As part of my year-long #StillStanding project, I post an average image of the spherical video recordings on Mastodon daily. These videos have black padding outside the fisheye-like images, and this padding also appears in the average image. It is possible to manually remove the black parts in some image editing software (of which open-source GIMP is my current favorite). However, as I recently started exploring ChatGPT for research, I decided to ask for help.

I have been running Python-based Jupyter Notebooks for some time but never thought about using environments before quite recently. I have heard people talking about environments, but I didn’t understand why I would need it. Two days ago, I tried to upgrade to the latest version of the Musical Gestures Toolbox for Python and got stuck in a dependency nightmare. I tried to upgrade one of the packages that choked, but that only led to other packages breaking.

Today, I ran a workshop called Video Visualization together with RITMO research assistant Joachim Poutaraud. The workshop was part of the Digital Scholarship Days 2023 organized by the University of Oslo Library, four days packed of hands-on tutorials of various useful things. Presentation slides made by Jupyter Notebook Joachim has done a fantastic job updating the Wiki with all the new things he has implemented in the toolbox. However, the Wiki is not the best thing to use in a workshop, it has too much information and would create an information overload for the participants.

I am doing some end of the year cleaning on my hard drive and just uploaded the Jupyter Notebook I used in the analysis of a mobile phone lying still earlier this year. For some future studies, I thought it would be interesting to explore the PDF export functionality from Jupyter. That worked very well except for that I didn’t get any title or author name on top: Then I found a solution on Stack Overflow.

After several years in the making, we finally “released” the Musical Gestures Toolbox for Python at the NordicSMC Conference this week. The toolbox is a collection of modules targeted at researchers working with video recordings. Below is a short video in which Bálint Laczkó and I briefly describe the toolbox: https://youtu.be/tZVX\_lDFrwc About MGT for Python The Musical Gestures Toolbox for Python includes video visualization techniques such as creating motion videos, motion history images, and motiongrams.

My PhD student Qichao Lan is at SMC in Malaga this week, presenting the paper: Lan, Qichao, Jim Tørresen, and Alexander Refsum Jensenius. “RaveForce: A Deep Reinforcement Learning Environment for Music Generation.” Proceedings of the Sound and Music Computing Conference. Malaga, 2019. Download The framework that Qichao has developed runs nicely with a bridge between Jupyter Notebook and SuperCollider. This opens for lots of interesting experiments in the years to come.

Richard Widerberg held a workshop today on using mobile python on Nokia phones running Symbian OS S60. He has gathered some links to everything that is needed to get a connection up and running with PD. Now I got a simple script up and running and communicating with Max/MSP through the serial object. It works, but it feels a bit limiting to only have one-dimensional control joystick up/down + number keys for interaction.