Read s3 time-series target data #78
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zkamvar
approved these changes
Mar 5, 2025
R/utils-target_data.R
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| target_data_path <- hub_path$path("target-data") | ||
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| td_files <- target_data_path$ls(allow_not_found = TRUE) | ||
| ts_files <- td_files[grepl(target_type, td_files, ignore.case = TRUE)] |
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| ts_files <- td_files[grepl(target_type, td_files, ignore.case = TRUE)] | |
| ts_files <- td_files[startsWith(td_files, target_type)] |
Since we are being prescriptive on the names, I don't think we need to be case insensitive. Also, startsWith() is faster than grepl() and prevents situations where we end up matching something like showtime-series1E02.mkv.
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Good catch. To ensure we also pick up trailing characters too and also apply the same filter to local file systems, I implemented more thorough and consistent checks in f2a471d
…ed target_type name are excluded
Co-authored-by: Zhian N. Kamvar <[email protected]>
Co-authored-by: Zhian N. Kamvar <[email protected]>
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This PR resoves #75 by expanding on #71 but adding class based methods and adapting higher level functions to access target time-series data from the cloud.
This approach should make it easier to mirror cloud functionality when we move on to oracle data.
The PR also adds some cloud/target data utilities
I've also memoised some SubTreeFileSystem methods to reduce calls to s3. Should probably pay attention that it doesn't cause any unexpected behaviour.