Phase 1, done

README.md

@@ -8,12 +8,12 @@ simulate the main processes and drivers within this system**." (Malleson, Heppen
 We developed the model on top of [https://github.com/projectmesa/mesa] mesa wolf_sheep example
 We thank David Massa e all the contributors of the mesa project. 
 
-**Alan Rafael Dill**
-
 **Lígia Mori Madeira**
 
 **Bernardo Alves Furtado**
 
+**Alan Rafael Dill**
+
 We are considering two models (see the other one at github.com/bafurtado/armas: 
 
 ### 1. Home violence model

output.csv

@@ -0,0 +1,48 @@
+metropolis;stress
+MANAUS;0.015413056522166494
+BELEM;0.01512778215826908
+MACAPA;0.015607207428302933
+SAO LUIS;0.015062071229671074
+TERESINA;0.01544716915866165
+FORTALEZA;0.015075663587447868
+CRAJUBAR;0.01570076100482962
+NATAL;0.015241406571013211
+JOAO PESSOA;0.01497565012527498
+CAMPINA GRANDE;0.015654850859894858
+RECIFE;0.015032789843866133
+PETROLINA - JUAZEIRO;0.01607362134415093
+MACEIO;0.01528808566628607
+ARACAJU;0.015479135597806221
+SALVADOR;0.014744720963030065
+FEIRA DE SANTANA;0.014944898840595439
+ILHEUS - ITABUNA;0.01596026156975715
+PETROLINA - JUAZEIRO;0.01629604953265254
+BELO HORIZONTE;0.014808226812753164
+JUIZ DE FORA;0.014188831874965972
+IPATINGA;0.014848129735741197
+UBERLANDIA;0.014507885582531754
+VITORIA;0.014436284883361126
+VOLTA REDONDA - BARRA MANSA;0.01434090352000408
+RIO DE JANEIRO;0.014132121977181649
+CAMPOS DOS GOYTACAZES;0.015525989683022205
+SAO PAULO;0.01402184252526661
+CAMPINAS;0.014295169188475414
+SOROCABA;0.014288853104210839
+SAO JOSE DO RIO PRETO;0.014154699166614054
+SANTOS;0.014477763794973767
+JUNDIAI;0.014447826390115088
+SAO JOSE DOS CAMPOS;0.014813393194991245
+RIBEIRAO PRETO;0.014282103250052828
+CURITIBA;0.013963751892614224
+LONDRINA;0.014337898961405684
+MARINGA;0.014184831524900588
+JOINVILLE;0.01514952607844631
+FLORIANOPOLIS;0.01395010849509571
+PORTO ALEGRE;0.014251968616385667
+NOVO HAMBURGO - SAO LEOPOLDO;0.014985636318288337
+CAXIAS DO SUL;0.014572435814835017
+PELOTAS - RIO GRANDE;0.014365054765180133
+CAMPO GRANDE;0.014262793357552735
+CUIABA;0.01445571862551349
+GOIANIA;0.014960733344703651
+BRASILIA;0.014677902107105205

violence/input/generator.py

@@ -23,7 +23,7 @@
               "ILHEUS - ITABUNA", "PETROLINA - JUAZEIRO", "BELO HORIZONTE", "JUIZ DE FORA", "IPATINGA", "UBERLANDIA",
               "VITORIA", "VOLTA REDONDA - BARRA MANSA", "RIO DE JANEIRO", "CAMPOS DOS GOYTACAZES", "SAO PAULO",
               "CAMPINAS", "SOROCABA", "SAO JOSE DO RIO PRETO", "SANTOS", "JUNDIAI", "SAO JOSE DOS CAMPOS",
-              "RIBEIRAO PRETO", "CURITIBA" "LONDRINA", "MARINGA", "JOINVILLE", "FLORIANOPOLIS", "PORTO ALEGRE",
+              "RIBEIRAO PRETO", "CURITIBA", "LONDRINA", "MARINGA", "JOINVILLE", "FLORIANOPOLIS", "PORTO ALEGRE",
               "NOVO HAMBURGO - SAO LEOPOLDO", "CAXIAS DO SUL", "PELOTAS - RIO GRANDE", "CAMPO GRANDE", "CUIABA",
               "GOIANIA", "BRASILIA"]
 

violence/model.py

@@ -20,6 +20,7 @@
 from violence.agents import Person, Family
 from violence.schedule import RandomActivationByBreed
 from violence.input import generator
+from violence.input.generator import metropolis
 
 
 class Home(Model):
@@ -183,10 +184,8 @@ def run_model(self, step_count=200):
             print('Final number People: ', self.schedule.get_breed_count(Person))
 
 
-if __name__ == '__main__':
-    # Bernardo's debugging
-    # Running all metropolis
-    from violence.input.generator import metropolis
+def generate_output():
+    # Running for all metropolis
     output = pd.DataFrame(columns=['metropolis', 'stress'])
     for metro in metropolis:
         home = Home(metro=metro)
@@ -195,3 +194,13 @@ def run_model(self, step_count=200):
         model_df = home.datacollector.get_model_vars_dataframe()
         output.loc[output.shape[0]] = [metro, model_df.loc[9, 'Stress']]
     output.to_csv('output.csv', sep=';', index=False)
+
+
+if __name__ == '__main__':
+    # Bernardo's debugging
+    generate_output()
+    # metro = 'CURITIBA'
+    # home = Home(metro=metro)
+    # for i in range(5):
+    #     home.step()
+    pass