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39 lines
2.9 KiB
Markdown
39 lines
2.9 KiB
Markdown
# TODO
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* Look up acsm input file requirements in "acsm_annual/actris_acsm_converter/ACSM_converter.bat"
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* Inputs: actris_acsm_converter/src/data/JFJ/in/JFJ_ACSM-017_2023.txt, actris_acsm_converter/data/JFJ/in/JFJ_ACSM-017_FLAGS_2023.txt
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* Converter relies on acsm_annual/actris_acsm_converter/src/ to generate EBAS files
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* Part 3: Get standard and site specific temperature and pressure from Nora's script these need to be updated as part of the EBAS file generation process
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* [WIP] Simplify flag generation for **species**. Specifically, reconcile auto and manual flags for a single column in the flag table and then broadcasting the value to the rest. This is because the flag is the same for all species variables.
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* [New] Finish command line interface for visualize_datatable_vars and add modes, --flags, --dataset, and save to figures folder in repo
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* Implement flagging-app specific data operations such as:
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1. [New item] When verify flags from checklist is active, enable delete-flag button to delete flag associated with active cell on table.
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2. [New item] When verify and ready to trasnfer items on checklist are active, enable record-flags button to record verified flags into the HDF5 file.
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3. [New item] When all checklist items active, enable apply button to apply flags to the time series data and save it to the HDF5 file.
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1. ~~Define data manager obj with apply flags behavior.~~
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2. Define metadata answering who did the flagging and quality assurance tests?
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3. Update intruments/dictionaries/ACSM_TOFWARE_flags.yaml and instruments/readers/flag_reader.py to describe metadata elements based on dictionary.
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4. ~~Update DIMA data integration pipeline to allowed user-defined file naming template~~
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5. ~~Design and implement flag visualization feature: click flag on table and display on figure shaded region when feature is enabled~~
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6. Implement schema validator on yaml/json representation of hdf5 metadata
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7. Implement updates to 'actris level' and 'processing_script' after operation applied to data/file?
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* ~~When `Create Flag` is clicked, modify the title to indicate that we are in flagging mode and ROIs can be drawn by dragging.~~
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* ~~Update `Commit Flag` logic:~~
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~~3. Update recorded flags directory, and add provenance information to each flag (which instrument and channel belongs to).~~
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* Record collected flag information initially in a YAML or JSON file. Is this faster than writing directly to the HDF5 file?
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* Should we actively transfer collected flags by clicking a button? after commit button is pressed, each flag is now stored in an independent json file
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* Enable some form of chunk storage and visualization from the HDF5 file. Iterate over chunks for faster display versus access time.
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1. Do I need to modify DIMA?
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2. What is a good chunk size?
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3. What Dash component can we use to iterate over the chunks?
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