Somewhat. I want it to be difficult to separate the data from the report. Basically I want the report itself to be ingestible as input to other steps, so it's m

Somewhat. I want it to be difficult to separate the data from the report. Basically I want the report itself to be ingestible as input to other steps, so it's more of a "documented data" with the analysis results available. The inputs are documented but the final results can be restored without reevaluating the entire notebook. I don't want the entire workspace saved, just the final results. I hoped there was some magic to inject and restore a python object from a cell using some form of introspection. The testing I do is annual equipment performance evaluation. I'd like to be able to process each test and then feed the results into longitudinal monitoring. One thing I am considering is adding a library or extension to papermill that automatically creates a workspace hdf5 or dill or whatever that I can store individual variables into. After studying the ipynb JSON it just seems odd that you can't just store blobs as attachments. But what I understand is it has to do with the kernels and notebooks running as separate processes and passing things around as notifications. So basically the kernels don't have any access to the cells or any sorts of introspection. With papermill you have parameters, there's just not any "return values" in the processed notebooks. If it existed you could treat "reports" more easily as cached function evaluations.