Data Science: MongoDB Sky Searches with Geospatial Queries

This is the fourth and, for now, final set of posts in my tutorial on using MongoDB No-SQL databases for astronomical work. We've created a database of Brown Dwarf objects making use of Python 3.7's Dataclasses, we've also stored header metadata for a variety of FITS files, and we've written functions to perform cone searches using HEALPix. Today, we're looking again at how to query the sky, but this time using MongoDB's built-in geospatial's functionality. As before, I provide a Jupyter notebook where those interested can follow along.

Data Science: MongoDB Sky Searches with HEALPix

This is the third blog post in a series about utilizing MongoDB NoSQL databases with astronomical data. Prior posts introduced how to store astronomical objects and how to store FITS header metadata. On today's post, we'll visit one of the most common things we do in astronomy- the cone search. In other words, how to do you search your database for objects in the sky that are located close to your input coordinates. Today we'll be tackling that problem "from scratch" utilizing HEALPix rather than any built-in functionality. As before, I provide a Jupyter notebook in my GitHub repo for those who may want more details and to run it on their own.

Data Science: Astronomy FITS Headers in MongoDB

This is the second post I have about using MongoDB NoSQL databases with astronomical data. If you'd like a refresher about what that means, check out my first post, where I describe how to ingest a custom BrownDwarf class object into these type of databases. Today, we're looking at a more general problem- metadata. Metadata is the information that describes the how, when, where of the data itself. For example, which telescope took the data, at what time of night, for how long, with what filter, etc etc. A lot of this information is encapsulated in the data files itself and, currently, the most commonly used format in astronomy is the FITS file.

In this post, we'll have a look at how we can extract the metadata from a FITS file and load it into our NoSQL database. As before, I provide a Jupyter notebook if you'd like to run the code yourself.

Data Science: Python Dataclasses and MongoDB

Over the past few weeks, I've been playing a bit with some NoSQL databases, in particular, with MongoDB. This is one particular type of database known as a document-store database and it works primarily by saving JSON formatted 'documents'. While exploring this technology and working on some Python code, I realized how easy it is to convert a standard Python class into a dictionary and how dictionaries readily translate into JSON. With this knowledge in hand, a light-bulb went off in my head as I realized I could make use of the new dataclasses implemented as part of Python 3.7 and quickly create a working database with minimal code.

In this post, I'll describe some of the ideas I had in mind while working through this and, if you want to try this on your own, I can point you to this Jupyter notebook where I work out this example.