def calc():
with open("series.dat") as fdata:
series = pickle.load(fdata).T
averages = error.mean(series)
variances = error.variance(series, averages)
# jackknife stuff
jackknife_errors = np.array([error.jackknife_analysis(series, k, averages)
for k in range(1, 2001)])
# binning sutff
binning_results = np.array([error.binning_analysis(series, k, averages,
variances)
for k in range(1, 2001)])
binning_errors = binning_results[:,1,:]
# autocorrelation stuff
autocorrelation_results = error.autocorrelation_analysis(series, averages, variances)
autocorrelation_errors = autocorrelation_results[1]
np.save("jackknife.npy", jackknife_errors)
np.save("binning.npy", binning_results)
np.save("autocorrelation.npy", autocorrelation_results)
print "Binning:", binning_errors[-1]
print "Jackknife:", jackknife_errors[-1]
print "Autocorrelation:", autocorrelation_errors
def plot():
binning_results = np.load("binning.npy")
jackknife_errors = np.load("jackknife.npy")
plt.subplot(211)
plt.ylabel("$\epsilon^2$")
plt.plot(jackknife_errors.T[0], label = "1")
plt.plot(jackknife_errors.T[1], label = "2")
plt.plot(jackknife_errors.T[2], label = "3")
plt.plot(jackknife_errors.T[4], label = "5")
plt.legend(loc = "upper left")
binning_times = binning_results[:,0,:]
binning_errors = binning_results[:,1,:]
plt.subplot(212)
plt.xlabel("$k$")
plt.ylabel(r"$10^3\times\epsilon^2$")
plt.plot(1e3 * jackknife_errors.T[3], label = "4")
plt.legend(loc = "upper left")
plt.savefig("errors-jackknife.pdf")
plt.clf()
# plot last data set seperately because it's pretty small compared to the
# others
plt.subplot(211)
plt.ylabel("$\epsilon^2$")
plt.plot(binning_errors.T[0], label = "1")
plt.plot(binning_errors.T[1], label = "2")
plt.plot(binning_errors.T[2], label = "3")
plt.plot(binning_errors.T[4], label = "5")
plt.legend(loc = "upper left")
plt.subplot(212)
plt.xlabel("$k$")
plt.ylabel(r"$10^3\times\epsilon^2$")
plt.plot(1e3 * binning_errors.T[3], label = "4")
plt.legend(loc = "upper left")
plt.savefig("errors-binning.pdf")
plt.clf()
plt.subplot(211)
plt.ylabel(r"$\tau$")
plt.plot(binning_times.T[0], label = "1")
plt.plot(binning_times.T[1], label = "2")
plt.plot(binning_times.T[2], label = "3")
plt.plot(binning_times.T[4], label = "5")
plt.legend(loc = "upper left")
plt.subplot(212)
plt.xlabel("$k$")
plt.ylabel(r"$\tau$")
plt.plot(binning_times.T[3], label = "4")
plt.legend(loc = "upper left")
plt.savefig("blocking-tau.pdf")
plt.clf()
series = np.load("series.dat").T
n = series.shape[1]
N = 2000
mean = error.mean(series)
var = error.variance(series, mean)
# we could have saved this during the actual autocorrelation analysis
acf = np.array(
[[error.autocorrelation_function(series[:,i], k, mean[i], var[i])
for k in range(N)] for i in range(n)]
)
iat = np.array([[sum(acf[i,:k]) for k in range(N)] for i in range(n)])
plt.subplot(211)
plt.ylabel(r"$A(k)$")
for i, d in enumerate(acf):
plt.plot(d, label = "{}".format(i + 1))
plt.legend()
plt.subplot(212)
plt.xlabel("$k$")
plt.ylabel(r"$\tau(k)$")
for i, d in enumerate(iat):
plt.plot(d, label = "{}".format(i + 1))
plt.legend()
plt.savefig("autocorrelation.pdf")