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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 0.4

  • Remaining basic processing and analysis steps:

    • denoising (via SVD or similar)

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

  • References in processing and analysis steps?

    • May be useful to automatically create a list of references to cite (report) depending on the methods actually used.

    • May need an implementation of some kind of bibliographic record

  • Reporter: Method for adding dict representations of datasets to context

  • 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.

  • Expand use cases: reports

  • Recipes: Subrecipes that can be included in recipes

  • Interfacing towards fitting/simulation frameworks

  • 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)

For later versions

  • Interpolation

    • for ND with N>2

    • different types of interpolation

  • Templates for creating derived packages

  • Logging

  • Tabular representations of characteristics extracted from datasets

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


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.


Flesh out these additional DatasetAnnotation classes, particularly in light of the newly created PlotAnnotation classes that may eventually be a way to graphically display the dataset annotations.

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Clarifly the difference between the HistoryRecord and Record classes, and explain which is used when and how.

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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

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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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While generally, recipe-driven data analysis works well in practice, improving usability and robustness is high on the list. This includes ( but may not be limited to) a parser for recipes performing a static analysis of their syntax and is particularly useful for larger datasets and/or longer lists of tasks. Potential steps in this direction:

  • Add check() method to aspecd.tasks.Task

  • Define required parameters in a (private) attribute of either the individual task level or even on the level of the underlying objects

  • Potentially reuse the _sanitise_parameters() method.

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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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