Technology Tales

Catching keyboard interruptions in a Python script for a more orderly exit

A while back, I was using a Python script to watch a folder and process photos in there, whenever a new one was added. Eventually, I ended up with a few of these because I was unable to work out a way to get multiple folders watched in the same script.

In each of them, though, I needed a tidy way to exit a running script in place of the stream of consciousness that often emerges when you do such things to it. Because I knew what was happening anyway, I needed a script to terminate quietly and set to uncover a way to achieve this.

What came up was something like the code that you see below. While I naturally did some customisations, I kept the essential construct to capture keyboard interruption shortcuts, like the use of CTRL + C in a Linux command line interface.

if __name__ == '__main__':
    try:
        main()
    except KeyboardInterrupt:
        print('Interrupted')
        try:
            sys.exit(130)
        except SystemExit:
            os._exit(130)

What is happening above is that the interruption operation is captured in a nested TRY/EXCEPT block. The outer block catches the interruption, while the inner one runs through different ways of performing the script termination. For the first termination function call, you need to call the SYS package and the second needs the OS one, so you need to declare these at the start of your script.

Of the two, SYS.EXIT is preferable because it invokes clean-up activities while OS._EXIT does not, which might explain the “_” prefix in the second of these. In fact, calling SYS.EXIT is not that different to issuing RAISE SYSTEMEXIT instead because that lies underneath it. Here OS._EXIT is the fallback for when SYS.EXIT fails, and it is not all that desirable given the abrupt action that it invokes.

The exit code of 130 is fed to both, since that is what is issued when you terminate a running instance of an application on Linux anyway. Using 0 could negate any means of picking up what has happened if you have downstream processing. In my use case, everything was standalone, so that did not matter so much.

AttributeError: module ‘PIL’ has no attribute ‘Image’

One of my websites has an online photo gallery. This has been a long-term activity that has taken several forms over the years. Once HTML and JavaScript based, it then was powered by Perl before PHP and MySQL came along to take things from there.

While that remains how it works, the publishing side of things has used its own selection of mechanisms over the same time span. Perl and XML were the backbone until Python and Markdown took over. There was a time when ImageMagick and GraphicsMagick handled image processing, but Python now does that as well.

That was when the error message gracing the title of this post came to my notice. Everything was working well when executed in Spyder, but the message appears when I tried running things using Python on the command line. PIL is the abbreviated name for the Python 3 pillow package; there was one called PIL in the Python 2 days.

For me, pillow loads, resizes and creates new images, which is handy for adding borders and copyright/source information to each image as well as creating thumbnails. All this happens in memory and that makes everything go quickly, much faster than disk-based tools like ImageMagick and GraphicsMagick.

Of course, nothing is going to happen if the package cannot be loaded, and that is what the error message is about. Linux is what I mainly use, so that is the context for this scenario. What I was doing was something like the following in the Python script:

import PIL

Then, I referred to PIL.Image when I needed it, and this could not be found when the script was run from the command line (BASH). The solution was to add something like the following:

from PIL import Image

That sorted it, and I must have run into trouble with PIL.ImageFilter too, since I now load it in the same manner. In both cases, I could just refer to Image or ImageFilter as I required and without the dot syntax. However, you need to make sure that there is no clash with anything in another loaded Python package when doing this.

Resolving a clash between Homebrew and Python

For reasons that I cannot recall now, I installed the Hugo static website generator on my Linux system and web servers using Homebrew. The only reason that I suggest is that it might have been a way to get the latest version at the time since Linux Mint only does major changes every two years, keeping it in line with long-term support editions of Ubuntu.

When Homebrew was installed, it changed the lookup path for command line executables by adding the following line to my .bashrc file:

eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)"

This executed the following lines:

export HOMEBREW_PREFIX="/home/linuxbrew/.linuxbrew";
export HOMEBREW_CELLAR="/home/linuxbrew/.linuxbrew/Cellar";
export HOMEBREW_REPOSITORY="/home/linuxbrew/.linuxbrew/Homebrew";
export PATH="/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin${PATH+:$PATH}";
export MANPATH="/home/linuxbrew/.linuxbrew/share/man${MANPATH+:$MANPATH}:";
export INFOPATH="/home/linuxbrew/.linuxbrew/share/info:${INFOPATH:-}";

While the result suits Homebrew, it changed the setup of Python and its packages on my system. Eventually, this had undesirable consequences like messing up how Spyder started so I wanted to change this. There are other things that I have automated using Python and these were not working either.

One way that I have seen suggested is to execute the following command but I cannot vouch for this:

brew unlink python

What I did was to comment out the offending line in .bashrc and replace it with the following:

export PATH="$PATH:/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin"

export HOMEBREW_PREFIX="/home/linuxbrew/.linuxbrew";
export HOMEBREW_CELLAR="/home/linuxbrew/.linuxbrew/Cellar";
export HOMEBREW_REPOSITORY="/home/linuxbrew/.linuxbrew/Homebrew";

export MANPATH="/home/linuxbrew/.linuxbrew/share/man${MANPATH+:$MANPATH}:";
export INFOPATH="${INFOPATH:-}/home/linuxbrew/.linuxbrew/share/info";

The first of these adds the Homebrew paths to the end of the PATH variable instead of the start of the same as was the case before. This means that system folders get searched for executable files before the Homebrew ones. It also means that Python packages are loaded from my user area and not the Homebrew one as was the case under its own terms. There are other things to remember with Python packages such as not having a version installed at the system level and another at the user one since these will conflict with one another.

So far, the result of the Homebrew changes is not unsatisfactory and I will watch for any rough edges that need addressing. If something comes up, then I will set things up in another way.

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.

Removing a Julia package

While I have been programming with SAS for a few decades and it remains a lynchpin in the world of clinical development in the pharmaceutical industry, other technologies like R and Python are gaining a foothold. Two years ago, I started to look at those languages with personal projects being a great way of facilitating this. In addition, I got to hear of Julia and got to try that too. That journey continues since I have put it into use for importing and backing up photos, and there are other possible uses too.

Recently, I updated Julia to version 1.8.2 but ran into a problem with the DataArrays package that I had installed so I decided to remove it since it was added during experimentation. The Pkg package that is used for package management is documented but I had not gotten to that so some web searching ensued. It turns out that there are two ways of doing this. One uses the REPL: after pressing the ] key, the following command gets issued:

rm DataArrays

When all is done, pressing the delete or backspace keys returns things to normal. This also can be done in a script as well as the REPL and the following line works in both instances:

using Pkg; Pkg.rm("DataArrays")

The semicolon is used to separate two commands issued in the same line but they can be on different lines or issued separately just as well. Naturally, DataArrays is just an example here so you just replace that with the name of whatever other package you need to remove. Since we can get carried away when downloading packages, there are times when a clean-up is needed to remove redundant packages so knowing how to remove any clutter is invaluable.

Getting custom Python imports to work in Visual Studio Code

While I continue to use Spyder as my preferred Python code editor, I also tried out Visual Studio Code. Handily, this Integrated Development Environment also has facilities for working with R and Julia code as well as MarkDown text editing and adding the required extensions is enough for these applications; it helps that there is an unofficial Grammarly extension for content creation.

My Python code development makes use of the Pylance extension and it works a little differently from Spyder when it comes to including files using import statements. Spyder will look into the folder where the base script is located but the default behaviour of Pylance is that it looks in the root path of your workspace. This meant that any code that ran successfully in Spyder failed in Visual Studio Code.

The way around this was to add the required location using the python.analysis.extraPaths setting for the workspace. That meant opening Settings by navigating to File > Preferences > Settings in the menu system and entering python.analysis.extraPaths into the search box. That took me to the section that I needed and I then clicked on Add Item before entering the required path and clicking on the OK button. That was enough to fix the problem and all worked as it should after that.

Useful Python packages for working with data

My response to changes in the technology stack used in clinical research is to develop some familiarity with programming and scripting platforms that complement and compete with SAS, a system with which I have been programming since 2000. One of these has been R but Python is another that has taken up my attention and I now also have Julia in my sights as well. There may be others to assess in the fullness of time.

While I first started to explore the Data Science world in the autumn of 2017, it was in the autumn of 2019 that I began to complete LinkedIn training courses on the subject. Good though they were, I find that I need to actually use a tool in order to better understand it. At that time, I did get to hear about Python packages like Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn and Beautiful Soup  though it took until of spring of this year for me to start gaining some hands-on experience with using any of these.

During the summer of 2020, I attended a BCS webinar on the CodeGrades initiative, a programming mentoring scheme inspired by the way classical musicianship is assessed. In fact, one of the main progenitors is a trained classical musician and teacher of classical music who turned to Python programming when starting a family so as to have a more stable income. The approach is that a student selects a project and works their way through it with mentoring and periodic assessments carried out in a gentle and discursive manner. Of course, the project has to be engaging for the learning experience to stay the course and that point came through in the webinar.

That is one lesson that resonates with me with subjects as diverse as web server performance and the ongoing pandemic pandemic supplying data and there are other sources of public data to examine as well before looking through my own personal archive gathered over the decades. Some subjects are uplifting while others are more foreboding but the key thing is that they sustain interest and offer opportunities for new learning. Without being able to dream up new things to try, my knowledge of R and Python would not be as extensive as it is and I hope that it will help with learning Julia too.

In the main, my own learning has been a solo effort with consultation of documentation along with web searches that have brought me to the likes of Real Python, Stack Abuse, Data Viz with Python and R and others for longer tutorials as well as threads on Stack Overflow. Usually, the web searching begins when I need a steer on a particular or a way to resolve a particular error or warning message but books always are worth reading even if that is the slower route. Those from the Dummies series or from O’Reilly have proved must useful so far but I do need to read them more completely than I already have; it is all too tempting to go with the try the “programming and search for solutions as you go” approach instead.

To get going, many choose the Anaconda distribution to get Jupyter notebook functionality but I prefer a more traditional editor so Spyder has been my tool of choice for Python programming and there are others like PyCharm as well. Spyder itself is written in Python so it can be installed using pip from PyPi like other Python packages. It has other dependencies like Pylint for code management activities but these get installed behind the scenes.

The packages that I first met in 2019 may be the mainstays for doing data science but I have discovered others since then. It also seems that there is porosity between the worlds of R an Python so you get some Python packages aping R packages and R has the Reticulate package for executing Python code. There are Python counterparts to such Tidyverse stables as dply and ggplot2 in the form of Siuba and Plotnine, respectively. The syntax of these packages are not direct copies of what is executed in R but they are close enough for there to be enough familiarity for added user friendliness compared to Pandas or Matplotlib. The interoperability does not stop there for there is SQLAlchemy for connecting to MySQL and other databases (PyMySQL is needed as well) and there also is SASPy for interacting with SAS Viya.

Pyhton may not have the speed of Julia but there are plenty of packages for working with larger workloads. Of these, Dask, Modin and RAPIDS all have there uses for dealing with data volumes that make Pandas code crawl. As if to prove that there are plenty of libraries for various forms of data analytics, data science, artificial intelligence and machine learning, there also are the likes of Keras, TensorFlow and NetworkX. These are just a selection of what is available and there is no need not to check out more. It may be tempting to stick with the most popular packages all the time, especially when they do so much, but it never hurst to keep an open mind either.

A new look

Things have been changing on here. Much of that has been behind the scenes with a move to a new VPS for extra speed and all the upheaval that brings. It also gained me a better system for less money than the old upgrade path was costing me and everything feels more responsive as well. Extra work has gone into securing the website as well and I have learned a lot as that has progressed. New lessons were added to older, and sometimes forgotten, ones.

The more obvious change for those who have been here before is that the visual appearance has been refreshed. A new theme has been applied with a multitude of tweaks to make it feel unique and to iron out any rough edges that there may be. This remains a WordPress-based website and new theme is a variant of the Appointee subtheme of the Appointment theme. WordPress does only supports child theming but not grandchild theming so I had to make a copy of Appointee of my own so I could modify things as I see fit.

To my eyes, things do look cleaner, crisper and brighter so I hope that it feels the same to you. Like so many designs these days, the basis is the Bootstrap framework and that is no bad thing in my mind though the standardisation may be too much for some tastes. What has become challenging is that it is getter harder to find new spins on more traditional layouts with everything going for a more magazine-like appearance and summaries being shown on the front page instead of complete articles. That probably reflects how things are going for websites these days so it may be that the next refresh could be more home grown and that is a while away yet.

As the website heads towards its sixteenth year, there is bound to be continuing change. In some ways, I prefer that some things remain unchanged so I use the classic editor instead of Gutenburg because that works best for me. Block-based editing is not for me since I prefer to tinker with code anyway. Still, not all of its influences can be avoided and I have needed to figure out the new widgets interface. It did not feel that intuitive but I suppose that I will grow accustomed to it.

My interest in technology continues even if it saddens me at time and some things do not impress me; the Windows 11 taskbar is one of those so I will not be in any hurry to move away from Windows 10. Still, the pandemic has offered its own learning with virtual conferencing allowing one to lurk and learn new things. For me, this has included R, Python, Julia and DevOps among other things. That proved worthwhile during a time with many restrictions. All that could yield more content yet and some already is on the way.

As ever, it is my own direct working with technology that yields some real niche ideas that others have not covered. With so many technology blogs out there, they may be getting less and less easy to find but everyone has their own journey so I hope to encounter more of them. There remain times when doing precedes telling and that is how it is on here. It is not all about appearances since content matters as much as it ever did.

A little bit of abstraction

Data science has remained in my awareness since 2017 though my work is more on its fringes in clinical research. In fact, I have been involved more in the standardisation and automation of more traditional data reporting than in the needs of data modelling such as data engineering or other similar disciplines. Much of this effort has meant the use of SAS, with which I have programmed since 2000 and for which I have a licence (an expensive commodity, it has to be said), but other technologies are being explored with R, Python and Julia being among them.

The change in technological scope does bring an element of excitement and new interest but there is also some sadness when tried and trusted technologies meet with newer competition and valued skills are no longer as career securing as they once were. Still, there is plenty of online training out there and I already have collected some of my thoughts on this. The learning continues and the need for repositioning is also clear.

The journey also has brought some curios to my notice. One of these is This Person Does Not Exist, a website building photos of non-existent faces using machine learning. Recently, I learned of others like it such as This Artwork Does Not Exist, This Cat Does Not Exist, This Horse Does Not Exist, and This Chemical Does Not Exist. The last of these probably should be entitled “This Molecule Does Not Exist (Yet)” since it is a fictitious molecular structure that has been created and what you get is an actual moving image that spins it around in three-dimensional space. The one with dynamically generated abstract art is the main inspiration for this piece and is of more interest to me while the other two are more explanatory though the horse website is not so successful in its execution and one can ask why we need more cat pictures.

To some, the idea of creating fake pictures may feel a little foreboding and that especially applies to photos of people and the livelihoods of any content creators. Nevertheless, these sources of imagery have their legitimate uses such as decorating websites or brochures and that is where my interest is piqued. After all, there are some subjects where pictures can be scarce so any form of decoration that enlivens an article has to have some use. Technology websites like this one can feature images too with screenshots and device photos being commonplace but they can all look like each other, hence the need for a little more variety and having pictures often increases the choice of website themes as well since so many need images to make them work or stand out. As ever, being sparing with any new innovations remains in order so that is how I approach this matter as well.

When a hard drive is unrecognised by the Linux hddtemp command

One should not do a new PC build in the middle of a heatwave if you do not want to be concerned about how fast fans are spinning and how hot things are getting. Yet, that is what I did last month after delaying the act for numerous months.

My efforts mean that I have a system built around an AMD Ryzen 9 5950X CPU and a Gigabyte X570 Aorus Pro with 64 GB of memory and things are settling down after the initial upheaval. That also meant some adjustments to the CPU fan profile in the BIOS for quieter running while the the use of Be Quiet! Dark Rock 4 cooler also helps as does a Be Quiet! Silent Wings 3 case fan. All are components from trusted brands though I wonder how much abuse they got during their installation and subsequent running in.

Fan noise is a non-quantitative indicator of heat levels as much as touch so more quantitative means are in order. Aside from using a thermocouple device, there are in-built sensors too. My using Linux Mint means that I have the sensors command from the lm-sensors package for checking on CPU and other temperatures though hddtemp is what you need for checking on the same for hard drives. The latter can be used as follows:

sudo hddtemp /dev/sda /dev/sdb

This has to happen using administrator access and a list of drives needs to be provided because it cannot find them by itself. In my case, I have no mechanical hard drives installed in non-NAS systems and I even got to replacing a 6 TB Western Digital Green disk with an 8 TB SSD but I got the following when I tried checking on things with hddtemp:

WARNING: Drive /dev/sda doesn't seem to have a temperature sensor.
WARNING: This doesn't mean it hasn't got one.
WARNING: If you are sure it has one, please contact me ([email protected]).
WARNING: See --help, --debug and --drivebase options.
/dev/sda: Samsung SSD 870 QVO 8TB: no sensor

The cause of the message for me was that there is no entry for Samsung SSD 870 QVO 8TB in /etc/hddtemp.db so that needed to be added there. Before that could be rectified, I needed to get some additional information using smartmontools and these needed to be installed using the following command:

sudo apt-get install smartmontools

What I needed to do was check the drive’s SMART data output for extra information and that was achieved using the following command:

sudo smartctl /dev/sda -a | grep -i Temp

What this does is to look for the temperature information from smartctl output using the grep command with output from the first being passed to the second through a pipe. This yielded the following:

190 Airflow_Temperature_Cel 0x0032 072 050 000 Old_age Always - 28

The first number in the above (190) is the thermal sensor’s attribute identifier and that was needed in what got added to /etc/hddtemp.db. The following command added the necessary data to the aforementioned file:

echo \"Samsung SSD 870 QVO 8TB\" 190 C \"Samsung SSD 870 QVO 8TB\" | sudo tee -a /etc/hddtemp.db

Here, the output of the echo command was passed to the tee command for adding to the end of the file. In the echo command output, the first part is the name of the drive, the second is the heat sensor identifier, the third is the temperature scale (C for Celsius or F for Fahrenheit) and the last part is the label (it can be anything that you like but I kept it the same as the name). On re-running the hddtemp command, I got output like the following so all was as I needed it to be.

/dev/sda: Samsung SSD 870 QVO 8TB: 28°C

Since then, temperatures may have cooled and the weather become more like what we usually get but I am still keeping an eye on things, especially when the system is put under load using Perl, R, Python or SAS. There may be further modifications such as changing the case or even adding water cooling, not least to have a cooler power supply unit, but nothing is being rushed as I monitor things to my satisfaction.