Skip to main content

Algorithms for TW

Project description

GitHub PyPI PyPI - Python Version

TW Complex

Repo with algorithms to divide ally villages into front and back in TW.

Underneath it is a problem of dividing a set of 2D points A according to the min_radius and max_radius distances from a set of other 2D points B, which can be solved most simply by counting the distances from each point in the first set A to all points in the second set B one by one.

Instalation

pip install tw_complex

Basic usage

from tw_complex import CDistBrute
import numpy as np

# The code for Example 1 below

points1 = np.random.rand(10000, 2) + [2, 0]
points2 = np.random.rand(15000, 2)
min_radius = 1.4
max_radius = 2

precise_front, precise_back = CDistBrute(
    ally_villages=points1,
    enemy_villages=points2,
    min_radius=min_radius,
    max_radius=max_radius,
).result()

Examples (before -> after)

Example 1

Ally: 10000 points
Enemy: 15000 points
min_radius: 1.4
max_radius: 2

example1

Example 2

Ally: 2500 points
Enemy: 6000 points
min_radius: 4
max_radius: 10

example2

Example 3

Ally: 20000 points
Enemy: 20000 points
min_radius: 20
max_radius: 60

example3

Example 4

Ally: 20000 points
Enemy: 20000 points
min_radius: 10
max_radius: 120

example4

Running locally

You will need to have poetry installed.

git clone https://github.com/rafsaf/tw-complex.git
cd tw-complex
poetry install

Code lives in tw-complex folder, and you may also test algorithms running in main folder

# In main folder
# eg. ~/Desktop/tw-complex

pytest

For CDistAndKNN it looks like

# tests/test_cdist.py

from tw_complex import CDistAndKNN
import tests.utils as utils


def test_CDistAndKNN():
    utils.run_all_tests(CDistAndKNN, "CDistAndKNN", _precision=0.8, draw=True)

# Go with `draw=False` if you do not want to use pyplot to show diagrams

Some hardcoded tests are located in tests/utils.py, it uses brute force for calculating exact result, then compare it to given algorithm using basic maths. You can even compare it to brute force itself (eg. using diffrent _precision). For new test there should be another file in tests/test_name_of_file_in_tw_complex_folder.py with pretty much the same content as above.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tw_complex-1.1.0.tar.gz (5.7 kB view details)

Uploaded Source

Built Distribution

tw_complex-1.1.0-py3-none-any.whl (6.9 kB view details)

Uploaded Python 3

File details

Details for the file tw_complex-1.1.0.tar.gz.

File metadata

  • Download URL: tw_complex-1.1.0.tar.gz
  • Upload date:
  • Size: 5.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for tw_complex-1.1.0.tar.gz
Algorithm Hash digest
SHA256 4e0ae77ff713d5f6e616ab7a353bc86bd02f7d612732b08eb35d0d7baf009dd6
MD5 fc5cee0c7999d678a11e36d1a59a073d
BLAKE2b-256 9a9bd90690c8cfced295d239bc3921a8d328a5e756dfa3aba886d40aa0b6cd83

See more details on using hashes here.

File details

Details for the file tw_complex-1.1.0-py3-none-any.whl.

File metadata

  • Download URL: tw_complex-1.1.0-py3-none-any.whl
  • Upload date:
  • Size: 6.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for tw_complex-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 35af93df9e2ebd18f5cd376b56e7119521c82499d1b4aa89a9817421dcc5bd6d
MD5 e5d13cafc5a7508d802f0345f4c09c0c
BLAKE2b-256 9eec666d3700c8663f6606aa23fbc5bcbc7b5cb716a3698af6e8b047952833c4

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page