Py65 65816 - Add support for the 65C816 to py65

Overview

Add support for the 65C816 to py65

Py65 (https://github.com/mnaberez/py65) is a great simulator for the 6502. Recently I added support for interrupts (https://github.com/tmr4/py65_int) and a debug window (https://github.com/tmr4/py65_debug_window). After success with these modifications, I decided to try adding support for the 65C816. Luckily, py65 is open-source and enhancing it isn't very difficult.

This repository provides a framework for adding support for the 65C816 to py65. I've included the modules I've developed to simulate and test the 65C816. As noted below, a few modifications are needed to the core py65 modules as well.

Screenshot

Screenshot of py65 running Liara Forth on a simulated 65C816

Contents

I've included the main device module, mpu65c816.py, to add simulation support for the 65C816 to py65. I've also include several modules for testing the 65C816 simulation. These include the main unit test module, test_mpu65c816.py, and support modules, test_mpu65816_Common6502.py and test_mpu65816_Common65c02.py, derived largely from similarly named py65 test modules, to test the 65C816 emulation mode simulation. I've also included a binary file, liara.bin, that I modified from Scot W. Stevenson's Liara Forth (https://github.com/scotws/LiaraForth) to work with py65 simulating the 65C816. Note that I'm a Python newbie and appreciate any feedback to make these better.

  • mpu65c816.py

The 65C816 device.

  • test_mpu65c816.py

The main unit test module for the 65C816.

  • test_mpu65816_Common6502.py

Unit tests for 65C816 emulation mode.

  • test_mpu65816_Common65c02.py

Additional 65C02 based unit tests for 65C816 emulation mode.

  • liara.bin

A modified version of Scot W. Stevenson's Liara Forth (https://github.com/scotws/LiaraForth) for testing. Liara Forth is designed to run on the Western Design Center's W65C265SXB development board (https://www.westerndesigncenter.com/wdc/documentation/W65C265SXB.pdf). I've modified the Liara Forth binary to interface with alternate I/O addresses rather than those used by the development board.

Modifications to core py65 modules

The following modifications are needed for py65 to simulate the 65C816:

  1. monitor.py
  • Add a reference to new 65C816 MPU class from devices.mpu65c816 import MPU as CMOS65C816
  • Add the '65C816': CMOS65C816 pair to the Microprocessors dictionary.

License

The mpu65c816.py, test_mpu65816_Common6502.py and test_mpu65816_Common65c02.py modules contain large portions of code from or derived from py65 which is covered by a BSD 3-Clause License. I've included that license as required.

Running the 65C816 Unit Tests

You can run the unit tests with python -m unittest test_mpu65c816.py. The 65C816 simulation passes the py65 6502- and 65C02-based test (507 in total) in emulation mode. Some of tests were modified to run properly with the new device. I still have to create the tests for native mode operations (not a small task). I expect these to take some time and I expect these will turn up many errors in my code.

Testing the 65C816 Simulation with Liara Forth

It wasn't easy to find a sizable program to test with the new 65C816 simulation. You can run the slightly modified version of Liara Forth with python monitor.py -m 65c816 -l liara.bin -g 5000 -i fff0 -o fff1.

Limitations

  1. The new 65C816 device is largely untested. I plan to update it as I work on supporting hardware and code. Use at your own risk. Some know issues:
  • FIXED: ROL and ROR haven't been updated for a 16 bit accumulator.
  • Extra cycle counts haven't been considered for any new to 65816 opcodes.
  • ADC and SBC in decimal mode are likely invalid in 16 bit.
  • Native mode hasn't been tested outside of bank 0. Assume it will fail for this until it is tested. Currently only 3 banks of memory are modeled, by py65 default, but this can easily be changed.
  • The simulation is meant to emulate the actual W65C816. Modelling so far has been based on the 65816 Programming Manual only. I intend to test at least some code against the W65C265SXB development board.
  • Currently no way to break to the py65 monitor.
  • Register wrapping of Direct page addressing modes need tested.
  1. While Liara Forth runs in py65 with the new 65C816 device, it isn't hard to make it crash. I believe this is due to my code, rather than Liara Forth, even though it is marked as an ALPHA version. Liara Forth runs entirely in bank 0. There is no way to break to the monitor since Liara Forth was designed to run on hardware only. It can only be ended with a control-C.

  2. I've successfully run a non-interrupt version of my own 6502 Forth in the new 65C816 device in emulation mode. This isn't surprising since much of the code comes from py65 6502 and 65C02 devices. I expect an interrupt version of it will run as well, but I haven't tested this. I expect that many 6502 programs will run in emulation mode. Note however, that there are differences between the 65C816 operating in emulation mode and the 6502/65C02 that could cause problems with your program.

Status

  • Initial commit: January 11, 2022
  • Successfully tested my 65C02 Forth in emulation mode
  • Was able to run Liara Forth in native mode in block 0.
    • FIXED: (Many words cause it to crash (likely due to one of the limitations listed above).)
    • Currently all numbers print out as 0. Unclear why.

Next Steps

  • Resolve simulator issues with running Liara Forth. I view this as a robust test of the 65816 simulator, other than bank switching, which Liara Forth doesn't handle out of the box. Some hardware specific Liara Forth features will not work with the simulator (KEY? for example which is hardwired to a W65C265SXB development board specific address indicating whether a key has been pressed).
  • Add native mode unit tests.
Multilingual word vectors in 78 languages

Aligning the fastText vectors of 78 languages Facebook recently open-sourced word vectors in 89 languages. However these vectors are monolingual; mean

Babylon Health 1.2k Dec 17, 2022
An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

Khalid Saifullah 37 Sep 05, 2022
A number of methods in order to perform Natural Language Processing on live data derived from Twitter

A number of methods in order to perform Natural Language Processing on live data derived from Twitter

1 Nov 24, 2021
A sentence aligner for comparable corpora

About Yalign is a tool for extracting parallel sentences from comparable corpora. Statistical Machine Translation relies on parallel corpora (eg.. eur

Machinalis 128 Aug 24, 2022
Natural Language Processing library built with AllenNLP 🌲🌱

Custom Natural Language Processing with big and small models 🌲🌱

Recognai 65 Sep 13, 2022
ETM - R package for Topic Modelling in Embedding Spaces

ETM - R package for Topic Modelling in Embedding Spaces This repository contains an R package called topicmodels.etm which is an implementation of ETM

bnosac 37 Nov 06, 2022
this repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

1 Nov 02, 2021
Implementation of the Hybrid Perception Block and Dual-Pruned Self-Attention block from the ITTR paper for Image to Image Translation using Transformers

ITTR - Pytorch Implementation of the Hybrid Perception Block (HPB) and Dual-Pruned Self-Attention (DPSA) block from the ITTR paper for Image to Image

Phil Wang 17 Dec 23, 2022
translate using your voice

speech-to-text-translator Usage translate using your voice description this project makes translating a word easy, all you have to do is speak and...

1 Oct 18, 2021
End-to-end MLOps pipeline of a BERT model for emotion classification.

image source EmoBERT-MLOps The goal of this repository is to build an end-to-end MLOps pipeline based on the MLOps course from Made with ML, but this

Dimitre Oliveira 4 Nov 06, 2022
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Dec 30, 2022
The SVO-Probes Dataset for Verb Understanding

The SVO-Probes Dataset for Verb Understanding This repository contains the SVO-Probes benchmark designed to probe for Subject, Verb, and Object unders

DeepMind 20 Nov 30, 2022
a test times augmentation toolkit based on paddle2.0.

Patta Image Test Time Augmentation with Paddle2.0! Input | # input batch of images / / /|\ \ \ # apply

AgentMaker 110 Dec 03, 2022
Baseline code for Korean open domain question answering(ODQA)

Open-Domain Question Answering(ODQA)λŠ” λ‹€μ–‘ν•œ μ£Όμ œμ— λŒ€ν•œ λ¬Έμ„œ μ§‘ν•©μœΌλ‘œλΆ€ν„° μžμ—°μ–΄ μ§ˆμ˜μ— λŒ€ν•œ 닡변을 μ°Ύμ•„μ˜€λŠ” taskμž…λ‹ˆλ‹€. μ΄λ•Œ μ‚¬μš©μž μ§ˆμ˜μ— λ‹΅λ³€ν•˜κΈ° μœ„ν•΄ μ£Όμ–΄μ§€λŠ” 지문이 λ”°λ‘œ μ‘΄μž¬ν•˜μ§€ μ•ŠμŠ΅λ‹ˆλ‹€. λ”°λΌμ„œ 사전에 κ΅¬μΆ•λ˜μ–΄μžˆλŠ” Knowl

VUMBLEB 69 Nov 04, 2022
A simple implementation of N-gram language model.

About A simple implementation of N-gram language model. Requirements numpy Data preparation Corpus Training data for the N-gram model, a text file lik

4 Nov 24, 2021
SciBERT is a BERT model trained on scientific text.

SciBERT is a BERT model trained on scientific text.

AI2 1.2k Dec 24, 2022
Uncomplete archive of files from the European Nopsled Team

European Nopsled CTF Archive This is an archive of collected material from various Capture the Flag competitions that the European Nopsled team played

European Nopsled 4 Nov 24, 2021
Ecco is a python library for exploring and explaining Natural Language Processing models using interactive visualizations.

Visualize, analyze, and explore NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BER

Jay Alammar 1.6k Dec 25, 2022
ADCS - Automatic Defect Classification System (ADCS) for SSMC

Table of Contents Table of Contents ADCS Overview Summary Operator's Guide Demo System Design System Logic Training Mode Production System Flow Folder

Tam Zher Min 2 Jun 24, 2022
Sample data associated with the Aurora-BP study

The Aurora-BP Study and Dataset This repository contains sample code, sample data, and explanatory information for working with the Aurora-BP dataset

Microsoft 16 Dec 12, 2022