Deprecating Healpy For Polygon Queries: A Better Solution

Hey guys! Today, let's dive into why we're moving away from the healpy module for querying polygons and how we're leveling up with the mocpy library. Trust me, this is going to make our lives a whole lot easier, especially when dealing with those tricky "convex" polygons.

The Problem with healpy

So, what's the deal with healpy? Well, querying polygons using healpy has been a bit of a headache. It's not just about the speed, although that's definitely a factor. The real kicker is that it throws an error when it encounters a polygon it thinks is "convex." This limitation is a major buzzkill, especially when you're trying to work with footprint polygons for projects like TESS and LSST. Imagine trying to map out the sky, and your tool just throws its hands up in the air because a polygon looks a little too round!

The current implementation has several limitations that impact its usability and performance. Firstly, the speed at which healpy processes these queries is less than ideal, leading to longer processing times and potential bottlenecks in our workflows. Secondly, and more critically, healpy struggles with polygons it deems "convex," resulting in errors and preventing us from querying these shapes effectively. This is particularly problematic for projects like TESS and LSST, where accurate and efficient polygon querying is essential for data analysis and sky mapping. The inability to handle these polygons limits the scope of our analysis and necessitates the exploration of alternative solutions.

Moreover, healpy's limitations extend beyond just convex polygons. It also falls short in terms of scalability, especially when dealing with high-resolution healpix grids and large numbers of polygons. As our datasets grow and our analytical needs become more complex, these limitations become increasingly apparent. It's clear that we need a more robust and versatile tool that can handle a wide range of polygon shapes and sizes while maintaining acceptable performance levels. This is where mocpy comes in, offering a promising alternative to healpy with its improved efficiency and ability to handle complex polygon shapes.

Enter mocpy: The Hero We Need

Here's where mocpy swoops in to save the day! We're rewriting our Footprint.query_pixels method using the mocpy library, and here's why you should be excited:

• Speed and Efficiency: mocpy is significantly faster and more efficient than healpy, especially when dealing with high-resolution healpix grids. This means less waiting around and more actual work getting done. Plus, it scales much better, so it won't slow down as your data gets bigger. Also mocpy handles all "convex" polygons without breaking a sweat. No more error messages! Finally, mocpy is a champ when it comes to handling multiple polygons. It can process them all at once without breaking a sweat, making it perfect for large-scale analyses.
• No More Convexity Issues: Remember those pesky "convex" polygons? mocpy laughs in their face! It handles them without any issues, so you can query those TESS and LSST footprints without pulling your hair out.

mocpy isn't just about fixing the problems with healpy; it's about providing a more powerful and flexible tool for all our polygon querying needs. Its ability to handle complex shapes, scale efficiently, and process multiple polygons simultaneously makes it a game-changer for our data analysis workflows. By switching to mocpy, we're not just avoiding errors; we're unlocking new possibilities for exploring and understanding the vast amounts of astronomical data at our fingertips.

A Tiny Caveat (But Nothing to Worry About)

Okay, so there's one tiny thing to keep in mind. As you crank up the order (resolution), there's a slight difference in how the pixels are calculated. But before you panic, I've done some digging, and the differences are super minimal. We're talking sub-percent level, and they mostly show up on the edges of the polygons. So, for all intents and purposes, it's negligible. When switching tools or libraries, it's natural to have concerns about potential discrepancies in results. However, in this case, the differences are so small that they shouldn't have a noticeable impact on our analyses. The benefits of mocpy far outweigh this minor caveat, making it the clear choice for our polygon querying needs.

Also, there's another bonus, our previous method returns the result in the RING healpix format, whereas it should be nested.

The Nitty-Gritty: Functional Requirements

So, what exactly do we expect from this new and improved Footprint.query_pixels method? Here's the rundown:

1. Query Polygons Like a Boss: It should be able to query polygons just as well as the old method, if not better.
2. Conquer Convex Polygons: Those "convex" polygons should be no match for our new method. No more errors!
3. NESTed Pixels: The results should be returned in the NEST healpix format, which is the correct format.
4. Ace the Tests: It should pass all (or at least be very close to passing) all the existing tests. We want to make sure we're not breaking anything in the process.

These functional requirements ensure that the new method not only addresses the limitations of healpy but also maintains compatibility with existing workflows and standards. By being able to query polygons as before, handle convex polygons without issues, return pixels in the correct NEST format, and pass existing tests, we can seamlessly transition to mocpy without disrupting our data analysis processes. These requirements provide a clear roadmap for the development and implementation of the new method, ensuring that it meets our needs and expectations.

In Conclusion

Switching from healpy to mocpy for querying polygons is a no-brainer. It's faster, more efficient, handles those tricky "convex" polygons, and returns results in the correct format. Plus, the minor differences in pixel calculation are so small that they're basically negligible. So, let's embrace the power of mocpy and take our data analysis to the next level!