeline_ml_caqtl_visualization-0.2.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "dc1564a08dbca1c0c0916329d7b781dfbafe3e70316ac3f1bfd920242b72fe24",
"md5": "031bd50b7b40d682cb09040833ae3d8f",
"sha256": "b2c96e85283edff2f1daa2adc74ee3cf8d0aacae42d05cb3b9773e60b17e73b2"
},
"downloads": -1,
"filename": "data_pipeline_ml_caqtl_visualization-0.2.1.tar.gz",
"has_sig": false,
"md5_digest": "031bd50b7b40d682cb09040833ae3d8f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.13,>=3.10",
"size": 34387,
"upload_time": "2024-10-22T02:37:07",
"upload_time_iso_8601": "2024-10-22T02:37:07.513132Z",
"url": "https://files.pythonhosted.org/packages/dc/15/64a08dbca1c0c0916329d7b781dfbafe3e70316ac3f1bfd920242b72fe24/data_pipeline_ml_caqtl_visualization-0.2.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-22 02:37:07",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mcvickerlab",
"github_project": "CaVarCNN",
"github_not_found": true,
"lcname": "data-pipeline-ml-caqtl-visualization"
}
JSON
