无法重新填充 ElectrodeGroup 数据联合表

Question

I'm a researcher in Loren Frank's lab at UCSF using datajoint and files in the nwb format. I made some changes to our code for defining entries in our ElectrodeGroup table, and was hoping to test those by deleting an entry in the table and regenerating it with the new code. I was able to delete the entry, but cannot repopulate it. In particular, when I run ElectrodeGroup.populate() or ElectrodeGroup.populate({"nwb_file_name": my_file_name}), no changes are made to the table. I confirmed that the electrode group I deleted and am trying to regenerate is defined in the original nwb file. I am seeking input on why the populate command seems to not be working here. Thanks in advance for any help!

Answer 1

This user also contacted our team through another channel. Sharing the solution below for future users, in reference to this schema . In short, the populate process is reserved for unique upstream primary keys.

Since the ElectrodeGroup's only upstream table dependency is Session, the make method will only be called if there are no electrode groups for that session. This is because from the perspective of DataJoint, the only 'guaranteed' knowledge about what should exist for this table is defined solely by the presence/absence of related upstream records. Since the 'new' primary 'electrode_group_name' attribute is defined by the ElectrodeGroup table itself, DataJoint doesn't know how many copies will be created by make, and so simply invokes make 1 time per Session, expecting the single make invocation to fully define all possible electrode_group_name values the table will use. If there is one value for that session, no work needs to be done, so no make() invocation occurs.

There are a couple possible solutions:

1. Model the electrode group explicitly, with a table defines the existence of an electrode group (eg, ElectrodeGroupConfiguration). This ElectrodeGroup would then inherit primary keys from both Session and ElectrodeGroupConfiguration. The ElectrodeGroup make function would be adjusted to load that unique keys across upstream tables.
2. Adjust the make function to handle the partial insert/update case, and call the make function directly with the desired primary key when these kinds of 'abnormal' updates need to occur.

Method #1 is 'cleanest' w/r/t to the DataJoint data model (explicitly modeled data dependencies using make/populate), whereas #2 is slightly 'escaping' the DataJoint data model in a controlled way to achieve a desired schema/data result.