Technology Tales

Some online writing tools

Every week, I get an email newsletter from Woody’s Office Watch. This was something to which I started subscribing in the 1990’s but I took a break from it for a good while for reasons that I cannot recall and returned to it only in recent years. This week’s issue featured a list of online paraphrasing tools that are part of what is offered by Quillbot, Paraphraser, Dupli Checker and Pre Post Seo. Each got their own reviews in the newsletter so I will just outline other features in this posting.

In Quillbot’s case, the toolkit includes a grammar checker, summary generator, and citation generator. In addition to the online offering, there are extensions for Microsoft Word, Google Chrome, and Google Docs. In addition to the free version, a paid subscription option is available.

In spite of the name, Paraphraser is about more than what the title purports to do. There is article rewriting, plagiarism checking, grammar checking and text summarisation. Because there is no premium version, the offering is funded by advertising and it will not work with an ad blocker enabled. The mention of plagiarism suggests a perhaps murkier side to writing that cuts both ways: one is to avoid copying other work while another is the avoidance of groundless accusations of copying.

It was appear that the main role of Dupli Checker is to avoid accusations of plagiarism by checking what you write yet there is a grammar checker as well as a paraphrasing tool on there too. When I tried it, the English that it produced looked a little convoluted and there is a lack of fluency in what is written on its website as well. Together with a free offering that is supported by ads that were not blocked by my ad blocker, there are premium subscriptions too.

In web publishing, they say that content is king so the appearance of an option using the acronym for Search Engine Optimisation in it name may not be as strange as it might as first glance. There are numerous tools here with both free and paid tiers of service. While paraphrasing and plagiarism checking get top billing in the main menu on the home page, further inspection reveals that there is a lot more to check on this site.

In writing, inspiration is a fleeting and ephemeral quantity so anything that helps with this has to be of interest. While any rewriting of initial content may appear less smooth than the starting point, any help with the creation process cannot go amiss. For that reason alone, I might be tempted to try these tools from time to time and they might assist with proof reading as well because that can be a hit and miss affair for some.

What about a book on WordPress development?

Here’s something that I would really appreciate: a book on code cutting for WordPress customisation and extension. Having the Codex is all very fine but having a dead tree compendium that you can peruse at your leisure is a definite bonus. If there was a way to get the Codex on PDF and print it all off for easier reading, that would be progress. I believe that a publisher did plan to bring to the market something like what I want but author and publisher parted company, a pity. It would be great to see something like Apress’ Pro Drupal Development for WordPress. What about it, Automattic folks?

SAS books now on Safari

Being a Safari subscriber, I found a pleasant surprise awaiting me in this month’s email newsletter: eBooks from SAS Books are now available on Safari. Having a quick look, I found a small but useful selection. Topics like the SQL procedure, the Macro language and Enterprise Guide caught my eye but there’s more than this on offer. It’ll be interesting to see where this leads…

Setting up a WD My Book Live NAS on Ubuntu GNOME 13.10

The official line from Western Digital is that they do not support the use of their My Book Live NAS drives with Linux or UNIX. However, what that means is that they only develop tools for accessing their products for Windows and maybe OS X. It still doesn’t mean that you cannot access the drive’s configuration settings by pointing your web browser at http://mybooklive.local/. In fact, not having those extra tools is no drawback at all since the drive can be accessed through your file manager of choice under the Network section and the default name is MyBookLive too so you easily can find the thing once it is connected to a router or switch anyway.

Once you are in the servers web configuration area, you can do things like changing its name, updating its firmware, finding out what network has been assigned to it, creating and deleting file shares, password protecting file shares and other things. These are the kinds of things that come in handy if you are going to have a more permanent connection to the NAS from a PC that runs Linux. The steps that I describe have worked on Ubuntu 12.04 and 13.10 with the GNOME  desktop environment.

What I was surprised to discover that you cannot just set up a symbolic link that points to a file share. Instead, it needs to be mounted and this can be done from the command line using mount or at start-up with /etc/fstab. For this to happen, you need the Common Internet File System utilities and these are added as follows if you need them (check on the Software Software or in Synaptic):

sudo apt-get install cifs-utils

Once these are added, you can add a line like the following to /etc/fstab:

//[NAS IP address]/[file share name] /[file system mount point] cifs
credentials=[full file location]/.creds,
iocharset=utf8,
sec=ntlm,
gid=1000,
uid=1000,
file_mode=0775,
dir_mode=0775
0 0

Though I have broken it over several lines above, this is one unwrapped line in /etc/fstab with all the fields in square brackets populated for your system and with no brackets around these. Though there are other ways to specify the server, using its IP address is what has given me the most success and this is found under Settings > Network on the web console. Next up is the actual file share name on the NAS and I have used a custom them instead of the default of Public. The NAS file share needs to be mounted to an actual directory in your file system like /media/nas or whatever you like and you will need to create this beforehand. After that, you have to specify the file system and it is cifs instead of more conventional alternatives like ext4 or swap. After this and before the final two space delimited zeroes in the line comes the chunk that deals with the security of the mount point.

What I have done in my case is to have a password protected file share and the user ID and password have been placed in a file in my home area with only the owner having read and write permissions for it (600 in chmod-speak). Preceding the filename with a “.” also affords extra invisibility. That file then is populated with the user ID and password like the following. Of course, the bracketed values have to be replaced with what you have in your case.

username=[NAS file share user ID]
password=[NAS file share password]

With the credentials file created, its options have to be set. First, there is the character set of the file (usually UTF-8 and I got error code 79 when I mistyped this) and the security that is to be applied to the credentials (ntlm in this case). To save having no write access to the mounted file share, the uid and gid for your user needs specification with 1000 being the values for the first non-root user created on a Linux system. After that, it does no harm to set the file and directory permissions because they only can be set at mount time; using chmod, chown and chgrp later on has no effect whatsoever. Here, I have set permissions to read, write and execute for the owner and the user group while only allowing read and execute access for everyone else (that’s 775 in the world of chmod).

All of what I have described here worked for me and had to gleaned from disparate sources like Mount Windows Shares Permanently from the Ubuntu Wiki, another blog entry regarding the permissions settings for a CIFS mount point and an Ubuntu forum posting on mounting CIFS with UTF-8 support. Because of the scattering of information, I just felt that it needed to all together in one place for others to use and I hope that fulfils someone else’s needs in a similar way to mine.

More thoughts on learning to use digital imaging software

Databases & Programming

The world of UNIX appears to attract those interested in the more technical aspects of computing. Since Linux is cut from the same lineage, it is apt to include lists of computing languages. Both scripting and programming appear here despite the title, itself shortened for the sake of brevity. Since much code cutting involves working with databases, these appear here too.

In time, I plan to correct the imbalance between programming and scripting languages that currently exists. The original list was bare, so descriptions have been added and will be more and more needed should there be any expansion of what you find here.

Programming and Scripting Languages

Apache Groovy

My first encounter with an implementation of this language was with that belonging to a statistical computing environment (SCE) and that remains an ongoing dalliance. It is easy to think of Groovy as a way of working with a Java-based API using a scripting language and it certainly feels like that. Saying that, it all works better if you know Java, though you do have to watch for the development of domain-specific language capability. That last comment probably applies to the aforementioned SCE in that it has its own object and method hierarchy that means that not all standard Groovy functionality is available.

Clojure

Clojure is a dynamic, functional programming language that runs on the Java Virtual Machine (JVM) and is designed for building robust and scalable software applications. It is characterised by its emphasis on immutability, persistent data structures, and seamless interoperability with Java. Clojure embraces the Lisp programming language’s principles, providing a concise syntax and powerful abstractions for managing state, concurrency, and functional programming paradigms. With its focus on simplicity, expressiveness, and the ability to leverage the vast Java ecosystem, Clojure enables developers to create efficient and maintainable code for a wide range of applications.

Erlang

Go

Computing languages often get strange names like single letters or small words like this one; that means that you need to look for “Golang” in any online search. In any case, Go was originated at Google and numbered among its inventors was one of the creators of the C programming language. The intent here is massively multithreaded system programming using stand-alone executable components while retaining or enhancing code readability. Another facet is the ability to function efficiently in distributed computing environments like those at SoundCloud or Uber. A variety of different tools have been written using the language and these include the ever pervasive Docker and Kubernetes.

Julia

It remains an odd decision to give a computing language a girl’s name, but the purpose is a serious one. Often, there is a trade-off between speed of code writing and speed of execution with the result being that data programming involves prototyping in one language and porting to another for production usage. The first group includes R and Python while the second includes C, C++, FORTRAN and even Java, so there is an element of translation involved that often means that different people are involved, which adds an element of error caused by misunderstandings. This gets described as the two language problem and Julia’s major raison d’être is the avoidance of that: its top-line description is that it is as quick to program as Python but runs as fast as C because of its just-in-time compilation, multiple dispatch and in-built multithreading. This also allows for extensive capabilities for scientific computing that go beyond machine learning and an example comes in the number of differential equation solvers that are available. It also helps that meta-programming makes everything more generalisable.

Perl

It has been around since the 1980’s and still pervades though it is not as dominant as it once was for creating dynamic websites or system administration. PHP has taken on much of the former while Python is making inroads into the latter. Still, no list would be complete with complete without a mention of the once ubiquitous scripting language and it once powered my online photo gallery. It may be an easier language, but there is plenty of documentation on the web with Perldoc, Perl Maven and Perlmeister being some good places to look, and Dan Massey has some interesting articles on his site too. Not only that, but it is extensible too, with plenty of extra modules to be found on CPAN.

PHP

This usurper has taken the place of Perl for powering many of the world’s websites. That the language is less verbose probably helps its case and many if not most CMS packages make use of its versatility.

Python

It may be Google’s preferred scripting language for system administration but it is its usefulness for Data Science where it really has shone in the eyes of many. There are numerous packages for data wrangling, data visualisation and machine learning that make the language ever present in any Data Scientist’s toolbox and looking in the PyPi archive will allow you to find what you need. It also has its place in web scripting too, even if it is not as pervasive as PHP though CMS’s like Plone run on Python and there is the Django framework together with the Gunicorn web server.

OpenJDK

One of the acts of Jonathon Schwartz while he was head at Sun Microsystems was to make Java open source after more than a decade of its being largely proprietary and this is the website for the project. Of course, his more notable act at Sun was to sell the company to Oracle, but that’s another story altogether…

R

This is an open-source implementation of the S language that is much appreciated by statisticians and is much used in the teaching of the subject. The base language only has so much functionality but there are many packages available that do just that and there are many to find on repositories like the CRAN and others can be found on various GitHub repositories, though these tend to be more experimental in nature. There are commonly used and well-supported mainstays that everyone uses, but there always is a need to verify that a particular package does what it claims to do. Given that, there are possibilities for data wrangling, data tabulation, data visualisation and data science. While quick to code, R is slow to execute compared with others and I have found that Python is faster but it still has a use for smaller data sets; both keep their temporary data sets in system memory so that will help.

Rust

It came as a surprise that this Mozilla-originated language is gaining traction in scientific data analysis, possibly because it is a fast multithreaded counterpart to C and C++ with some added safety features (though these can be turned off if needed and extra care gets taken). The downsizing of Mozilla led to a sharp reduction in its team of Rust developers and the Rust Foundation has been set up to oversee the language instead. There are online books like The Rust Programming Language and the Rust Cookbook, with the first of these also having paper and e-book counterparts from No Starch Press. For those interested in a more interactive introduction, there also is the Tour of Rust.

Databases

MariaDB

This essentially is a fork of MySQL (see below) now that Oracle owns it. The originators of MySQL are the creators of MariaDB so their claims of it being a drop-in replacement for it may have some traction. So far, I have seen no exodus from MySQL, though.

MySQL

After being in the hands of a number of owners until it incongruously came into the custodianship of Oracle (who of course already had and still have one of their own), the database system that powers many dynamic websites almost remains a de facto standard and looks set to remain thus for now.

MongoDB

This may a document-based and not a relationship database like many of us understand them but it still is being touted as an alternative to the more mainstream competition. Database technology isn’t just about SQL and MongoDB champions a NoSQL approach; it sounds as if the emergence of XML might be what’s facilitating the NoSQL database technologies.

PostgreSQL

This project may have more open-source credibility than MySQL, but it seems to remain in its shadow, though that may be explained by its being a more complex piece of software to use (at least, that has been my experience, anyway). It so happens that this is what Debian installs if you specify the web server option at operating system installation time.

A self-hosted online photo album option

I was perusing a recent copy of Linux Format and encountered a feature describing a self-hosted alternative to the likes of Flickr: Gallery. From my quick look, it looks fully featured, offering themes and even shopping cart facilities for those who want to sell their wares. The screenshots on the open-source project’s website look promising but, for a fuller appraisal, I would need to spend some time trying to bend it to my will. Before anyone mentions it, I am aware that WordPress can be used for photoblogging, but this tool seems to take things a bit further. It’s the sort of thing about which I might have wondered, given the pervasiveness of content management systems these days. My own custom-built photo gallery is devoid of a slick back end, hence why Gallery caught my eye, but I’ll continue with it and may even get to adding the needful myself.

One reason why you cannot merge cells in Excel

One handy thing that I didn’t realise that you could do with Excel until the last few months was the ability to share an open workbook between users and collate any changes that are made (it seems that a form of version control is behind this). From what I have seen, Excel seems to manage changes to shared spreadsheets rather well. When you save yours, it adds updates from other users and warns if any edits collide with one another. To activate it in Excel 2003, all that needs doing is for you to go to the Share Workbook entry on the Tools and tick the appropriate checkbox in the resulting dialogue box. In 2007 and 2010, look for the Share Workbook icon in the Review tab on the ribbon to get the same dialogue box popping up.

That’s not to say that it doesn’t have its restrictions though and I have found that the merging of cells is made unavailable but that can be sorted by unsharing and resharing the workbook when no one else is using it. As to why cell merger is switched off by sharing, I have a few ideas. Maybe, they couldn’t make it work reliably (can happen with large software development projects like the creation of a new version of Excel) or decided that it would have consequences for other users that are too inconvenient. Either way, we cannot merge cells in shared workbooks and that’s the way that things are for now. Some may not worry about this though since they reckon that cell merging is undesirable anyway; well, don’t go doing it in any spreadsheet that is likely to be read in by another program or you could cause trouble.

A look at the Julia programming language

Several open-source computing languages get mentioned when talking about working with data. Among these are R and Python, but there are others; Julia is another one of these. It took a while before I got to check out Julia because I felt the need to get acquainted with R and Python beforehand. There are others like Lua to investigate too, but that can wait for now.

With the way that R is making an incursion into clinical data reporting analysis following the passage of decades when SAS was predominant, my explorations of Julia are inspired by a certain contrariness on my part. Alongside some small personal projects, there has been some reading in (digital) book form and online. Concerning the latter of these, there are useful tutorials like Introduction to Data Science: Learn Julia Programming, Maths & Data Science from Scratch or Julia Programming: a Hands-on Tutorial. Like what happens with R, there are online versions of published books available free of charge, and they include Julia Data Science and Interactive Visualization and Plotting with Julia. Video learning can help too and Jane Herriman has recorded and shared useful beginner’s guides on YouTube that start with the basics before heading onto more advanced subjects like multiple dispatch, broadcasting and metaprogramming.

This piece of learning has been made of simple self-inspired puzzles before moving on to anything more complex. That differs from my dalliance with R and Python, where I ventured into complexity first, not least because of testing them out with public COVID data. Eventually, I got around to doing that with Julia too though my interest was beginning to wane by then, and Julia’s abilities for creating multipage PDF files were such that PDF Toolkit was needed to help with this. Along the way, I have made use of such packages as CSV.jl, DataFrames.jl, DataFramesMeta, Plots, Gadfly.jl, XLSX.jl and JSON3.jl, among others. After that, there is PrettyTables.jl to try out, and anyone can look at the Beautiful Makie website to see what Makie can do. There are plenty of other packages creating graphs such as SpatialGraphs.jl, PGFPlotsX and GRUtils.jl. For formatting numbers, options include Format.jl and Humanize.jl.

So far, my primary usage has been with personal financial data together with automated processing and backup of photo files. The photo file processing has taken advantage of the ability to compile Julia scripts for added speed because just-in-time compilation always means there is a lag before the real work begins.

VS Code is my chosen editor for working with Julia scripts, since it has a plugin for the language. That adds the REPL, syntax highlighting, execution and data frame viewing capabilities that once were added to the now defunct Atom editor by its own plugin. While it would be nice to have a keyboard shortcut for script execution, the whole thing works well and is regularly updated.

Naturally, there have been a load of queries as I have gone along and the Julia Documentation has been consulted as well as Julia Discourse and Stack Overflow. The latter pair have become regular landing spots on many a Google search. One example followed a glitch that I encountered after a Julia upgrade when I asked a question about this and was directed to the XLSX.jl Migration Guides where I got the information that I needed to fix my code for it to run properly.

There is more learning to do as I continue to use Julia for various things. Once compiled, it does run fast like it has been promised. The syntax paradigm is akin to R and Python, but there are Julia-specific features too. If you have used the others, the learning curve is lessened but not eliminated completely. This is not an object-oriented language as such, but its functional nature makes it familiar enough for getting going with it. In short, the project has come a long way since it started more than ten years ago. There is much for the scientific programmer, but only time will tell if it usurped its older competitors. For now, I will remain interested in it.

A web development toolbox