Wide-scale testing on Earthscope

[kkappler]

Scripts are being kept in earthscope_tests branch, in aurora/test_utils/earthscope for now.

Tasks include

• [x] Accessing XML files from SPUD This is done in 00_catalog_SPUD.py
• [x] Method for testing mt_metadata I/O on SPUD XMLs Done in 01_test_load_spud_tfs.py Results of these tests are being posted to mt_metadata issue 143
• [x] method for extracting remote references from tf Prototypes exist but needs review/discussion
• [x] add list of RR stations to tf review csv Prototype exists, but since extraction of RR is unstable, so is this.
• [x] verify that lists of RR stations are accurate -- spot checking manually a few to start
• [x] Method for mth5 builds from Earthscope for processing 03_test_download_from_earthscope.py
• [x] Tracking for test results of mth5 builds
• [x] Address Issue #275
• [x] Address Issue #276

Karl ToDo

• [x] Driver for aurora processing / iterator over valid xml with data to generate TFs
• [x] TF comparison methods

[kkappler]

This task has run an entire first pass with aurora results in reasonable agreement with spud in most cases.

A follow up task is to take the six stages of testing:

• 00_catalog_SPUD.py
• 01_test_load_spud_tfs.py
• 02_test_station_inventory_valid.py
• 03_test_download_from_earthscope.py
• 04_test_processing_with_aurora.py
• 05_compare_tfs.py

and wrap them in a common framework. Towards this I forked a widescale_test branch off of earthscope_tests where each of the six steps can be wrapped as an instance of a WideScaleTest class. The idea is that each test has an output table (defined by a schema), and that the table is prepared into a dataframe and then dask iterates over the df.

I am attaching a previous result csv from stage 00, 01, 02 here for comparison with the updated version.

00_spud_xml_scrape.csv

01_spud_xml_review_2023-09-07_203451.csv

[kkappler]

All stages have successfully executed on gadi, as well as on my local machine.

If we were going to do this again, I would make the following updates:

1. More testing of dask, it was not clear that dask was speeding things up much for stage 01
2. Merge the h5 files, either into one archive, or at least one archive per survey, rather than one mth5 per station.

For reference, I zipped and attached the summary tables csvs from gadi

summary_tables_gadi_20230928.zip