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.

• Finishing Power Colors: My immediate target was getting the Power Colors implementation over the finish line. Classifying spectral states based on power spectral shapes is a huge deal in X-ray binaries.

• Porting from Python to Julia: Bringing this capability from the established Python ecosystem to our native Julia environment felt like unlocking a new superpower.

• Rigorous Testing: It required strict testing against established scientific benchmarks, but finally seeing those PR checks turn green was an absolute thrill.

Leveling Up: Core Spectral Types: Weeks 3 & 4

With the confidence boost from the Power Colors merge, Weeks 3 and 4 were all about the heavy lifting: the Core Spectral Types.

If you've used Stingray.jl before, you might remember that functions like avg_cs_from_events would just spit out a raw DataFrame. Sure, it got the job done, but it wasn't exactly user-friendly for building a high-level scientific pipeline. Our mission? Build rock-solid, type-safe Julia structs that not only map beautifully to the Python Stingray API but also squeeze out every ounce of Julia's performance through multiple dispatch.

I am super pumped to announce that the Core Spectral Types are officially baked in! We rolled out the AbstractCrossspectrum hierarchy, featuring the brand-new Crossspectrum{T} and Powerspectrum{T} types.

Here is a quick look at how slick the reverberation lag measurement looks now:

# Reverberation lag measurement using our new Core Spectral Types

ev = readevents("observation.fits")

soft_ev = filter_energy(e -> 0.3 <= e <= 1.0, ev)  # soft band (thermal disk)

hard_ev = filter_energy(e -> 2.0 <= e <= 10.0, ev)  # hard band (Comptonized corona)

# Meet the new Crossspectrum type!

cs = Crossspectrum(soft_ev, hard_ev, segment_size=64.0, dt=1/256)

lags = time_lag(cs)  # Grab those frequency-dependent time lags

coh = coherence(cs)  # Intrinsic coherence, calculated seamlessly

# Visualizing the data is literally a one-liner now

plot(cs, :lag_frequency)  # lag-frequency plot

Observed X-ray Lag-Frequency Spectrum showing reverberation dip

Under the Hood: Supercharging Stingray.jl

The real magic happens beneath the surface. By designing Crossspectrum parametrically (Crossspectrum{T<:Real}), we guaranteed type stability. This means Julia's Just-In-Time (JIT) compiler can optimize the math down to the bare metal.

Check out a snippet of the foundation we laid in crossspectrum.jl:

struct Crossspectrum{T<:Real} <: AbstractCrossspectrum

    freq::Vector{T}

    power::Vector{Complex{T}}

    power_err::Vector{Complex{T}}

    unnorm_power::Vector{Complex{T}}

    unnorm_power_err::Vector{Complex{T}}

    pds1::Union{Nothing, Vector{T}}

    pds2::Union{Nothing, Vector{T}}

    df::T

    dt::T

    n::Int

    m::Int

    # ... tracking all the crucial metadata internally!

end

By ditching the raw DataFrame returns in favor of these robust structs, the developer ergonomics have improved tenfold:

• Strong Typing: No more hunting for array columns, everything you need is strongly typed.

• Ready for Analysis: The data is immediately ready for complex downstream computations.

• Smart Constructors: We wired up convenient constructors that ingest EventList or LightCurve objects directly.

• Automated GTIs: These constructors take care of Good Time Intervals (GTIs) automatically under the hood.

What’s Next on the Radar?

We've covered a lot of ground, but we are far from done. Here is the roadmap for the upcoming weeks:

1. Frequency Rebinning: This is the very next hurdle. We are actively working on logarithmic (geometric) and linear frequency rebinning for the Powerspectrum and Crossspectrum objects. Getting the logarithmic rebinning dialed in is non-negotiable for high-quality broadband noise studies, as it drastically improves the signal-to-noise ratio at high frequencies.

X-ray Power Density Spectrum with a QPO peak at 5 Hz

2. Covariance Spectra and the Lag-Energy Framework: With rebinning out of the way, we will pivot to the variability-vs-energy framework the holy grail for mapping X-ray reverberations.

3. Lomb-Scargle Periodograms: Bringing Lomb-Scargle into our shiny new type system to handle unevenly sampled datasets like a champ.

This GSoC journey has been an absolute blast so far. Marrying the expressive flexibility of Python with the raw speed of Julia isn't without its brain-bending moments, but seeing those beautiful lag-frequency plots render from our native Julia types makes every hour of debugging worth it.

Catch you in the next update as we dive deep into frequency rebinning!