jupyter

5 Articles

Seeing Fireworks In A Different Light

January 6, 2024 by 5 Comments

If you’re worried that [Roman Dvořák]’s spectroscopic analysis of fireworks is going to ruin New Year’s Eve or the Fourth of July, relax — the science of this build only adds to the fun.

Not that there’s nothing to worry about with fireworks, of course; there are plenty of nasty chemicals in there, and we can say from first-hand experience that getting hit in the face and chest with shrapnel from a shell is an unpleasant experience. [Roman]’s goal with this experiment is pretty simple: to see if it’s possible to cobble together a spectrograph to identify the elements that light up the sky during a pyrotechnic display. The camera rig was mainly assembled from readily available gear, including a Chronos monochrome high-speed camera and a 500-mm telescopic lens. A 100 line/mm grating was attached between the lens and the camera, a finding scope was attached, and the whole thing went onto a sturdy tripod.

From a perch above Prague on New Year’s Eve, [Roman] collected a ton of images in RAW12 format. The files were converted to TIFFs by a Python script and converted to video by FFmpeg. Frames with good spectra were selected for analysis using a Jupyter Notebook project. Spectra were selected by moving the cursor across the image using slider controls, converting pixel positions into wavelengths.

There are some optical improvements [Roman] would like to make, especially in aiming and focusing the camera; as he says, the dynamic and unpredictable nature of fireworks makes them difficult to photograph. As for identifying elements in the spectra, that’s on the to-do list until he can find a library of spectra to use. Or, there’s always DIY Raman spectroscopy. Continue reading “Seeing Fireworks In A Different Light” →

Using Statistics Instead Of Sensors

April 3, 2022 by 9 Comments

Statistics often gets a bad rap in mathematics circles for being less than concrete at best, and being downright misleading at worst. While these sentiments might ring true for things like political polling, it hides the fact that statistical methods can be put to good use in engineering systems with fantastic results. [Mark Smith], for example, has been working on an espresso machine which can make the perfect shot of coffee, and turned to one of the tools in the statistics toolbox in order to solve a problem rather than adding another sensor to his complex coffee-brewing machine.

To make espresso, steam is generated which is then forced through finely ground coffee. [Mark] found that his espresso machine was often pouring too much or too little coffee, and in order to improve his machine’s accuracy in this area he turned to the linear regression parameter R2, also known as the coefficient of determination. By using a machine learning algorithm tuned to this value, which assesses predictable variation in a data set, a computer can more easily tell when the coffee begins pouring out of the portafilter and into the espresso cup based on the pressure and water flow in the machine itself rather than using some other input such as the weight of the cup.

We have seen in the past how seriously [Mark] takes his coffee-making, and this is another step in a series of improvements he has made to his equipment. In this iteration, he has additionally produced a simulation in JupyterLab to better assist him in modeling the system and making even more accurate predictions. It’s quite a bit more effort than adding sensors, but since his espresso machine already included quite a bit of computing power it’s not too big a leap for him to make.

Fortran Goes Interactive

May 3, 2019 by 8 Comments

When you think of Fortran you probably think of punched cards and green bar paper. While it is true that Fortran isn’t the go-to language it used to be — pun unintentional — it still has a vibrant community of people who do serious number crunching. However, many members of that community have been seduced away by interactive tools that are also good at number crunching like MATLAB, Julian, and Python with special libraries. The LFortran project aims to create a Fortran environment with interactivity like Python, but retaining the speed that Fortran is known for.

The resulting tool is impressive. You can use it from Jupyter, can parse code targeting existing Fortran compilers, and supports Linux, Mac, and Windows. There is development to make the code fully interoperable with other languages like C or Python as well as take advantage of GPUs and other specialized hardware. They are also zeroing in on full Fortran 2018 support.

Continue reading “Fortran Goes Interactive” →

Quantum Computing With QISKit

April 2, 2019 by 3 Comments

We all know that quantum computing is coming, but it is hard to know how to get started with it. [Mtreinish] suggests Qiskit — an Apache Licensed SDK for developing quantum applications. He has a presentation he gave in Singapore that you can see below, and a notebook you can go through on GitHub. If you are impatient, you can even run the notebook online through Google.

The tools can work against several backends including a simulator or the real hardware available from IBM. The official site has a different notebook you can use as a tutorial. Interestingly, the foundation of all Qiskit programs is “Terra” (the Earth) and permeating all Qiskit elements is Aer or air. There are also fire and water elements. At the bottom of the official notebook, you’ll find a lot of community notebooks that go deeper into specific topics.

Continue reading “Quantum Computing With QISKit” →

Drops Of Jupyter Notebooks: How To Keep Notes In The Information Age

February 22, 2019 by 32 Comments

Our digital world is so much more interactive than the paper one it has been replacing. That becomes very obvious in the features of Jupyter Notebooks. The point is to make your data beautiful, organized, interactive, and shareable. And you can do all of this with just a bit of simple coding.

We already leveraged computer power by moving from paper spreadsheets to digital spreadsheets, but they are limited. One thing I’ve seen over and over again — and occasionally been guilty of myself — is spreadsheet abuse. That is, using a spreadsheet program to do something I probably ought to write a program to do. For those times that you want something quick but want something more than a spreadsheet, you should check out Jupyter Notebooks. The system is most commonly associated with Python, but it isn’t Python-specific. There are over 100 languages supported — many community-developed. You can even install a C++ interpreter backend for it. Because of the client/server architecture, it is very simple to share notebooks with other users.

You can — in theory — use Jupyter for anything you could use Python for. In practice, it seems to get a lot of workout with people analyzing large data sets, doing machine learning, and similar tasks.

The Good: Simple, Powerful, Extensible

The idea is simple. Think of a Markdown-enabled web page that can connect to a backend (a kernel, in Jupyter-speak). The backend can run on your machine or remotely and will support some kind of language — often Python. The document has cells that line up vertically (like a single wide spreadsheet column). For example, here’s a simple notebook I created to explain how a bunch of sine waves add up to a square wave:

Continue reading “Drops Of Jupyter Notebooks: How To Keep Notes In The Information Age” →