Metadata-Version: 2.1
Name: py-graph-em
Version: 0.0.3
Summary: Graph Based EM
Home-page: https://github.com/nmdp-bioinformatics/py-graph-em
Author: Pradeep Bashyal
Author-email: pbashyal@nmdp.org
License: LGPL 3.0
Description: Graph Based Multi-Race EM
        =========================
        
        ## Pre-requisites
        
        Make a virtual environment and activate it, run `make venv`
        ```shell
        make venv
        source venv/bin/activate
        ```
        
        Install all the development dependencies. Will install packages from all `requirements-*.txt` files.
        ```shell
        make install
        ```
        
        ### Data Directory Structure
        
        Donor files are in `data` directory, specified in the conf file.
        
        ```
        data
        └── ct_mr_don_10.txt
        ```
        
        ### conf Directory Structure
        
        Configure input/output directories and EM specific parameters.
        
        ```
        conf
        └── minimal-configuration.json
        ```
        
        # Example EM run
        
        Run the `test_em` script to produce frequences from the example data.
        ```
        python test_em.py
        ```
        
        Results can be found in `output` directory.
        
        
        How to contribute:
        
        1. Fork the repository: https://github.com/nmdp-bioinformatics/py-graph-em.git
           This will create a new repository with the given name e.g. `py-graph-em.`
        2. Clone the repository locally
            ```shell
            git clone  https://github.com/pbashyal-nmdp/py-graph-em.git
            cd py-graph-em
            ```
        3. Make a virtual environment and activate it, run `make venv`
           ```shell
            > make venv
              python3 -m venv venv --prompt py-graph-em-venv
              =====================================================================
            To activate the new virtual environment, execute the following from your shell
            source venv/bin/activate
           ```
        4. Source the virtual environment
           ```shell
           source venv/bin/activate
           ```
        5. Development workflow is driven through `Makefile`. Use `make` to list show all targets.
           ```
            > make
            clean                remove all build, test, coverage and Python artifacts
            clean-build          remove build artifacts
            clean-pyc            remove Python file artifacts
            clean-test           remove test and coverage artifacts
            lint                 check style with flake8
            behave               run the behave tests, generate and serve report
            pytest               run tests quickly with the default Python
            test                 run all(BDD and unit) tests
            coverage             check code coverage quickly with the default Python
            dist                 builds source and wheel package
            docker-build         build a docker image for the service
            docker               build a docker image for the service
            install              install the package to the active Python's site-packages
            venv                 creates a Python3 virtualenv environment in venv
            activate             activate a virtual environment. Run `make venv` before activating.
           ```
        6. Install all the development dependencies. Will install packages from all `requirements-*.txt` files.
           ```shell
            make install
           ```
        7. The Gherkin Feature files, step files and pytest files go in `tests` directory:
            ```
            tests
            |-- features
            |   |-- algorithm
            |   |   `-- SLUG\ Match.feature
            |   `-- definition
            |       `-- Class\ I\ HLA\ Alleles.feature
            |-- steps
            |   |-- HLA_alleles.py
            |   `-- SLUG_match.py
            `-- unit
                `-- test_my_project_template.py
            ```
        8. Package Module files go in the `my_project_template` directory.
            ```
            my_project_template
            |-- __init__.py
            |-- algorithm
            |   `-- match.py
            |-- model
            |   |-- allele.py
            |   `-- slug.py
            `-- my_project_template.py
            ```
        9. Run all tests with `make test` or different tests with `make behave` or `make pytest`. `make behave` will generate report files and open the browser to the report.
        10. Use `python app.py` to run the Flask service app in debug mode. Service will be available at http://localhost:8080/
        11. Use `make docker-build` to build a docker image using the current `Dockerfile`.
        12. `make docker` will build and run the docker image with the service.  Service will be available at http://localhost:8080/
        
        
        
        The py-graph-em code was developed in the [YOLO Lab](https://yolo.math.biu.ac.il/) and at the [NMDP](https://bethematch.org/). Please cite [Israeli, S., Gragert, L., Maiers, M., & Louzoun, Y. (2021). HLA haplotype frequency estimation for heterogeneous populations using a graph-based imputation algorithm. Human Immunology, 82(10), 746-757.‏](https://www.sciencedirect.com/science/article/pii/S0198885921001750?casa_token=Ob0ufT6jBLMAAAAA:uFVlu1R0wFBkqQ8rhztoCppH_EGnOnygJaTYwmT-EvHfKFIISI2Sc2GcTu8CJ9F3MPxc53ZuizTe)
        
        
        =======
        History
        =======
        
        0.0.1 (2021-08-25)
        ------------------
        
        * First release on PyPI.
        
Keywords: Graph,EM
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.8
Description-Content-Type: text/markdown
