Metadata-Version: 2.1
Name: knox-mi-graph
Version: 1.0.5
Summary: Knowledge graph builder
Home-page: https://git.its.aau.dk/Knox/mi-graph
Author: Foersteholdet
Author-email: sw514e20@cs.aau.dk
License: UNKNOWN
Description: # knox-d
        MI-graph (Machine learning graph)
        
        machine learning experts, do things
        
        ## Usage
        run the following commands
        - `pip install --extra-index-url https://repos.knox.cs.aau.dk knox-mi-graph`
        - `mi_graph input.json`
        
        You can try it out with our example, and put a -v for visualisation
        
        `mi_graph examples/grundfos_test.json`
        
        ### Run under development
        at root level of the repository run:
        
        `python mi_graph file.json`
        
        
        ## Setup: virtualenv environment
        Make sure pip is up-to-date:
        
        `python -m pip install --upgrade pip`
        
        Install `virtualenv`:
        
        `python -m pip install virtualenv`
        
        ### Activate environment
        To create an environment run:
        
        `python -m venv env`
        
        to create an environment with the name "env". Now to activate the environment run the activate script based on your OS in "env/Scripts".
        
        On windows 10 you can run:
        
        `env/Scripts/Activate.ps1`
        
        in your powershell, to activate the environment.
        
        If running scripts is denied - run the following command to remove the restriction. [Stackoverflow](https://stackoverflow.com/questions/4037939/powershell-says-execution-of-scripts-is-disabled-on-this-system)
        
        `set-executionpolicy remotesigned`
        
        ### Install dependencies
        To install dependencies run:
        
        `pip install -r .\requirements.txt`
        
        If you encounter problems see [this](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
        
        ## Setup: conda environment
        Install conda from [their website](https://docs.anaconda.com/anaconda/install/). (We recommend the mini version / miniconda)
        
        Initialize the environment with:
        - `conda create --name knox-env python=3.8`
        - `activate knox-env`
        - `pip install -r requirements.txt`
        And you are good to go.
        
        
        ## Test dependencies
        For developers, you also need to install the test requirements:
        - `pip install -r tests/requirements.txt`
        
        ## pylint
        Before you make a pull request to master, you should run branch though pylint.
        
        you can use `pylint_runner` to run all folders in the solution, or with pylint like so
        Run: 
        ```bash
        pip install pylint
        ```
        And then
        ```bash
        pylint folder/
        ```
        
        ## Build module
        You cannot be in a virtual environment, when building
        - `python3 setup sdist bdist_wheel`
        - `python3 -m twine upload dist/*`
            
        TODO
        - [ ] upload to knox repo instead of pypi 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
