You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
import csv
|
|
import math
|
|
import random
|
|
import matplotlib.pyplot as plt
|
|
class Mensch(object):
|
|
def __init__(self):
|
|
#toImplement
|
|
def getCoordinates(self, bdl):
|
|
bdlDict = {'Vorarlberg': [0, 0], 'Tirol': [1, 0], 'Kärnten': [2, 0], 'Salzburg': [0, 1],
|
|
'Steiermark': [1, 1], 'Burgenland': [2, 1], 'Oberösterreich': [0, 2],
|
|
'Niederösterreich': [1, 2], 'Wien': [2, 2]}
|
|
return [int(bdlDict[bdl][0]) + random.random(), int(bdlDict[bdl][1]) + random.random()]
|
|
def die(self):
|
|
#toImplement
|
|
def infect(self, day):
|
|
#toImplement
|
|
#Distanz = math.sqrt((self.Koordinaten[0] - Population[randIndex].Koordinaten[0]) * (self.Koordinaten[0] - Population[randIndex].Koordinaten[0]) + (self.Koordinaten[1] - Population[randIndex].Koordinaten[1]) * (self.Koordinaten[1] - Population[randIndex].Koordinaten[1]))
|
|
class Simulation(object):
|
|
def __init__(self, population):
|
|
self.Population = population
|
|
def simulate(self):
|
|
#toImplement
|
|
# Visualisierung
|
|
# Scatter Plot
|
|
x_inf = []
|
|
y_inf = []
|
|
x_fit = []
|
|
y_fit = []
|
|
for human in Population:
|
|
if (human.Infiziert):
|
|
x_inf.append(human.Koordinaten[0])
|
|
y_inf.append(human.Koordinaten[1])
|
|
else:
|
|
x_fit.append(human.Koordinaten[0])
|
|
y_fit.append(human.Koordinaten[1])
|
|
plt.title("Population")
|
|
plt.scatter(x_inf, y_inf, c="red", s=5)
|
|
plt.scatter(x_fit, y_fit, c="blue", s=2)
|
|
plt.draw()
|
|
if (i % 5 == 0):
|
|
plt.savefig('result/populationOnDay' + str(i) + '.png')
|
|
plt.pause(0.0001)
|
|
plt.clf()
|
|
if __name__ == '__main__':
|
|
print("Generate Population:")
|
|
Populationsfaktor = 0.01
|
|
AnteilFrauen = 0.507
|
|
Population = []
|
|
bevAnzahl = 0
|
|
bevDict = {}
|
|
gebDict = {}
|
|
gestDict = {}
|
|
#read data from Statistik Austria
|
|
with open('./data/Bevölkerung.csv') as bevoelkerung:
|
|
csv_reader = csv.reader(bevoelkerung, delimiter = ';')
|
|
rowcnt = 0
|
|
for row in csv_reader:
|
|
rowcnt = rowcnt + 1
|
|
if rowcnt == 2:
|
|
bevAnzahl = int(int(row[-1])*Populationsfaktor)
|
|
if rowcnt > 2:
|
|
bevDict[row[0]] = row[-1]
|
|
with open('./data/Geburten2021.csv') as geburten:
|
|
csv_reader = csv.reader(geburten, delimiter=';')
|
|
rowcnt = 0
|
|
for row in csv_reader:
|
|
rowcnt = rowcnt + 1
|
|
if rowcnt > 2:
|
|
gebDict[row[0]] = [row[-2], row[-1]]
|
|
with open('./data/Gestorbene2021.csv') as gestorbene:
|
|
csv_reader = csv.reader(gestorbene, delimiter=';')
|
|
rowcnt = 0
|
|
for row in csv_reader:
|
|
rowcnt = rowcnt + 1
|
|
if rowcnt > 2:
|
|
gestDict[row[0]] = [row[-2], row[-1]]
|
|
print(bevDict)
|
|
print(gebDict)
|
|
print(gestDict)
|
|
print(bevAnzahl)
|
|
print(random.random())
|
|
for i in range(int(bevAnzahl)):
|
|
gender = 1
|
|
if(random.random()>0.507):
|
|
gender = 0
|
|
age = random.randint(0, 101)
|
|
bdl = ""
|
|
randBdl = random.random()
|
|
BdlCount = 0
|
|
for key in bevDict.keys():
|
|
BdlCount += int(bevDict[key])
|
|
checkWlsk = (BdlCount*Populationsfaktor)/int(bevAnzahl)
|
|
if(randBdl < checkWlsk):
|
|
bdl = str(key)
|
|
break
|
|
Population.append(Mensch(age, gender, bdl))
|
|
Simulation = Simulation(Population)
|
|
Simulation.simulate()
|