moeyensj / difi Goto Github PK

Quivr: https://www.github.com/spenczar/quivr

Add LSST eups build table for compatibility with the LSST stack

Add analysis code for observations that given a set of algorithmic constrains (such as: the cadence between observations, maximum and minimum linkage ranges, etc) will calculate if known objects within that set of observations should be findable.

Add option for the user to define and pass a findability metric to analyzeObservations.

difi should support a configuration file / class with defaults defining metric parameters and column mapping dictionaries.

Linkage plotting code: given some set of linkage_ids, plot them!

Add statistics on the number of objects found, the number of objects findable, the number of linkages that are contaminated, etc...

For use on minor planet linking it would be useful to have a set of user-defined object level population statistics.

• How many NEOs are findable and were linked, etc...
• How many KBOs...

Add support for Docker containers, update README with appropriate installation instructions.

• Add build worflow
• Add build + packaging worklow for PyPi and conda
• Add conda recipe

If the linkageMembers truth column has a different dtype than the observations truth column, pandas joins will not work as intended. This should be fixed by insuring all dtypes are the same and warning the user if not. A keyword can be added to convert the truth column to the "object" dtype.

difi currently will run a multiprocessing pool if the user wants to, however, if others want to use difi in a pipeline that itself is using multiple processes then the current shared_memory implementation will break.

difi's findability criterion currently doesn't require observations to occur within a 15 day window. We should make sure it does. (Made this with @mjuric)

E.g. currently difi would say that one of my objects that has 2 observations on each of nights 266, 287, 288 and 312 is findable but it isn't really because those don't fit in 15 days.

When the user gives a column name or dictionary of the classes to which every observation belongs to, there should be a warning thrown if not all of the unique truths in the data set appear in the given classes.