Enhancing SOAR Queries: Improved Error Handling and Support for Distance-Based Filtering

Improved Error Handling and Support for Distance-Based Filtering

Introduction

Over the past few weeks, I’ve been working on addressing and enhancing certain functionalities within the sunpy-soar package. This post delves into the two main improvements I’ve implemented: better error handling for server downtime and the introduction of support for distance-based query filtering.

Improved Error Handling for Server Downtime

Previously, when the SOAR server was down, a generic JSONDecodeError would be raised. This was less than ideal as it did not provide a clear indication of what the actual issue was. To improve this, I worked on implementing a more descriptive error message that would be raised in such scenarios.

r = requests.get(f"{tap_endpoint}/sync", params=payload)
try:
    response_json = r.json()
except JSONDecodeError:
    msg = "Server returned an invalid JSON response. The SOAR server may be down or not functioning correctly."
    raise RuntimeError(msg)

With this change, users will now see a RuntimeError with a clear message indicating that the server may be down or not functioning correctly, which makes troubleshooting much easier.

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Blog 4: Testing the New Coalignment with a Catch

Now that we’ve agreed on the structure of the co-alignment API and also laid down its foundation, all that’s left is to validate it through actual tests and generate some gallery examples.

First try with coaligning EIS raster with AIA map

Let’s take a closer look at the EIS raster first. I found an AIA image close to the date_average of the raster.

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Taking on the Gaps: First Approaches of the Temporal Interpolation

 

Introduction

In the study of Active Galactic Nuclei (AGNs), accurately interpolating light curve data is crucial for overcoming the challenge of observational gaps. This post elaborates on the implementation of both basic and advanced interpolation methods to enhance data continuity in AGN light curves.

Maximizing Coverage Across AGN Light Curves

An essential step in our analysis of AGN light curves was to establish a benchmark for maximum coverage in each observational band. This process involves determining the most comprehensive temporal span for which we have data, ensuring that our interpolation methods are aligned with these time frames.

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