def invert_migrations_test():
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
migration_balances = {
'2011': 109,
'2012': 91,
'2013': 82,
'2014': 99,
'2015': -161,
'2016': 217,
'2017': 250
}
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics()
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors(custom_migrations=migration_balances)
simulate(population)
def control_test():
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics()
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors()
simulate(population)
def main():
print("Loading natality and mortality data")
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
# migrations_ages = [0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0]
migrations_ages = [
27, 50, 0, 0, 107, 122, 153, 169, 0, 82, 99, 0, 0, 0, 0, 0, 0, 0
]
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics()
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors()
simulate(population)
def flatten_mortality_test():
mortality_data = Parser.get_mortality_data()
for age in mortality_data:
mortality_data[age] = 20
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics()
population.set_mortality(mortality_data) # BEST = "ksone"
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors()
simulate(population)
def custom_popualation_workplaces_test():
workers_filepath = "fake-workers-survey.xlsx"
_ = SurveyGenerator(workplaces_list="workplaces-aldeia-redondos.csv",
workers_filepath=workers_filepath)
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics(workers_survey=workers_filepath)
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors()
simulate(population)
def custom_popualation_schools_test():
students_filepath = "fake-students-survey.xlsx"
_ = SurveyGenerator(schools_list="schools-outeiro-das-galegas.csv",
students_filepath=students_filepath)
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics(student_surveys=students_filepath)
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors()
simulate(population)
def custom_population_origin_test():
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
custom_index = 25
population = PopulationGenerator.init_population_census_2011(
custom_origin_index=custom_index)
print("All population initiated on", population.zones[custom_index])
population.init_dynamics()
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors()
simulate(population)
def double_mortality_test():
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
deaths_py = Parser.get_mortality_data_2011_2018()
deaths_py.pop(2018, None)
for year in deaths_py:
deaths_py[year] = deaths_py[year] * 2
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics()
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors(custom_mortality=deaths_py)
simulate(population)
def double_natality_test():
mortality_data = Parser.get_mortality_data()
natality_data = Parser.get_natality_data()
migrations_ages = [
0, 0, 0, 0, 111, 44, 206, 71, 0, 28, 49, 0, 75, 0, 0, 0, 0, 0
]
natality_data = Parser.get_natality_data_2011_2018()
natality_data.pop(2018, None)
for year in natality_data:
natality_data[year] *= 2
population = PopulationGenerator.init_population_census_2011()
population.init_dynamics()
population.set_mortality(mortality_data)
population.set_natality(natality_data)
population.set_migrations(migrations_ages)
population.train_predictiors(custom_natality=natality_data)
population.BIRTHS_PER_YEAR = population.BIRTHS_PER_YEAR * 2
simulate(population)
def get_population_distribution(self):
filepath = basepath + "pombal-detailed.csv"
df = pandas.read_csv(filepath)
resize_ratio = PopulationGenerator.get_resize_ratio()
for i, row in df.iterrows():
tot = int(round(row['Total'] * resize_ratio, 0))
pens_num = int(round(row['Pensionistas'] * resize_ratio, 0))
stud_num = int(round(row['Estudantes'] * resize_ratio, 0))
prim_num = int(round(row['Setor primário'] * resize_ratio, 0))
sec_num = int(round(row['Secundário'] * resize_ratio, 0))
tert_num = int(round(row['Terciário'] * resize_ratio, 0))
df.loc[i, 'Total'] = tot
df.loc[i, 'Pensionistas'] = pens_num
df.loc[i, 'Estudantes'] = stud_num
df.loc[i, 'Setor primário'] = prim_num
df.loc[i, 'Secundário'] = sec_num
df.loc[i, 'Terciário'] = tert_num
df = df.drop(df.index[47])
self.population_distibution = df
import PopulationGenerator import time population = PopulationGenerator.init_population_census_2011() dynamics = population.dynamics dynamics.get_od_matrix()
os.makedirs(directory)
if not os.path.exists(directory + '/raw'):
os.makedirs(directory + '/raw')
if not os.path.exists(directory + '/image'):
os.makedirs(directory + '/image')
total_populations = {}
for zone in Zones.get():
print 'getting age pyramids of', zone
total_populations[zone] = []
pyramid = PopulationGenerator.zones_pyramids[str(zone)]
for year in YEARS:
directory = './Population/zone_' + str(zone)
check_directory_exists(directory)
pyramid = PopulationGenerator.make_pyramid_older(
pyramid, year - 1, year)
path = './Population/zone_' + str(zone) + '/raw/pyramid_year_' + str(
year) + '.csv'
Printer.pyramid_to_file(pyramid, path)
Printer.pyramid_to_image(
pyramid, './Population/zone_' + str(zone) +
'/image/pyramid_year_' + str(year) + '.png')
total_populations[zone].append(
PopulationGenerator.calculate_total_population(pyramid))
Printer.print_total_population(
total_populations[zone],
'./Population/zone_' + str(zone) + '/raw/total.csv')
Printer.line_graph(YEARS, total_populations[zone],
'./Population/zone_' + str(zone) + '/image/total.png')
penalolen_population = []
import Fitness
import PopulationGenerator
import PrintFunctions
import ProduceNewGeneration
import Classes
population = PopulationGenerator.GeneratePopulation()
chr = Classes.Chromosome
flag = False
i = 1
gn = 1
while(flag==False):
print("\n\n\n\n")
print("Generation # ",gn,"\n")
population = ProduceNewGeneration.produceGeneration(population)
PrintFunctions.printPopulation(population)
population.populationLimit = population.chromosomeList.__len__()
gn = gn + 1
for x in population.chromosomeList:
if(x.fitness>10):
chr = x
PrintFunctions.printChromosome(chr)
flag = True