Metadata-Version: 2.1
Name: mcvoronoi
Version: 0.0.3
Summary: mcvoronoi package
Home-page: https://github.com/abcnishant007/mcvoronoi
Author: Kusum Kumari
Author-email: kusum.kumarisjce@gmail.com
License: UNKNOWN
Description: # mcvoronoi 
        Computing [voronoi](https://en.wikipedia.org/wiki/Voronoi_diagram) areas using monte carlo simulation
        
        
        ### Prerequisites (required modules)
        
        - python_requires='>=3.6'
        ```sh
        $ python3 --version 
        ```
        If not installed, visit official site for python [here](https://www.python.org/downloads/) and download the latest version of Python.
        
        - numpy
        ```sh
        $ pip3 install numpy
        ```
        - sklearn
        ```sh
        $ pip3 install sklearn
        ```
        - matplotlib
        ```sh
        $ pip3 install matplotlib
        ```
        
        ### Installation
        
        ```sh
        $ pip3 install mcvoronoi
        ```
        - in main.py file *example code to use the module*:
        ```sh
        import numpy as np
        import mcvoronoi 
        
        
        points = np.random.rand(10, 2)  # a numpy array of 10 input co-ordinates
        lat_lon_area, mean_percentage_error = mcvoronoi.voronoi_area(points, voronoi_plot_enabled=True, NUM_COLORS=5)
        ```
        
        ### Parameters to the function 
        
        | 	  Input Type	| 						Input					| 	Default_Value	|
        | ------------------| ----------------------------------------------|-------------------|
        | numpy array		| input_coordinates		|	No default value|
        | integer 			| number_of_iterations 							|	50				|
        | integer			| number_of_trials_per_iteration 				|	10000			|
        | boolean			| error_plot_enabled 							|	True			|
        | boolean			| voronoi_plot_enabled 							|	False			|
        | float				| sizeOfMarker 									|	0.5				|
        | integer			| NUM_COLORS									|	20				|
        
        
        ### Returned values
        
        |	  Return Type	| 						Output														|
        | ------------------| ----------------------------------------------------------------------------------|
        | python dict		| key = (x,y), value = % of area of the smallest rectangle enclosing all input_coordinates, len(lat_lon_area) is same as number of input_coordinates  			|
        | plot  			| line graph of % error vs trial number (saved as .png)							|
        | plot				| voronoi Diagram with pts & random pts closest to points marked in NUM_COLORS(saved as .png)		|
        | float				| mean % error at the last trial									 						|
        
        
        ### Credits
        
        |	  Author	    | 		Contribution                                    														|        Email					|
        | ------------------| --------------------------------------------------------------------------------------------------------------|-------------------------------|
        | Kusum Kumari      | code standardization; code extension to include useful functionalities; creation and maintenance of mcvoronoi python library | kusum.kumarisjce@gmail.com    |
        | Nishant Kumar 	| initial working solution using [MC](https://en.wikipedia.org/wiki/Monte_Carlo_method) simulation for voronoi areas | abc.nishant007@gmail.com     			|
        
        
        ### Contributing
        
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to add/change.
        
        
        ### License
        
        [MIT](https://choosealicense.com/licenses/mit/)
        
        
        ### Output plots
        
        [mean_errors_plot](https://user-images.githubusercontent.com/9101260/84084935-0e5a7380-aa17-11ea-9519-7887a4a35cc0.png) 
        [vornoi_colored_areas](https://user-images.githubusercontent.com/9101260/84084884-ed921e00-aa16-11ea-97b6-edfb4c98c397.png) 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
