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Programming: Forth, Bash and Python

Submitted by Roy Schestowitz on Thursday 3rd of October 2019 10:59:20 AM Filed under
Development
  • 7 Excellent Free Books to Learn Forth

    Forth is an imperative stack-based programming language, and a member of the class of extensible interactive languages. It was created by Charles Moore in 1970 to control telescopes in observatories using small computers. Because of its roots, Forth stresses efficiency, compactness, flexible and efficient hardware/software interaction.

    Forth has a number of properties that contrast it from many other programming languages. In particular, Forth has no inherent keywords and is extensible. It is both a low level and high level language. It has the interesting property of being able to compile itself into a new compiler, debug itself and to experiment in real time as the system is built. Forth is an extremely flexible language, with high portability, compact source and object code, and a language that is easy to learn, program and debug. It has an incremental compiler, an interpreter and a very fast edit-compile-test cycle. Forth uses a stack to pass data between words, and it uses the raw memory for more permanent storage. It also lets coders write their own control structures.

    Forth has often being deployed in embedded systems due to the compactness of object code. Forth is also used in boot loaders such as Open Firmware (developed by Sun Microsystems) as well as scientific fields such as astronomy, mathematics, oceanography and electrical engineering.

  • Writing Comments in Bash Scripts

    When writing Bash scripts, it is always a good practice to make your code clean and easily understandable. Organizing your code in blocks, indenting, giving variables and functions descriptive names are several ways to do this. Another way to improve the readability of your code is by using comments. A comment is a human-readable explanation or annotation that is written in the shell script. Adding comments to your Bash scripts will save you a lot of time and effort when you look at your code in the future.

  • 13 Python Natural Language Processing Tools

    Natural language processing (NLP) is an exciting field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. It includes word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering.

    In our formative years, we master the basics of spoken and written language. However, the vast majority of us do not progress past some basic processing rules when we learn how to handle text in our applications. Yet unstructured software comprises the majority of the data we see. NLP is the technology for dealing with our all-pervasive product: human language, as it appears in social media, emails, web pages, tweets, product descriptions, newspaper stories, and scientific articles, in thousands of languages and variants.

  • Calculating Rayleigh Reflectance using Py6S

    A user of Py6S recently contacted me to ask if it was possible to get an output of Rayleigh reflectance from Py6S. Unfortunately this email wasn’t sent to the Py6s Google Group, so I thought I’d write a blog post explaining how to do this, and showing a few outputs (reminder: please post Py6S questions there rather than emailing me directly, then people with questions in the future can find the answers there rather than asking again).

    So, first of all, what is Rayleigh reflectance? Well, it’s the reflectance (as measured at the top-of-atmosphere) that is caused by Rayleigh scattering in the atmosphere. This is the wavelength-dependent scattering of light by gas molecules in the atmosphere – and it is an inescapable effect of light passing through the atmosphere.

    So, on to how to calculate it in Py6S. Unfortunately the underlying 6S model doesn’t provide Rayleigh reflectance as an output, so we have to do a bit more work to calculate it.

  • Using the Python zip() Function for Parallel Iteration

    Python’s zip() function creates an iterator that will aggregate elements from two or more iterables. You can use the resulting iterator to quickly and consistently solve common programming problems, like creating dictionaries. In this tutorial, you’ll discover the logic behind the Python zip() function and how you can use it to solve real-world problems.

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