Python Notes: Introduction

  1. The information presented here is intended for educational use.
  2. The information presented here is provided free of charge, as-is, with no warranty of any kind.
  3. Edit: 2025-07-12
  1. Introduction
  2. my example Python programs
  3. Shifting over to Python
  4. mod_cgi vs mod_wsgi (experiments)
  5. Machine Learning + Deep Learning
  6. External Links

Introduction

Hacking - Noodling with BASIC

Anyone who ever played with BASIC on personal computers in the 1970s or 1980s will recognize the importance of the BASIC language for noodling around. Although the creators of BASIC intended it for use in teaching computer programming concepts to FORTRAN students at Dartmouth College (photo), noodling around made BASIC ideal for teaching concepts in science, engineering, finance, and math.

BASIC on 8-bit personal computers in the 1970s was usually implemented in ROM, and every implementation was different (Apple2, Radio Shack TRS-80, HeathKit-H8).

The IBM-PC in 1981 (the first popular 16-bit desktop computer) also offered BASIC in ROM (IBM BASIC).

Shortly after that, Microsoft began publishing 16-bit software products like GW-BASIC (1983),  QuickBASIC (1985), QBASIC (1991) and Visual BASIC (1991) which worked well on 16-bit operating systems like MS-DOS (1981) up through Windows-3.11 (1992). These 16-bit language interpreters were also supported on 32-bit operating systems, starting with Windows-95 up through Windows-7.

The big problem today is that 64-bit computers run 64-bit operating systems, like Windows-10, where 64-bit and 32-bit programs are supported, but native support for 16-bit programs has been dropped. Technical work-arounds exist including "setting up a virtual machine" on your 64-bit OS, but why go to all that bother when all you want to do is noodle around? Perhaps it is time for you to ditch BASIC for Python.

Hacking - Noodling with Python

Many people reading this will not know that Python was first created in 1991 to produce an internet-friendly BASIC-like scripting tool for use under the CGI (common gateway interface) specification to extend web server functionality. Python quickly morphed into a language which could easily support both "internet servers and clients" as well as "standalone applications". Here is a short list:

comment: The good folks at Google could see much potential in python2 so hired python's original author ( Guido van Rossum ) at Google's expense, to produce python3. There is no room here to tell that story here, so look for it elsewhere.
Python does not have these BASIC imitations:
Python3 has no difficulty with these statements:

Imagine using numbers of this size to index your data arrays

(a few of my) Example Programs (alphabetical order)

link description Notes
calendar a very simple calendar generator BASIC-to-Python conversion examples
compiling caching Python compiling - Python file caching article for nerds and speed demons
DFT - FFT Discrete Fourier Transform -
Fast Fourier Transform		 BASIC-to-Python conversion examples (not interested in BASIC? Then see my FFT demo 4 rows below)
dh standalone Diffie-Hellman key exchange demo (interactive) Python interactive standalone application (called from a shell prompt)
dh web Diffie-Hellman key exchange demo (web) Python web application (just to show you how)
click here: https://neilrieck.net/dh_demo.html
caveat: this client-server demo is a rather junky looking "how to" proof-of-concept, so should not be considered anything I would consider production quality (for example, the JS file employs pure JavaScript rather than calling jQuery)
Easter compute the date of Easter for any given year it all starts with determining the date of the first full moon on, or after. the Spring equinox.
FFT via numpy
 Fast Fourier Transform demo a very simple demo on how to combine 3 analog signals into one, then invoke FFT to convert from the time domain to the frequency domain
employs: numpy
FFT web via numpy
(code coming soon)		 Fast Fourier Transform demo (web) Python web application (just to show you how)
employs: numpy , matlibplot , mpdl3 click here: https://neilrieck.net/fft_demo.html
caveat: this client-server demo is a rather junky looking "how to" proof-of-concept, so should not be considered anything I would consider production quality (for example, the JS file employs pure JavaScript rather than calling jQuery)
fun with floats fun with floats and decimals be careful how you initialize float and decimal data; this program also shows how to properly use the exec() function in python3
http website tester shows how to fetch a web page this technique works with both http as well as https
https harder demo shows how to try harder shows how to connect to an older https site
logical_name_demo logical name demo caveat: this will only work on OpenVMS systems
money rounding money rounding demo required for GAAP (generally accepted accounting principles)
pix-of-day picture of the day generator extracts picture-of-the-day info from a relational database which is then passed to JavaScript.
Click this link (https://neilrieck.net) to see it in action:
1) the top right corner will show a NASA chart for last month's CO2 readings
2) four seconds later, the image and text will switch to something extracted from my database (usually a scientist of the day)
3) the pictures will cycle back for ten iterations
4) hit the F12 key then click CONSOLE to observe log messages
caveat: this client-server demo is a rather junky looking "how to" proof-of-concept, so should not be considered anything I would consider production quality (for example, the JS file employs pure JavaScript rather than calling jQuery)
RMS demo
 fooling around with root-mean-square fooling around with root-mean-square (the basis for all A.C. voltage measurements)

A few of my client-server demos:

  1. Diffie-Hellman key exchange
  2. FFT wave analysis

Shifting over to Python

mod_cgi vs mod_wsgi

Machine Learning - Deep Learning

Executive Summary
  1. Just as it is nearly impossible to develop a working knowledge of Calculus without a working knowledge of Algebra, I am now convinced that you cannot develop a working knowledge Deep Machine Learning without a working knowledge Python
  2. Unlike other programming projects, you cannot develop a deep machine learning application, then publish it, then move onto something else. These systems are only as good as their training data, which means they should constantly be updated (so the programmer becomes a teacher?)
  3. Artificial Intelligence does not mean Artificial Consciousness. While some modern A.I. systems might pass a Turing Test by Joe citizen, a specialist will always be able to do a better job. (think about the Voight-Kampf interview between Rick Deckard and Rachel in the movie Bladerunner. Rachel knew that eating 'raw oysters' was okay but didn't know that serving 'boiled dog' was wrong)

Anyone considering a course on machine learning or deep learning should learn Python immediately where most examples will include one (or more) of these free Python libraries

NumPy Library to add array support to Python2
Also adds support for matrix mathematics		 https://en.wikipedia.org/wiki/NumPy Community - 2006
scikit-learn first generation learning library https://en.wikipedia.org/wiki/Scikit-learn Google Summer of Code - 2007
TensorFlow second generation learning library https://en.wikipedia.org/wiki/TensorFlow Google - 2015
Keras second generation learning library https://en.wikipedia.org/wiki/Keras Google - 2015
PyTorch second generation learning library https://en.wikipedia.org/wiki/PyTorch Facebook - 2017

The roots of artificial intelligence (and expert systems, and machine learning) go back to the 1940s and 1950s so contain a lot of non-computer terminology. Modern computer technologists might wish to start here:

External Links

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 Neil Rieck
 Waterloo, Ontario, Canada.