Tag Archive Linux

A quick look at the 7G Firewall

October 17th, 2021

There is a simple principal with the 7G Firewall from Perishable press: it is a set of mod_rewrite rules for the Apache web server that can be added to a .htaccess file and there also is a version for the Nginx web server as well. These check query strings, request Uniform Resource Identifiers (URI’s), user agents, remote hosts, HTTP referrers and request methods for any anomalies and blocks those that appear dubious.

Unfortunately, I found that the rules heavily slowed down a website with which I tried them so I am going have to wait until that is moved to a faster system before I really can give them a go. This can be a problem with security tools as I also found with adding a modsec jail to a Fail2Ban instance. As it happens, both sets of observations were made using the GTmetrix tool so it seems that there is a trade off between security and speed that needs to be assessed before adding anything to block unwanted web visitors.

Useful Python packages for working with data

October 14th, 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. 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.

Stopping Firefox from launching on the wrong virtual desktop on Linux Mint

October 12th, 2021

During the summer, I discovered that Firefox was steadfastly opening on the same virtual desktop on Linux Mint (the Cinnamon version) regardless of the one on which it was started. Being a creature of habit who routinely opens Firefox within the same virtual desktop all the time, this was not something that I had noticed until the upheaval of a system rebuild. The supposed cause is setting the browser to reopen tabs from the preceding session. The settings change according to the version of Firefox but it is found in Settings > General in the version in which I am writing these words (Firefox Developer Edition 94.0b4) and the text beside the tick box is Open previous windows and tabs.

While disabling the aforementioned setting could work, there is another less intrusive solution. This needs the opening of a new tab and the entering of the address about:config in the address bar. If you see a warning message about the consequences of proceed further, accept responsibility using the interface as you do just that. In the resulting field marked Search preference name, enter the text widget.disable-workspace-management and toggle the setting from false to true in order to activate it. Then, Firefox should open on the desktop where you want it and not some other default location.

Something to watch with the SYSODSESCAPECHAR automatic SAS macro variable

October 10th, 2021

Recently, a client of mine updated one of their systems from SAS 9.4 M5 to SAS 9.4 M7. In spite of performing due diligence regarding changes between the maintenance release, a change in behaviour of the SYSODSESCAPECHAR automatic macro variable surprised them. The macro variable captures the assignment of the ODS escape character used to prefix RTF codes for page numbering and other things. That setting is made using an ODS ESCAPECHAR statement like the following:

ods escapechar="~";

In the M5 release, the tilde character in this example was output by the automatic macro variable but that changed in the M7 release to 7E, the hexadecimal code for the same and this tripped up one of their validated macro programs used in output production. The adopted solution was to use the escape sequence (*ESC*) that gave the same outcome that was there before the change. That was less verbose than alternative code changing the hexadecimal code into the expected ASCII character that follows.

data _null_;
call symput("new",byte(input("&sysodsescapechar.",hex.)));

The above supplies a hexadecimal code to the BYTE function for correct rendering with the SYMPUT routine assigning the resulting value to a macro variable named new. Just using the escape sequence is far more succinct though there is now an added validation need once user pilot testing has completed. In my line of business, the updating of code is the quickest part of many such changes; documentation and testing always take longer.

Limiting Google Drive upload & synchronisation speeds using Trickle

October 9th, 2021

Having had a mishap that lost me some photos in the early days of my dalliance with digital photography, I have been far more careful since then and that now applies to other files as well. Doing regular backups is a must that you find reiterated by many different authors and the current computing climate makes doing that more vital than it ever was.

So, as well as having various local backups, I also have remote ones in the form of OneDrive, Dropbox and Google Drive. These more correctly may be file synchronisation services but disciplined use can make them useful as additional storage facilities in the interests of maintaining added resilience. There also are dedicated backup services that I have seen reviewed in the likes of PC Pro magazine but I have to make use of those.


Part of my process for dealing with new digital photo files is to back them up to Google Drive and I did that with a Windows client in the early days but then moved to Insync running on Linux Mint. One drawback to the approach is that this hogs the upload bandwidth of an internet connection that has yet to move to fibre from copper cabling. Having fibre connections to a local cabinet helps but a 100 KiB/s upload speed is easily overwhelmed and digital photo file sizes keep increasing. It does not help that I insist on using more flexible raw formats like DNG, CR2 or CR3 either.

Making less images could help to cut the load but I still come away from an excursion with many files because I get so besotted with my surroundings. This means that upload sessions take numerous hours and can extend across calendar days. Ultimately, this makes my internet connection far less usable so I want to throttle upload speed much like what is possible in the Transmission BitTorrent client or in the Dropbox client. Unfortunately, this is not available in Insync so I have tried using the trickle command instead and an example is below:

trickle -d 2000 -u 50 insync

Here, the upload speed is limited to 50 KiB/s while the download speed is limited to 2000 KiB/s. In my case, the latter of these hardly matters while the former leaves me with acceptable internet usability. Insync does not work smoothly with this, however, so occasional restarts are needed to keep file uploads progressing and CPU load also is higher. As rough as the user experience feels, uploads can continue in parallel with other work.


One other option that I am exploring is the use of the command line tool gdrive and this appears to work well with trickle. After downloading and installing the tool, getting going is a matter of issuing the following command and following the instructions:

gdrive about

On web servers, I even have the tool backing up things to Google Drive on a scheduled basis. Because of a Google Drive limitation that I have encountered not only with gdrive but also with Insync and the Google’s own Windows Google Drive client, synchronisation only can happen with two new folders, one local and the other remote. Handily, gdrive supports the usual bash style commands for working with remote directories so something like the following will create a directory on Google Drive:

gdrive mkdir ttdc [ID for parent folder]

Here, the ID for the parent folder may be omitted but it can be obtained by going to Google Drive online and getting a link location by right clicking on a folder and choosing the appropriate context menu item. This gets you something like the following and the required identifier is found between the last slash and the first question mark in the address string (so as not to share any real links, I made the address more general below):

https://drive.google.com/drive/folders/[remote folder ID]?usp=sharing

Then, synchronisation uses a command like the following:

gdrive sync upload [local folder or file path] [remote folder ID]

There also is the option to do a one way upload and this is the form of the command used:

gdrive upload [local folder or file path] -p [remote folder ID]

Because every file or folder object has its own ID on Google Drive, it is possible to create two objects on there that appear to have the same name though that is sure to cause confusion even if you know what is happening. It is possible in each of the above to throttle them using trickle as well:

trickle -d 2000 -u 50 gdrive sync upload [local folder or file path] [remote folder ID]
trickle -d 2000 -u 50 gdrive upload [local folder or file path] -p [remote folder ID]

Handily, this works without the added drama seen with Insync and lends itself to scripting as well so it could be something that I will incorporate into my current workflow. One thing that needs to be watched is file upload failures but there may be ways to catch those and retry them so that would another thing that needs doing. This is built into Insync and it would be a learning opportunity if I was to stick with gdrive instead.