Framework for abstracting Amiga debuggers and access to AmigaOS libraries and devices.

Overview

Framework for abstracting Amiga debuggers.

This project provides abstration to control an Amiga remotely using a debugger.

The APIs are not yet stable.

I include an end-user ready GUI tool based on this, amigaXfer, as a preview.

amigaXfer

This is a tool for data transfer between an Amiga and another computer using the serial port. No agent required on Amiga's side, as it uses the kickstart rom's debugger to take control of the Amiga.

There's multiple ways to get into this debugger. A simple one is through Workbench's debug menu, present when wb is loaded using loadwb -debug.

Selecting the Debug, RomWack or SAD menu option in Workbench 1.x/2.x/3.x will then enter the debugger and enable amigaXfer usage.

Alternatively, it is possible to bootstrap an Amiga for which no bootable disks are available.

https://rvalles.net/bootstrapping-an-amiga-without-a-bootable-amiga-floppy.html

amigaXfer runs on multiple platforms. Windows binaries are provided in release binary builds. Python 3.8+, PySerial and wxPython are required if running from sources.

It is able to e.g. read/write/compare floppies, install bootblocks, send/receive files and dump the kickstart rom.

Highlights:

  • Uses the kickstart's serial debugger, and thus it does not require an agent.
  • Supports RomWack (AmigaOS 1.x, 2.x) and SAD (AmigaOS 3.x) builtin debuggers.
  • High speed transfers; 512kbps possible on basic 68000 @ 7MHz A500.
  • Can be used to bootstrap an Amiga for which no bootable disks are available.
  • Checksums (CRC32/ISO-HDLC) used throughout to ensure transfer integrity.
You might also like...
In this project, we'll be making our own screen recorder in Python using some libraries.

Screen Recorder in Python Project Description: In this project, we'll be making our own screen recorder in Python using some libraries. Requirements:

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!
A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Note: This is an alpha (preview) version which is still under refining. nn-Meter is a novel and efficient system to accurately predict the inference l

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices.
CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices.

CenterFace Introduce CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices. Recent Update 2019.09.

High performance Cross-platform Inference-engine, you could run Anakin on x86-cpu,arm, nv-gpu, amd-gpu,bitmain and cambricon devices.

Anakin2.0 Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineer

CondenseNet: Light weighted CNN for mobile devices
CondenseNet: Light weighted CNN for mobile devices

CondenseNets This repository contains the code (in PyTorch) for "CondenseNet: An Efficient DenseNet using Learned Group Convolutions" paper by Gao Hua

Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle

TF Watcher TF Watcher is a simple to use Python package and web app which allows you to monitor 👀 your Machine Learning training or testing process o

Comments
  • Crash with AmigaOS 3.2 / 68060 / Fastmem (I-cache)

    Crash with AmigaOS 3.2 / 68060 / Fastmem (I-cache)

    I'm very impressed with this project. Really marvellous and nicely laid out code.

    I am however seeing a crash when starting this on OS 3.2. I'm not sure if its OS3.2, MMULib or my accelerator card that might be causing the issue. The crash happens randomly transferring and running the snippets.

    OS3.2 has romwack.

    My hardware setup is a full 68060 with MMULib and 128Mb of SDRAM.

    Interestingly I can manually create a script and run AllocMem over and over but no issues. I'm happy to help dig into the whys but some hints might be useful.

    My end goal is to simply have a cross development environment with a serial cable.

    opened by terriblefire 23
Releases(1.1.2)
  • 1.1.2(Aug 21, 2022)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 or higher, 32bit or 64bit. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45/46/47 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    If doing the floppyless bootstrap process, as a free and open source alternative to Workbench disks, Nico Bendlin's HelloAmi will boot all the way up to Workbench. He kindly enabled the Workbench's debug menu on my request, which involved some research work on his end.

    Changes

    • SetupDialog
      • Open serial in exclusive mode if possible.
      • Support for interrupting the DEL-sending CrashEntry routine.
    • BootblockTool
      • Remove stale code from debug/optdebug bootblocks.
      • New "noboot" bootblock: Amiga won't boot it. DOS can still access.
    • RomTool
      • Fix: Progressbar progress display was not accurate.
    • Fix: Clear icache on code upload (020+). (Thanks to TerribleFire, issue #1)
    • Improved log output.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them. A makefile is provided.

    Note that this version has changed the assembly files. Re-copy or rebuild.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.1.2_win32.zip(11.72 MB)
  • 1.1.1(Jul 8, 2021)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 or higher, 32bit or 64bit. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45/46/47 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    If doing the floppyless bootstrap process, as a free and open source alternative to Workbench disks, Nico Bendlin's HelloAmi will boot all the way up to Workbench. He kindly enabled the Workbench's debug menu on my request, which involved some research work on his end.

    Changes

    • SetupDialog
      • Detect missing m68k objects.
      • Better UX on connection issues.
    • Bootblock Tool
      • New bootblocks:
        • exitstrap is a hack to actually exit strap's init routine.
        • warmdos is exitstrap + start dos via WarmCapture(). A curiosity.
    • DOS Tool (preview)
      • BUGFIX: Fixed crash with AmigaOS 2.x and setpatch.
      • File transfers can now be interrupted.
    • Improved log output.

    Thanks to Ralf Hoffmann for AmigaOS 2.x issue report and testing fix and Daniel Doran for pre-release testing.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them.

    Note that the assembler files have changed. Current objects are needed for the new library function calling mechanism (related to the fix for the setpatch issue with AmigaOS 2 mentioned above). Re-copy or rebuild.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.1.1_win32.zip(11.72 MB)
  • 1.1.0(May 18, 2021)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 or higher, 32bit or 64bit. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45/46 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    If doing the floppyless bootstrap process, as a free and open source alternative to Workbench disks, Nico Bendlin's HelloAmi will boot all the way up to Workbench. He kindly enabled the Workbench's debug menu on my request, which involved some research work on his end.

    Changes

    • SetupDialog
      • ResetFirst will reboot machine during connection.
        • Writing floppies is slightly faster in this environment, due to less tasks running.
        • DosTool not usable in this environment due to dos.library being not yet initialized.
        • Allows entry via non-critical guru right click.
    • FloppyTool
      • BUGFIX: Fixed tool not working at all and instead spitting FCh ioerr on some machines.
        • Thanks to Michael Kagerbauer for reporting issue and testing fix.
      • Disk2ADF will now retry reads 5 times before giving up.
      • More user friendly IO error reporting.
      • Thanks to Michael Kagerbauer for feedback on old IOERR reporting.
    • BootblockTool
      • Better error reporting.
    • BUGFIX: Fixed issue in workaround for WRITE_BYTE SAD bug (kick v39).
    • Workaround introduced for SAD reboot function ACK bug.
      • SAD doesn't check TSRE after writing ACK to SERDAT; reboot will interrupt ACK on a fast CPU.
      • Don't bother waiting for ACK.
    • Floppyless Bootstrap should now work on all kickstarts.
      • Tested on kickstart 34/37/39/40/45/46.
    • Size SetupDialog/RomTool windows to contents.
      • Thanks to Alexander Fritsch for feedback/screenshots on window sizing issues with some win7 themes.
    • Cleaned up tool startup/cleanup procedures for all tools.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them.

    Note that the assembler files have changed. Current objects are needed for the floppyXfer server bugfix. Re-copy or rebuild.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.1.0_win32.zip(11.61 MB)
  • 1.0.1(Apr 2, 2021)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 32bit or higher. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    Changes

    • RomTool
      • Initialization GUI work outside GUI thread issue fixed.
      • Kickstart detection logic is now slightly more clever.
      • Can now be interrupted mid-dumping.
      • Larger transfer blocks, faster dumping.
      • Timer added.
      • Debug text output added.
    • DosTool
      • Target directory can safely contain a trailing slash.
      • Buffer size scales with free RAM, up to 256KB. Faster.
      • Timer added.
    • FloppyTool
      • Progressbar added.
    • UI improvements.
    • Documentation improvements.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them.

    Note: Reissued win32 zip, due to an issue unpacking it with win7. It does not appear to like advcomp'd zips.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.0.1-newzip_win32.zip(11.81 MB)
  • 1.0.0(Mar 25, 2021)

    First release of amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port.

    Binaries should work on Windows 7 32bit or higher.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built blobs from the Windows archive. Else, vasm or phxass will build them.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.0.0_win32.zip(11.91 MB)
Owner
Roc Vallès
Roc Vallès
DockStream: A Docking Wrapper to Enhance De Novo Molecular Design

DockStream Description DockStream is a docking wrapper providing access to a collection of ligand embedders and docking backends. Docking execution an

AstraZeneca - Molecular AI 72 Jan 02, 2023
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

Nonuniform-to-Uniform Quantization This repository contains the training code of N2UQ introduced in our CVPR 2022 paper: "Nonuniform-to-Uniform Quanti

Zechun Liu 60 Dec 28, 2022
Continuous Time LiDAR odometry

CT-ICP: Elastic SLAM for LiDAR sensors This repository implements the SLAM CT-ICP (see our article), a lightweight, precise and versatile pure LiDAR o

385 Dec 29, 2022
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

TradingGym TradingGym is a toolkit for training and backtesting the reinforcement learning algorithms. This was inspired by OpenAI Gym and imitated th

Yvictor 1.1k Jan 02, 2023
A collection of scripts I developed for personal and working projects.

A collection of scripts I developed for personal and working projects Table of contents Introduction Repository diagram structure List of scripts pyth

Gianluca Bianco 109 Dec 26, 2022
A simple implementation of Kalman filter in single object tracking

kalman-filter-in-single-object-tracking A simple implementation of Kalman filter in single object tracking https://www.bilibili.com/video/BV1Qf4y1J7D4

130 Dec 26, 2022
Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification

S-multi-SNE Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification A repository containing the code to reproduce the findings

Theodoulos Rodosthenous 3 Apr 15, 2022
[ICCV 2021] Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural radiance fields for view synthesis.

MVSNeRF Project page | Paper This repository contains a pytorch lightning implementation for the ICCV 2021 paper: MVSNeRF: Fast Generalizable Radiance

Anpei Chen 529 Dec 30, 2022
pytorch implementation of trDesign

trdesign-pytorch This repository is a PyTorch implementation of the trDesign paper based on the official TensorFlow implementation. The initial port o

Learn Ventures Inc. 41 Dec 29, 2022
Generating Band-Limited Adversarial Surfaces Using Neural Networks

Generating Band-Limited Adversarial Surfaces Using Neural Networks This is the official repository of the technical report that was published on arXiv

3 Jul 26, 2022
Dataset and Source code of paper 'Enhancing Keyphrase Extraction from Academic Articles with their Reference Information'.

Enhancing Keyphrase Extraction from Academic Articles with their Reference Information Overview Dataset and code for paper "Enhancing Keyphrase Extrac

15 Nov 24, 2022
KIND: an Italian Multi-Domain Dataset for Named Entity Recognition

KIND (Kessler Italian Named-entities Dataset) KIND is an Italian dataset for Named-Entity Recognition. It contains more than one million tokens with t

Digital Humanities 5 Jun 21, 2022
ICCV2021 Oral SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

Sign-Agnostic Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page This repository contains the implementation

64 Jan 05, 2023
[ICCV 2021] Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

Counterfactual Attention Learning Created by Yongming Rao*, Guangyi Chen*, Jiwen Lu, Jie Zhou This repository contains PyTorch implementation for ICCV

Yongming Rao 90 Dec 31, 2022
PAWS 🐾 Predicting View-Assignments with Support Samples

This repo provides a PyTorch implementation of PAWS (predicting view assignments with support samples), as described in the paper Semi-Supervised Learning of Visual Features by Non-Parametrically Pre

Facebook Research 437 Dec 23, 2022
How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

Bogdan Kulynych 49 Nov 05, 2022
Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch

SRDenseNet-pytorch Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICC

wxy 114 Nov 26, 2022
[ICLR'21] Counterfactual Generative Networks

This repository contains the code for the ICLR 2021 paper "Counterfactual Generative Networks" by Axel Sauer and Andreas Geiger. If you want to take the CGN for a spin and generate counterfactual ima

88 Jan 02, 2023
Implementation of the "PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences" paper.

PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences Introduction Point cloud sequences are irregular and unordered in the spatial dimen

Hehe Fan 63 Dec 09, 2022
LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations

LIMEcraft LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations The LIMEcraft algorithm is an explanatory method based on

MI^2 DataLab 4 Aug 01, 2022