CoDICE L1 decompression and unpacking data updates #762
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… individual integer values
…d "hard coded" into the configuration dictionary; Added spin sector config variable
bourque
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Ins: CoDICE
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Aug 20, 2024
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Aug 20, 2024
|
Closes #767 |
…cessing pipeline a bit more readable; added proper numbers for positions/energies/spin_sectors
…arrays by positions, spin_sectors, and energies
bourque
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Aug 28, 2024
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| CATDESC: Azimuth | ||
| FIELDNAM: Azimuth | ||
| FORMAT: I2 | ||
| LABLAXIS: Azimuth |
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I'm planning to provide better labels/descriptions for these in an upcoming PR where I address nomenclature
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Aug 30, 2024
imap_processing/codice/codice_l1a.py
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| # For CoDICE-lo, data are a 3D arrays with a shape representing | ||
| # [<num_positions>,<num_spin_sectors>,<num_energy_steps>] | ||
| if self.instrument == "lo": | ||
| self.data = np.array(science_values_decompressed, dtype=np.uint).reshape( |
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do you mean to add dtype=np.uint32 or 64?
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Yes, I think this can be uint32. This prompted me to read up on what the difference between np.uint and np.uint32 was, and it sounds like uint32 is a better choice:
np.uint:
This is an alias for the default unsigned integer type in NumPy, which is platform-dependent. On most platforms, np.uint is equivalent to np.uint32, meaning it is a 32-bit unsigned integer. However, this can vary based on the platform and version of NumPy.
np.uint32:
This explicitly defines a 32-bit unsigned integer. It can store values from 0 to 232−1232−1, which is 0 to 4,294,967,295. This is a fixed-size type and is not platform-dependent.
imap_processing/codice/decompress.py
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| """ | ||
| Apply 8-bit to 32-bit Lossy B decompression algorithm. | ||
| Apply 8-bit to 32-bit Lossy A decompression algorithm. |
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Suggested change
| Apply 8-bit to 32-bit Lossy A decompression algorithm. | |
| Apply 8-bit to 32-bit Lossy B decompression algorithm. |
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Thanks for catching that! It was a bad copy/paste
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Change Summary
Overview
This PR makes improvements to how CoDICE data is unpacked and structured as data arrays in the CDF data products. Prior to SIT-3, this step of the algorithm was mostly glossed over in order to get CDFs created to support the test, but I am now going back to properly implement how the data arrays get stored. This PR also makes improvements to the decompression algorithm, namely making it so that an entire binary string can be decompressed at once, instead of individual values.
Updated Files
imap_processing/cdf/config/imap_codice_l1a/b_variable_attrs.yamlspin_sectorcoordinateimap_processing/codice/codice_l1a.pyspin_sectorcoordinate; splitunpack_science_datainto two functions, one for eachhiandlo, since they have slightly differently ways of upacking data.imap_processing/codice/constants.pyspin_sector; removed Collapse Table lookups, as they are not needed because I am defining configurations via theDATA_PRODUCT_CONFIGURATIONSdictionaries.imap_processing/codice/decompress.pyimap_processing/tests/codice/test_codice_l1a.pyimap_processing/tests/codice/test_decompress.py