Metadata-Version: 2.1
Name: FoodNetX
Version: 0.2
Summary: Library to create databases using graphs from networkx. Covers the challenge of Foodvisor
Home-page: https://github.com/Jor-G-ete/FoodNetX
Author: Jorge Lopez Marcos
Author-email: jlomar2005@hotmail.com
Maintainer: Jorge Lopez Marcos
Maintainer-email: jlomar2005@hotmail.com
License: GNU General Public License v3.0
Download-URL: https://github.com/Jor-G-ete/FoodNetX/archive/v0.2.tar.gz
Project-URL: Documentation, https://github.com/Jor-G-ete/FoodNetX
Project-URL: Source Code, https://github.com/Jor-G-ete/FoodNetX/blob/master/FoodNetX/database.py
Description: # FoodVisor Challenge 1
        
        [![](https://img.shields.io/pypi/v/FoodNetX.svg)]()
        [![](https://img.shields.io/pypi/pyversions/FoodNetX.svg)](https://pypi.org/project/FoodNetX/)
        [![](https://img.shields.io/pypi/l/FoodNetX.svg)](https://github.com/Jor-G-ete/FoodNetX/blob/master/LICENSE)
        [![](https://img.shields.io/github/downloads/Jor-G-ete/FoodNetX/total)]()
        [![](https://img.shields.io/github/last-commit/Jor-G-ete/FoodNetx)]()
        [![](https://img.shields.io/github/v/release/Jor-G-ete/FoodNetX)]()
        [![](https://img.shields.io/github/v/tag/Jor-G-ete/FoodNetx)]()
        
        FoodNetx is a library created using the python package *NetworkX*. It creates a database based in graphs, in concrete a Directed graph, in which could occur cycles. The main idea is cover the challenge of Foodvisor ( [Challenge1](https://github.com/Foodvisor/coding-assignment) and [Challenge2](https://github.com/Foodvisor/home-assignment) ) but any other use is welcomed, please if it used reference this Github and its creator.
        
        ## Installation
        
        ### Source file
        
        1. Download the source file from github
        2. Unzip and navigate to the folder containing `setup.py` and other files
        3. Run the following command: `python setup.py install`
        
        ### Pip
        
        ```python3
            pip3 install FoodNetX
        ```
        
        ## Testing and replicating the results for the Foodvisor challenges
        
        Download and install the python package *FoodNetx*, then clone the repo.
        In the Github repository has been left a folder called *Test*, where tests are stored.
        To execute any test, just use the following shell command from inside the github repo:
        
        ```shell
        python3 ./Test/TestFile.py
        ```
        
        Example:
        
        ```bash
        python3 ./Test/FoodVisor_Challenge1_test.py
        ```
        
        If you use an IDE for programming just select the test file desired and just click Run.
        
        ### Test's outputs
        
        **FoodVisor_Challenge1_test.py**: 
        
        ```python
        {"img001": "granularity_staged", "img002": "valid"}
        {"img001": "granularity_staged", "img002": "coverage_staged", "img003": "invalid"}
        ```
        
        
        
        ##  New modules in development  
        
        By now the foodvisor challenge #2 is being developed and implemented to join this package at the end of this week
        
        ## Python Compatibility
        
        * [Python](http://www.python.com) - v3.7
        
        ### Note
        
        This library has been created with the help of [Networkx](https://networkx.github.io/mat) and [Matplotlib](https://matplotlib.org/)
        
        ### 
Keywords: python3.7,NeuroSky,Graphs,Trees
Platform: Windows
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Natural Language :: English
Classifier: Natural Language :: Spanish
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.7
Description-Content-Type: text/markdown
