Comments (2)
inside predict()
model_predictions = self.apply_model(blah blah, return='all')
state_vars = {v:model_predictions[i] for f,i in self.state_variables}
if state_vars_to_return = 'all':
return state_vars
else:
return state_vars[state_vars_to_return]
or something like that...
from grasslandmodels.
from grasslandmodels.
Related Issues (20)
- Generate paper figure
- loading pre-fit models HOT 2
- need phenograss model for NDVI HOT 1
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- have preloaded ndvi data
- remove Wcap from Choler PR1 HOT 1
- make phenograss b1 correct
- check that no extra predictor args are passed to fit
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- create test for numerous dimensions on prediction HOT 2
- fit using LEAP global optimization method instead of scipy.optimize HOT 2
- clean up Choler models HOT 1
- set default Vmax to 0.9999 HOT 1
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from grasslandmodels.