pyPhenology
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sdtaylor
Plant phenology models in python with a scikit-learn inspired API
If there are unexpected columns in the obs data.frame with NA's, they will be dropped due to "lack" of temp data, cause the super long temp data.frame will end up...
setting `debug=True` in the bootstrap and weighted ensemble models doesn't give any useful info.
the naive model expects these in the predictor data, but throws an error if they're in both predictor and observation data. Both are kinda reasonable.
Currently only the final models are saved. but to refit the weights with the newest data will require all the old iterations.
spring warming model is essentially a uniforc model w/ some fixed params parallel model is mix of the alternating and parallel model (ie. triangle response for chilling and an exponential...
- [x] uniforc - [x] unichill - [x] alternating - [x] macro scale budburst - [x] m1 - requires daylength calculation and associated `site_info` - [x] linear - [x] sequential...
potentially methods to clean some raw data and put it in a format used by the package - takes dates of phenophases (a la NPN) and convert to DOY with...