Updated tutorial1

tutorials/tutorial1/input.ini

@@ -13,14 +13,19 @@ n_tau_hyb=1000
 [update]
 swap_vector="1 0 3 2 5 4  1 2 3 4 5 0"
 
+# We measure the coefficients of the Green's function directly (from l=0 to n_legendre-1).
+# At the end of the simulation, we transform the data to G(tau) and G(iomega_n).
+# The parameters measurement.G1.n_tau and measurement.G1.n_matsubara only affect this postprocess.
 [measurement.G1]
 n_legendre=50
 n_tau=1000
 n_matsubara=500
 
-[measurement.two_time_G2]
-on=1
-n_legendre=50
-
-[measurement.equal_time_G2]
-on=1
+# If you measure many observables in a single simulation, thermalization may take very long.
+# Do not use the following options unless you understand the behavior of the worm sampling very well.
+# [measurement.two_time_G2]
+# on=1
+# n_legendre=50
+#
+# [measurement.equal_time_G2]
+# on=1

tutorials/tutorial1/plot.py

@@ -32,6 +32,8 @@ def load_g(path):
 
     r["Sign"] = h5['/simulation/results/Sign/mean/value'].value
 
+    r["Sign_count"] = h5['/simulation/results/Sign/count'].value
+
     #r["Equal_time_G1"] = h5['/EQUAL_TIME_G1'].value[:,:,0] + 1J*h5['/EQUAL_TIME_G1'].value[:,:,1]
 
     return r
@@ -74,6 +76,8 @@ def load_g(path):
     gomega_l = result_list[i]["Gomega"]
     #equal_time_G1 = result_list[i]["Equal_time_G1"]
 
+    print "The number of measurements is ", result_list[i]["Sign_count"]
+
     print "sign=",sign
     #occ = 0.0
     #for i_f in range(nf):
@@ -98,5 +102,5 @@ def load_g(path):
 plt.legend(loc='best',shadow=True,frameon=False,prop={'size' : 12})
 
 plt.tight_layout()
-plt.savefig("GF.pdf")
+plt.savefig("GF.eps")
 plt.close(1)