RLBot Python bindings for the Rust crate rl_ball_sym

Overview

RLBot Python bindings for rl_ball_sym 0.6

Prerequisites:

Steps to build the Python bindings

  1. Download this repository
  2. Run cargo_build_release.bat
  3. A new file, called rl_ball_sym.pyd, will appear
  4. Copy rl_ball_sym.pyd to your Python project's source folder
  5. import rl_ball_sym in your Python file

Basic usage in an RLBot script to render the path prediction

See script.cfg and script.py for a pre-made script that renders the framework's ball path prediction in green and the rl_ball_sym's ball path prediction in red.

from traceback import print_exc

from rlbot.agents.base_script import BaseScript
from rlbot.utils.structures.game_data_struct import GameTickPacket

import rl_ball_sym as rlbs


class rl_ball_sym(BaseScript):
    def __init__(self):
        super().__init__("rl_ball_sym")

    def main(self):
        rlbs.load_soccar()

        while 1:
            try:
                self.packet: GameTickPacket = self.wait_game_tick_packet()
                current_location = self.packet.game_ball.physics.location
                current_velocity = self.packet.game_ball.physics.velocity
                current_angular_velocity = self.packet.game_ball.physics.angular_velocity

                rlbs.set_ball({
                    "time": self.packet.game_info.seconds_elapsed,
                    "location": [current_location.x, current_location.y, current_location.z],
                    "velocity": [current_velocity.x, current_velocity.y, current_velocity.z],
                    "angular_velocity": [current_angular_velocity.x, current_angular_velocity.y, current_angular_velocity.z],
                })

                path_prediction = rlbs.get_ball_prediction_struct()

                self.renderer.begin_rendering()
                self.renderer.draw_polyline_3d(tuple((path_prediction["slices"][i]["location"][0], path_prediction["slices"][i]["location"][1], path_prediction["slices"][i]["location"][2]) for i in range(0, path_prediction["num_slices"], 4)), self.renderer.red())
                self.renderer.end_rendering()
            except Exception:
                print_exc()


if __name__ == "__main__":
    rl_ball_sym = rl_ball_sym()
    rl_ball_sym.main()

Example ball prediction struct

Normal

[
    {
        "time": 0.008333,
        "location": [
            -2283.9,
            1683.8,
            323.4,
        ],
        "velocity": [
            1273.4,
            -39.7,
            757.6,
        ]
    },
    {
        "time": 0.025,
        "location": [
            -2262.6,
            1683.1,
            335.9,
        ],
        "velocity": [
            1272.7,
            -39.7,
            746.4,
        ]
    }
    ...
]

Full

[
    {
        "time": 0.008333,
        "location": [
            -2283.9,
            1683.8,
            323.4,
        ],
        "velocity": [
            1273.4,
            -39.7,
            757.6,
        ]
        "angular_velocity": [
            2.3,
            -0.8,
            3.8,
        }
    },
    {
        "time": 0.016666,
        "location": [
            -2273.3,
            1683.4,
            329.7,
        ],
        "velocity": [
            1273.1,
            -39.7,
            752.0,
        ],
        "angular_velocity": [
            2.3,
            -0.8,
            3.8
        ]
    }
    ...
]

__doc__

Returns the string rl_ball_sym is a Rust implementation of ball path prediction for Rocket League; Inspired by Samuel (Chip) P. Mish's C++ utils called RLUtilities

load_soccar

Loads in the field for a standard soccar game.

load_dropshot

Loads in the field for a standard dropshot game.

load_hoops

Loads in the field for a standard hoops game.

set_ball

Sets information related to the ball. Accepts a Python dictionary. You don't have to set everything - you can exclude keys at will.

time

The seconds that the game has elapsed for.

location

The ball's location, in an array in the format [x, y, z].

velocity

The ball's velocity, in an array in the format [x, y, z].

angular_velocity

The ball's angular velocity, in an array in the format [x, y, z].

radius

The ball's radius.

Defaults:

  • Soccar - 91.25
  • Dropshot - 100.45
  • Hoops - 91.25

collision_radius

The ball's collision radius.

Defaults:

  • Soccar - 93.15
  • Dropshot - 103.6
  • Hoops - 93.15

set_gravity

Sets information about game's gravity.

Accepts an array in the format [x, y, z].

step_ball

Steps the ball by 1/120 seconds into the future every time it's called.

For convience, also returns the new information about the ball.

Example:

{
    "time": 0.008333,
    "location": [
        -2283.9,
        1683.8,
        323.4,
    ],
    "velocity": [
        1273.4,
        -39.7,
        757.6,
    ]
    "angular_velocity": [
        2.3,
        -0.8,
        3.8,
    }
}

get_ball_prediction_struct

Equivalent to calling step_ball() 720 times (6 seconds).

Returns a normal-type ball prediction struct.

get_ball_prediction_struct takes 0.3ms to execute

get_ball_prediction_struct_full

Equivalent to calling step_ball() 720 times (6 seconds).

Returns a full-type ball prediction struct.

get_ball_prediction_struct_full takes 0.54ms to execute

get_ball_prediction_struct_for_time

Equivalent to calling step_ball() 120 * time times.

Returns a normal-type ball prediction struct.

time

The seconds into the future that the ball path prediction should be generated.

get_ball_prediction_struct_full_for_time

Equivalent to calling step_ball() 120 * time times.

Returns a full-type ball prediction struct.

time

The seconds into the future that the ball path prediction should be generated.

You might also like...
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).

Crab - A Recommendation Engine library for Python Crab is a flexible, fast recommender engine for Python that integrates classic information filtering r

Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models
Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models

Python scripts to detect faces using Python with the BlazeFace Tensorflow Lite models. Tested on Windows 10, Tensorflow 2.4.0 (Python 3.8).

A fast python implementation of Ray Tracing in One Weekend using python and Taichi
A fast python implementation of Ray Tracing in One Weekend using python and Taichi

ray-tracing-one-weekend-taichi A fast python implementation of Ray Tracing in One Weekend using python and Taichi. Taichi is a simple "Domain specific

Technical Indicators implemented in Python only using Numpy-Pandas as Magic  - Very Very Fast! Very tiny!  Stock Market Financial Technical Analysis Python library .  Quant Trading automation or cryptocoin exchange
Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

MyTT Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python

This is an open source python repository for various python tests

Welcome to Py-tests This is an open source python repository for various python tests. This is in response to the hacktoberfest2021 challenge. It is a

Composable transformations of Python+NumPy programsComposable transformations of Python+NumPy programs

Chex Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help: Instrument your code (e.g. assertions) Debug

Automatic self-diagnosis program (python required)Automatic self-diagnosis program (python required)

auto-self-checker 자동으로 자가진단 해주는 프로그램(python 필요) 중요 이 프로그램이 실행될때에는 절대로 마우스포인터를 움직이거나 키보드를 건드리면 안된다(화면인식, 마우스포인터로 직접 클릭) 사용법 프로그램을 구동할 폴더 내의 cmd창에서 pip

POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction
POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

POPPY: Physical Optics Propagation in Python POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propaga

Space-invaders - Simple Game created using Python & PyGame, as my Beginner Python Project
Space-invaders - Simple Game created using Python & PyGame, as my Beginner Python Project

Space Invaders This is a simple SPACE INVADER game create using PYGAME whihc hav

Releases(v1.0.0)
Owner
Eric Veilleux
I know HTML/CSS/JS, Java, Python, C, and Rust.
Eric Veilleux
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

108 Dec 17, 2022
Face Library is an open source package for accurate and real-time face detection and recognition

Face Library Face Library is an open source package for accurate and real-time face detection and recognition. The package is built over OpenCV and us

52 Nov 09, 2022
A very tiny, very simple, and very secure file encryption tool.

Picocrypt is a very tiny (hence "Pico"), very simple, yet very secure file encryption tool. It uses the modern ChaCha20-Poly1305 cipher suite as well

Evan Su 1k Dec 30, 2022
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations Requirements python 3.6 torch 1.9 numpy 1.19 Quick Start The experimen

DMIRLAB 4 Oct 16, 2022
TensorFlow implementation of ENet

TensorFlow-ENet TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. This model was tested on th

Kwotsin 255 Oct 17, 2022
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

Breaking the Curse of Space Explosion: Towards Effcient NAS with Curriculum Search Pytorch implementation for "Breaking the Curse of Space Explosion:

guoyong 17 Jan 03, 2023
MicRank is a Learning to Rank neural channel selection framework where a DNN is trained to rank microphone channels.

MicRank: Learning to Rank Microphones for Distant Speech Recognition Application Scenario Many applications nowadays envision the presence of multiple

Samuele Cornell 20 Nov 10, 2022
Project Aquarium is a SUSE-sponsored open source project aiming at becoming an easy to use, rock solid storage appliance based on Ceph.

Project Aquarium Project Aquarium is a SUSE-sponsored open source project aiming at becoming an easy to use, rock solid storage appliance based on Cep

Aquarist Labs 73 Jul 21, 2022
Code for the paper "Implicit Representations of Meaning in Neural Language Models"

Implicit Representations of Meaning in Neural Language Models Preliminaries Create and set up a conda environment as follows: conda create -n state-pr

Belinda Li 39 Nov 03, 2022
Using Python to Play Cyberpunk 2077

CyberPython 2077 Using Python to Play Cyberpunk 2077 This repo will contain code from the Cyberpython 2077 video series on Youtube (youtube.

Harrison 118 Oct 18, 2022
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
PyTorch implementation of the Crafting Better Contrastive Views for Siamese Representation Learning

Crafting Better Contrastive Views for Siamese Representation Learning This is the official PyTorch implementation of the ContrastiveCrop paper: @artic

249 Dec 28, 2022
Vector.ai assignment

fabio-tests-nisargatman Low Level Approach: ###Tables: continents: id*, name, population, area, createdAt, updatedAt countries: id*, name, population,

Ravi Pullagurla 1 Nov 09, 2021
An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results

EasyDatas An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results Installation pip install git+https

Ximing Yang 4 Dec 14, 2021
Baseline for the Spoofing-aware Speaker Verification Challenge 2022

Introduction This repository contains several materials that supplements the Spoofing-Aware Speaker Verification (SASV) Challenge 2022 including: calc

40 Dec 28, 2022
Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging"

Deep Optics for Single-shot High-dynamic-range Imaging Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging" CVPR, 2

Stanford Computational Imaging Lab 40 Dec 12, 2022
The best solution of the Weather Prediction track in the Yandex Shifts challenge

yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re

Ivan Yu. Bondarenko 15 Dec 18, 2022
AntroPy: entropy and complexity of (EEG) time-series in Python

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to e

Raphael Vallat 153 Dec 27, 2022
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Jan 03, 2023
(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"

SSR (NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductivefew-shot classification" [Paper] [Project webpage]

xshen 29 Dec 06, 2022