# calc eigenvalues from R matrix
# should return in descending order (largest will be first)
evals, matrix_v = np.linalg.eig(R) # matrix_v --> normalized eigenvectors
# largest eigenvalue is R0
R0 = max(evals)
return R0
###################################################
if __name__ == "__main__":
# READ POPULATION DATA FROM FILE
popdata = csv.reader(open('Dropbox/Anne_Bansal_lab/SDI_Data/totalpop_age.csv', 'r'),delimiter = ',')
dict_popdata, ages, years = func.import_popdata(popdata, 0, 1, 2)
dict_childpop, dict_adultpop = func.pop_child_adult (dict_popdata, years)
# READ GERMAN CONTACT DATA FROM FILE
filename_germ_contact_data = 'Dropbox/Anne_Bansal_lab/Contact_Data/polymod_germany_contact_matrix_Mossong_2008.csv'
filename_germ_pop_data = 'Dropbox/Anne_Bansal_lab/UNdata_Export_2008_Germany_Population.csv'
# DEFINE DISEASE PARAMETERS
beta = 0.0165
gamma = 0.2
# CALCULATE ALPHA
year = 2010
alpha = func.calc_alpha(year, dict_childpop, dict_adultpop)
# CALCULATE R MATRIX
R = calc_R_matrix (beta, gamma, alpha)
# AGE SPLIT #
# POLYMOD contact study (Ref 22 in Apollini 2013)
## Children: (5 - 19)
## Adults: (20 - 69)
#popdata
#totalpop_age.csv
# import csv file of pop data
popdata = csv.reader(open('Dropbox/Anne_Bansal_lab/SDI_Data/totalpop_age.csv', 'r'),delimiter = ',')
# import data into dicts
d_pop_for_yr_age, ages, years = func.import_popdata(popdata, 0, 1, 2)
#group ages into children and adults
d_childpop, d_adultpop = func.pop_child_adult(d_pop_for_yr_age, years)
# set value of alpha from U.S. pop data
year = 2010 # decided to use pop from 2010 bc most recent
a = func.pop_child_frac(year, d_childpop, d_adultpop)
#print a # = 0.239 for 2010 with 60-69; was 0.27 without 60-69
metro, ages = func.assign_metro(a)
print metro
#totpop of metros = 10817
# ids of ppl = [0 - 10816]