mcvoronoi

Computing voronoi areas using monte carlo simulation

Prerequisites (required modules)

• python_requires='>=3.6'

$ python3 --version 

If not installed, visit official site for python here and download the latest version of Python.

• numpy

$ pip3 install numpy

• sklearn

$ pip3 install sklearn

• matplotlib

$ pip3 install matplotlib

Installation

$ pip3 install mcvoronoi

• in main.py file example code to use the module:

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	[email protected]
Nishant Kumar	initial working solution using MC simulation for voronoi areas	[email protected]

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to add/change.

License

MIT

Output plots