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test_datasets.py
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test_datasets.py
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import collections
import os
import pickle
import unittest
import numpy as np
import pygsti
from ..testutils import BaseTestCase, compare_files, temp_files, regenerate_references
class TestDataSetMethods(BaseTestCase):
def test_from_scratch(self):
# Create a dataset from scratch
ds = pygsti.data.DataSet(outcome_labels=['0', '1'])
ds.add_count_dict( ('Gx',), {'0': 10, '1': 90} )
ds[ ('Gx',) ] = {'0': 10, '1': 90}
ds[ ('Gx',) ]['0'] = 10
ds[ ('Gx',) ]['1'] = 90
with self.assertRaises(NotImplementedError):
ds[ ('Gx',) ]['new'] = 20 # assignment can't create *new* outcome labels (yet)
ds.add_count_dict( ('Gy','Gy'), {'FooBar': 10, '1': 90 }) # OK to add outcome labels on the fly
ds.add_count_dict( ('Gy','Gy'), {'1': 90 }) # now all outcome labels OK now
ds.add_count_dict(('Gy','Gy'), pygsti.baseobjs.OutcomeLabelDict([('0', 10), ('1', 90)]))
ds.done_adding_data()
#Pickle and unpickle
with open(temp_files + '/dataset.pickle', 'wb') as datasetfile:
pickle.dump(ds, datasetfile)
ds_from_pkl = None
with open(temp_files + '/dataset.pickle', 'rb') as datasetfile:
ds_from_pkl = pickle.load(datasetfile)
self.assertEqual(ds_from_pkl[('Gx',)]['0'], 10)
self.assertAlmostEqual(ds_from_pkl[('Gx',)].fractions['0'], 0.1)
# Invoke the DataSet constructor other ways
gstrs = [ ('Gx',), ('Gx','Gy'), ('Gy',) ]
gstrInds = collections.OrderedDict( [ (('Gx',),0), (('Gx','Gy'),1), (('Gy',),2) ] )
gstrInds_static = collections.OrderedDict([(pygsti.circuits.Circuit(('Gx',)), slice(0, 2)),
(pygsti.circuits.Circuit(('Gx', 'Gy')), slice(2, 4)),
(pygsti.circuits.Circuit(('Gy',)), slice(4, 6))])
olInds = collections.OrderedDict( [ ('0',0), ('1',1) ] )
oli = np.array([0,1],'i')
oli_static = np.array( [0,1]*3, 'd' ) # 3 operation sequences * 2 outcome labels each
time_static = np.zeros( (6,), 'd' )
reps_static = 10*np.ones( (6,), 'd' )
oli_nonstc = [ oli, oli, oli ] # each item has num_outcomes elements
time_nonstc = [ np.zeros(2,'d'), np.zeros(2,'d'), np.zeros(2,'d') ]
reps_nonstc = [ 10*np.ones(2,'i'), 10*np.ones(2,'i'), 10*np.ones(2,'i') ]
ds2 = pygsti.data.DataSet(oli_nonstc, time_nonstc, reps_nonstc,
circuits=gstrs, outcome_labels=['0','1'])
ds4 = pygsti.data.DataSet(oli_static, time_static, reps_static,
circuit_indices=gstrInds_static, outcome_labels=['0','1'], static=True)
ds2.add_counts_from_dataset(ds)
#Loading and saving
ds2.save(temp_files + "/nonstatic_dataset.saved")
ds2.save(temp_files + "/nonstatic_dataset.saved.gz")
with open(temp_files + "/nonstatic_dataset.stream","wb") as streamfile:
ds2.save(streamfile)
ds4.save(temp_files + "/static_dataset.saved")
ds4.save(temp_files + "/static_dataset.saved.gz")
with open(temp_files + "/static_dataset.stream","wb") as streamfile:
ds4.save(streamfile)
ds2.load(temp_files + "/nonstatic_dataset.saved")
ds2.load(temp_files + "/nonstatic_dataset.saved.gz")
with open(temp_files + "/nonstatic_dataset.stream","rb") as streamfile:
ds2.load(streamfile)
ds4.load(temp_files + "/static_dataset.saved")
ds4.load(temp_files + "/static_dataset.saved.gz")
with open(temp_files + "/static_dataset.stream","rb") as streamfile:
ds2.load(streamfile)
#Test loading a deprecated dataset file
#dsDeprecated = pygsti.data.DataSet(file_to_load_from=compare_files + "/deprecated.dataset")
def test_from_file(self):
# creating and loading a text-format dataset file
dataset_txt = \
"""## Columns = 0 count, 1 count
{} 0 100
Gx 10 90
GxGy 40 60
Gx^4 20 80
"""
with open(temp_files + "/TinyDataset.txt","w") as output:
output.write(dataset_txt)
ds = pygsti.io.read_dataset(temp_files + "/TinyDataset.txt")
self.assertEqual(ds[()][('0',)], 0)
print(ds.cirIndex.keys())
print(('Gx','Gy') in ds)
print(('Gx','Gy') in ds.keys())
self.assertEqual(ds[('Gx','Gy')][('1',)], 60)
dataset_txt2 = \
"""## Columns = 0 count, 1 count
{} 0 100
Gx 10 90
GxGy 40 60
Gx^4 20 80
"""
with open(temp_files + "/TinyDataset2.txt","w") as output:
output.write(dataset_txt2)
ds2 = pygsti.io.read_dataset(temp_files + "/TinyDataset2.txt")
self.assertEqualDatasets(ds, ds2)
def test_generate_fake_data(self):
model = pygsti.models.modelconstruction.create_explicit_model_from_expressions([('Q0',)], ['Gi', 'Gx', 'Gy', 'Gz'],
[ "I(Q0)","X(pi/8,Q0)", "Y(pi/8,Q0)", "Z(pi/2,Q0)"])
depol_gateset = model.depolarize(op_noise=0.1,spam_noise=0)
fids = pygsti.circuits.to_circuits([(), ('Gx',), ('Gy'), ('Gx', 'Gx')])
germs = pygsti.circuits.to_circuits([('Gi',), ('Gx',), ('Gy'), ('Gi', 'Gi', 'Gi')])
circuits = pygsti.circuits.create_circuits(
"f0+T(germ,N)+f1", f0=fids, f1=fids, germ=germs, N=3,
T=pygsti.circuits.repeat_with_max_length,
order=["germ","f0","f1"])
pygsti.remove_duplicates_in_place(circuits)
ds_none = pygsti.data.simulate_data(depol_gateset, circuits,
num_samples=1000, sample_error='none')
ds_round = pygsti.data.simulate_data(depol_gateset, circuits,
num_samples=1000, sample_error='round')
ds_otherds = pygsti.data.simulate_data(ds_none, circuits,
num_samples=None, sample_error='none')
# TO SEED SAVED FILE, RUN BELOW LINES:
if regenerate_references():
pygsti.io.write_dataset(compare_files + "/Fake_Dataset_none.txt", ds_none, circuits)
pygsti.io.write_dataset(compare_files + "/Fake_Dataset_round.txt", ds_round, circuits)
bDeepTesting = bool( 'PYGSTI_DEEP_TESTING' in os.environ and
os.environ['PYGSTI_DEEP_TESTING'].lower() in ("yes","1","true") )
#Do not test *random* data for equality unless "deep testing", since different
# versions/installs of numpy give different random numbers and we don't expect
# data will be equal.
saved_ds = pygsti.io.read_dataset(compare_files + "/Fake_Dataset_none.txt", cache=True)
#print("SAVED = ",saved_ds)
#print("NONE = ",ds_none)
self.assertEqualDatasets(ds_none, saved_ds)
saved_ds = pygsti.io.read_dataset(compare_files + "/Fake_Dataset_round.txt")
self.assertEqualDatasets(ds_round, saved_ds)
def test_multi_dataset(self):
multi_dataset_txt = \
"""## Columns = DS0 0 count, DS0 1 count, DS1 0 count, DS1 1 count
{} 0 100 0 100
Gx 10 90 10 90
GxGy 40 60 40 60
Gx^4 20 80 20 80
"""
with open(temp_files + "/TinyMultiDataset.txt","w") as output:
output.write(multi_dataset_txt)
multiDS = pygsti.io.read_multidataset(temp_files + "/TinyMultiDataset.txt", cache=True)
bad_multi_dataset_txt = \
"""## Columns = DS0 0 count, DS0 1 count, DS1 0 count, DS1 1 count
{} 0 100 0 100
FooBar 10 90 10 90
GxGy 40 60 40 60
Gx^4 20 80 20 80
"""
with open(temp_files + "/BadTinyMultiDataset.txt","w") as output:
output.write(bad_multi_dataset_txt)
with self.assertRaises(ValueError):
pygsti.io.read_multidataset(temp_files + "/BadTinyMultiDataset.txt")
gstrInds = collections.OrderedDict([(pygsti.circuits.Circuit(('Gx',)), slice(0, 2)),
(pygsti.circuits.Circuit(('Gx', 'Gy')), slice(2, 4)),
(pygsti.circuits.Circuit(('Gy',)), slice(4, 6))])
olInds = collections.OrderedDict( [ ('0',0), ('1',1) ] )
ds1_oli = np.array( [0,1]*3, 'i' ) # 3 operation sequences * 2 outcome labels
ds1_time = np.zeros(6,'d')
ds1_rep = 10*np.ones(6,'i')
ds2_oli = np.array( [0,1]*3, 'i' ) # 3 operation sequences * 2 outcome labels
ds2_time = np.zeros(6,'d')
ds2_rep = 5*np.ones(6,'i')
mds_oli = collections.OrderedDict( [ ('ds1', ds1_oli), ('ds2', ds2_oli) ] )
mds_time = collections.OrderedDict( [ ('ds1', ds1_time), ('ds2', ds2_time) ] )
mds_rep = collections.OrderedDict( [ ('ds1', ds1_rep), ('ds2', ds2_rep) ] )
mds2 = pygsti.data.MultiDataSet(mds_oli, mds_time, mds_rep, circuit_indices=gstrInds,
outcome_labels=['0','1'])
mds3 = pygsti.data.MultiDataSet(mds_oli, mds_time, mds_rep, circuit_indices=gstrInds,
outcome_label_indices=olInds)
mds4 = pygsti.data.MultiDataSet(outcome_labels=['0', '1'])
mds5 = pygsti.data.MultiDataSet()
#Create a multidataset with time dependence and no rep counts
ds1_time = np.array(np.arange(0,6),'d')
ds2_oli = np.array( [0,1]*3, 'i' ) # 3 operation sequences * 2 outcome labels
ds2_time = np.array(np.arange(2,8),'d')
mds_oli = collections.OrderedDict( [ ('ds1', ds1_oli), ('ds2', ds2_oli) ] )
mds_time = collections.OrderedDict( [ ('ds1', ds1_time), ('ds2', ds2_time) ] )
mdsNoReps = pygsti.data.MultiDataSet(mds_oli, mds_time, None, circuit_indices=gstrInds,
outcome_labels=['0','1'])
#Create some data to test adding data to multidataset
ds = pygsti.data.DataSet(outcome_labels=['0', '1'])
ds.add_count_dict( (), {'0': 10, '1': 90} )
ds.add_count_dict( ('Gx',), {'0': 10, '1': 90} )
ds.add_count_dict( ('Gx','Gy'), {'0': 20, '1':80} )
ds.add_count_dict( ('Gx','Gx','Gx','Gx'), {'0': 20, '1':80} )
ds.done_adding_data()
multiDS['myDS'] = ds
#Pickle and unpickle
with open(temp_files + '/multidataset.pickle', 'wb') as picklefile:
pickle.dump(multiDS, picklefile)
mds_from_pkl = None
with open(temp_files + '/multidataset.pickle', 'rb') as picklefile:
mds_from_pkl = pickle.load(picklefile)
self.assertEqual(mds_from_pkl['DS0'][('Gx',)]['0'], 10)
#Loading and saving
multiDS.save(temp_files + "/multidataset.saved")
multiDS.save(temp_files + "/multidataset.saved.gz")
mdsNoReps.save(temp_files + "/multidataset_noreps.saved")
with open(temp_files + "/multidataset.stream","wb") as streamfile:
multiDS.save(streamfile)
multiDS.load(temp_files + "/multidataset.saved")
multiDS.load(temp_files + "/multidataset.saved.gz")
mdsNoReps.load(temp_files + "/multidataset_noreps.saved")
with open(temp_files + "/multidataset.stream","rb") as streamfile:
multiDS.load(streamfile)
multiDS2 = pygsti.data.MultiDataSet(file_to_load_from=temp_files + "/multidataset.saved")
#Finally, add a dataset w/reps to a multidataset without them
mdsNoReps.add_dataset('DSwReps', mds2['ds1'])
def test_tddataset_construction(self):
#Create a non-static already initialized dataset
circuits = pygsti.circuits.to_circuits([('Gx',), ('Gy', 'Gx')])
gatestringIndices = collections.OrderedDict([ (mdl,i) for i,mdl in enumerate(circuits)])
oliData = [ np.array([0,1,0]), np.array([1,1,0]) ]
timeData = [ np.array([1.0,2.0,3.0]), np.array([4.0,5.0,6.0]) ]
repData = [ np.array([1,1,1]), np.array([2,2,2]) ]
oli = collections.OrderedDict( [(('0',),0), (('1',),1)] )
ds = pygsti.data.DataSet(oliData, timeData, repData, circuits, None,
['0','1'], None, static=False)
ds = pygsti.data.DataSet(oliData, timeData, repData, None, gatestringIndices,
None, oli, static=False) #provide operation sequence & spam label index dicts instead of lists
ds = pygsti.data.DataSet(oliData, timeData, None, None, gatestringIndices,
None, oli) #no rep data is OK - just assumes 1; bStatic=False is default
#Test loading a non-static set from a saved file
ds.save(temp_files + "/test_tddataset.saved")
ds3 = pygsti.data.DataSet(file_to_load_from=temp_files + "/test_tddataset.saved")
#Create an static already initialized dataset
ds = pygsti.data.DataSet(outcome_labels=['0', '1'])
CIR = pygsti.circuits.Circuit #no auto-convert to Circuits when using circuit_indices
gatestringIndices = collections.OrderedDict([ #always need this when creating a static dataset
( CIR(('Gx',)) , slice(0,3) ), # (now a dict of *slices* into flattened 1D
( CIR(('Gy','Gx')), slice(3,6) ) ]) # data arrays)
oliData = np.array([0,1,0,1,1,0])
timeData = np.array([1.0,2.0,3.0,4.0,5.0,6.0])
repData = np.array([1,1,1,2,2,2])
oli = collections.OrderedDict( [(('0',),0), (('1',),1)] )
ds = pygsti.data.DataSet(oliData, timeData, repData, None, gatestringIndices,
['0','1'], None, static=True)
ds = pygsti.data.DataSet(oliData, timeData, repData, None, gatestringIndices,
None, oli, static=True) #provide spam label index dict instead of list
ds = pygsti.data.DataSet(oliData, timeData, None, None, gatestringIndices,
None, oli, static=True) #no rep data is OK - just assumes 1
#Test loading a static set from a saved file
ds.save(temp_files + "/test_tddataset.saved")
ds3 = pygsti.data.DataSet(file_to_load_from=temp_files + "/test_tddataset.saved")
def test_tddataset_methods(self):
# Create a dataset from scratch
def printInfo(ds, opstr):
print( "*** %s info ***" % str(opstr))
print( ds[opstr] )
print( ds[opstr].oli )
print( ds[opstr].time )
print( ds[opstr].reps )
print( ds[opstr].outcomes )
print( ds[opstr].expanded_ol )
print( ds[opstr].expanded_oli )
print( ds[opstr].expanded_times )
print( ds[opstr].counts )
print( ds[opstr].fractions )
print( ds[opstr].total )
print( ds[opstr].fractions['0'] )
print( "[0] (int) = ",ds[opstr][0] ) # integer index
print( "[0.0] (float) = ",ds[opstr][0.0] ) # time index
print( "['0'] (str) = ",ds[opstr]['0'] ) # outcome-label index
print( "[('0',)] (tuple) = ",ds[opstr][('0',)] ) # outcome-label index
print( "at time 0 = ", ds[opstr].counts_at_time(0.0) )
all_times, _ = ds[opstr].timeseries('all')
print( "series('all') = ", ds[opstr].timeseries('all') )
print( "series('0') = ",ds[opstr].timeseries('0') )
print( "series('1') = ",ds[opstr].timeseries('1') )
print( "series('0',alltimes) = ",ds[opstr].timeseries('0', all_times) )
print( len(ds[opstr]) )
print("\n")
ds = pygsti.data.DataSet(outcome_labels=['0', '1'])
ds.add_raw_series_data( ('Gx',),
['0','0','1','0','1','0','1','1','1','0'],
[0.0, 0.2, 0.5, 0.6, 0.7, 0.9, 1.1, 1.3, 1.35, 1.5], None)
printInfo(ds, ('Gx',) )
ds[('Gy','Gy')] = (['0','1'], [0.0, 1.0]) #add via spam-labels, times
dsNoReps = ds.copy() #tests copy() before any rep-data is added
ds.add_raw_series_data( ('Gy',),['0','1'],[0.0, 1.0], [3,7]) #using repetitions
ds.add_series_data( ('Gy','Gy'), [ {'0': 2, '1': 8}, {'0': 6, '1': 4}, {'1': 10} ],
[0.0, 1.2, 2.4])
OD = collections.OrderedDict
ds.add_series_data( ('Gy','Gy','Gy'), [ OD([('0',2),('1',8)]), OD([('0',6),('1',4)]), OD([('1',10)]) ],
[0.0, 1.2, 2.4]) # add with ordered dicts
ds[('Gx','Gx')] = (['0','1'], [0.0, 1.0], [10,10]) #add via spam-labels, times, reps
ds[('Gx','Gy')] = (['0','1'], [0.0, 1.0]) #add via spam-labels, times *after* we've added rep data
printInfo(ds, ('Gy',) )
printInfo(ds, ('Gy','Gy') )
ds.add_raw_series_data( ('Gx','Gx'),['0','1'],[0.0, 1.0], [6,14], overwrite_existing=True) #the default
ds.add_raw_series_data( ('Gx','Gx'),['0','1'],[1.0, 2.0], [5,10], overwrite_existing=False)
#Setting (spamlabel,time,count) data
ds[('Gx',)][0] = ('1',0.1,1)
ds[('Gy',)][1] = ('0',0.4,3)
dsNoReps[('Gx',)][0] = ('1',0.1,1) # reps must == 1
dsNoReps[('Gy','Gy')][1] = ('0',0.4) # or be omitted
printInfo(ds, ('Gx',) )
printInfo(ds, ('Gy',) )
ds.done_adding_data()
dsNoReps.done_adding_data()
print("Whole thing:")
print(ds)
dsWritable = ds.copy_nonstatic()
dsWritable[('Gx',)][0] = ('1',0.1,1)
dsWritable.add_raw_series_data( ('Gy','Gx'),['0','1'],[0.0, 1.0], [2,2])
dsWritable.add_series_from_dataset(ds)
#Pickle and unpickle
with open(temp_files + '/tddataset.pickle', 'wb') as datasetfile:
pickle.dump(ds, datasetfile)
ds_from_pkl = None
with open(temp_files + '/tddataset.pickle', 'rb') as datasetfile:
ds_from_pkl = pickle.load(datasetfile)
#Loading and saving
ds.save(temp_files + "/nonstatic_tddataset.saved")
ds.save(temp_files + "/nonstatic_tddataset.saved.gz")
with open(temp_files + "/nonstatic_tddataset.stream","wb") as streamfile:
ds.save(streamfile)
dsWritable.save(temp_files + "/static_tddataset.saved")
dsWritable.save(temp_files + "/static_tddataset.saved.gz")
with open(temp_files + "/static_tddataset.stream","wb") as streamfile:
dsWritable.save(streamfile)
ds.load(temp_files + "/nonstatic_tddataset.saved")
ds.load(temp_files + "/nonstatic_tddataset.saved.gz")
with open(temp_files + "/nonstatic_tddataset.stream","rb") as streamfile:
ds.load(streamfile)
dsWritable.load(temp_files + "/static_tddataset.saved")
dsWritable.load(temp_files + "/static_tddataset.saved.gz")
with open(temp_files + "/static_tddataset.stream","rb") as streamfile:
dsWritable.load(streamfile)
def test_deprecated_dataset(self):
with open(compare_files + '/deprecated.dataset', 'rb') as datasetfile:
ds_from_pkl = pickle.load(datasetfile)
def test_tddataset_from_file(self):
# creating and loading a text-format dataset file
# NOTE: left of = sign is letter alias, right of = sign is spam label
dataset_txt = \
"""## 0 = 0
## 1 = 1
{} 011001
Gx 111000111
Gy 11001100
"""
with open(temp_files + "/TDDataset.txt","w") as output:
output.write(dataset_txt)
ds = pygsti.io.read_time_dependent_dataset(temp_files + "/TDDataset.txt")
self.assertEqual(ds[()].fractions['1'], 0.5)
self.assertEqual(ds[('Gy',)].fractions['1'], 0.5)
self.assertEqual(ds[('Gx',)].total, 9)
bad_dataset_txt = \
"""## 0 = 0
## 1 = 1
Foobar 011001
Gx 111000111
Gy 11001100
"""
with open(temp_files + "/BadTDDataset.txt","w") as output:
output.write(bad_dataset_txt)
with self.assertRaises(ValueError):
pygsti.io.read_time_dependent_dataset(temp_files + "/BadTDDataset.txt")
@unittest.skip("We probably won't be able to unpickle old files given the amount of refactoring")
def test_load_old_dataset(self):
#pygsti.baseobjs.results.enable_old_python_results_unpickling()
with pygsti.io.enable_old_object_unpickling():
with open(compare_files + "/pygsti0.9.6.dataset.pkl", 'rb') as f:
ds = pickle.load(f)
#pygsti.baseobjs.results.disable_old_python_results_unpickling()
#pygsti.io.disable_old_object_unpickling()
with open(temp_files + "/repickle_old_dataset.pkl", 'wb') as f:
pickle.dump(ds, f)
with pygsti.io.enable_old_object_unpickling("0.9.7"):
with open(compare_files + "/pygsti0.9.7.dataset.pkl", 'rb') as f:
ds = pickle.load(f)
with open(temp_files + "/repickle_old_dataset.pkl", 'wb') as f:
pickle.dump(ds, f)
def test_auxinfo(self):
# creating and loading a text-format dataset file w/auxiliary info
dataset_txt = \
"""## Columns = 0 count, 1 count
{} 0 100 # 'test':45
Gx 10 90 # (3,4): "value"
GxGy 40 60 # "can be": "anything", "allowed in": "a python dict", 4: {"example": "this"}
Gx^4 20 80
"""
with open(temp_files + "/AuxDataset.txt","w") as output:
output.write(dataset_txt)
ds = pygsti.io.read_dataset(temp_files + "/AuxDataset.txt")
self.assertEqual(ds[()][('0',)], 0)
self.assertEqual(ds[('Gx','Gy')][('1',)], 60)
self.assertEqual(ds[()].aux, {"test":45})
self.assertEqual(ds[('Gx','Gy')].aux, {"can be": "anything", "allowed in": "a python dict", 4: {"example": "this"}})
self.assertEqual(ds[('Gx',)].aux, { (3,4): "value" })
self.assertEqual(ds[('Gx','Gx','Gx','Gx')].aux, {})
if __name__ == "__main__":
unittest.main(verbosity=2)