Make it possible to work with multiple HDF repos

[root-11]

Use case:

1. User U is a member of team who is doing a data audit of client C who stores data in multiple columns.
2. Only common denominator is a column "site" which exists in all tables.
3. The team has a list of all sites and has a team drive T, which is the team's cleaned data.
4. User U needs to reads through C's data to find tables that nobody else in the team have worked on. U shares it in online spreadsheet.
5. Once U has cleaned the data, s/he uploads it to T.

Drives:

/tmp/tablite.hdf5   # local working memory on SSD.
/C/datasources.hdf5  # source data from the client (read only access)
/T/clean_data.hdf5  # teams cleaned data.

Workflow:

prerequisite: User selects site 'Corrusant'

site_name= 'Corrusant'

all_tables = Table.reload_stored_tables('/C/datasources.hdf5')  # rapid remote header level read-only access.

# sift through all tables and create tables for site on localhost using the default 'H5_STORAGE':
wip = [ t.any(**{'site': lambda x:x=site_name}) for t in all_tables) ]  

# undisclosed process used to clean up the data.

# create/append to a super table with all data from all sites. Note that this table may already exist!
super_table = Table(H5_location="/T/clean_data.hdf5")  
for table in wip:
    super_table.stack(table)  # append to the team wide super table.

At this point clean data exists in a super table for the whole team to work on.

[root-11]

Done! This is possible since version 2023.6