from __future__ import print_function
import os
import sys
import time
import json
import pygsti
%pylab inline
#Load example quantities from files
gs_target = pygsti.io.load_gateset("tutorial_files/Example_Gateset.txt")
gs_mc2gst = pygsti.io.load_gateset("tutorial_files/Example_MC2GST_Gateset.txt")
ds = pygsti.io.load_dataset("tutorial_files/Example_Dataset.txt", cache=True)
fiducials = pygsti.io.load_gatestring_list("tutorial_files/Example_FiducialList.txt")
germs = pygsti.io.load_gatestring_list("tutorial_files/Example_GermsList.txt")
maxLengths = json.load(open("tutorial_files/Example_maxLengths.json","r"))
specs = pygsti.construction.build_spam_specs(fiducials)
Here we do parametric bootstrapping, as indicated by the 'parametric' argument below. The output is eventually stored in the "mean" and "std" GateSets, which hold the mean and standard deviation values of the set of bootstrapped gatesets (after gauge optimization). It is this latter "standard deviation Gateset" which holds the collection of error bars. Note: due to print setting issues, the outputs that are printed here will not necessarily reflect the true accuracy of the estimates made.
#The number of simulated datasets & gatesets made for bootstrapping purposes.
# For good statistics, should probably be greater than 10.
numGatesets=10
param_boot_gatesets = pygsti.drivers.make_bootstrap_gatesets(
numGatesets, ds, 'parametric', fiducials, fiducials, germs, maxLengths,
inputGateSet=gs_mc2gst, startSeed=0, constrainToTP=False, returnData=False,
verbosity=2)
gauge_opt_pboot_gatesets = pygsti.drivers.gauge_optimize_gs_list(param_boot_gatesets, gs_mc2gst,
constrainToTP=False, plot=True)
pboot_mean = pygsti.drivers.to_mean_gateset(gauge_opt_pboot_gatesets, gs_mc2gst)
pboot_std = pygsti.drivers.to_std_gateset(gauge_opt_pboot_gatesets, gs_mc2gst)
#Summary of the error bars
print("Parametric bootstrapped error bars, with", numGatesets, "resamples\n")
print("Error in rho vec:")
print(pboot_std['rho0'], end='\n\n')
print("Error in E vec:")
print(pboot_std['E0'], end='\n\n')
print("Error in Gi:")
print(pboot_std['Gi'], end='\n\n')
print("Error in Gx:")
print(pboot_std['Gx'], end='\n\n')
print("Error in Gy:")
print(pboot_std['Gy'])
Here we do non-parametric bootstrapping, as indicated by the 'nonparametric' argument below. The output is again eventually stored in the "mean" and "std" GateSets, which hold the mean and standard deviation values of the set of bootstrapped gatesets (after gauge optimization). It is this latter "standard deviation Gateset" which holds the collection of error bars. Note: due to print setting issues, the outputs that are printed here will not necessarily reflect the true accuracy of the estimates made.
(Technical note: ddof = 1 is by default used when computing the standard deviation -- see numpy.std -- meaning that we are computing a standard deviation of the sample, not of the population.)
#The number of simulated datasets & gatesets made for bootstrapping purposes.
# For good statistics, should probably be greater than 10.
numGatesets=10
nonparam_boot_gatesets = pygsti.drivers.make_bootstrap_gatesets(
numGatesets, ds, 'nonparametric', fiducials, fiducials, germs, maxLengths,
targetGateSet=gs_mc2gst, startSeed=0, constrainToTP=False, returnData=False,
verbosity=2)
gauge_opt_npboot_gatesets = pygsti.drivers.gauge_optimize_gs_list(nonparam_boot_gatesets, gs_mc2gst,
constrainToTP=False, plot=True)
npboot_mean = pygsti.drivers.to_mean_gateset(gauge_opt_npboot_gatesets, gs_mc2gst)
npboot_std = pygsti.drivers.to_std_gateset(gauge_opt_npboot_gatesets, gs_mc2gst)
#Summary of the error bars
print("Non-parametric bootstrapped error bars, with", numGatesets, "resamples\n")
print("Error in rho vec:")
print(npboot_std['rho0'], end='\n\n')
print("Error in E vec:")
print(npboot_std['E0'], end='\n\n')
print("Error in Gi:")
print(npboot_std['Gi'], end='\n\n')
print("Error in Gx:")
print(npboot_std['Gx'], end='\n\n')
print("Error in Gy:")
print(npboot_std['Gy'])
loglog(npboot_std.to_vector(),pboot_std.to_vector(),'.')
loglog(np.logspace(-4,-2,10),np.logspace(-4,-2,10),'--')
xlabel('Non-parametric')
ylabel('Parametric')
xlim((1e-4,1e-2)); ylim((1e-4,1e-2))
title('Scatter plot comparing param vs. non-param bootstrapping error bars.')