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A few ideas how to develop the project further, currently a list as a reminder for the main developers themselves, in no particular order, though with a tendency to list more important aspects first:

For version 0.5

  • Report task:

    • Add figure captions to context if available

    • Operating on recipes, i.e. report on all tasks in a recipe

    • Adding arbitrary dict representations of properties of datasets/results to context

  • Default report templates for each type of processing/analysis task

    Includes deciding where to store these templates, whether to have them stored in different directories for different languages, and alike. Ideally, templates should be copied to a user-accessible directory for modifying there. (See experience gained implementing pymetacode)

  • Expand use cases: reports

  • Recipes: Subrecipes that can be included in recipes

  • Handling of mapper recipe (from package source, not from directory)

For later versions

  • Recipe-driven data analysis

    • Functionality to create recipe structure/structure for tasks as YAML (for use either with CLI or with web interface)

  • Remaining basic processing and analysis steps:

    • denoising (via SVD or similar)

    • SNREstimation with explicitly providing noise (using both, processing and analysis)

  • Interpolation

    • for ND with N>2

    • different types of interpolation

  • Templates for creating derived packages

  • Tabular representations of characteristics extracted from datasets

  • Plotter: Factory to create single plots of each given dataset.

  • Basic maths in values of recipes (ranges, basic numpy functions)?

    May impair the platform-independence of the recipe (i.e., tying it to Python/NumPy)


A list of todos, extracted from the code and documentation itself, and only meant as convenience for the main developers. Ideally, this list will be empty at some point.


How to handle noisy data in case of area normalisation, as this would probably account for double the noise if simply taking the absolute?

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  • Make type of interpolation controllable

  • Check for ways to make it work with ND, N>2

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  • Make type of interpolation controllable

  • Make number of points controllable (in absolute numbers as well as minimum and maximum points with respect to datasets)

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There is a number of things that are not yet implemented, but highly recommended for a working recipe-driven data analysis that follows good practice for reproducible research. This includes (but may not be limited to):

  • Parser for recipes performing a static analysis of their syntax. Useful particularly for larger datasets and/or longer lists of tasks.

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Can recipes have LOIs themselves and therefore be retrieved from the extended data safe? Might be a sensible option, although generic (and at the same time unique) LOIs for recipes are much harder to create than LOIs for datasets and alike.

Generally, the concept of a LOI is nothing a recipe needs to know about. But it does know about an ID of any kind. Whether this ID is a (local) path or a LOI doesn’t matter. Somewhere in the ASpecD framework there may exist a resolver (factory) for handling IDs of any kind and eventually retrieving the respective information.

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Things to add:

  • Reports

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