/
testGateSets.py
1501 lines (1215 loc) · 73.1 KB
/
testGateSets.py
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import logging
mpl_logger = logging.getLogger('matplotlib')
mpl_logger.setLevel(logging.WARNING)
import unittest
import itertools
import collections
import pygsti
import numpy as np
import warnings
import pickle
import os
from ..testutils import BaseTestCase, compare_files, temp_files
#from pygsti.objects.mapforwardsim import MapForwardSimulator
#Note: calcs expect tuples (or Circuits) of *Labels*
from pygsti.objects import Label as L
from pygsti.construction import std1Q_XYI
def Ls(*args):
""" Convert args to a tuple to Labels """
return tuple([L(x) for x in args])
FD_JAC_PLACES = 5 # loose checking when computing finite difference derivatives (currently in map calcs)
FD_HESS_PLACES = 1 # looser checking when computing finite difference hessians (currently in map calcs)
# This class is for unifying some models that get used in this file and in testGateSets2.py
class GateSetTestCase(BaseTestCase):
def setUp(self):
super(GateSetTestCase, self).setUp()
#OK for these tests, since we test user interface?
#Set Model objects to "strict" mode for testing
pygsti.objects.ExplicitOpModel._strict = False
self.model = pygsti.construction.build_explicit_model(
[('Q0',)],['Gi','Gx','Gy'],
[ "I(Q0)","X(pi/8,Q0)", "Y(pi/8,Q0)"])
self.tp_gateset = pygsti.construction.build_explicit_model(
[('Q0',)],['Gi','Gx','Gy'],
[ "I(Q0)","X(pi/8,Q0)", "Y(pi/8,Q0)"],
parameterization="TP")
self.static_gateset = pygsti.construction.build_explicit_model(
[('Q0',)],['Gi','Gx','Gy'],
[ "I(Q0)","X(pi/8,Q0)", "Y(pi/8,Q0)"],
parameterization="static")
self.mgateset = self.model.copy()
#self.mgateset._calcClass = MapForwardSimulator
self.mgateset.set_simtype('map')
class TestGateSetMethods(GateSetTestCase):
def test_creation(self):
self.assertIsInstance(self.model, pygsti.objects.Model)
def test_pickling(self):
p = pickle.dumps(self.model.preps)
preps = pickle.loads(p)
self.assertEqual(list(preps.keys()), list(self.model.preps.keys()))
p = pickle.dumps(self.model.povms)
povms = pickle.loads(p)
self.assertEqual(list(povms.keys()), list(self.model.povms.keys()))
p = pickle.dumps(self.model.operations)
gates = pickle.loads(p)
self.assertEqual(list(gates.keys()), list(self.model.operations.keys()))
self.model._clean_paramvec()
print("BEFORE: ",self.model.dirty, self.model.povms['Mdefault'].dirty)
p = pickle.dumps(self.model)
g = pickle.loads(p)
print("AFTER ",g.dirty, g.povms['Mdefault'].dirty)
g._clean_paramvec()
self.assertAlmostEqual(self.model.frobeniusdist(g), 0.0)
def test_counting(self):
self.assertEqual( len(self.model.preps), 1)
self.assertEqual( len(self.model.povms['Mdefault']), 2)
for default_param in ("full","TP","static"):
print("Case: default_param = ",default_param)
nOperations = 3 if default_param in ("full","TP") else 0
nSPVecs = 1 if default_param in ("full","TP") else 0
if default_param == "full": nEVecs = 2
elif default_param == "TP": nEVecs = 1 #complement doesn't add params
else: nEVecs = 0
nParamsPerGate = 16 if default_param == "full" else 12
nParamsPerSP = 4 if default_param == "full" else 3
nParams = nOperations * nParamsPerGate + nSPVecs * nParamsPerSP + nEVecs * 4
self.model.set_all_parameterizations(default_param)
for lbl,obj in self.model.preps.items():
print(lbl,':',obj.gpindices)
for lbl,obj in self.model.povms.items():
print(lbl,':',obj.gpindices)
for lbl,obj in self.model.operations.items():
print(lbl,':',obj.gpindices)
print("NPARAMS = ",self.model.num_params())
self.assertEqual(self.model.num_params(), nParams)
self.assertEqual(list(self.model.preps.keys()), ["rho0"])
self.assertEqual(list(self.model.povms.keys()), ["Mdefault"])
def test_getset_full(self):
self.getset_helper(self.model)
def test_getset_tp(self):
self.getset_helper(self.tp_gateset)
def test_getset_static(self):
self.getset_helper(self.static_gateset)
def getset_helper(self, mdl):
#Test default prep/effects
default_prep = mdl.prep
self.assertArraysAlmostEqual(default_prep,mdl.preps["rho0"])
default_povm = mdl.effects
assert(set(default_povm.keys()) == set(['0','1']))
v = np.array( [[1.0/np.sqrt(2)],[0],[0],[1.0/np.sqrt(2)]], 'd')
#mdl['identity'] = v
#w = mdl['identity']
#self.assertArraysAlmostEqual(w,v)
mdl['rho1'] = v
w = mdl['rho1']
self.assertArraysAlmostEqual(w,v)
# Can't just assign to POVM...
#mdl['Mdefault']['2'] = v
#w = mdl['Mdefault']['2']
#self.assertArraysAlmostEqual(w,v)
Gi_matrix = np.identity(4, 'd')
self.assertTrue( isinstance(mdl['Gi'], pygsti.objects.LinearOperator) )
Gi_test_matrix = np.random.random( (4,4) )
Gi_test_matrix[0,:] = [1,0,0,0] # so TP mode works
Gi_test = pygsti.objects.FullDenseOp( Gi_test_matrix )
print("POINT 1")
try:
mdl["Gi"] = Gi_test_matrix #set operation matrix
except ValueError:
pass # can't always set via matrix - e.g. doesn't work for *static* case
print("POINT 2")
mdl["Gi"] = Gi_test #set gate object
self.assertArraysAlmostEqual( mdl['Gi'], Gi_test_matrix )
print("DEL")
del mdl.preps['rho1']
with self.assertRaises(KeyError):
mdl.preps['foobar'] = [1.0/np.sqrt(2),0,0,0] #bad key prefix
with self.assertRaises(KeyError):
print("COPYING")
gs2 = mdl.copy()
error = gs2.povms['foobar']
Iz = pygsti.obj.Instrument( [('0', np.random.random( (4,4) ))] )
mdl["Iz"] = Iz #set an instrument
Iz2 = mdl["Iz"] #get an instrument
deriv = mdl.deriv_wrt_params()
def test_copy(self):
cp = self.model.copy()
self.assertAlmostEqual( self.model.frobeniusdist(cp), 0 )
self.assertAlmostEqual( self.model.jtracedist(cp), 0 )
self.assertAlmostEqual( self.model.diamonddist(cp), 0 )
def test_vectorize(self):
cp = self.model.copy()
v = cp.to_vector()
cp.from_vector(v)
self.assertAlmostEqual( self.model.frobeniusdist(cp), 0 )
def test_transform(self):
T = np.array([[ 0.36862036, 0.49241519, 0.35903944, 0.90069522],
[ 0.12347698, 0.45060548, 0.61671491, 0.64854769],
[ 0.4038386 , 0.89518315, 0.20206879, 0.6484708 ],
[ 0.44878029, 0.42095514, 0.27645424, 0.41766033]]) #some random array
Tinv = np.linalg.inv(T)
elT = pygsti.objects.FullGaugeGroupElement(T)
cp = self.model.copy()
cp.set_all_parameterizations('full') # so POVM can be transformed...
cp.transform(elT)
self.assertAlmostEqual( self.model.frobeniusdist(cp, T, normalize=False), 0 ) #test out normalize=False
self.assertAlmostEqual( self.model.jtracedist(cp, T), 0 )
self.assertAlmostEqual( self.model.diamonddist(cp, T), 0 )
for opLabel in cp.operations:
self.assertArraysAlmostEqual(cp[opLabel], np.dot(Tinv, np.dot(self.model[opLabel], T)))
for prepLabel in cp.preps:
self.assertArraysAlmostEqual(cp[prepLabel], np.dot(Tinv, self.model[prepLabel]))
for povmLabel in cp.povms:
for effectLabel,eVec in cp.povms[povmLabel].items():
self.assertArraysAlmostEqual(eVec, np.dot(np.transpose(T), self.model.povms[povmLabel][effectLabel]))
def test_simple_multiplicationA(self):
circuit = ('Gx','Gy')
p1 = np.dot( self.model['Gy'], self.model['Gx'] )
p2 = self.model.product(circuit, bScale=False)
p3,scale = self.model.product(circuit, bScale=True)
self.assertArraysAlmostEqual(p1,p2)
self.assertArraysAlmostEqual(p1,scale*p3)
#Artificially reset the "smallness" threshold for scaling to be
# sure to engate the scaling machinery
PORIG = pygsti.objects.matrixforwardsim.PSMALL; pygsti.objects.matrixforwardsim.PSMALL = 10
p4,scale = self.model.product(circuit, bScale=True)
pygsti.objects.matrixforwardsim.PSMALL = PORIG
self.assertArraysAlmostEqual(p1,scale*p4)
dp = self.model.dproduct(circuit)
dp_flat = self.model.dproduct(circuit,flat=True)
def test_simple_multiplicationB(self):
circuit = ('Gx','Gy','Gy')
p1 = np.dot( self.model['Gy'], np.dot( self.model['Gy'], self.model['Gx'] ))
p2 = self.model.product(circuit, bScale=False)
p3,scale = self.model.product(circuit, bScale=True)
self.assertArraysAlmostEqual(p1,p2)
self.assertArraysAlmostEqual(p1,scale*p3)
#Artificially reset the "smallness" threshold for scaling to be
# sure to engate the scaling machinery
PORIG = pygsti.objects.matrixforwardsim.PSMALL; pygsti.objects.matrixforwardsim.PSMALL = 10
p4,scale = self.model.product(circuit, bScale=True)
pygsti.objects.matrixforwardsim.PSMALL = PORIG
self.assertArraysAlmostEqual(p1,scale*p4)
def test_bulk_multiplication(self):
gatestring1 = ('Gx','Gy')
gatestring2 = ('Gx','Gy','Gy')
evt,lookup,outcome_lookup = self.model.bulk_evaltree( [gatestring1,gatestring2] )
p1 = np.dot( self.model['Gy'], self.model['Gx'] )
p2 = np.dot( self.model['Gy'], np.dot( self.model['Gy'], self.model['Gx'] ))
bulk_prods = self.model.bulk_product(evt)
bulk_prods_scaled, scaleVals = self.model.bulk_product(evt, bScale=True)
bulk_prods2 = scaleVals[:,None,None] * bulk_prods_scaled
self.assertArraysAlmostEqual(bulk_prods[ 0 ],p1)
self.assertArraysAlmostEqual(bulk_prods[ 1 ],p2)
self.assertArraysAlmostEqual(bulk_prods2[ 0 ],p1)
self.assertArraysAlmostEqual(bulk_prods2[ 1 ],p2)
#Artificially reset the "smallness" threshold for scaling to be
# sure to engate the scaling machinery
PORIG = pygsti.objects.matrixforwardsim.PSMALL; pygsti.objects.matrixforwardsim.PSMALL = 10
bulk_prods_scaled, scaleVals3 = self.model.bulk_product(evt, bScale=True)
bulk_prods3 = scaleVals3[:,None,None] * bulk_prods_scaled
pygsti.objects.matrixforwardsim.PSMALL = PORIG
self.assertArraysAlmostEqual(bulk_prods3[0],p1)
self.assertArraysAlmostEqual(bulk_prods3[1],p2)
#tag on a few extra EvalTree tests
debug_stuff = evt.get_analysis_plot_infos()
def test_simple_probabilityA(self):
circuit = ('Gx','Gy')
p0a = np.dot( np.transpose(self.model.povms['Mdefault']['0']),
np.dot( self.model['Gy'],
np.dot(self.model['Gx'],
self.model.preps['rho0'])))
probs = self.model.probs(circuit)
p0b,p1b = probs[('0',)], probs[('1',)]
self.assertArraysAlmostEqual(p0a,p0b)
self.assertArraysAlmostEqual(1.0-p0a,p1b)
dprobs = self.model.dprobs(circuit)
dprobs2 = self.model.dprobs(circuit,returnPr=True)
self.assertArraysAlmostEqual(dprobs[('0',)],dprobs2[('0',)][0])
self.assertArraysAlmostEqual(dprobs[('1',)],dprobs2[('1',)][0])
#Compare with map-based computation
mprobs = self.mgateset.probs(circuit)
mp0b,mp1b = mprobs[('0',)], mprobs[('1',)]
self.assertArraysAlmostEqual(p0b,mp0b)
self.assertArraysAlmostEqual(p1b,mp1b)
mdprobs = self.mgateset.dprobs(circuit)
mdprobs2 = self.mgateset.dprobs(circuit,returnPr=True)
self.assertArraysAlmostEqual(dprobs[('0',)],mdprobs[('0',)])
self.assertArraysAlmostEqual(dprobs[('1',)],mdprobs[('1',)])
self.assertArraysAlmostEqual(dprobs[('0',)],mdprobs2[('0',)][0])
self.assertArraysAlmostEqual(dprobs[('1',)],mdprobs2[('1',)][0])
def test_simple_probabilityB(self):
circuit = ('Gx','Gy','Gy')
p1 = np.dot( np.transpose(self.model.povms['Mdefault']['0']),
np.dot( self.model['Gy'],
np.dot( self.model['Gy'],
np.dot(self.model['Gx'],
self.model.preps['rho0']))))
p2 = self.model.probs(circuit)[('0',)]
self.assertSingleElemArrayAlmostEqual(p1, p2)
gateset_with_nan = self.model.copy()
gateset_with_nan['rho0'][:] = np.nan
gateset_with_nan.to_vector()
self.assertWarns(gateset_with_nan.probs,circuit)
self.assertWarns(gateset_with_nan.probs,circuit*5) # long circuit: warning uses elipsis
mgateset_with_nan = self.mgateset.copy()
mgateset_with_nan['rho0'][:] = np.nan
self.assertWarns(mgateset_with_nan.probs,circuit)
self.assertWarns(mgateset_with_nan.probs,circuit*5) # long circuit: warning uses elipsis
def test_bulk_probabilities(self):
gatestring1 = pygsti.obj.Circuit(('Gx','Gy'))
gatestring2 = pygsti.obj.Circuit(('Gx','Gy','Gy'))
evt,lookup,outcome_lookup = self.model.bulk_evaltree( [gatestring1,gatestring2] )
mevt,mlookup,moutcome_lookup = self.mgateset.bulk_evaltree( [gatestring1,gatestring2] )
p1 = np.dot( np.transpose(self.model.povms['Mdefault']['0']),
np.dot( self.model['Gy'],
np.dot(self.model['Gx'],
self.model.preps['rho0'])))
p2 = np.dot( np.transpose(self.model.povms['Mdefault']['0']),
np.dot( self.model['Gy'],
np.dot( self.model['Gy'],
np.dot(self.model['Gx'],
self.model.preps['rho0']))))
#bulk_pr removed
##check == true could raise a warning if a mismatch is detected
#bulk_pr = self.assertNoWarnings(self.model.bulk_pr,'0',evt,check=True)
#bulk_pr_m = self.assertNoWarnings(self.model.bulk_pr,'1',evt,check=True)
#mbulk_pr = self.assertNoWarnings(self.mgateset.bulk_pr,'0',mevt,check=True)
#mbulk_pr_m = self.assertNoWarnings(self.mgateset.bulk_pr,'1',mevt,check=True)
#self.assertSingleElemArrayAlmostEqual(p1, bulk_pr[0])
#self.assertSingleElemArrayAlmostEqual(p2, bulk_pr[1])
#self.assertSingleElemArrayAlmostEqual(1.0 - p1, bulk_pr_m[0])
#self.assertSingleElemArrayAlmostEqual(1.0 - p2, bulk_pr_m[1])
#self.assertSingleElemArrayAlmostEqual(p1, mbulk_pr[0])
#self.assertSingleElemArrayAlmostEqual(p2, mbulk_pr[1])
#self.assertSingleElemArrayAlmostEqual(1.0 - p1, mbulk_pr_m[0])
#self.assertSingleElemArrayAlmostEqual(1.0 - p2, mbulk_pr_m[1])
#non-bulk probabilities (again?)
probs1 = self.model.probs(gatestring1)
probs2 = self.model.probs(gatestring2)
mprobs1 = self.mgateset.probs(gatestring1)
mprobs2 = self.mgateset.probs(gatestring2)
self.assertSingleElemArrayAlmostEqual(p1, probs1[('0',)])
self.assertSingleElemArrayAlmostEqual(p2, probs2[('0',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p1, probs1[('1',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p2, probs2[('1',)])
self.assertSingleElemArrayAlmostEqual(p1, mprobs1[('0',)])
self.assertSingleElemArrayAlmostEqual(p2, mprobs2[('0',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p1, mprobs1[('1',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p2, mprobs2[('1',)])
#bulk_probs
bulk_probs = self.assertNoWarnings(self.model.bulk_probs,[gatestring1,gatestring2],check=True)
mbulk_probs = self.assertNoWarnings(self.mgateset.bulk_probs,[gatestring1,gatestring2],check=True)
self.assertSingleElemArrayAlmostEqual(p1, bulk_probs[gatestring1][('0',)])
self.assertSingleElemArrayAlmostEqual(p2, bulk_probs[gatestring2][('0',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p1, bulk_probs[gatestring1][('1',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p2, bulk_probs[gatestring2][('1',)])
self.assertSingleElemArrayAlmostEqual(p1, mbulk_probs[gatestring1][('0',)])
self.assertSingleElemArrayAlmostEqual(p2, mbulk_probs[gatestring2][('0',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p1, mbulk_probs[gatestring1][('1',)])
self.assertSingleElemArrayAlmostEqual(1.0 - p2, mbulk_probs[gatestring2][('1',)])
def elIndx(iOpStr, outcome):
inds = pygsti.tools.indices(lookup[iOpStr]) if isinstance(lookup[iOpStr],slice) \
else lookup[iOpStr] #an index array
return inds[ outcome_lookup[iOpStr].index( outcome ) ]
def melIndx(iOpStr, outcome):
inds = pygsti.tools.indices(mlookup[iOpStr]) if isinstance(mlookup[iOpStr],slice) \
else mlookup[iOpStr] #an index array
return inds[ moutcome_lookup[iOpStr].index( outcome ) ]
nElements = evt.num_final_elements()
probs_to_fill = np.empty( nElements, 'd')
mprobs_to_fill = np.empty( nElements, 'd')
self.assertNoWarnings(self.model.bulk_fill_probs, probs_to_fill, evt, check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_probs, mprobs_to_fill, mevt, check=True)
self.assertSingleElemArrayAlmostEqual(p1, probs_to_fill[ elIndx(0, ('0',)) ])
self.assertSingleElemArrayAlmostEqual(p2, probs_to_fill[ elIndx(1, ('0',))])
self.assertSingleElemArrayAlmostEqual(1-p1, probs_to_fill[ elIndx(0, ('1',))])
self.assertSingleElemArrayAlmostEqual(1-p2, probs_to_fill[ elIndx(1, ('1',))])
self.assertSingleElemArrayAlmostEqual(p1, mprobs_to_fill[ melIndx(0, ('0',))])
self.assertSingleElemArrayAlmostEqual(p2, mprobs_to_fill[ melIndx(1, ('0',))])
self.assertSingleElemArrayAlmostEqual(1-p1, mprobs_to_fill[ melIndx(0, ('1',))])
self.assertSingleElemArrayAlmostEqual(1-p2, mprobs_to_fill[ melIndx(1, ('1',))])
#test with split eval tree
evt_split = evt.copy(); lookup_splt = evt_split.split(lookup,numSubTrees=2)
mevt_split = mevt.copy(); mlookup_splt = mevt_split.split(mlookup,numSubTrees=2)
probs_to_fill_splt = np.empty( nElements, 'd')
mprobs_to_fill_splt = np.empty( nElements, 'd')
bulk_probs_splt = self.assertNoWarnings(self.model.bulk_fill_probs,
probs_to_fill_splt, evt_split, check=True)
mbulk_probs_splt = self.assertNoWarnings(self.mgateset.bulk_fill_probs,
mprobs_to_fill_splt, mevt_split, check=True)
evt_split.print_analysis()
mevt_split.print_analysis()
#Note: Outcome labels stay in same order across tree splits (i.e.
# evalTree.split() doesn't need to update outcome_lookup)
for i,opstr in enumerate([gatestring1,gatestring2]): #original operation sequences
self.assertArraysAlmostEqual(probs_to_fill[ lookup[i] ],
probs_to_fill_splt[ lookup_splt[i] ])
self.assertArraysAlmostEqual(mprobs_to_fill[ mlookup[i] ],
mprobs_to_fill_splt[ mlookup_splt[i] ])
#Also check map vs matrix fills:
assert(outcome_lookup[i] == moutcome_lookup[i]) # should stay in same ordering... I think
self.assertArraysAlmostEqual(probs_to_fill[ lookup[i] ],
mprobs_to_fill[ mlookup[i] ], places=FD_JAC_PLACES)
prods = self.model.bulk_product(evt) #TODO: test output?
def test_derivatives(self):
gatestring0 = pygsti.obj.Circuit(('Gi',)) #,'Gx'
gatestring1 = pygsti.obj.Circuit(('Gx','Gy'))
gatestring2 = pygsti.obj.Circuit(('Gx','Gy','Gy'))
circuitList = [gatestring0,gatestring1,gatestring2]
evt,lookup,outcome_lookup = self.model.bulk_evaltree( circuitList )
mevt,mlookup,moutcome_lookup = self.mgateset.bulk_evaltree( circuitList )
dP0 = self.model.dprobs(gatestring0)[('0',)]
dP1 = self.model.dprobs(gatestring1)[('0',)]
dP2 = self.model.dprobs(gatestring2)[('0',)]
dP0m = self.model.dprobs(gatestring0)[('1',)]
dP1m = self.model.dprobs(gatestring1)[('1',)]
dP2m = self.model.dprobs(gatestring2)[('1',)]
#Removed bulk_dpr
#bulk_dP = self.model.bulk_dpr('0', evt, returnPr=False, check=True)
#bulk_dP_m = self.model.bulk_dpr('1', evt, returnPr=False, check=True)
#bulk_dP_chk, bulk_P = self.model.bulk_dpr('0', evt, returnPr=True, check=False)
#bulk_dP_m_chk, bulk_Pm = self.model.bulk_dpr('1', evt, returnPr=True, check=False)
#
#mbulk_dP = self.mgateset.bulk_dpr('0', mevt, returnPr=False, check=True)
#mbulk_dP_m = self.mgateset.bulk_dpr('1', mevt, returnPr=False, check=True)
#mbulk_dP_chk, mbulk_P = self.mgateset.bulk_dpr('0', mevt, returnPr=True, check=False)
#mbulk_dP_m_chk, mbulk_Pm = self.mgateset.bulk_dpr('1', mevt, returnPr=True, check=False)
#
#self.assertArraysAlmostEqual(bulk_dP,bulk_dP_chk)
#self.assertArraysAlmostEqual(bulk_dP[0,:],dP0)
#self.assertArraysAlmostEqual(bulk_dP[1,:],dP1)
#self.assertArraysAlmostEqual(bulk_dP[2,:],dP2)
#self.assertArraysAlmostEqual(bulk_dP_m,bulk_dP_m_chk)
#self.assertArraysAlmostEqual(bulk_dP_m[0,:],dP0m)
#self.assertArraysAlmostEqual(bulk_dP_m[1,:],dP1m)
#self.assertArraysAlmostEqual(bulk_dP_m[2,:],dP2m)
#
#self.assertArraysAlmostEqual(mbulk_dP,mbulk_dP_chk, places=FD_JAC_PLACES) #relax tolerance for
#self.assertArraysAlmostEqual(mbulk_dP[0,:],dP0, places=FD_JAC_PLACES) # finite diff derivs...
#self.assertArraysAlmostEqual(mbulk_dP[1,:],dP1, places=FD_JAC_PLACES)
#self.assertArraysAlmostEqual(mbulk_dP[2,:],dP2, places=FD_JAC_PLACES)
#self.assertArraysAlmostEqual(mbulk_dP_m,mbulk_dP_m_chk, places=FD_JAC_PLACES)
#self.assertArraysAlmostEqual(mbulk_dP_m[0,:],dP0m, places=FD_JAC_PLACES)
#self.assertArraysAlmostEqual(mbulk_dP_m[1,:],dP1m, places=FD_JAC_PLACES)
#self.assertArraysAlmostEqual(mbulk_dP_m[2,:],dP2m, places=FD_JAC_PLACES)
dProbs0 = self.model.dprobs(gatestring0)
dProbs1 = self.model.dprobs(gatestring1)
dProbs2 = self.model.dprobs(gatestring2)
mdProbs0 = self.mgateset.dprobs(gatestring0)
mdProbs1 = self.mgateset.dprobs(gatestring1)
mdProbs2 = self.mgateset.dprobs(gatestring2)
dProbs0b = self.model.dprobs(gatestring0, returnPr=True)
mdProbs0b = self.mgateset.dprobs(gatestring0, returnPr=True)
self.assertArraysAlmostEqual(dProbs0[('0',)], dP0)
self.assertArraysAlmostEqual(dProbs1[('0',)], dP1)
self.assertArraysAlmostEqual(dProbs2[('0',)], dP2)
self.assertArraysAlmostEqual(mdProbs0[('0',)], dP0, places=FD_JAC_PLACES)
self.assertArraysAlmostEqual(mdProbs1[('0',)], dP1, places=FD_JAC_PLACES)
self.assertArraysAlmostEqual(mdProbs2[('0',)], dP2, places=FD_JAC_PLACES)
bulk_dProbs = self.assertNoWarnings(self.model.bulk_dprobs,
circuitList, returnPr=False, check=True)
bulk_dProbs_chk = self.assertNoWarnings(self.model.bulk_dprobs,
circuitList, returnPr=True, check=True)
mbulk_dProbs = self.assertNoWarnings(self.mgateset.bulk_dprobs,
circuitList, returnPr=False, check=True)
mbulk_dProbs_chk = self.assertNoWarnings(self.mgateset.bulk_dprobs,
circuitList, returnPr=True, check=True)
for opstr in circuitList:
for outLbl in bulk_dProbs[opstr]:
self.assertArraysAlmostEqual(bulk_dProbs[opstr][outLbl],
bulk_dProbs_chk[opstr][outLbl][0]) #[0] b/c _chk also contains probs
self.assertArraysAlmostEqual(mbulk_dProbs[opstr][outLbl],
mbulk_dProbs_chk[opstr][outLbl][0]) #[0] b/c _chk also contains probs
self.assertArraysAlmostEqual(bulk_dProbs[opstr][outLbl],
mbulk_dProbs[opstr][outLbl], places=FD_JAC_PLACES) # map vs. matrix
self.assertArraysAlmostEqual(bulk_dProbs[gatestring0][('0',)],dP0)
self.assertArraysAlmostEqual(bulk_dProbs[gatestring1][('0',)],dP1)
self.assertArraysAlmostEqual(bulk_dProbs[gatestring2][('0',)],dP2)
self.assertArraysAlmostEqual(mbulk_dProbs[gatestring0][('0',)],mdProbs0[('0',)])
self.assertArraysAlmostEqual(mbulk_dProbs[gatestring1][('0',)],mdProbs1[('0',)])
self.assertArraysAlmostEqual(mbulk_dProbs[gatestring2][('0',)],mdProbs2[('0',)])
nElements = evt.num_final_elements(); nParams = self.model.num_params()
probs_to_fill = np.empty( nElements, 'd')
dprobs_to_fill = np.empty( (nElements,nParams), 'd')
dprobs_to_fillB = np.empty( (nElements,nParams), 'd')
mprobs_to_fill = np.empty( nElements, 'd')
mdprobs_to_fill = np.empty( (nElements,nParams), 'd')
mdprobs_to_fillB = np.empty( (nElements,nParams), 'd')
spam_label_rows = { '0': 0, '1': 1 }
self.assertNoWarnings(self.model.bulk_fill_dprobs, dprobs_to_fill, evt,
prMxToFill=probs_to_fill,check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_dprobs, mdprobs_to_fill, mevt,
prMxToFill=mprobs_to_fill,check=True)
def elIndx(iOpStr, outcome):
inds = pygsti.tools.indices(lookup[iOpStr]) if isinstance(lookup[iOpStr],slice) \
else lookup[iOpStr] #an index array
return inds[ outcome_lookup[iOpStr].index( outcome ) ]
def melIndx(iOpStr, outcome):
inds = pygsti.tools.indices(mlookup[iOpStr]) if isinstance(mlookup[iOpStr],slice) \
else mlookup[iOpStr] #an index array
return inds[ moutcome_lookup[iOpStr].index( outcome ) ]
self.assertArraysAlmostEqual(dprobs_to_fill[elIndx(0,('0',)),:],dP0)
self.assertArraysAlmostEqual(dprobs_to_fill[elIndx(1,('0',)),:],dP1)
self.assertArraysAlmostEqual(dprobs_to_fill[elIndx(2,('0',)),:],dP2)
self.assertArraysAlmostEqual(mdprobs_to_fill[melIndx(0,('0',)),:],dP0, places=FD_JAC_PLACES)
self.assertArraysAlmostEqual(mdprobs_to_fill[melIndx(1,('0',)),:],dP1, places=FD_JAC_PLACES)
self.assertArraysAlmostEqual(mdprobs_to_fill[melIndx(2,('0',)),:],dP2, places=FD_JAC_PLACES)
#without probs
self.assertNoWarnings(self.model.bulk_fill_dprobs, dprobs_to_fillB, evt, check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_dprobs, mdprobs_to_fillB, mevt, check=True)
self.assertArraysAlmostEqual(dprobs_to_fill,dprobs_to_fillB)
self.assertArraysAlmostEqual(mdprobs_to_fill,mdprobs_to_fillB, places=FD_JAC_PLACES)
#Artificially reset the "smallness" threshold for scaling
# to be sure to engate the scaling machinery
PORIG = pygsti.objects.matrixforwardsim.PSMALL; pygsti.objects.matrixforwardsim.PSMALL = 10
DORIG = pygsti.objects.matrixforwardsim.DSMALL; pygsti.objects.matrixforwardsim.DSMALL = 10
self.model.bulk_fill_dprobs(dprobs_to_fillB, evt, check=True)
self.assertArraysAlmostEqual(dprobs_to_fill,dprobs_to_fillB)
pygsti.objects.matrixforwardsim.PSMALL = PORIG
self.model.bulk_fill_dprobs(dprobs_to_fillB, evt, check=True)
self.assertArraysAlmostEqual(dprobs_to_fill,dprobs_to_fillB)
pygsti.objects.matrixforwardsim.DSMALL = DORIG
#test with split eval tree
evt_split = evt.copy(); lookup_splt = evt_split.split(lookup,numSubTrees=2)
mevt_split = mevt.copy(); mlookup_splt = mevt_split.split(mlookup,numSubTrees=2)
dprobs_to_fill_splt = np.empty( (nElements,nParams), 'd')
mdprobs_to_fill_splt = np.empty( (nElements,nParams), 'd')
self.assertNoWarnings(self.model.bulk_fill_dprobs, dprobs_to_fill_splt, evt_split,
prMxToFill=None,check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_dprobs, mdprobs_to_fill_splt, mevt_split,
prMxToFill=None,check=True)
#Note: Outcome labels stay in same order across tree splits (i.e.
# evalTree.split() doesn't need to update outcome_lookup)
for i,opstr in enumerate(circuitList): #original operation sequences
print("Circuit %d: comparing " % i,lookup[i]," to ", lookup_splt[i])
self.assertArraysAlmostEqual(dprobs_to_fill[ lookup[i] ],
dprobs_to_fill_splt[ lookup_splt[i] ])
print("Circuit %d: comparing " % i,mlookup[i]," to ", mlookup_splt[i])
self.assertArraysAlmostEqual(mdprobs_to_fill[ mlookup[i] ],
mdprobs_to_fill_splt[ mlookup_splt[i] ])
#Also check map vs matrix fills:
assert(outcome_lookup[i] == moutcome_lookup[i]) # should stay in same ordering... I think
self.assertArraysAlmostEqual(dprobs_to_fill[ lookup[i] ],
mdprobs_to_fill[ mlookup[i] ], places=FD_JAC_PLACES)
dProds = self.model.bulk_dproduct(evt) #TODO: test output?
with self.assertRaises(AttributeError): #NotImplementedError):
self.mgateset.bulk_dproduct(mevt) # map-based computation doesn't compute "products"
def test_hessians(self):
gatestring0 = pygsti.obj.Circuit(('Gi','Gx'))
gatestring1 = pygsti.obj.Circuit(('Gx','Gy'))
gatestring2 = pygsti.obj.Circuit(('Gx','Gy','Gy'))
circuitList = pygsti.construction.circuit_list([gatestring0,gatestring1,gatestring2])
evt,lookup,outcome_lookup = self.model.bulk_evaltree( [gatestring0,gatestring1,gatestring2] )
mevt,mlookup,moutcome_lookup = self.mgateset.bulk_evaltree( [gatestring0,gatestring1,gatestring2] )
hP0 = self.model.hprobs(gatestring0)[('0',)]
hP1 = self.model.hprobs(gatestring1)[('0',)]
hP2 = self.model.hprobs(gatestring2)[('0',)]
hP0m = self.model.hprobs(gatestring0)[('1',)]
hP1m = self.model.hprobs(gatestring1)[('1',)]
hP2m = self.model.hprobs(gatestring2)[('1',)]
hP0b,P0 = self.model.hprobs(gatestring0, returnPr=True)[('0',)]
hP0b,dP0 = self.model.hprobs(gatestring0, returnDeriv=True)[('0',)]
hP0mb,P0m = self.model.hprobs(gatestring0, returnPr=True)[('1',)]
hP0mb,dP0m = self.model.hprobs(gatestring0, returnDeriv=True)[('1',)]
#Removed bulk_hpr
#bulk_hP = self.model.bulk_hpr('0', evt, returnPr=False, returnDeriv=False, check=True)
#bulk_hP_m = self.model.bulk_hpr('1', evt, returnPr=False, returnDeriv=False, check=True)
#bulk_hP_chk, bulk_dP, bulk_P = self.model.bulk_hpr('0', evt, returnPr=True, returnDeriv=True, check=False)
#bulk_hP_m_chk, bulk_dP_m, bulk_P_m = self.model.bulk_hpr('1', evt, returnPr=True, returnDeriv=True, check=False)
#
#mbulk_hP = self.mgateset.bulk_hpr('0', mevt, returnPr=False, returnDeriv=False, check=True)
#mbulk_hP_m = self.mgateset.bulk_hpr('1', mevt, returnPr=False, returnDeriv=False, check=True)
#mbulk_hP_chk, mbulk_dP, mbulk_P = self.mgateset.bulk_hpr('0', mevt, returnPr=True, returnDeriv=True, check=False)
#mbulk_hP_m_chk, mbulk_dP_m, mbulk_P_m = self.mgateset.bulk_hpr('1', mevt, returnPr=True, returnDeriv=True, check=False)
#
#self.assertArraysAlmostEqual(bulk_hP,bulk_hP_chk)
#self.assertArraysAlmostEqual(bulk_hP[0,:,:],hP0)
#self.assertArraysAlmostEqual(bulk_hP[1,:,:],hP1)
#self.assertArraysAlmostEqual(bulk_hP[2,:,:],hP2)
#self.assertArraysAlmostEqual(bulk_hP_m,bulk_hP_m_chk)
#self.assertArraysAlmostEqual(bulk_hP_m[0,:,:],hP0m)
#self.assertArraysAlmostEqual(bulk_hP_m[1,:,:],hP1m)
#self.assertArraysAlmostEqual(bulk_hP_m[2,:,:],hP2m)
#
#self.assertArraysAlmostEqual(mbulk_hP,mbulk_hP_chk, places=FD_HESS_PLACES)
#print("DB: hP0 = ",hP0)
#print("DB: mhP0 = ",mbulk_hP[0,:,:])
#self.assertArraysAlmostEqual(mbulk_hP[0,:,:],hP0, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mbulk_hP[1,:,:],hP1, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mbulk_hP[2,:,:],hP2, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mbulk_hP_m,mbulk_hP_m_chk, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mbulk_hP_m[0,:,:],hP0m, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mbulk_hP_m[1,:,:],hP1m, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mbulk_hP_m[2,:,:],hP2m, places=FD_HESS_PLACES)
hProbs0 = self.model.hprobs(gatestring0)
hProbs1 = self.model.hprobs(gatestring1)
hProbs2 = self.model.hprobs(gatestring2)
mhProbs0 = self.mgateset.hprobs(gatestring0)
mhProbs1 = self.mgateset.hprobs(gatestring1)
mhProbs2 = self.mgateset.hprobs(gatestring2)
self.assertArraysAlmostEqual(hProbs0[('0',)], hP0)
self.assertArraysAlmostEqual(hProbs1[('0',)], hP1)
self.assertArraysAlmostEqual(hProbs2[('0',)], hP2)
self.assertArraysAlmostEqual(mhProbs0[('0',)], hP0, places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mhProbs1[('0',)], hP1, places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mhProbs2[('0',)], hP2, places=FD_HESS_PLACES)
bulk_hProbs = self.assertNoWarnings(self.model.bulk_hprobs,
circuitList, returnPr=False, check=True)
bulk_hProbs_chk = self.assertNoWarnings(self.model.bulk_hprobs,
circuitList, returnPr=True, check=True)
mbulk_hProbs = self.assertNoWarnings(self.mgateset.bulk_hprobs,
circuitList, returnPr=False, check=True)
mbulk_hProbs_chk = self.assertNoWarnings(self.mgateset.bulk_hprobs,
circuitList, returnPr=True, check=True)
for opstr in circuitList:
for outLbl in bulk_hProbs[opstr]:
self.assertArraysAlmostEqual(bulk_hProbs[opstr][outLbl],
bulk_hProbs_chk[opstr][outLbl][0]) #[0] b/c _chk also contains probs
self.assertArraysAlmostEqual(mbulk_hProbs[opstr][outLbl],
mbulk_hProbs_chk[opstr][outLbl][0]) #[0] b/c _chk also contains probs
self.assertArraysAlmostEqual(bulk_hProbs[opstr][outLbl],
mbulk_hProbs[opstr][outLbl], places=FD_HESS_PLACES) # map vs. matrix
self.assertArraysAlmostEqual(bulk_hProbs[gatestring0][('0',)],hP0)
self.assertArraysAlmostEqual(bulk_hProbs[gatestring1][('0',)],hP1)
self.assertArraysAlmostEqual(bulk_hProbs[gatestring2][('0',)],hP2)
self.assertArraysAlmostEqual(mbulk_hProbs[gatestring0][('0',)],mhProbs0[('0',)], places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mbulk_hProbs[gatestring1][('0',)],mhProbs1[('0',)], places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mbulk_hProbs[gatestring2][('0',)],mhProbs2[('0',)], places=FD_HESS_PLACES)
#Vary keyword args
hProbs0b = self.model.hprobs(gatestring0,returnPr=True)
hProbs0c = self.model.hprobs(gatestring0,returnDeriv=True)
hProbs0d = self.model.hprobs(gatestring0,returnDeriv=True,returnPr=True)
bulk_hProbs_B = self.model.bulk_hprobs(circuitList, returnPr=True, returnDeriv=True)
bulk_hProbs_C = self.model.bulk_hprobs(circuitList, returnDeriv=True)
mhProbs0b = self.mgateset.hprobs(gatestring0,returnPr=True)
mhProbs0c = self.mgateset.hprobs(gatestring0,returnDeriv=True)
mhProbs0d = self.mgateset.hprobs(gatestring0,returnDeriv=True,returnPr=True)
mbulk_hProbs_B = self.mgateset.bulk_hprobs(circuitList, returnPr=True, returnDeriv=True)
mbulk_hProbs_C = self.mgateset.bulk_hprobs(circuitList, returnDeriv=True)
nElements = evt.num_final_elements(); nParams = self.model.num_params()
probs_to_fill = np.empty( nElements, 'd')
probs_to_fillB = np.empty( nElements, 'd')
dprobs_to_fill = np.empty( (nElements,nParams), 'd')
dprobs_to_fillB = np.empty( (nElements,nParams), 'd')
hprobs_to_fill = np.empty( (nElements,nParams,nParams), 'd')
hprobs_to_fillB = np.empty( (nElements,nParams,nParams), 'd')
mprobs_to_fill = np.empty( nElements, 'd')
mprobs_to_fillB = np.empty( nElements, 'd')
mdprobs_to_fill = np.empty( (nElements,nParams), 'd')
mdprobs_to_fillB = np.empty( (nElements,nParams), 'd')
mhprobs_to_fill = np.empty( (nElements,nParams,nParams), 'd')
mhprobs_to_fillB = np.empty( (nElements,nParams,nParams), 'd')
spam_label_rows = { '0': 0, '1': 1 }
self.assertNoWarnings(self.model.bulk_fill_hprobs, hprobs_to_fill, evt,
prMxToFill=probs_to_fill, derivMxToFill=dprobs_to_fill, check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_hprobs, mhprobs_to_fill, mevt,
prMxToFill=mprobs_to_fill, derivMxToFill=mdprobs_to_fill, check=True)
def elIndx(iOpStr, outcome):
inds = pygsti.tools.indices(lookup[iOpStr]) if isinstance(lookup[iOpStr],slice) \
else lookup[iOpStr] #an index array
return inds[ outcome_lookup[iOpStr].index( outcome ) ]
def melIndx(iOpStr, outcome):
inds = pygsti.tools.indices(mlookup[iOpStr]) if isinstance(mlookup[iOpStr],slice) \
else mlookup[iOpStr] #an index array
return inds[ moutcome_lookup[iOpStr].index( outcome ) ]
self.assertArraysAlmostEqual(hprobs_to_fill[elIndx(0,('0',)),:,:],hP0)
self.assertArraysAlmostEqual(hprobs_to_fill[elIndx(1,('0',)),:,:],hP1)
self.assertArraysAlmostEqual(hprobs_to_fill[elIndx(2,('0',)),:,:],hP2)
self.assertArraysAlmostEqual(mhprobs_to_fill[melIndx(0,('0',)),:,:],hP0, places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mhprobs_to_fill[melIndx(1,('0',)),:,:],hP1, places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mhprobs_to_fill[melIndx(2,('0',)),:,:],hP2, places=FD_HESS_PLACES)
#without derivative
self.assertNoWarnings(self.model.bulk_fill_hprobs, hprobs_to_fillB, evt,
prMxToFill=probs_to_fillB, check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_hprobs, mhprobs_to_fillB, mevt,
prMxToFill=mprobs_to_fillB, check=True)
self.assertArraysAlmostEqual(hprobs_to_fill,hprobs_to_fillB)
self.assertArraysAlmostEqual(probs_to_fill,probs_to_fillB)
self.assertArraysAlmostEqual(mhprobs_to_fill,mhprobs_to_fillB, places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mprobs_to_fill,mprobs_to_fillB, places=FD_HESS_PLACES)
#without probs
self.assertNoWarnings(self.model.bulk_fill_hprobs, hprobs_to_fillB, evt,
derivMxToFill=dprobs_to_fillB, check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_hprobs, mhprobs_to_fillB, mevt,
derivMxToFill=mdprobs_to_fillB, check=True)
self.assertArraysAlmostEqual(hprobs_to_fill,hprobs_to_fillB)
self.assertArraysAlmostEqual(dprobs_to_fill,dprobs_to_fillB)
self.assertArraysAlmostEqual(mhprobs_to_fill,mhprobs_to_fillB, places=FD_HESS_PLACES)
self.assertArraysAlmostEqual(mdprobs_to_fill,mdprobs_to_fillB, places=FD_HESS_PLACES)
#without either
self.assertNoWarnings(self.model.bulk_fill_hprobs, hprobs_to_fillB, evt, check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_hprobs, mhprobs_to_fillB, mevt, check=True)
self.assertArraysAlmostEqual(hprobs_to_fill,hprobs_to_fillB)
self.assertArraysAlmostEqual(mhprobs_to_fill,mhprobs_to_fillB, places=FD_HESS_PLACES)
#Artificially reset the "smallness" threshold for scaling
# to be sure to engate the scaling machinery
PORIG = pygsti.objects.matrixforwardsim.PSMALL; pygsti.objects.matrixforwardsim.PSMALL = 10
DORIG = pygsti.objects.matrixforwardsim.DSMALL; pygsti.objects.matrixforwardsim.DSMALL = 10
HORIG = pygsti.objects.matrixforwardsim.HSMALL; pygsti.objects.matrixforwardsim.HSMALL = 10
self.model.bulk_fill_hprobs(hprobs_to_fillB, evt, check=True)
self.assertArraysAlmostEqual(hprobs_to_fill,hprobs_to_fillB)
pygsti.objects.matrixforwardsim.PSMALL = PORIG
self.model.bulk_fill_hprobs(hprobs_to_fillB, evt, check=True)
self.assertArraysAlmostEqual(hprobs_to_fill,hprobs_to_fillB)
pygsti.objects.matrixforwardsim.DSMALL = DORIG
pygsti.objects.matrixforwardsim.HSMALL = HORIG
#test with split eval tree
evt_split = evt.copy(); lookup_splt = evt_split.split(lookup,maxSubTreeSize=4)
mevt_split = mevt.copy(); mlookup_splt = mevt_split.split(mlookup,numSubTrees=2)
hprobs_to_fill_splt = np.empty( (nElements,nParams,nParams), 'd')
mhprobs_to_fill_splt = np.empty( (nElements,nParams,nParams), 'd')
self.assertNoWarnings(self.model.bulk_fill_hprobs, hprobs_to_fill_splt, evt_split, check=True)
self.assertNoWarnings(self.mgateset.bulk_fill_hprobs, mhprobs_to_fill_splt, mevt_split, check=True)
#Note: Outcome labels stay in same order across tree splits (i.e.
# evalTree.split() doesn't need to update outcome_lookup)
for i,opstr in enumerate(circuitList): #original operation sequences
self.assertArraysAlmostEqual(hprobs_to_fill[ lookup[i] ],
hprobs_to_fill_splt[ lookup_splt[i] ])
self.assertArraysAlmostEqual(mhprobs_to_fill[ mlookup[i] ],
mhprobs_to_fill_splt[ mlookup_splt[i] ], places=FD_HESS_PLACES)
#Also check map vs matrix fills:
assert(outcome_lookup[i] == moutcome_lookup[i]) # should stay in same ordering... I think
self.assertArraysAlmostEqual(hprobs_to_fill[ lookup[i] ],
mhprobs_to_fill[ mlookup[i] ], places=FD_HESS_PLACES)
#products
N = self.model.get_dimension()**2 #number of elements in a operation matrix
hProds = self.model.bulk_hproduct(evt)
hProdsB,scales = self.model.bulk_hproduct(evt, bScale=True)
self.assertArraysAlmostEqual(hProds, scales[:,None,None,None,None]*hProdsB)
hProdsFlat = self.model.bulk_hproduct(evt, flat=True, bScale=False)
hProdsFlatB,S1 = self.model.bulk_hproduct(evt, flat=True, bScale=True)
self.assertArraysAlmostEqual(hProdsFlat, np.repeat(S1,N)[:,None,None]*hProdsFlatB)
hProdsC, dProdsC, prodsC = self.model.bulk_hproduct(evt, bReturnDProdsAndProds=True, bScale=False)
hProdsD, dProdsD, prodsD, S2 = self.model.bulk_hproduct(evt, bReturnDProdsAndProds=True, bScale=True)
self.assertArraysAlmostEqual(hProds, hProdsC)
self.assertArraysAlmostEqual(hProds, S2[:,None,None,None,None]*hProdsD)
self.assertArraysAlmostEqual(dProdsC, S2[:,None,None,None]*dProdsD)
self.assertArraysAlmostEqual(prodsC, S2[:,None,None]*prodsD)
hProdsF, dProdsF, prodsF = self.model.bulk_hproduct(evt, bReturnDProdsAndProds=True, flat=True, bScale=False)
hProdsF2, dProdsF2, prodsF2, S3 = self.model.bulk_hproduct(evt, bReturnDProdsAndProds=True, flat=True, bScale=True)
self.assertArraysAlmostEqual(hProdsFlat, hProdsF)
self.assertArraysAlmostEqual(hProdsFlat, np.repeat(S3,N)[:,None,None]*hProdsF2)
self.assertArraysAlmostEqual(dProdsF, np.repeat(S3,N)[:,None]*dProdsF2)
self.assertArraysAlmostEqual(prodsF, S3[:,None,None]*prodsF2)
nP = self.model.num_params()
hcols = []
d12cols = []
slicesList = [ (slice(0,nP),slice(i,i+1)) for i in range(nP) ]
for s1,s2, hprobs_col, dprobs12_col in self.model.bulk_hprobs_by_block(
evt, slicesList, True):
hcols.append(hprobs_col)
d12cols.append(dprobs12_col)
all_hcols = np.concatenate( hcols, axis=2 ) #axes = (spam+circuit, derivParam1, derivParam2)
all_d12cols = np.concatenate( d12cols, axis=2 )
dprobs12 = dprobs_to_fill[:,:,None] * dprobs_to_fill[:,None,:]
#NOTE: Currently bulk_hprobs_by_block isn't implemented in map calculator - but it could
# (and probably should) be later on, at which point the commented code here and
# below would test it.
#mhcols = []
#md12cols = []
#mslicesList = [ (slice(0,nP),slice(i,i+1)) for i in range(nP) ]
#for s1,s2, hprobs_col, dprobs12_col in self.mgateset.bulk_hprobs_by_block(
# mevt, mslicesList, True):
# mhcols.append(hprobs_col)
# md12cols.append(dprobs12_col)
#mall_hcols = np.concatenate( mhcols, axis=2 ) #axes = (spam+circuit, derivParam1, derivParam2)
#mall_d12cols = np.concatenate( md12cols, axis=2 )
#mdprobs12 = mdprobs_to_fill[:,:,None] * mdprobs_to_fill[:,None,:]
self.assertArraysAlmostEqual(all_hcols,hprobs_to_fill)
self.assertArraysAlmostEqual(all_d12cols,dprobs12)
#self.assertArraysAlmostEqual(mall_hcols,mhprobs_to_fill, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mall_d12cols,mdprobs12, places=FD_HESS_PLACES)
#
#self.assertArraysAlmostEqual(mall_hcols,all_hcols, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mall_d12cols,all_d12cols, places=FD_HESS_PLACES)
hcols = []
d12cols = []
slicesList = [ (slice(0,nP),slice(i,i+1)) for i in range(1,10) ]
for s1,s2, hprobs_col, dprobs12_col in self.model.bulk_hprobs_by_block(
evt, slicesList, True):
hcols.append(hprobs_col)
d12cols.append(dprobs12_col)
all_hcols = np.concatenate( hcols, axis=2 ) #axes = (spam+circuit, derivParam1, derivParam2)
all_d12cols = np.concatenate( d12cols, axis=2 )
#mhcols = []
#md12cols = []
#mslicesList = [ (slice(0,nP),slice(i,i+1)) for i in range(1,10) ]
#for s1,s2, hprobs_col, dprobs12_col in self.mgateset.bulk_hprobs_by_block(
# spam_label_rows, mevt, mslicesList, True):
# mhcols.append(hprobs_col)
# md12cols.append(dprobs12_col)
#mall_hcols = np.concatenate( mhcols, axis=2 ) #axes = (spam+circuit, derivParam1, derivParam2)
#mall_d12cols = np.concatenate( md12cols, axis=2 )
self.assertArraysAlmostEqual(all_hcols,hprobs_to_fill[:,:,1:10])
self.assertArraysAlmostEqual(all_d12cols,dprobs12[:,:,1:10])
#self.assertArraysAlmostEqual(mall_hcols,mhprobs_to_fill[:,:,1:10], places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mall_d12cols,mdprobs12[:,:,1:10], places=FD_HESS_PLACES)
#
#self.assertArraysAlmostEqual(mall_hcols,all_hcols, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mall_d12cols,all_d12cols, places=FD_HESS_PLACES)
hcols = []
d12cols = []
slicesList = [ (slice(2,12),slice(i,i+1)) for i in range(1,10) ]
for s1,s2, hprobs_col, dprobs12_col in self.model.bulk_hprobs_by_block(
evt, slicesList, True):
hcols.append(hprobs_col)
d12cols.append(dprobs12_col)
all_hcols = np.concatenate( hcols, axis=2 ) #axes = (spam+circuit, derivParam1, derivParam2)
all_d12cols = np.concatenate( d12cols, axis=2 )
#mhcols = []
#md12cols = []
#mslicesList = [ (slice(2,12),slice(i,i+1)) for i in range(1,10) ]
#for s1,s2, hprobs_col, dprobs12_col in self.mgateset.bulk_hprobs_by_block(
# mevt, mslicesList, True):
# mhcols.append(hprobs_col)
# md12cols.append(dprobs12_col)
#mall_hcols = np.concatenate( mhcols, axis=2 ) #axes = (spam+circuit, derivParam1, derivParam2)
#mall_d12cols = np.concatenate( md12cols, axis=2 )
self.assertArraysAlmostEqual(all_hcols,hprobs_to_fill[:,2:12,1:10])
self.assertArraysAlmostEqual(all_d12cols,dprobs12[:,2:12,1:10])
#self.assertArraysAlmostEqual(mall_hcols,mhprobs_to_fill[:,2:12,1:10], places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mall_d12cols,mdprobs12[:,2:12,1:10], places=FD_HESS_PLACES)
#
#self.assertArraysAlmostEqual(mall_hcols,all_hcols, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mall_d12cols,all_d12cols, places=FD_HESS_PLACES)
hprobs_by_block = np.zeros(hprobs_to_fill.shape,'d')
dprobs12_by_block = np.zeros(dprobs12.shape,'d')
#mhprobs_by_block = np.zeros(mhprobs_to_fill.shape,'d')
#mdprobs12_by_block = np.zeros(mdprobs12.shape,'d')
blocks1 = pygsti.tools.mpitools.slice_up_range(nP, 3)
blocks2 = pygsti.tools.mpitools.slice_up_range(nP, 5)
slicesList = list(itertools.product(blocks1,blocks2))
for s1,s2, hprobs_blk, dprobs12_blk in self.model.bulk_hprobs_by_block(
evt, slicesList, True):
hprobs_by_block[:,s1,s2] = hprobs_blk
dprobs12_by_block[:,s1,s2] = dprobs12_blk
#again, but no dprobs12
hprobs_by_block2 = np.zeros(hprobs_to_fill.shape,'d')
for s1,s2, hprobs_blk in self.model.bulk_hprobs_by_block(
evt, slicesList, False):
hprobs_by_block2[:,s1,s2] = hprobs_blk
#for s1,s2, hprobs_blk, dprobs12_blk in self.mgateset.bulk_hprobs_by_block(
# mevt, slicesList, True):
# mhprobs_by_block[:,s1,s2] = hprobs_blk
# mdprobs12_by_block[:,s1,s2] = dprobs12_blk
self.assertArraysAlmostEqual(hprobs_by_block,hprobs_to_fill)
self.assertArraysAlmostEqual(hprobs_by_block2,hprobs_to_fill)
self.assertArraysAlmostEqual(dprobs12_by_block,dprobs12)
#self.assertArraysAlmostEqual(mhprobs_by_block,hprobs_to_fill, places=FD_HESS_PLACES)
#self.assertArraysAlmostEqual(mdprobs12_by_block,dprobs12, places=FD_HESS_PLACES)
#print("****DEBUG HESSIAN BY COL****")
#print("shape = ",all_hcols.shape)
#to_check = hprobs_to_fill[:,2:12,1:10]
#for si in range(all_hcols.shape[0]):
# for stri in range(all_hcols.shape[1]):
# diff = np.linalg.norm(all_hcols[si,stri]-to_check[si,stri])
# print("[%d,%d] diff = %g" % (si,stri,diff))
# if diff > 1e-6:
# for i in range(all_hcols.shape[2]):
# for j in range(all_hcols.shape[3]):