def process_optional_input(pop = None, conc = None, port_y = None):
global port_name, port_ambient_conc, port_y0, port_c0, port_pop_struc, port_pop, base_conc, port_real_em, ratio
if pop is not None:
port_pop = pop
if conc is not None:
port_ambient_conc = conc
# call Port class to do calc, if use inputs his own y, then pass y to port class instead of y0 to calc ymin
if port_y is None:
port = Port(port_name, port_ambient_conc, port_y0, port_c0, port_pop_struc, port_pop, base_conc, port_real_em, ratio)
indicator = 0
else:
port = Port(port_name, port_ambient_conc, port_y, port_c0, port_pop_struc, port_pop, base_conc, port_real_em, ratio)
indicator = 1
name_t = port_name
ambient_t = port_ambient_conc
y0_t = port_y0
c0_t = port_c0
pop_struc_t = port_pop_struc
pop_t = port_pop
base_t = base_conc
em_t = port_real_em
#print impact.key
impact = port.get_impact(indicator)
# process/aggregate the impact in some way
result_disease_age = {}
result_disease_time = {}
result_disease_zone = {}
result_disease_age_yll = {}
for key in impact.keys():
if key != 'ARI':
impact_all_age = np.empty(age_group)
for i in range(age_group):
impact_all_age[i] = sum(impact[key][i][4, :])
result_disease_age[key] = np.around(impact_all_age, 2)
else:
result_disease_age[key] = np.around(np.array([sum(impact[key][0][4, :])]),2)
# does not matter if ARI or not
for key in impact.keys():
impact_sum_age = sum(impact[key])
result_disease_zone[key] = np.around(impact_sum_age[4, :],2)
#print impact_sum_age.key
temp = np.empty(time_interval)
for i in range(time_interval):
temp[i] = sum(impact_sum_age[i, :])
result_disease_time[key] = np.around(temp, 2)
#sum_age_t = impact_sum_age
#print sum_age_t.key
for key in impact.keys():
if key != "ARI":
result_disease_age_yll[key] = np.around(np.array(life_exp) * result_disease_age[key], 2)
#-- add total col for yll except its ARI
result_disease_age_yll[key] = np.append(result_disease_age_yll[key], sum(result_disease_age_yll[key]))
#-- add total col for age except its ARI
result_disease_age[key] = np.append(result_disease_age[key], sum(result_disease_age[key]))
else:
result_disease_age_yll[key] = np.around(np.array(life_exp[0]) * result_disease_age[key], 2)
#exp_t = life_exp
#age_t = result_disease_age_yll["ARI"]
#print exp_t.key
#--- Add N/A and total in cols for ARI
result_disease_age['ARI'] = np.append(result_disease_age['ARI'], np.array([ 0 for i in range(age_group - 1)]))
result_disease_age['ARI'] = np.append(result_disease_age['ARI'], result_disease_age['ARI'][0])
result_disease_age_yll['ARI'] = np.append(result_disease_age_yll['ARI'], np.array([ 0 for i in range(age_group - 1)]))
result_disease_age_yll['ARI'] = np.append(result_disease_age_yll['ARI'], result_disease_age_yll['ARI'][0])
#-- add total row below each table
#result_disease_age['Total'] = np.zeros(age_group + 1)
#result_disease_time['Total'] = np.zeros(age_group + 1)
#result_disease_age_yll['Total'] = np.zeros(age_group + 1)
#result_disease_zone['Total'] = np.zeros(age_group + 1)
#for i in range(age_group + 1):
# for key in result_disease_age.keys():
# if key!= 'Total' and result_disease_age[key][i] != 'N/A':
# result_disease_age['Total'][i] = result_disease_age['Total'][i] + float(result_disease_age[key][i])
#-- get rid of 0-4 value for age time before adding up cols
#result_disease_age['LC'][0] = 0
#result_disease_age['CP'][0] = 0
#result_disease_age_yll['LC'][0] = 0
#result_disease_age_yll['CP'][0] = 0
#-- add total row below table
add_total(result_disease_age)
add_total(result_disease_time, time_interval)
add_total(result_disease_age_yll)
add_total(result_disease_zone, len(result_disease_zone['ARI']))
return {'age': result_disease_age, 'time': result_disease_time, 'zone': result_disease_zone, 'yll': result_disease_age_yll, 'indicator': indicator}
