Besides cash flow tables, costs and incomes associated with operations or tree value models can also be defined with cash flow models; i.e., models predicting the unit price changes over time based on some variables.
All cash flow models have the same set of input parameters:
Below is an example of a stochastic timber assortment price model:
def random_prices(table, date, variables, parameters, errors):
ret = True
index = (1,2)
# the initial year is now fixed to 2008
n = date.year - 2008 + 1
# load the prices and compute standardized delta (increment), simulate
# prices for the next n years
prices, delta_st = _load_prices()
P = _simulate(prices, delta_st, n, 2)
# store the prices of the last year
table.set_values(index, PINELOG, P[-1,0])
table.set_values(index, SPRUCELOG, P[-1,1])
table.set_values(index, BIRCHLOG1, P[-1,2])
table.set_values(index, BIRCHLOG2, P[-1,2])
table.set_values(index, PINEPULP, P[-1,3])
table.set_values(index, SPRUCEPULP, P[-1,4])
table.set_values(index, BIRCHPULP1, P[-1,5])
table.set_values(index, BIRCHPULP2, P[-1,5])
return ret