Diploma Thesis Project - Kernel integrals in time--distance helioseismology

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Table of Contents

• Installation
• Check JSOC data completness
• Usage
• Contributing
• License

Installation

To install and run this project, follow these steps:

1. Clone this repository.
2. Navigate to the project directory.
3. Follow the steps mentioned in Usage chapter

Check JSOC data completness

This section covers modules in jsoc_query_checker package. The folder structure of this package looks as follows:

jsoc_query_checker
├── __init__.py
├── check_jsoc_queries.py
├── dates_to_queries.txt
├── prepare_queries_from_dates.py
├── queries_to_check
└── run_check_jsoc_queries.pbs

Main aims of this package:

• create JSOC query strings for 24 hour HMI Dopplergrams for provided dates
• check if the data for these queries stored on JSOC are complete
• provide a file containing queries for complete data and queries for incomplete data

How to use:

1. Save the dates you want to convert to queries into a file dates_to_queries.txt in format YYYYMMDD.
2. Run python3 prepare_queries_from_dates.py. You should then see a new file queries_to_check.txt.
3. Set your JSOC e-mail, method and protocol constants in check_jsoc_queries.py.
4. Run python3 check_jsoc_queries.py.
5. Queries with complete data will be stored in queries_with_complete_data.txt while incomplete data queries will be stored in incomplete_data_queries.json.

Note

Documentation for other packages and modules will be gradually added.

Usage

Warning

The code is still in a stage of development so it might be a little wild sometimes. Consider yourself warned.

Note

The code is developed mainly to be run on a cluster as PBS jobs but separate tasks can be run on local machine via python as well.

The basic usage on a cluster can be described in a very simplified pipeline as follows: datacube_pipeline_prerequisitor.py --> (download_selected_queries.py) --> datacube_pipeline_runner.py

1. datacube_pipeline_prerequisitor.py prepares the whole folder structure base on the inputs we give to it. The configuration is done at the begnning of the file via Python constants. After running the script, two helper files located at ./datacube_pipeline_helper_files/ are made/altered:

   • datacube_maker_inputs.json containing various directory and file paths needed for a correct run of subsequent .pbs files
   • requests_ready_for_download.json containing pairs of JSOC requests and the paths to folders where the data from these requests will be stored

2. If you need to download some data before running the datacube and travel-time pipelines, you have to run download_selected_queries.py as a middle step. This will go through the requests_ready_for_download.json file and download all the requests stored there into the paths mentioned as values (each request:path pair gets deleted from the file after it's download is complete without failure). If you want to run the downloads, drink your coffee ☕ and enjoy your life, try running run_download_queries.pbs. It will run the downloads as jobs on the cluster, so you can logout and enjoy your life.

3. Last but not least, when all your data is ready, you can run datacube_pipeline_runner.py which will query all the jobs (both for creating datacubes and for running traveltime calculations) on cluster.

Warning

When you post more datacubes you might want to change your name on the cluster to something like Fantomas so no-one knows who's running the thing. The reason is that it takes so much RAM and CPU that your colleagues might become somewhat hostile towards you pretty quickly for stalling their work. Safety first!

Contributing

If you would like to contribute to this project, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature: git checkout -b feature-name.
3. Make your changes and commit them: git commit -m 'Description of your changes'.
4. Push to the branch: git push origin feature-name.
5. Open a pull request with a detailed description of the changes.

License

This project is licensed under the MIT License.

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