FAF/SCFA Replay Parser Library

MazorNoob

This thing is good, it parses tick count real fast, it's thanks to this thing that we get to preview in-game time for replays, you should adore it

HideLord

Really clean. Great work

Askaholic

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.

Nooby

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

Katharsas

Mavor most iconic unit confirmed.

Zeldafanboy

Lol "air".

Giebmasse

im need mass pls

Could be interesting seeing more replays parsed and data analyzed/presented.

meatontable

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 ?

Nooby

@meatontable What information can you extract here?

Relationship of unit experemental built to winning in next 10 mins would be good

meatontable

Sorry for delay. I'm doing this for fun when I'm free. Of course, a detecting winning conditions is a good goal.

dragonite

Found this thread after ages to be here. Unbelievable. Guys, you're great!