![]() ![]() The query language for the VO is the Astronomical Data Query Language (ADQL), which is similar to the more general Structured Query Language (SQL, pronounced either ”S–Q–L” or ”sequel”). A number of astronomical data bases are organised in the shape of a Virtual Observatory (VO) - data bases that conform to certain common standards to enable easy access for all astronomers. In order to do so, we must submit a data base query, or query for short, to the data base: a formalized request for data, written in a specific query language. We need to tell the data base which specific set of data we would like to access. When we do not download the data in the form of files, but instead access astronomical data bases, there is an additional issue. In those cases, it is even more important than usual for your analysis to include suitable cross-checks and tests to ensure that it is indeed doing what you intend it to do. Using a module which is a “black box” for you represents a step in your analysis that you do not fully understand. When have become more advanced, ready-made modules represent a different problem: In research, it is important that you understand the different steps in any analysis you are doing. While you are still learning, you will want to avoid some of those higer-level modules and re-invent at least some of the wheels in question, since writing a routine for completing some specified astronomical task is a good way of understanding what that particular task, and the astronomy behind it. Some routines may be adapted to a specific telescope, allowing you to reduce and analyze that telescope’s data. An ephemeris module will help you find the position of Solar System Bodies, for instance. Specialised astronomical modules provide you with tools for higher-level operations that are routine in astronomy, but not elsewhere. What qualifies as a routine operation will depend on context, of course. Then it becomes time to make use of a different kind of tool: That is when, again, you start writing a bit of code that helps you choose the right entries from the catalogues, and to produce helpful diagrams – plots and histograms – that allow you to make sense of your data. But in all other cases, including almost all of the interesting ones, your analysis will need a little more flexibility. In some of the simplest cases, you might get away with loading the catalogue in Microsoft Excel and start analyzing your data in there. But at some point, sooner rather than later, you will want to do something more specialised, and more automatised, than application software can provide. For simple image operations you might get by with firing up the DS9 software, for instance. But in astronomical research, application software is usually not enough. We will use some application software in the following, namely SAOImage DS9 for images and TOPCAT for operations involving tables. 11.4 A simple two-dimensional simulation.11.1 Step-by-step numerical integration: Euler method.10 Astronomical image manipulation with Python.8 Basic plotting with Python and Matplotlib.7.7 Strings and base n numbers as lists. ![]() 7.6 Variable types, lists, arrays and speed.7.3 Operations involving more than one list.7 Taming long data sets: Lists in Python.4.3 Connecting with a Virtual Observatory (VO) service.3.7 Photometry with regions and statistics.3.3 Coordinates: Finding your way around the image.3.2 A first look at the Eagle Nebula M16.2.7 High-level data: catalogues and tables.2.3 Images: darkframes and flatfielding.
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