mcvoronoi package
Project description
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 | |
---|---|---|
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 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
Output plots
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