Issue resolved
The problem with classes was resolved by making factory functions to encase them in, and then they were not conflicting with each other.
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Ahmed Hossam
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code29563
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DeadSpheroid
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Deus1704
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Kartik Mandar
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Lucas Martin Garcia
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Manit Singh
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ViciousEagle03
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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.
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.
New issue propped up
When changing the whole architecture, a new issue has propped. An initialised object of the class can not be initialised again. So I am not able to revert back the home UI back dynamically.
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.
Finally the issue resolves
Damn, those 2 weeks were stressful, finally the issue is resolved. I am able to render HoloViews and thus Bokeh plots inside my program and in the process my codebase is much better than before.
Finishing up equilibrium spectra
- Tests have been added for the implementation of the Kurucz database and associated parts of the code
- Examples have been added relating to the Lorentzian broadening of atomic lines and partition functions.
Exploring OpenCL memory management
In this post, I hope to give a high level understanding of OpenCL's Memory Mechanisms
The basics
The project is almost finished
So, after I finished the main refactoring and the documentation strings, I started the testing phase.
I started with the smallest functions that don't call any non-tested function, and then went to the bigger functions.
Refactoring the codebase
I am refactoring the whole codebase to make it more maintainable and in that hoping to solve the issue with HoloViews. I am making the codebase fully pragmatic and declarative and using Parameterised classes. I hope so it resolves the issue with HoloViews.