Adventures & experiences in contemporary technology
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.
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.
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.
Like Fedora, Manjaro also installs a package for VirtualBox Guest Additions when you install the Linux distro in a VirtualBox virtual machine. However, it does have certain expectations when doing this. On many systems and my own is one of these, Linux guests are forced to use the VMSVGA virtual graphics controller while Windows guests are allowed to use the VBoxSVGA one. It is the latter that Manjaro expects so you get a message like the following appearing when the desktop environment has loaded:
Windows Resizing
Set your VirtualBox Graphics Controller to enable windows resizing
After ensuring that gcc, make, perl and kernel headers are installed, I usually install VirtualBox Guest Additions myself from the included ISO image and so I did the same with Manjaro. Doing that and restarting the virtual machine got me extra functionality like screen resizing and being able to copy and paste between the VM and elsewhere after choosing the Bidirectional setting in the menus under Devices > Shared Clipboard.
That still left an unwanted message popping up on startup. To get rid of that, I just needed to remove /etc/xdg/autostart/mhwd-vmsvga-alert.desktop. It can be deleted but I just moved it somewhere else and a restart proved that the message was gone as needed. Now everything is working as I wanted.
While some Linux distros like Fedora install VirtualBox drivers during installation time, I prefer to install the VirtualBox Guest Additions themselves. Before doing this, it is best to remove the virtualbox-guest-additions package from Fedora to avoid conflicts. After that, execute the following command to ensure that all prerequisites for the VirtualBox Guest Additions are in place prior to mounting the VirtualBox Guest Additions ISO image and installing from there:
sudo dnf -y install gcc automake make kernel-headers dkms bzip2 libxcrypt-compat kernel-devel perl
During the installation, you may encounter a message like the following:
ValueError: File context for /opt/VBoxGuestAdditions-<VERSION>/other/mount.vboxsf already defined
This is generated by SELinux so the following commands need executing before the VirtualBox Guest Additions installation is repeated:
sudo semanage fcontext -d /opt/VBoxGuestAdditions-<VERSION>/other/mount.vboxsf
sudo restorecon /opt/VBoxGuestAdditions-<VERSION>/other/mount.vboxsf
Without doing the above step and fixing the preceding error message, I had an issue with mounting of Shared Folders whereby the mount point was set up but no folder contents were displayed. This happened even when my user account was added to the vboxsf group and it proved to be the SELinux context issue that was the cause.
Recently, I needed to inactivate blocks of code in a Perl script while doing some testing. This is something that I often do in other computing languages so I sought the same in Perl. To do that, I need to use the POD methodology. This meant enclosing the code as follows.
=start
<< Code to be inactivated by inclusion in a comment >>
=cut
The =start line could use any word after the equality sign but it seems that =cut is needed to close the multi-line comment. If this was actual programming documentation, then the comment block should include some meaningful text for use with perldoc but that was not a concern here since the commenting statements would be removed afterwards anyway and it is good practice not to leave commented code in a production script or program to avoid any later confusion.
In my case, this facility allowed me to isolate the code that I needed to alter and test before putting everything back as needed. It also saved time since I did not need to individually comment out every executable line because multiple lines could be inactivated at a time.
In a previous post, I described how to add Perl modules in Linux Mint while mentioning that I hoped to add another that discusses the use of the Parallel::ForkManager module. This is that second post and I am going to keep things as simple and generic as they can be. There are other articles like one on the Perl Maven website that go into more detail.
The first thing to do is ensure that the Parallel::ForkManager module is called by your script and having the following line near the top will do just that. Without this step, the script will not be able to find the required module by itself and errors will be generated.
use Parallel::ForkManager;
Then, the maximum number of threads needs to be specified. While that can be achieved using a simple variable declaration, the following line reads this from the command used to invoke the script. It even tells a forgetful user what they need to do in its own terse manner. Here $0 is the name of the script and N is the number of threads. Not all these threads will get used and processing capacity will limit how many actually are in use so there is less chance of overwhelming a CPU.
my $forks = shift or die "Usage: $0 N\n";
Once the maximum number of available threads is known, the next step is to instantiate the Parallel::ForkManager object as follows to use these child processes:
my $pm = Parallel::ForkManager->new($forks);
With the Parallel::ForkManager object available, it is now possible to use it as part of a loop. A foreach loop works well though only a single array can be used with hashes being needed when other collections need interrogation. Two extra statements are needed with one to start a child process and another to end it.
foreach $t (@array) {
my $pid = $pm->start and next;
<< Other code to be processed >>
$pm->finish;
}
Since there often is other processing performed by script and it is possible to have multiple threaded loops in one, there needs to be a way of getting the parent process to wait until all the child processes have completed before moving from one step to another in the main script and that is what the following statement does. In short, it adds more control.
$pm->wait_all_children;
To close, there needs to be a comment on the advantages of parallel processing. Modern multi-core processors often get used in single threaded operations and that leaves most of the capacity unused. Utilising this extra power then shortens processing times markedly. To give you an idea of what can be achieved, I had a single script taking around 2.5 minutes to complete in single threaded mode while setting the maximum number of threads to 24 reduced this to just over half a minute while taking up 80% of the processing capacity. This was with an AMD Ryzen 7 2700X CPU with eight cores and a maximum of 16 processor threads. Surprisingly, using 16 as the maximum thread number only used half the processor capacity so it seems to be a matter of performing one’s own measurements when making these decisions.
My online travel photo gallery is a self-coded set of PHP scripts that read data from tables in a MySQL database. These tables are built from input XML files using a Perl script that itself creates and executes an SQL script. The Perl script also does some image processing using GraphicsMagick commands to resize images and to add copyright information and image framing. Because this processed one image at a time sequentially, it was taking several minutes to complete and only partly used the capacity of the PC that I used.
This led me to look at adding parallel processing and that is what brought me to looking at the Parallel::ForkManager Perl module. An alternative approach might have been to add new images in such a way as not to need the full run involving hundreds of image files, but that will take more work and I fancied having a look at parallelising things anyway.
If it was not there already, the first act would have been to install build-essential to get access to the cpan command. The following command accomplishes this:
sudo apt-get install build-essential
Once that is there, the cpan command needs to be run and some questions answered to get things going. The first question to answer is whether you want setup to be as automated as possible and the default answer of yes worked for me. The next question to answer regards the approach that cpan takes when installing modules and I chose sudo here (local::lib is the default value and manual is another option). After this, cpan drops into its own command shell. Here, I issued two more commands to continue the basic setup by updating CPAN.pm to the latest version and adding Bundle::CPAN to optimise the module further:
make install
install Bundle::CPAN
Continuing the last of these may need extra intervention to confirmation the suggested default of exit at one point in its operation and that takes a little time to complete. It is after this that Parallel::ForkManager can be installed using the following command:
install Parallel::ForkManager
That completed quickly and the cpan shell was exited using its exit command. Then, the new module was available in scripting after that. The actual use of this module is something that hope to describe in another post so I am ending this one here and the same process is just as applicable to setting up cpan and adding any other Perl CPAN module.
With my tendency to apply Linux updates using the command, I was happy to see that something similar was possible in FreeBSD too. The first step is to fire up a terminal session and drop into root using the su command. That needs the root superuser password in order to continue and the next step is to update the local repositories using the following command:
pkg update
After that, it is time download updated packages and install these by issuing this command:
pkg upgrade
Most of the time, that is sufficient but I discovered that there are times when the above fails and additional interventions are needed. What I had uncovered were dependency error messages and I set to looking around the web for remedies to this. One forum question that was similar to what I had met with the suggestion of consulting the file called UPDATING in /usr/ports/. An answer like that looks unhelpful but for the inclusion of advice where extra actions were needed. Also, there is a useful article on updating FreeBSD ports that gives more in the way of background knowledge so you understand the more about what needs doing.
Following both that and the UPDATING file resulted in my taking the following sequence of steps. The first act was to download and initialise the Ports Collection, a set of build instructions.
portsnap fetch extract
The above is a one time only action so future updates are done as follows:
portsnap fetch update
With an up to date Ports Collection in place, it was time to install portman:
pkg install portman
A look through /usr/ports/UPDTAING revealed the commands I needed for updating Python and Perl to address the dependency problem that I was having:
portmaster -o devel/py-setuptools27 devel/py-setuptools
portmaster -r py\*setuptools
With those completed, I re-ran pkg update again and all was well. The extra actions needed to get that result will not get forgotten and I am sharing them on here so I know where they are. If anyone else has use for them, that would be even better.
There is a bill making its way through the U.K. parliament at the moment that could reduce the power of copyright when it comes to images placed on the web. The current situation is that anyone who creates an image automatically holds the copyright for it. However, the new legislation will remove that if it becomes law as it stands. As it happens, the Royal Photographic Society is doing what it can to avoid any changes to what we have now. There may be the barrier of due diligence but how many of us take steps to mark our own intellectual property? For one, I have been less that attentive to this and now wonder if there is anything more that I should be doing. Others may copyleft their images but I don’t want to find myself unable to share my own photos because another party is claiming rights over them. There’s watermarking them but I also want to add something to the image metadata too.
That got me wondering about adding metadata to any images that I post online that assert my status as the copyright holder. It may not be perfect but any action is better than doing nothing at all. Given that I don’t post photos where EXIF metadata is stripped as part of the uploading process, it should be there to see for anyone who bothers to check and there may not be many who do.
Because I also wanted to batch process images, I looked for a command line tool to do the needful and found ExifTool. Being a Perl library, it is cross-platform so you can use it on Linux, Windows and even OS X. To install it on a Debian or Ubuntu based Linux distro, just use the following command:
sudo apt-get install libimage-exiftool-perl
The form of the command that I found useful for adding the actual copyright information is below:
exiftool -p “-copyright=(c) John …” -ext jpg -overwrite_original
The -p switch preserves the timestamp of the image file while the -overwrite_original one ensures that you don’t end up with unwanted backup files. The copyright message goes within the quotes along with the -copyright option. With a little shell scripting, you can traverse a directory structure and change the metadata for any image files contained in different sub-folders. If you wish to do more than this, there’s always the user documentation to be consulted.