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For #66#48
Also revise factor finder for sb
This has introduced a discrepancy in the simulation results originating
in factor finder
The Sw factor is now 3.75 for plot 1049300, before it was 1.1352
Probably because of:
- initial_age_sw = kwargs['startTageSw']
- years_to_bh_sw = kwargs['y2bh_Sw']
- species_comp_sw = kwargs['SC_Sw']
site_index = kwargs['SI_bh_Sw']
- present_density = kwargs['N_bh_SwT']
+ densities = kwargs['densities']
now, the species_composition is variable isntead of fixed at the time of
data
provided the time series of species_composition from the densities array
passes through the data in the appropriate year, this hsould not be
changed
it may not pass through it; and this was peculiar for aspen because
aspen density for young plots is more variable than other species
one option is to use a flag in the simulation functions to use constant
values for species_comp and present density (the values from the data year)
Essentially, the sdf functions solve the desntity functions for SDF given the
observed age, site index etc
if we use a function to generate the density functions such that they are only variable in SDF, a scipy solver can be used
this reduces our maintenance overhead and probably gives a speed improvement
This is likely also true for the basal area correction factor functions of which the aw function is currently the bottleneck in gypsy
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