def batch_run_classroom(j):
"""
Helper function.
The function containing the batchrunner for the classroom simulations.
:param j: j is the setup type. eg j=2 => setup type [2,2,2]
:return: Returns lists of average number of infected for every timestep.
Saves the raw data from the datacollector into a csv file.
"""
batch_run = BatchRunner(covid_Model,
variable_parameters={"N": range(24,25,1)},
fixed_parameters={"width": 11, "height": 11, "setUpType": [j]},
iterations=antal_iterationer,
max_steps=antal_tidsskridt_per_simulation)
batch_run.run_all() #run batchrunner
ordered_df = batch_run.get_collector_model()
data_list = list(ordered_df.values()) #saves batchrunner data in list
for i in range(len(data_list)):
data_list[i]['Iteration'] = i+1
pd.concat(data_list).to_csv('csvdata/classroom_test'+str(j)+'.csv')
num_of_infected = [0]*(antal_tidsskridt_per_simulation+1)
for i in range(len(data_list)):
temp_list = []
for k in range(len(data_list[i]["infected"])):
num_of_infected[k]+=data_list[i]["infected"][k]
temp_list.append(data_list[i]["infected"][k])
num_of_infected = [number / antal_iterationer for number in num_of_infected] #avg number of infected
return num_of_infected
def plot_busy(fix_par, var_par, model, iter, steps):
batch_run = BatchRunner(
model,
variable_parameters=var_par,
fixed_parameters=fix_par,
iterations=iter,
max_steps=steps,
model_reporters={"busy": lambda m: busy_employees(m)},
)
batch_run.run_all() #run batchrunner
data_list = list(batch_run.get_collector_model().values()
) # saves batchrunner data in a list
sum_of_busy = [0] * (steps + 1) #makes list for y-values
for i in range(len(data_list)):
for j in range(len(data_list[i]["busy"])):
sum_of_busy[j] += data_list[i]["busy"][
j] #at the right index add number of infected
sum_of_infected = [number / iter for number in sum_of_busy
] #divide list with number of iterations to get avg
time = [i
for i in range(0, steps + 1)] #makes list of x-values for plotting
plt.plot(time, sum_of_busy, label='# busy employees', color='Green')
# plt.plot(time, num_of_susceptible, label= 'Number of Susceptible', color = 'Green', linestyle='dashed')
plt.xlabel('Tidsskridt')
plt.ylabel('Mean busy employees')
plt.title('ved %s simulationer' % iter)
plt.legend()
return
def batch_run(j):
"""
Helper function.
The function containing the batchrunner for the full simulations.
:param j: j is the setup type. eg j=2 => setup type [2,2,2]
:return: Returns lists of average number of Infected, Susceptible, Recovered for every timestep.
Saves the raw data from the datacollector into a csv file.
"""
batch_run = BatchRunner(covid_Model,
variable_parameters={"N": range(24,25,1)},
fixed_parameters={"width": 26, "height": 38, "setUpType": [j,j,j]},
iterations=antal_iterationer,
max_steps=antal_tidsskridt_per_simulation,
model_reporters={"infected": lambda m: get_infected_count(m)})
batch_run.run_all() #run batchrunner
ordered_df = batch_run.get_collector_model()
data_list = list(ordered_df.values()) #saves batchrunner data in list
for i in range(len(data_list)):
data_list[i]['Iteration'] = i+1
pd.concat(data_list).to_csv('csvdata/rawdata'+str(j)+'.csv')
num_of_infected = [0]*(antal_tidsskridt_per_simulation+1)
num_of_susceptible = [0]*(antal_tidsskridt_per_simulation+1)
num_of_recovered = [0]*(antal_tidsskridt_per_simulation+1)
for i in range(len(data_list)):
for j in range(len(data_list[i]["infected"])):
num_of_infected[j]+=data_list[i]["infected"][j]
num_of_susceptible[j] += data_list[i]["Agent_count"][j]-(data_list[i]["infected"][j]+data_list[i]["recovered"][j]+number_of_vaccinated) #number of susceptible at each time step
num_of_recovered[j] += data_list[i]["recovered"][j]
num_of_infected =[number / antal_iterationer for number in num_of_infected] #avg number of infected
num_of_susceptible = [number / antal_iterationer for number in num_of_susceptible]
num_of_recovered = [number / antal_iterationer for number in num_of_recovered]
return num_of_infected, num_of_susceptible, num_of_recovered
def experiment_1(replicates=40, max_steps=200, graph_type="None"):
"""
Experiment 1 - Run simulations of civil violence with network model.
Function to generates data which are used for comparison of network topology influence on civil violence model.
"""
path = 'archives/saved_data_experiment_1_{0}_{1}'.format(
int(time.time()), graph_type)
configuration = read_configuration()
model_params = {}
model_params.update(
configuration
) # Overwritten user parameters don't appear in the graphic interface
model_params.update({'seed': None})
model_params['graph_type'] = graph_type
model_params['max_iter'] = max_steps
batch = BatchRunner(
CivilViolenceModel,
max_steps=max_steps,
iterations=replicates,
fixed_parameters=model_params,
model_reporters={
'All_Data': lambda m: m.datacollector,
"QUIESCENT": lambda m: m.count_type_citizens("QUIESCENT"),
"ACTIVE": lambda m: m.count_type_citizens("ACTIVE"),
"JAILED": lambda m: m.count_type_citizens("JAILED"),
"OUTBREAKS": lambda m: m.outbreaks
}, # attempt all
display_progress=True)
batch.run_all()
batch_df = batch.get_model_vars_dataframe()
batch_df = batch_df.drop('All_Data', axis=1)
data = batch_df
run_data = batch.get_collector_model()
with open(path, 'ab') as f:
np.save(f, data)
run_path = path + '_run'
with open(run_path, 'ab') as f:
np.save(f, run_data)
# one_agent_wealth = agent_wealth.xs(14, level="AgentID")
# one_agent_wealth.Wealth.plot()
# plt.show()
fixed_params = {"width": 10, "height": 10}
variable_params = {"N": range(10, 500, 10)}
batch_run = BatchRunner(ParkingModel,
variable_params,
fixed_params,
iterations=5,
max_steps=100,
model_reporters={"Gini": compute_gini})
batch_run.run_all()
run_data = batch_run.get_model_vars_dataframe()
run_data.head()
plt.scatter(run_data.N, run_data.Gini)
plt.show()
#Get the Agent DataCollection
data_collector_agents = batch_run.get_collector_agents()
print(data_collector_agents[(10, 2)])
#Get the Model DataCollection.
data_collector_model = batch_run.get_collector_model()
print(data_collector_model[(10, 1)])
