Upload and changes to Python 0.9.1 release (from 1991!) so that it would compile

Overview
This is Python, an extensible interpreted programming language that
combines remarkable power with very clear syntax.

This is version 0.9 (the first beta release), patchlevel 1.

Python can be used instead of shell, Awk or Perl scripts, to write
prototypes of real applications, or as an extension language of large
systems, you name it.  There are built-in modules that interface to
the operating system and to various window systems: X11, the Mac
window system (you need STDWIN for these two), and Silicon Graphics'
GL library.  It runs on most modern versions of UNIX, on the Mac, and
I wouldn't be surprised if it ran on MS-DOS unchanged.  I developed it
mostly on an SGI IRIS workstation (using IRIX 3.1 and 3.2) and on the
Mac, but have tested it also on SunOS (4.1) and BSD 4.3 (tahoe).

Building and installing Python is easy (but do read the Makefile).
A UNIX style manual page and extensive documentation (in LaTeX format)
are provided.  (In the beta release, the documentation is still under
development.)

Please try it out and send me your comments (on anything -- the
language design, implementation, portability, installation,
documentation) and the modules you wrote for it, to make the first
real release better.  If you needed to hack the source to get it to
compile and run on a particular machine, send me the fixes -- I'll try
to incorporate them into the next patch.  If you can't get it to work
at all, send me a *detailed* description of the problem and I may look
into it.

If you want to profit of the X11 or Mac window interface, you'll need
STDWIN.  This is a portable window system interface by the same
author.  The versions of STDWIN floating around on some archives are
not sufficiently up-to-date for use with Python.  I will distribute
the latest and greatest STDWIN version at about the same time as Python.

I am the author of Python:

	Guido van Rossum
	CWI, dept. CST
	Kruislaan 413
	1098 SJ  Amsterdam
	The Netherlands

	E-mail: [email protected]

The Python source is copyrighted, but you can freely use and copy it
as long as you don't change or remove the copyright:

/***********************************************************
Copyright 1991 by Stichting Mathematisch Centrum, Amsterdam, The
Netherlands.

                        All Rights Reserved

Permission to use, copy, modify, and distribute this software and its 
documentation for any purpose and without fee is hereby granted, 
provided that the above copyright notice appear in all copies and that
both that copyright notice and this permission notice appear in 
supporting documentation, and that the names of Stichting Mathematisch
Centrum or CWI not be used in advertising or publicity pertaining to
distribution of the software without specific, written prior permission.

STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

******************************************************************/
You might also like...
Implement yolov5 with Tensorrt C++ api, and integrate batchedNMSPlugin. A Python wrapper is also provided.
Implement yolov5 with Tensorrt C++ api, and integrate batchedNMSPlugin. A Python wrapper is also provided.

yolov5 Original codes from tensorrtx. I modified the yololayer and integrated batchedNMSPlugin. A yolov5s.wts is provided for fast demo. How to genera

ParaMonte: Plain Powerful Parallel Monte Carlo and MCMC Library for Python, MATLAB, Fortran, C++, C.
ParaMonte: Plain Powerful Parallel Monte Carlo and MCMC Library for Python, MATLAB, Fortran, C++, C.

Overview | Installation | Dependencies | Parallelism | Examples | Acknowledgments | License | Authors ParaMonte: Plain Powerful Parallel Monte Carlo L

This repository contains Python and C++ implementation of Robust Consistent Video Depth, as described in the paper Python and C++ implementation of
Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV Workshop @ CVPR 2021.

MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose Estimation This is a PyTorch and LibTorch implementation of MarkerPose: a

Source code for the TKET quantum compiler, Python bindings and utilities

tket Introduction This repository contains the full source code for tket, a quantum SDK. If you just want to use tket via Python, the easiest way is t

A C++ framework for MDPs and POMDPs with Python bindings
A C++ framework for MDPs and POMDPs with Python bindings

AI-Toolbox This C++ toolbox is aimed at representing and solving common AI problems, implementing an easy-to-use interface which should be hopefully e

Example of using  ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python
Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python

yolov5-opencv-cpp-python Example of performing inference with ultralytics YOLO V5, OpenCV 4.5.4 DNN, C++ and Python Looking for YOLO V4 OpenCV C++/Pyt

Example of using YOLO v4 with OpenCV, C++ and Python
Example of using YOLO v4 with OpenCV, C++ and Python

yolov4-opencv-cpp-python Example of performing inference with Darknet YOLO V4, OpenCV 4.4.0 DNN, C++ and Python Looking for YOLO V5 OpenCV C++/Python

Python to CLike language transpiler

Python to many CLike language transpiler Currently supports C++ and Rust. Preliminary support for Julia, Kotlin, Nim, Go and Dart. The main idea is th

Owner
Skip Montanaro
Skip Montanaro
Subscribe anime RSS to download, repack, upload to Baidu, and post on TSDM automatically.

AnimeRSSforTSDM This is a project to automatically transfer Anime to Baidu netdisk with some rules on TSDM. Watch this post in detail. System Flow Cha

InanitySnow 2 Sep 19, 2022
Compile and run/debug C or C++ code easily

run-c Compile and run/debug C or C++ code easily. Installation and Updating Install & Update Script To install or update run-c, you should run the ins

null 1 Dec 8, 2021
C++20 compile time compressed string tables

Squeeze - C++20 Compile time string compression Experiments in building complex compile time executed code using constexpr functions to generate a com

Ashley Roll 44 Nov 14, 2022
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Distributed (Deep) Machine Learning Community 23.5k Dec 2, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Nov 25, 2022
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.8k Nov 26, 2022
In-situ data analyses and machine learning with OpenFOAM and Python

PythonFOAM: In-situ data analyses with OpenFOAM and Python Using Python modules for in-situ data analytics with OpenFOAM 8. NOTE that this is NOT PyFO

Argonne Leadership Computing Facility - ALCF 113 Nov 12, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Nov 30, 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration

PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks b

null 60.7k Dec 3, 2022