- Implemented `formats.py` to define data structures and functions for handling map data, including reading and decoding shape and map items.
- Created `png.py` for generating PNG images from shape frames and pixel data.
- Developed `sorting.py` to manage the sorting and rendering order of map items based on their properties and spatial relationships.
- Introduced `render_all_maps.py` to facilitate the rendering of all maps for specified games, including command-line argument parsing and subprocess management for rendering tasks.
- Introduced new binary files for user data and database.
- Updated project state to include new paths for Spanish executable.
- Added detailed notes on Spanish cheat/debug differences in a new document.
- Revised mid-project plan to reflect findings from localized build comparison.
This commit introduces a comprehensive document outlining the various executable-patching attempts aimed at revealing the hidden retail usecode debugger within the CRUSADER.EXE file. The document serves multiple purposes, including preserving negative evidence, recording patch shapes and their rationales, and ensuring that runtime outcomes are linked to specific patch generations.
Key sections include:
- Ground rules for patching and validation processes.
- A table of stable facts regarding the debugger's structure and behavior.
- A detailed attempt log documenting each patch's shape, mechanical and runtime results, and verdicts.
- Root-cause findings from failed paths, providing insights into the challenges faced during the patching process.
- Current live candidates for further testing and exploration.
This documentation is intended to streamline future patching efforts and improve the understanding of the underlying mechanics of the debugger.
- Introduced README.md for the PyGhidra Crusader Toolkit, detailing setup and usage instructions.
- Added bootstrap_env.ps1 script to create and refresh the Python virtual environment with necessary packages.
- Implemented _tmp_patch_hidden_cheat_menu.py and _tmp_patch_hidden_cheat_menu_deferred.py scripts for patching specific memory addresses in Ghidra.
- Introduced a new command 'annotate-usecode' to import USECODE IR JSON annotation hints as Ghidra comments on compiled anchors.
- Added argument parsing for multiple IR JSON files, comment type selection, and a dry-run option.
- Implemented logic to read annotation records from the provided IR files and set comments on the corresponding addresses in Ghidra.
- Enhanced JSON schema to include response structure for the new command.
- Enhance `extract_eusecode_flx.py` to derive class event rows with additional metadata including derived body windows and repeated template statuses.
- Introduce `usecode_family_compare.py` for comparing event families, analyzing commonalities in event bodies, and generating reports on identical groups and differences.
- Implement new data structures for managing class event rows and family artifact specifications.
- Update output formats to include derived body information and repeated family regression checks.
- Ensure robust validation of repeated family expectations against actual extracted data.
- Introduced new file `vm_mask_ladder.tsv` containing detailed mappings for Crusader USECODE VM masks and their associated descriptors.
- Added comprehensive documentation in `scummvm-crusader-reference.md` outlining the structure, findings, and implications for reverse-engineering the Crusader engine within ScummVM.
- Created `usecode-roundtrip-ir.md` to document the plan for converting Crusader USECODE bytes into a human-readable format, detailing the container layout, event names, and intrinsic tables.
- Implemented a PowerShell script `temp_usecode_sample.ps1` for extracting and analyzing USECODE data from the Crusader FLX files, providing insights into class and event structures.
- Implemented a Python script to extract data from the EUSECODE.FLX file format.
- Defined data structures for candidate entries and extracted chunks using dataclasses.
- Added functions to read and parse the FLX table, extract candidate data, and generate human-readable output files.
- Included functionality for analyzing extracted data, including generating summaries, descriptors, and event family reports.
- Implemented utilities for calculating printable ratios, zero ratios, and identifying text-like data.
- Added support for writing various output formats, including JSON, TSV, and Markdown.
