Analysis Recipes
A recipe is a pipeline written down as a small JSON file: which blocks run, in which order, with which settings, over which frames. Save one to repeat an analysis on another trajectory, to share it with a colleague, or to let a script or an AI agent prepare the pipeline for you.In the app
Below the Run button in ANALYZE:- Export recipe downloads the current pipeline as
pipeline-YYYY-MM-DD.claviscage.json. - Import recipe reads such a file. You can also drop the file on the pipeline.
Algorithms of your own in a recipe
A recipe can carry functions you enrolled yourself (see the LIBRARY tab). When you import one that this browser does not have yet:- It is added to LIBRARY with the USER · UNVERIFIED badge, unpinned.
- Its code does not run. The pipeline leaves it out for now.
- A notice lists it. Read the code in LIBRARY, then choose Add to pipeline or Keep in LIBRARY only.
Methods that changed since the recipe was saved
Each engine block in a recipe records the version of its method description (descriptorVersion). If this app’s engine describes the method with a different version, for example because its parameters or outputs changed, the block is held out of the pipeline and a notice names it. Choose Add to pipeline to run it anyway, or leave it out.
Format: claviscage.recipe/v2
The app has one hydrogen-bond cutoff and one neighbour cutoff for all blocks. When a recipe gives several values, the first valid one is used. Values outside the ranges above are ignored.
For scripts and agents
The format is plain JSON on purpose, so any tool can write it: a Python script, a notebook, or an AI assistant asked to “count cages with TRACE and HTR on every 5th frame”. Write the file, then import it in the app. The block ids are the onesclavis_engine.list_methods() reports in the Python package; clavis_engine.describe_method(id) gives each method’s descriptor_version and parameter names.