Ais message decoding
AIS message decoding. 100% pure Python.
This module contains functions to decode and parse Automatic Identification System (AIS) serial messages. For detailed information about AIS refer to the AIS standard.
Using this module is easy. If you want to parse a file, that contains AIS messages, just copy the following code and replace
filename with your desired filename.
from pyais import FileReaderStream filename = "sample.ais" for msg in FileReaderStream(filename): decoded_message = msg.decode() ais_content = decoded_message.content
It is possible to directly convert messages into JSON.
from pyais import TCPStream for msg in TCPStream('ais.exploratorium.edu'): json_data = msg.decode().to_json()
You can also parse a single message encoded as bytes or from a string:
message = NMEAMessage(b"!AIVDM,1,1,,B,15M67FC000G?ufbE`FepT@3n00Sa,0*5C") message = NMEAMessage.from_string("!AIVDM,1,1,,B,15M67FC000G?ufbE`FepT@3n00Sa,0*5C")
See the example folder for more examples.
You may refer to the Code Review Stack Exchange question. After a some research I decided to use the bitarray module as foundation. This module uses a C extension under the hood and has a nice user interface in Python. Performance is also great. Decoding this sample with roughly 85k messages takes less than 6 seconds on my machine. For comparison, the C++ based libais module parses the same file in ~ 2 seconds.
This module is a private project of mine and does not claim to be complete. I try to improve and extend it, but there may be bugs. If you find such a bug feel free to submit an issue or even better create a pull-request. :-)
Currently this module is able to decode most message types. There are only a few exceptions. These are messages that only occur in very rare cases and that you will probably never observe. The module was able to completely decode a 4 hour stream with real-time data from San Francisco Bay Area without any errors or problems. If you find a bug or missing feature, please create an issue.
You should run all tests before you submit a new pull request to prevent regressions. Also run flake8.
python -m unittest discover tests && flake8
pip install coverage
coverage run --source=pyais -m unittest discover tests && coverage report -m && flake8
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