Ha... Had an instructor ask to disable downloads for a #datascience #Jupyter Hub instance because they'll be working with some sensitive data. Of course, the feature for that in RStudio is a posit Pro thingy... But the export path is uri path based and my coworker already hacked an ingress that logs you out. Now downloading files ALSO logs you out.
*insert maniacal laugh here*
Project #Jupyter recently completed its 2025 Executive Council (EC) election, marking an important transition in its leadership https://blog.jupyter.org/project-jupyters-2025-executive-council-elections-605b183ec64c
La dernière mise à jour de #VSCode est disponible (https://code.visualstudio.com/updates/v1_98), avec comme à chaque fois son lot de nouveautés concernant #Python (https://devblogs.microsoft.com/python/python-in-visual-studio-code-march-2025-release/) :
- amélioration dans l'intégration de la REPL Python dans le terminal
- paramétrage plus fin dans la recherche des scripts de #test (possibilité de définir un pattern)
- section automatique de longues chaînes de caractères
- différentes améliorations dans l'utilisation de notebook #jupyter
I have prepared a new design note using #Jupyter notebooks, this time for the verification of steel plate-girders. The files are here: https://codeberg.org/mgregoire/Jupyter-Structural-Engineering/
Beware: I haven't checked this one particularly carefully as yet. If you spot a problem, then please let me know.
I have prepared an updated version of my #Jupyter notebook for the design of #ReinforcedConcrete beams, now with calculations for shear resistance. The file is at https://codeberg.org/mgregoire/Jupyter-Structural-Engineering.git
2/2 The course is based on a #MOOC with #Jupyter Notebooks, videos, quizzes and case studies. "It's a very pragmatic approach, where we try to get students to question and understand where the ethical dimension lies" she says.
Videos: https://mediaspace.epfl.ch/channel/MOOC%2BResponsible%2BSoftware/54611
Notebook: https://gitlab.epfl.ch/responsiblesoftware/cs-290-responsible-software
Compare the Best Data Visualisation Libraries for Python and Pandas
I have no idea what magic makes this possible, but I love it: https://github.com/evcxr/evcxr/blob/main/evcxr_jupyter/README.md #Evcxr, a #Rust #Jupyter kernel.
I've been planning to actually sit down and start learning the language finally but have been putting it off.
But... a Rust REPL in Jupyter? Yes, Finally. #IPython/Jupyter have become my natural habitat over the past decade. This will make playing around with and learning to think in Rust *much* easier.
Episode #497 - Development Approach
So my "good" laptop - the most expensive thing I've bought in many years - continues to be sidelined because I'm stuck staring at the little laptop screen and can't have my nice external portrait display for #Jupyter notebooks... grr... arg.
Apparently and absurdly it seems to have something to do with #Linux V DRM. There are incomprehensible instructions online which fail to fix it. My hardware refuses to use an external display 'cause of DRM rules (cause I might watch a movie over HDMI?)
https://github.com/alessandro-fazzi/ruby-devel-notebook built for personal use, but you’re welcome to copy-paste/improve. Ships with #ruby 3.4.2 and the freshly released iruby 0.8.1
#notebook #jupyter
I pushed my firmware and my analysis notebook to Github.
The repository is here.
https://github.com/kbob/ESP32_Fan_Analysis
The Jupyter notebook that created the upthread graphs is here.
https://github.com/kbob/ESP32_Fan_Analysis/blob/bb9b301058848362991cbcb2453ec73eb7901afd/src/fan%20analysis.ipynb
Let's find those bands and see what's up. I analyzed both the half speed and full speed runs, as they both show the banding clearly.
The space between bands is 813-824 clocks, or 97-98 KHz. I can only guess why they're different.
If I understand Nyquist correctly, I can down sample my traces to 200 KHz and not lose any resolution.
My next hypothesis is that the tach signal is not a direct measurement of some analog signal but is generated by the fan controller and is quantized by the controller's clock.
So let's switch from looking at RPM to looking at intervals. This graph shows the same data, but the Y axis is the interval between edges, measured in ESP32 APB clocks (80 MHz). There are two pulses per fan revolution, so I've colored the two pulses differently.
I re-plotted the half speed run, calculating speed using the intervals between rising edges and the intervals between falling edges. These plots show both, all in one color. (Maybe I should have colorized them again.)
The small bands gaps are gone. There are still bands, but they're evenly spaced.
So what's going on with that banding? I calculated the time difference between every two tach edges and plotted them all together.
Hypothesis: There are four edges per rotation. Maybe the edges are not spaced exactly 90° apart.
Here, I've colored the bands for the constant speed runs. You can see that the rising edges are in one set of bands and the falling edges are in the others. So that's solved.
Avui a la feina he estat manegant per substituir el que hauria estat un notebook #iPyhon (o #JupyterLab) per un document en #orgmode (d'#emacs, esclar).
Moltes de les comparatives que hi ha pels internets fan moltes històries. Jo no en demano cap cosa gaire complexa: combinar text, LaTeX i blocs de codi, normalment scripts poc òptims per acabar fent gràfiques, i poder-ho organitzar tot una mica bé.
Amb aquests requisits, org-mode serveix perfectament, com també em serviria un notebook de #jupyter.
Com a avantatge, funciona amb les dreceres de teclat habituals de la meva configuració. En el jupyter, tot i que hi ha un complement per emular vim, no acaba de funcionar igual.
Com a desavantatge, quan un bloc de codi falla no sempre ho fa mostrant l'error, mentre que al notebook si. És un inconvenient menor que resolc amb una comanda extra, tangle, després de la qual tinc tot l'entorn de debug que podria voler, però bueno, per dir alguna cosa dolenta.
"Why Python for Data Analysis?"
https://wesmckinney.com/book/preliminaries
from "#Python for #DataAnalysis: #DataWrangling with #pandas, #NumPy & #Jupyter"
![screenshot from the start of the introduction at https://wesmckinney.com/book/preliminaries
1.2 Why Python for Data Analysis?
For many people, the Python programming language has strong appeal. Since its first appearance in 1991, Python has become one of the most popular interpreted programming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular since 2005 or so for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages, as they can be used to quickly write small programs, or scripts to automate other tasks. I don’t like the term “scripting languages,” as it carries a connotation that they cannot be used for building serious software. Among interpreted languages, for various historical and cultural reasons, Python has developed a large and active scientific computing and data analysis community. In the last 20 years, Python has gone from a bleeding-edge or "at your own risk" scientific computing language to one of the most important languages for data science, machine learning, and general software development in academia and industry.
For data analysis and interactive computing and data visualization, Python will inevitably draw comparisons with other open source and commercial programming languages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent years...
[and the page continues a bit]](https://media.ciberlandia.pt/ciberlandia-media/media_attachments/files/113/974/532/258/957/672/original/834b3742152a607a.png "screenshot from the start of the introduction at https://wesmckinney.com/book/preliminaries
1.2 Why Python for Data Analysis?
For many people, the Python programming language has strong appeal. Since its first appearance in 1991, Python has become one of the most popular interpreted programming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular since 2005 or so for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages, as they can be used to quickly write small programs, or scripts to automate other tasks. I don’t like the term “scripting languages,” as it carries a connotation that they cannot be used for building serious software. Among interpreted languages, for various historical and cultural reasons, Python has developed a large and active scientific computing and data analysis community. In the last 20 years, Python has gone from a bleeding-edge or \"at your own risk\" scientific computing language to one of the most important languages for data science, machine learning, and general software development in academia and industry.
For data analysis and interactive computing and data visualization, Python will inevitably draw comparisons with other open source and commercial programming languages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent years...
[and the page continues a bit]")
Anyway, there's more to come. Now that I've gotten the raw trace buffers slurped in and sanitized and figured out how to plot them, I can start applying some filters. I also know exactly how noisy the tach signal is.
I won't speculate yet about the speed banding in the constant speed tests or the initial humps, but I do have thoughts...
