You're reading an old version of this documentation. For up-to-date information, please have a look at v0.7.
Roadmap¶
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 0.2¶
Report task: Add figure captions to context if available
Remaining basic processing and analysis steps, such as algebra with datasets, slice extraction for >2D datasets, peak finding, SNR determination, denoising, filtering, noise, cut dataset and axis (to common range)
Normalising over parts of a dataset
aspecd.processing.ProcessingStep._set_defaults()method called beforeaspecd.processing.ProcessingStep._sanitise_parameters()Importer with parameters in recipe (e.g., for CSV importer)
Expand use cases
Plot task: default filename for saved figure
For later versions¶
Reporter: Method for adding dict representations of datasets to context
Report task: Operating on recipes, i.e. report on all tasks in a recipe
Report task: 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.
Templates for creating derived packages
Logging
Tabular representations of characteristics extracted from datasets
Plotter: Factory to create single plots of each given dataset. Probably needs a way to create default filenames (e.g. label + date?).
Todos¶
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.
Todo
How to handle noisy data in case of area normalisation, as this would probably account for double the noise if simply taking the absolute?
Todo
Make type of interpolation controllable
Check for ways to make it work with ND, N>2
Todo
Make type of interpolation controllable
Make number of points controllable (in absolute numbers as well as minimum and maximum points with respect to datasets)
Todo
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.
Todo
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.
Todo
Things to add:
example for an analysis step
Todo
Things to add:
Reports