Refactoring 2026 - #188
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not need to provide "formula" but metadata columns are iteratively tested and combined in a way to explain most of variability. Furthermore, one function tests all alpha- and beta- div metrics, instead of just one
"trajectory" functions
…lepath deprecate other sepp_ versions harmonize use of "verbose" parameter for _executor blastn: make word_size a cached parameter extract functions for executing stuff into execute.py
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This PR is a larger and partially braking braking change!
str()function to then compute a hash of this representation. For larger DataFrames, ellipsis were returned bystr(), such that only the first and last 10 rows / cols were actually included in the string. Changes in the larger rest of data did not effect the hash, i.e. collisions were potentially very HIGH / likely. I've changes that, which means for you, that ggmap most likely will not find existing cached results in your analysis working directory and you need to re-compute (or rename the cached files accordingly). No results were wrong at any time!execute.py: I decided to re-structure my spaghetti code a bit, i.e. move all functionality necessary to run external programs to the new file "execute.py"analyses.py:verboseis handled as an argument.sepp(which I think nobody used for long time) are now moved todeprecated.pyblastn_localcan now handle LARGE result hitlists by applying filters "early", i.e. while parsing individual chunkscorrelations.py: annoyed by manually constructing / testing "formulas" for the linear model for forward step redundancy analyses for effect size computation and the fact that I had to call alpha- and beta- versions manually, I put everything in one functionredundancyand implemented an "auto" mode which first iterates ALL metadata columns (except those defined inomit) and computes individual effects and than heuristically composes a model that explains most of variability. Note: a smart manual "design" might still out compete this automatic mechanism and you might want to iteratively add columns toomitif you know about confounders!snippets.py: I've added three more functions to visualize alpha- / beta- and ancom results of testing for differences for a metadata columnhue(e.g. healthy/diseased) across multiple timepointstime(e.g. days after intervention) in an optionally "stratified" (e.g. body site) fashion.