TOPIC: COMPUTER LANGUAGES
Getting Grav Admin2 to write files with the correct permissions
19th September 2026
When I found that the find and replace functionality in the article editor offered by Grav's Admin2 plugin was next to unusable, I opted to do what I needed on the server using nano. However, I then discovered that was not possible without the sudo facility even though my user account was in the www-data group. Here is the command that discovered that 644 (owner with read and write permissions, the rest as read only) was being used instead of 664 (owner and group with read and write permissions, the rest as read only):
stat -c '%a %U %G %n' filename.md
Using -c switch to define a custom format, the output is like this for what I needed it to be:
664 www-data www-data filename.md
Unpacking the codes, %a provides the permissions in their octal or numeric form, %U gives the owner, %G is the associated group and %n is the file name. Though I manually set the permissions, I found that they kept changing whenever a file was updated from the user interface. It appears that the file is copied to a different area and then moved back when edits have been completed.
If I had thought that this was going to be resolved by finding the right setting in the Grav configuration I was about to be disappointed. The actual solution turned out to be in very different place: the web server systemd service settings.
PHP-FPM
If the PHP-FPM service was being used, then things might have been simpler. The first step is to add a systemd override by opening up the service configuration:
sudo systemctl edit php8.3-fpm
Near the top of the file, add these lines (make sure that they are above any line that says everything gets deleted below it). Then, save the file.
[Service]
UMask=0002
Following that, restart the service (this is for version 8.3; clearly, the version number will need changing for any future release):
sudo systemctl restart php8.3-fpm
Next, it is time to check that the override is in place by using this command:
systemctl show php8.3-fpm -p UMask
When you see a result like the one below, Grav should be producing files with rw-rw-r-- or 664 permissions through the Admin2 editor. That is how it is with a Nginx set up that I have with PHP-FPM in action on there. What is happening is that it takes the likes of 777 and makes it 775; if the setting were 0022 (the default, it would appear to me), you would then get 755.
UMask=0002
Apache MOD-PHP
However, the issue initially surfaced on an Apache web server, and it took a while for the solution to be found. Confusingly, both PHP-FPM and MOD-PHP were on there. Then, it was a question of finding which one was in action because the latter is part of Apache and the former is a separate service.
Trying the PHP-FPM override described above did not work, which hinted that something else was needed. That was to follow a similar process to that for PHP-FPM, only to do it for Apache this time around. The first step to add the override after opening the relevant file is as follows:
sudo systemctl edit apache2
As before, these lines need to go near the top of the file, above any line warning of the loss of any changes made below it, before it is saved:
[Service]
UMask=0002
Then, it is time to restart the service:
sudo systemctl restart apache2
Next, the override status needs to be probed:
systemctl show apache2 -p UMask
Getting this result from the above command again demonstrates that all should be in order, which it was in my case:
UMask=0002
After all that, it may be time to switch from MOD-PHP to PHP-FPM anyway. At least, it guarantees configuration consistency and separates PHP interpretation from normal web server activities as well.
Broadening data science horizons: Useful Python packages for working with data
14th October 2021
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. While one of these has been R, 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 began 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 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 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 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. Though some subjects are uplifting while others are more foreboding, 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 are always worth reading even if that is the slower route. While those from the Dummies series or from O'Reilly have proved must useful so far, 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. Because Spyder itself is written in Python, 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 and 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 dplyr and ggplot2 in the form of Siuba and Plotnine, respectively. Though the syntax of these packages are not direct copies of what is executed in R, 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.
While Python may not have the speed of Julia, there are plenty of packages for working with larger workloads. Of these, Dask, Modin and RAPIDS all have their 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 always the possibility of checking out others. It may be tempting to stick with the most popular packages all the time, especially when they do so much, but it never hurts to keep an open mind either.