Jomini

A low level, performance oriented parser for EU4 save files and other PDS developed titles.

Jomini is the cornerstone of the Rakaly, an EU4 achievement leaderboard and save file analyzer. This library is also used in the Paradox Game Converters project to parse ironman EU4, CK3, and Imperator saves.

Features

• ✔ Versatile: Handle both plaintext and binary encoded data
• ✔ Fast: Parse data at over 1 GB/s
• ✔ Small: Compile with zero dependencies
• ✔ Safe: Extensively fuzzed against potential malicious input
• ✔ Ergonomic: Use serde-like macros to have parsing logic automatically implemented
• ✔ Embeddable: Cross platform native apps, statically compiled services, or in the browser via WASM
• ✔ Agnostic: Parse EU4, HOI4, Imperator, CK3, and Vic2 save and game files

Quick Start

Below is a demonstration on parsing plaintext data using jomini tools.

```rust use jomini::{JominiDeserialize, TextDeserializer};

[derive(JominiDeserialize, PartialEq, Debug)]

pub struct Model { human: bool, first: Option, #[jomini(alias = "forth")] fourth: u16, #[jomini(alias = "core", duplicated)] cores: Vec, names: Vec, }

let data = br#" human = yes forth = 10 core = "HAB" names = { "Johan" "Frederick" } core = FRA "#;

let expected = Model { human: true, first: None, fourth: 10, cores: vec!["HAB".tostring(), "FRA".tostring()], names: vec!["Johan".tostring(), "Frederick".tostring()], };

let actual: Model = TextDeserializer::fromwindows1252slice(data)?; assert_eq!(actual, expected); ```

Binary Parsing

Parsing data encoded in the binary format is done in a similar fashion but with an extra step. Tokens can be encoded into 16 integers, and so one must provide a map from these integers to their textual representations

```rust use jomini::{JominiDeserialize, BinaryDeserializer}; use std::collections::HashMap;

[derive(JominiDeserialize, PartialEq, Debug)]

struct MyStruct { field1: String, }

let data = [ 0x82, 0x2d, 0x01, 0x00, 0x0f, 0x00, 0x03, 0x00, 0x45, 0x4e, 0x47 ];

let mut map = HashMap::new(); map.insert(0x2d82, "field1");

let actual: MyStruct = BinaryDeserializer::fromeu4(&data[..], &map)?; asserteq!(actual, MyStruct { field1: "ENG".to_string() }); ```

When done correctly, one can use the same structure to represent both the plaintext and binary data without any duplication.

One can configure the behavior when a token is unknown (ie: fail immediately or try to continue).

Caveats

Caller is responsible for:

• Determining the correct format (text or binary) ahead of time
• Stripping off any header that may be present (eg: EU4txt / EU4bin)
• Providing the token resolver for the binary format
• Providing the conversion to reconcile how, for example, a date may be encoded as an integer in the binary format, but as a string when in plaintext.

The Mid-level API

If the automatic deserialization via JominiDeserialize is too high level, there is a mid-level api where one can easily iterate through the parsed document and interrogate fields for their information.

```rust use jomini::TextTape;

let data = b"name=aaa name=bbb core=123 name=ccc name=ddd"; let tape = TextTape::fromslice(data).unwrap(); let mut reader = tape.windows1252reader();

while let Some((key, op, value)) = reader.nextfield() { println!("{:?}={:?}", key.readstr(), value.readstr().unwrap()); } ```

One Level Lower

At the lowest layer, one can interact with the raw data directly via TextTape and BinaryTape.

```rust use jomini::{TextTape, TextToken, Scalar};

let data = b"foo=bar";

asserteq!( TextTape::fromslice(&data[..])?.tokens(), &[ TextToken::Unquoted(Scalar::new(b"foo")), TextToken::Unquoted(Scalar::new(b"bar")), ] ); ```

If one will only use TextTape and BinaryTape then jomini can be compiled without default features, resulting in a build without dependencies.

Write API

There are two targeted use cases for the write API. One is when a text tape is on hand. This is useful when one needs to reformat a document (note that comments are not preserved):

```rust use jomini::{TextTape, TextWriterBuilder};

let tape = TextTape::fromslice(b"hello = world")?; let mut out: Vec = Vec::new(); let mut writer = TextWriterBuilder::new().fromwriter(&mut out); writer.writetape(&tape)?; asserteq!(&out, b"hello=world\n"); ```

The writer normalizes any formatting issues. The writer is not able to losslessly write all parsed documents, but these are limited to truly esoteric situations and hope to be resolved in future releases.

The other use case is geared more towards incremental writing that can be found in melters or those crafting documents by hand. These use cases need to manually drive the writer:

rust use jomini::TextWriterBuilder; let mut out: Vec<u8> = Vec::new(); let mut writer = TextWriterBuilder::new().from_writer(&mut out); writer.write_unquoted(b"hello")?; writer.write_unquoted(b"world")?; writer.write_unquoted(b"foo")?; writer.write_unquoted(b"bar")?; assert_eq!(&out, b"hello=world\nfoo=bar\n");

Benchmarks

Benchmarks are ran with the following command:

cargo clean cargo bench -- '/ck3' find ./target -wholename "*/new/raw.csv" -print0 | xargs -0 xsv cat rows > assets/jomini-benchmarks.csv

And can be analyzed with the R script found in the assets directory.

Below is a graph generated from benchmarking on an arbitrary computer.