Rocket League replay parsing and analysis.
Project description
carball
Various tools for decompiling / analyzing Rocket League replays.
Requirements
- Python 3.6.7+ or 3.7+
- Windows, Mac or Linux
Install
Install from pip:
pip install carball
Clone for development
git clone https://github.com/SaltieRL/carball
cd carball/
python init.py
Examples / Usage
Decompile and analyze a replay:
import carball
analysis_manager = carball.analyze_replay_file('9EB5E5814D73F55B51A1BD9664D4CBF3.replay',
output_path='9EB5E5814D73F55B51A1BD9664D4CBF3.json',
overwrite=True)
proto_game = analysis_manager.get_protobuf_data()
# you can see more example of using the analysis manager below
Just decompile a replay to a JSON object:
import carball
_json = carball.decompile_replay('9EB5E5814D73F55B51A1BD9664D4CBF3.replay',
output_path='9EB5E5814D73F55B51A1BD9664D4CBF3.json',
overwrite=True)
Analyze a JSON game object:
import carball
import os
import gzip
from carball.json_parser.game import Game
from carball.analysis.analysis_manager import AnalysisManager
# _json is a JSON game object (from decompile_replay)
game = Game()
game.initialize(loaded_json=_json)
analysis_manager = AnalysisManager(game)
analysis_manager.create_analysis()
# write proto out to a file
# read api/*.proto for info on the object properties
with open(os.path.join('output.pts'), 'wb') as fo:
analysis_manager.write_proto_out_to_file(fo)
# write pandas dataframe out as a gzipped numpy array
with gzip.open(os.path.join('output.gzip'), 'wb') as fo:
analysis_manager.write_pandas_out_to_file(fo)
# return the proto object in python
proto_object = analysis_manager.get_protobuf_data()
# return the pandas data frame in python
dataframe = analysis_manager.get_data_frame()
Command Line
Carball comes with a command line tool to analyze replays. To use carball from the command line:
carball -i 9EB5E5814D73F55B51A1BD9664D4CBF3.replay --json analysis.json
To get the analysis in both json and protobuf and also the compressed replay frame data frame:
carball -i 9EB5E5814D73F55B51A1BD9664D4CBF3.replay --json analysis.json --proto analysis.pts --gzip frames.gzip
Command Line Arguments
usage: carball [-h] -i INPUT [--proto PROTO] [--json JSON] [--gzip GZIP] [-sd]
[-v] [-s]
Rocket League replay parsing and analysis.
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
Path to replay file that will be analyzed. Carball
expects a raw replay file unless --skip-decompile is
provided.
--proto PROTO The result of the analysis will be saved to this file
in protocol buffers format.
--json JSON The result of the analysis will be saved to this file
in json file format. This is not the decompiled replay
json from rattletrap.
--gzip GZIP The pandas dataframe will be saved to this file in a
compressed gzip format.
-sd, --skip-decompile
If set, carball will treat the input file as a json
file that Rattletrap outputs.
-v, --verbose Set the logging level to INFO. To set the logging
level to DEBUG use -vv.
-s, --silent Disable logging altogether.
Pipeline
If you want to add a new stat it is best to do it in the advanced stats section of the pipeline. You should look at:
If you want to see the output format of the stats created you can look here
Compile the proto files by running in this directory
setup.bat
(Windows) or setup.sh
(Linux/mac)
Tips
Linux set python3.6
as python
:
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.6 1
This assumes you already have 3.6 installed.
Linux Error (Potential):
PermissionError: [Errno 13] Permission denied: 'carball/rattletrap/rattletrap-6.2.2-linux'
Fix:
chmod +x "carball/rattletrap/rattletrap-6.2.2-linux"
Developing
For testing you must run pytest. For ides you can configure them to use the pytest runner.
Project details
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