09: Run copied code

Exercise sheet 9/latticescalar.py

@@ -1,7 +1,11 @@
 import numpy as np
+import argparse
+import time
+import h5py
+
+starttime = time.asctime()
+
 rng = np.random.default_rng()  
-import matplotlib.pylab as plt
-%matplotlib inline
 
 def potential_v(x,lamb):
 	'''Compute the potential function V(x).'''
@@ -49,9 +53,9 @@ kappas = np.linspace(0.08,0.18,11)
 width = 3
 num_sites = width**4
 delta = 1.5  # chosen to have ~ 50% acceptance
-equil_sweeps = 1000
+equil_sweeps = 10
 measure_sweeps = 2
-measurements = 2000
+measurements = 20
 
 mean_magn = []
 for kappa in kappas:
@@ -64,8 +68,18 @@ for kappa in kappas:
 	mean, err = batch_estimate(np.abs(magnetizations),lambda x:np.mean(x),10)
 	mean_magn.append([mean,err])
 
-plt.errorbar(kappas,[m[0] for m in mean_magn],yerr=[m[1] for m in mean_magn],fmt='-o')
+output_filename = 'preliminary_simulation.h5'
-plt.xlabel(r"$\kappa$")
+with h5py.File(output_filename,'a') as f:
-plt.ylabel(r"$|m|$")
+	if not "mean-magn" in f:
-plt.title(r"Absolute field average on $3^4$ lattice, $\lambda = 1.5$")
+		dataset = f.create_dataset("mean-magn", chunks=True, data=mean_magn)
-plt.show()
+		# 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()