Technology Tales

Clearing the Julia REPL

During development, there are times when you need to clear the Julia REPL. It can become so laden with content that it becomes hard to perform debugging of your code. One way to accomplish this is issuing the CTRL + L keyboard shortcut while focus is within the REPL; you need to click on it first. Another is to issue the following in the REPL itself:

print("\033c")

Here \033 is an escape code in octal format. It is often used in terminal control sequences. The c character is what resets the terminal to its initial state. Printing this sequence is what does the clearance, and variations can be used to clear other kinds of console screens too. That makes it a more generic solution.

Dropping to an underlying shell using the ; character is another possibility. Then, you can use the clear or cls commands as needed; the latter is for Windows systems.

One last option is to define a Julia function for doing this:

function clear_console()
    run(`clear`)  # or `cls` for Windows
end

Calling the clear_console function then clears the screen programmatically, allowing for greater automation. The run function is the one that sends that command in backticks to the underlying shell for execution. Even using that alone should work too.

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.

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 the 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 linchpin 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. Though the Pkg package that is used for package management is documented, I had not got to that, which meant that 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")

While the semicolon is used to separate two commands issued on the same line, they can be on different lines or issued separately just as well. Naturally, DataArrays is just an example here; 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.

To solve this issue, I added the location using the python.analysis.extraPaths setting for the workspace. I opened Settings by going to File > Preferences > Settings in the menu. I typed python.analysis.extraPaths in the search box. This showed me the correct section. I clicked on Add Item, entered the required path, and clicked OK. This resolved the problem, and everything worked properly afterwards.

Broadening data science horizons: 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. 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 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 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.

Getting Eclipse to start without incompatibility errors on Linux Mint 19.1

Recent curiosity about Java programming and Groovy scripting got me trying to start up the Eclipse IDE that I had installed on my main machine. What I got instead of a successful application startup was a message that included the following:

!MESSAGE Exception launching the Eclipse Platform:
!STACK
java.lang.ClassNotFoundException: org.eclipse.core.runtime.adaptor.EclipseStarter
at java.base/java.net.URLClassLoader.findClass(URLClassLoader.java:466)
at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:566)
at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:499)
at org.eclipse.equinox.launcher.Main.invokeFramework(Main.java:626)
at org.eclipse.equinox.launcher.Main.basicRun(Main.java:584)
at org.eclipse.equinox.launcher.Main.run(Main.java:1438)
at org.eclipse.equinox.launcher.Main.main(Main.java:1414)

The cause was a mismatch between Eclipse and the installed version of Java that it needed to run. After all, the software itself is written in the Java language and the installed version from the usual software repositories was too old for Java 11. The solution turned out to be installing a newer version as a Snap (Ubuntu's answer to Flatpak). The following command did the needful since snapd already was running on my machine:

sudo snap install eclipse --classic

The only part of the command that warrants extra comment is the --classic switch, since that is needed for a tool like Eclipse that needs to access a host file system. On executing, the software was downloaded from Snapcraft and then installed within its own bundle of dependencies. The latter adds a certain detachment from the underlying Linux installation and ensures that no messages appear because of incompatibilities like the one near the start of this post.

On Making PROC REPORT Work Harder

In the early years of my SAS programming career, there seemed to be just the one procedure to use if you wanted to create a summary table. That was TABULATE and it was great for generating columns according to the value of a variable such as the treatment received by a subject in a clinical study. To a point, it could generate statistics for you too, and I often used it to sum frequency and percentage variables. Since then, it seems to have been enhanced a little and surprised me with the statistics it could produce when I had a recent play. Here's the code:

proc tabulate data=sashelp.class;
    class sex;
    var age;
    table age*(n median*f=8. mean*f=8.1 std*f=8.1 min*f=8. max*f=8. lclm*f=8.1 uclm*f=8.1),sex
	  / misstext="0";
run;

When you compare that with the idea of creating one variable per column and then defining them in PROC REPORT as many do, it looks more elegant and the results aren't bad either, though they can be tweaked further from the quick example that I generated. That last comment brings me to the point that PROC REPORT seems to have taken over from TABULATE wherever I care to look these days, and I do ask myself if it is the right tool for that for which it is being used or if it is being used in the best way.

While using Data Step to create one variable per column in a PROC REPORT output doesn't strike me as the best way to write reusable code, there are ways to make PROC REPORT do more for you. For example, by defining GROUP, ACROSS and ANALYSIS columns in an output, you can persuade the procedure to do the summarising for you and there's some example code below with the comma nesting height under sex in the resulting table. Sums are created by default if you do this, and forgoing an analysis column definition means that you get a frequency table, not at all a useless thing in numerous instances.

proc report data=sashelp.class nowd missing;
    columns age sex,height;
    define age / group "Age";
    define sex / across "Sex";
    define height / analysis mean f=missing. "Mean Height";
run;

For those times when you need to create more heavily formatted statistics (summarising range as min-max rather showing min and max separately, for example), you might feel that the GROUP/ACROSS set-up's non-display of character values puts a stop to using that approach. However, I found that making every value combination unique and attaching a cell ID helps to work around the problem. Then, you can create a format control data set from the data like in the code below and create a format from that which you can apply to the cell ID's to display things as you need them. This method does make things more portable from situation to situation than adding or removing columns depending on the values of a classification variable.

proc sql noprint;
    create table cntlin as
        select distinct "fmtname" as fmtname, cellid as start, cellid as end, decode as label
            from report;
quit;

proc format lib=work cntlin=cnlin;
run;

About Perl's Binding Operator

While this piece is as much an aide de memoire for myself as anything else, putting it here seems worthwhile if it answers questions for others. The binding operators, =~ or !~, come in handy when you are framing conditional statements in Perl using Regular Expressions, for example, testing whether x =~ /\d+/ or not. The =~ variant is also used for changing strings using the s/[pattern1]/[pattern2]/ regular expression construct (here, s stands for "substitute"). What has brought this to mind is that I wanted to ensure that something was done for strings that did not contain a certain pattern, and that's where the !~ binding operator came in useful; ^~ might have come to mind for some reason, but it wasn't what I needed.

Are ten seconds enough?

Fasthosts, the hosting provider for what you find here, has, in their wisdom, decided to limit the execution time for ASP scripts to 15 seconds and 10 seconds for any others. I haven't used Perl sufficiently in this shared hosting set up to determine how that is affected. In contrast, I can share my experiences on the PHP side and you may have noticed occasional glitches. They have also disabled the set_time_limit PHP function, so you cannot easily address the matter yourself where you need to do it. You almost get the feeling that they don't trust the abilities, actions and oversight of their users. Personally, I reckon that the ten-second limit is too short and that something of the order of 20 or 30 seconds would be better. If it all gets too restrictive, I suppose that there are other providers, though I think that I would avoid resellers after a previous less than glorious experience. There's the dedicated server option too if I was feeling flush, not so likely given the economic times in which we live.