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examples: four walkthrough notebooks for the common API journeys - #8
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Each one takes something you would normally do by clicking in the app and does it from the API instead, explaining the wire formats that are not guessable from the client and what happens when you get them wrong. md_dr_pairwise_walkthrough upload, then pairwise + dose response md_workspace_walkthrough workspaces, tabs, registry, the grid md_qc_report_automation the QC Report automation, rebuilt md_pairwise_ora_walkthrough saved entity lists and pathway enrichment All four run end to end against dev. Checked in without saved outputs so readers see their own results. The ORA notebook additionally needs pandas and requests, noted in the folder README.
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What
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examples/walkthroughs/, four notebooks that each take something you would normally do by clicking in the app and do it from the API instead. They are written to be read as much as run: every wire format that is not guessable from the client is explained where it appears, along with what goes wrong if you get it different.md_dr_pairwise_walkthroughmd_workspace_walkthroughmd_qc_report_automationmd_pairwise_ora_walkthroughRead in that order; each assumes the one before it.
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All four run end to end against dev. The last one, as an example of the level of checking: 1876 entities through a pairwise, six entity lists created, 22 modules placed across two tabs, three
orajobs completed, and the resultingora_resultstables downloaded and inspected rather than trusted. Settings were read back from the API after creation rather than assumed from the create call succeeding.Notes for review
md_pairwise_ora_walkthroughneedspandasandrequests, which are not md-python dependencies. Called out in the folder README rather than added topyproject.toml, since it is the only notebook that needs them.00000000-...placeholders.Two things the notebooks document that are worth knowing independently
Module settings are validated for shape, not for meaning. The API returns a 422 for wrong keys or wrong types, but accepts a metadata column name that does not exist, or an enum value with the wrong capitalisation. Those surface later as an empty widget on a tab.
A required setting can still be empty. Several modules declare a setting required and default it to
[]. An empty list satisfies the requirement, so the module is created and renders nothing.