diff --git a/Exercise sheet 9/latticescalar.py b/Exercise sheet 9/latticescalar.py
index 90da42a..6a5be34 100644
--- a/Exercise sheet 9/latticescalar.py	
+++ b/Exercise sheet 9/latticescalar.py	
@@ -48,38 +48,42 @@ def batch_estimate(data,observable,k):
 	values = np.apply_along_axis(observable, 1, batches)
 	return np.mean(values), np.std(values)/np.sqrt(k-1)
 
-lamb = 1.5
-kappas = np.linspace(0.08,0.18,11)
-width = 3
-num_sites = width**4
-delta = 1.5  # chosen to have ~ 50% acceptance
-equil_sweeps = 10
-measure_sweeps = 2
-measurements = 20
+def main():
+	lamb = 1.5
+	kappas = np.linspace(0.08,0.18,11)
+	width = 3
+	num_sites = width**4
+	delta = 1.5  # chosen to have ~ 50% acceptance
+	equil_sweeps = 10
+	measure_sweeps = 2
+	measurements = 20
+	
+	mean_magn = []
+	for kappa in kappas:
+		phi_state = np.zeros((width,width,width,width))
+		run_scalar_MH(phi_state,lamb,kappa,delta,equil_sweeps * num_sites)
+		magnetizations = np.empty(measurements)
+		for i in range(measurements):
+			run_scalar_MH(phi_state,lamb,kappa,delta,measure_sweeps * num_sites)
+			magnetizations[i] = np.mean(phi_state)
+		mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10)
+		mean_magn.append([mean,err])
+	
+	output_filename = 'preliminary_simulation.h5'
+	with h5py.File(output_filename,'a') as f:
+		if not "mean-magn" in f:
+			dataset = f.create_dataset("mean-magn", chunks=True, data=mean_magn)
+			# store some information as metadata for the data set
+			dataset.attrs["lamb"] = lamb
+			dataset.attrs["kappas"] = kappas
+			dataset.attrs["width"] = width
+			dataset.attrs["num_sites"] = num_sites
+			dataset.attrs["delta"] = delta
+			dataset.attrs["equil_sweeps"] = equil_sweeps
+			dataset.attrs["measure_sweeps"] = measure_sweeps
+			dataset.attrs["measurements"] = measurements
+			dataset.attrs["start time"] = starttime
+			dataset.attrs["stop time"] = time.asctime()
 
-mean_magn = []
-for kappa in kappas:
-	phi_state = np.zeros((width,width,width,width))
-	run_scalar_MH(phi_state,lamb,kappa,delta,equil_sweeps * num_sites)
-	magnetizations = np.empty(measurements)
-	for i in range(measurements):
-		run_scalar_MH(phi_state,lamb,kappa,delta,measure_sweeps * num_sites)
-		magnetizations[i] = np.mean(phi_state)
-	mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10)
-	mean_magn.append([mean,err])
-
-output_filename = 'preliminary_simulation.h5'
-with h5py.File(output_filename,'a') as f:
-	if not "mean-magn" in f:
-		dataset = f.create_dataset("mean-magn", chunks=True, data=mean_magn)
-		# store some information as metadata for the data set
-		dataset.attrs["lamb"] = lamb
-		dataset.attrs["kappas"] = kappas
-		dataset.attrs["width"] = width
-		dataset.attrs["num_sites"] = num_sites
-		dataset.attrs["delta"] = delta
-		dataset.attrs["equil_sweeps"] = equil_sweeps
-		dataset.attrs["measure_sweeps"] = measure_sweeps
-		dataset.attrs["measurements"] = measurements
-		dataset.attrs["start time"] = starttime
-		dataset.attrs["stop time"] = time.asctime()
+if __name__ == "__main__":
+	main()