Rebinning Updates!

 

Updates on Rebinning in Stingray.jl

Welcome back! Over the past couple of weeks, we have been working hard on expanding the capabilities of Stingray.jl. One of the key features we've recently added is data rebinning. Rebinning is an essential part of time series analysis, especially when dealing with noisy light curves or power spectra where we want to trade off some resolution for a higher signal-to-noise ratio.
In this post, we'll walk through the two new rebinning functions we've introduced: rebin_data for linear rebinning and rebin_data_log for logarithmic rebinning.

Linear Rebinning with rebin_data

The rebin_data function allows you to linearly rebin your data array to a new, coarser resolution.

You can choose to either sum or average the data points falling into the new bins using the method keyword argument.

It also automatically propagates uncertainties if you provide them!Here is a quick example of how you can use it to rebin a light curve or a signal:

using Stingray using Plots

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Coming up for Air

After the absolute gauntlet of the past few weeks, this week finally offered a little bit of breathing room. I'm not going to lie, I needed it. But "lighter" in the open-source world doesn't mean stopping; it just means you finally have the time to sit back and actually think before you code.

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Expanding Our Toolkit

It’s Time for Another Update

Times have have been incredibly productive and exciting. If you read my last update, you’ll know we successfully wrapped up the implementation of the Core Spectral Types. With that strong foundation in place, the real fun has begun.


The Next Frontier: Advanced Methods

Now that we have the core types for handling our spectral data, we've started diving into the algorithms that make Stingray so powerful for X-ray astronomers.

Conquering the Lomb-Scargle Periodogram

Implementing the Lomb-Scargle periodogram. For those who might not be familiar, astronomical data is notoriously messy.

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Comfort is a Trap

I've realized something over these last few weeks. Comfort is a trap.

In Python, everything is comfortable. You have these beautiful, high-level wrappers that hide the ugly reality of how the machine actually works. But if I want to do work that matters—if I want to build things that last—I can't stay in the comfortable layers. I have to go down to where it's dark and unforgiving.

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GSoC 2026 – Weeks 3 & 4: Looking Beyond the Code

Understanding the Infrastructure

The past two weeks have been quite different from the start of the coding period.

Instead of spending most of my time implementing new functionality, I found myself exploring a part of open source development that I had very little experience with before: testing infrastructure, CI workflows and release tooling.

One thing I've learned so far is that writing code is only part of maintaining a project. Making sure that code continues to work months or years later is an equally important challenge.

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 Lift off: Weeks 1 & 2The firs...

 

Lift off: Weeks 1 & 2

The first month of Google Summer of Code has been nothing short of a whirlwind. Here's a quick breakdown of how the first two weeks went down:

  • Navigating the Codebase: If you've ever stared at a massive codebase and wondered where to begin, you know how my first few days felt. Getting my hands dirty made things click into place rapidly.

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