FAF/SCFA Replay Parser Library
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Really clean. Great work
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I updated the development version to clean up the command output a bit.
Old:
├── VerifyChecksum { digest: [168, 55, 122, 87, 70, 60, 17, 145, 224, 174, 52, 71, 2, 143, 109, 2], tick: 0 } ├── IssueCommand(GameCommand { entity_ids: [0], id: 0, coordinated_attack_cmd_id: 4294967295, type_: 8, arg2: -1, target: Position(Position { x: 667.5, y: 18.679688, z: 357.5 }), arg3: 0, formation: None, blueprint: "urb1103", arg4: 0, arg5: 1, arg6: 1, upgrades: Nil, clear_queue: None }) ├── LuaSimCallback { func: "SyncValueFromUi", args: Table({Unicode("id"): String("0"), Unicode("Specialization"): String("ALL"), Unicode("AffectName"): String("PowerDamage")}), selection: [] }
New:
├── VerifyChecksum { digest: a8377a57463c1191e0ae3447028f6d02, tick: 0 } ├── IssueCommand(GameCommand { entity_ids: [0], id: 0, coordinated_attack_cmd_id: -1, type: BuildMobile, arg2: -1, target: Position { x: 667.5, y: 18.679688, z: 357.5 }, arg3: 0, formation: None, blueprint: "urb1103", arg4: 0, arg5: 1, arg6: 1, upgrades: Nil, clear_queue: None }) ├── LuaSimCallback { func: "SyncValueFromUi", args: {"id": "0", "Specialization": "ALL", "AffectName": "PowerDamage"}, selection: [] }
The commands should be a lot easier to read now. New versions of the pre-compiled binaries are also pushed, link in the OP.
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I have downloaded a few thousand replays and used your python libary to parse all chat in them. 12MB of text was parsed for this.
I have quite a big text file for each FAF username. I know it would be better to use userID and I will in future things. Does anyone want this data?
The chat has been filtered to remove "notify" events and also "Units / Mass / Power sent"
I do plan to do some kind of node analysis on who plays with who next
who is associated with which map
association of maps with ratings -
Mavor most iconic unit confirmed.
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Lol "air".
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im need mass pls
Could be interesting seeing more replays parsed and data analyzed/presented.
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I have tested a python binding and it is OK. What kind of the data analyze do you want ?
Winning fraction? most killed fraction ? popular (unpopular) units ? -
@meatontable What information can you extract here?
Relationship of unit experemental built to winning in next 10 mins would be good
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Sorry for delay. I'm doing this for fun when I'm free. Of course, a detecting winning conditions is a good goal.
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Found this thread after ages to be here. Unbelievable. Guys, you're great!