Technology Tales

Claude Projects: Reusing your favourite AI prompts

Some things that I do with Anthropic Claude, I end up repeating. Generating titles for pieces of text or rewriting text to make it read better are activities that happen a lot. Others would include the generation of single word previews for a piece or creating a summary.

Python or R scripts come in handy for summarisation, either for a social media post or for introduction into other content. In fact, this is how I go much of the time. Nevertheless, I found another option: using Projects in the Claude web interface.

These allow you to store a prompt that you reuse a lot in the Project Knowledge panel. Otherwise, you need to supply a title and a description too. Once completed, you just add your text in there for the AI to do the rest. Title generation and text rewriting already are set up like this, and keywords could follow. It is a great way to reuse and refine prompts that you use a lot.

Dealing with this Python error message on Windows: UnicodeEncodeError: 'charmap' codec can't encode characters in position 56-57: character maps to <undefined>

Recently, I got caught out by the above message when summarising some text using Python and Open AI's API while working within VS Code. There was no problem on Linux or macOS, but it was triggered on the Windows command line from within VS Code. Unlike the Julia or R REPL's, everything in Python gets executed in the console like this:

& "C:/Program Files/Python313/python.exe" script.py

The Windows command line shell operated with cp1252 character encoding, and that was tripping up the code like the following:

with open("out.txt", "w") as file:
    file.write(new_text)

The cure was to specify the encoding of the output text as utf-8:

with open("out.txt", "w", encoding='utf-8') as file:
    file.write(new_text)

After that, all was well and text was written to a file like in the other operating systems. One other thing to note is that the use of backslashes in file paths is another gotcha. Adding an r before the quotes gets around this to escape the contents, like using double backslashes. Using forward slashes is another option.

with open(r"c:\temp\out.txt", "w", encoding='utf-8') as file:
    file.write(new_text)

Getting Pylance to recognise locally installed packages in VSCode running on Linux Mint

When using VSCode on Linux Mint, I noticed that it was not finding any Python package installed into my user area, as is my usual way of working. Thus, it was being highlighted as being missing when it was already there.

The solution was to navigate to File > Preferences > Settings and click on the Open Settings (JSON) icon in the top right-hand corner of the app to add something like the following snippet in there.

"python.analysis.extraPaths": [
"/home/[user]/.local/bin"
]

Once you have added your user account to the above, saving the file is the next step. Then, VSCode needs a restart to pick up the new location. After that, the false positives get eliminated.

What to do an error appears when using pip to install Python packages on Linux Mint 22

After upgrading to Linux Mint 22, the following message appeared when attempting to install Python packages using the pip command:

error: externally-managed-environment

× This environment is externally managed
╰─> To install Python packages system-wide, try apt install
python3-xyz, where xyz is the package you are trying to
install.

If you wish to install a non-Debian-packaged Python package,
create a virtual environment using python3 -m venv path/to/venv.
Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make
sure you have python3-full installed.

If you wish to install a non-Debian packaged Python application,
it may be easiest to use pipx install xyz, which will manage a
virtual environment for you. Make sure you have pipx installed.

See /usr/share/doc/python3.12/README.venv for more information.

note: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.
hint: See PEP 668 for the detailed specification.

This will frustrate anyone following how-tos on the web, so users will need to know about it. On something like Linux Mint, the repositories may not be as up-to-date as PyPI, so picking up the very latest version has its advantages. Thus, I initially used the unrecommended --break-system-packages switch to get things going as before, since doing never broke anything before. While the way of working feels like an overkill in some ways, using pipx probably is the way forward as long as things work as I want them to do.

There is wisdom in using virtual environments too, especially when AI models are involved. For most of what I get to do, that may be getting too elaborate. Then, deleting or renaming the message file in /usr/lib/python3.12/EXTERNALLY-MANAGED is tempting if that gets around things, as retrograde as that probably is. After all, I never broke anything before this message started to appear, possibly since my interests are data related.

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 because Linux Mint only does major changes like that 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 command adds Homebrew paths to the end of the PATH variable rather than the beginning, which was the previous arrangement. This ensures system folders are searched for executable files before Homebrew folders. It also means Python packages load from my user area instead of the Homebrew location, which happened under Homebrew's default configuration. When working with Python packages, remember not to install one version at the system level and another in your user area, as this creates conflicts.

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 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.