Use scipy solvers for finding SDF and correction factors #48
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jo-tham
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Dec 28, 2016
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)
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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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