py-graph-em-temp


Namepy-graph-em-temp JSON
Version 0.0.4 PyPI version JSON
download
home_pagehttps://github.com/Regev32/py-graph-em
SummaryGraph Based EM
upload_time2025-02-17 12:11:48
maintainerNone
docs_urlNone
authorRegev Yehezkel Imra
requires_python>=3.8
licenseLGPL 3.0
keywords graph em
VCS
bugtrack_url
requirements toml py-graph-imputation
Travis-CI No Travis.
coveralls test coverage No coveralls.
            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 (2025-02-16)
------------------

* First release on PyPI.

            

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    "description": "Graph Based Multi-Race EM\n=========================\n\n## Pre-requisites\n\nMake a virtual environment and activate it, run `make venv`\n```shell\nmake venv\nsource venv/bin/activate\n```\n\nInstall all the development dependencies. Will install packages from all `requirements-*.txt` files.\n```shell\nmake install\n```\n\n### Data Directory Structure\n\nDonor files are in `data` directory, specified in the conf file.\n\n```\ndata\n\u2514\u2500\u2500 ct_mr_don_10.txt\n```\n\n### conf Directory Structure\n\nConfigure input/output directories and EM specific parameters.\n\n```\nconf\n\u2514\u2500\u2500 minimal-configuration.json\n```\n\n# Example EM run\n\nRun the `test_em` script to produce frequences from the example data.\n```\npython test_em.py\n```\n\nResults can be found in `output` directory.\n\n\nHow to contribute:\n\n1. Fork the repository: https://github.com/nmdp-bioinformatics/py-graph-em.git\n   This will create a new repository with the given name e.g. `py-graph-em.`\n2. Clone the repository locally\n    ```shell\n    git clone  https://github.com/pbashyal-nmdp/py-graph-em.git\n    cd py-graph-em\n    ```\n3. Make a virtual environment and activate it, run `make venv`\n   ```shell\n    > make venv\n      python3 -m venv venv --prompt py-graph-em-venv\n      =====================================================================\n    To activate the new virtual environment, execute the following from your shell\n    source venv/bin/activate\n   ```\n4. Source the virtual environment\n   ```shell\n   source venv/bin/activate\n   ```\n5. Development workflow is driven through `Makefile`. Use `make` to list show all targets.\n   ```\n    > make\n    clean                remove all build, test, coverage and Python artifacts\n    clean-build          remove build artifacts\n    clean-pyc            remove Python file artifacts\n    clean-test           remove test and coverage artifacts\n    lint                 check style with flake8\n    behave               run the behave tests, generate and serve report\n    pytest               run tests quickly with the default Python\n    test                 run all(BDD and unit) tests\n    coverage             check code coverage quickly with the default Python\n    dist                 builds source and wheel package\n    docker-build         build a docker image for the service\n    docker               build a docker image for the service\n    install              install the package to the active Python's site-packages\n    venv                 creates a Python3 virtualenv environment in venv\n    activate             activate a virtual environment. Run `make venv` before activating.\n   ```\n6. Install all the development dependencies. Will install packages from all `requirements-*.txt` files.\n   ```shell\n    make install\n   ```\n7. The Gherkin Feature files, step files and pytest files go in `tests` directory:\n    ```\n    tests\n    |-- features\n    |   |-- algorithm\n    |   |   `-- SLUG\\ Match.feature\n    |   `-- definition\n    |       `-- Class\\ I\\ HLA\\ Alleles.feature\n    |-- steps\n    |   |-- HLA_alleles.py\n    |   `-- SLUG_match.py\n    `-- unit\n        `-- test_my_project_template.py\n    ```\n8. Package Module files go in the `my_project_template` directory.\n    ```\n    my_project_template\n    |-- __init__.py\n    |-- algorithm\n    |   `-- match.py\n    |-- model\n    |   |-- allele.py\n    |   `-- slug.py\n    `-- my_project_template.py\n    ```\n9. 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.\n10. Use `python app.py` to run the Flask service app in debug mode. Service will be available at http://localhost:8080/\n11. Use `make docker-build` to build a docker image using the current `Dockerfile`.\n12. `make docker` will build and run the docker image with the service.  Service will be available at http://localhost:8080/\n\n\n\nThe 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.\u200f](https://www.sciencedirect.com/science/article/pii/S0198885921001750?casa_token=Ob0ufT6jBLMAAAAA:uFVlu1R0wFBkqQ8rhztoCppH_EGnOnygJaTYwmT-EvHfKFIISI2Sc2GcTu8CJ9F3MPxc53ZuizTe)\n\n\n=======\nHistory\n=======\n\n0.0.1 (2025-02-16)\n------------------\n\n* First release on PyPI.\n",
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