def load_pygsti_dataset(filename):
"""
Loads a pygsti dataset from file.
This is a wrapper that just checks the first line, and replaces it with the newer outcome specification
format if its the old type.
"""
try:
# file = open(filename, "r")
open(filename, "r")
except IOError:
print("File not found, or other file IO error.")
# lines = file.readlines()
# file.close()
# if lines[0] == "## Columns = 00 count, 01 count, 10 count, 11 count\n":
# lines[0] = "## Columns = 0:0 count, 0:1 count, 1:0 count, 1:1 count\n"
# file = open(filename, "w")
# file.writelines(lines)
# file.close()
data = _pygio.read_dataset(filename)
return data
def test_load_ignore_zero_count_lines3(self, pth):
contents = ("## Outcomes = 0, 1\n"
"Gc1 0:1 1:1 # {'test': 1}\n"
"Gc2 # {'test': 1}\n")
with open(pth, 'w') as f:
f.write(contents)
ds = io.read_dataset(pth, ignore_zero_count_lines=False)
self.assertEqual(ds[Circuit('Gc1')]['0'], 1)
self.assertEqual(ds[Circuit('Gc2')]['0'], 0)
self.assertEqual(ds[Circuit('Gc1')].aux['test'], 1)
self.assertEqual(ds[Circuit('Gc2')].aux['test'], 1)
def _load_dataset(data_filename_or_set, comm, verbosity):
"""Loads a DataSet from the data_filename_or_set argument of functions in this module."""
printer = _baseobjs.VerbosityPrinter.create_printer(verbosity, comm)
if isinstance(data_filename_or_set, str):
if comm is None or comm.Get_rank() == 0:
if _os.path.splitext(data_filename_or_set)[1] == ".pkl":
with open(data_filename_or_set, 'rb') as pklfile:
ds = _pickle.load(pklfile)
else:
ds = _io.read_dataset(data_filename_or_set, True, "aggregate",
printer)
if comm is not None: comm.bcast(ds, root=0)
else:
ds = comm.bcast(None, root=0)
else:
ds = data_filename_or_set # assume a Dataset object
return ds
def test_load_ignore_zero_count_lines4(self, pth):
c1 = Circuit('Gc1')
c2 = Circuit('Gc2')
c3 = Circuit('Gc3')
ds = DataSet()
ds.add_count_dict(c1, {}, aux={'test': 1})
ds.add_count_dict(c2, {'0': 1}, aux={'test': 1})
ds.add_count_dict(c3, {}, aux={'test': 1})
#print(ds)
io.write_dataset(pth, ds, fixed_column_mode=False)
ds = io.read_dataset(pth, ignore_zero_count_lines=False)
self.assertEqual(ds[c1]['0'], 0)
self.assertEqual(ds[c2]['0'], 1)
self.assertEqual(
ds[c3]['0'],
0) # especially make sure last line is read in properly!
self.assertEqual(ds[c1].aux['test'], 1)
self.assertEqual(ds[c2].aux['test'], 1)
self.assertEqual(ds[c3].aux['test'], 1)
def load_data_into_benchmarker(dsfilenames=None,
summarydatasets_filenames=None,
summarydatasets_folder=None,
predicted_summarydatasets_folders={},
verbosity=1):
"""
todo
"""
if len(predicted_summarydatasets_folders) > 0:
assert (summarydatasets_folder is not None)
#if len(predicted_summarydatasets_folders) > 1:
# raise NotImplementedError("This is not yet supported!")
if dsfilenames is not None:
# If it is a filename, then we import the dataset from file.
if isinstance(dsfilenames, str):
dsfilenames = [
dsfilenames,
]
elif not isinstance(dsfilenames, list):
raise ValueError("dsfilenames must be a str or a list of strings!")
mds = _mds.MultiDataSet()
for dsfn_ind, dsfn in enumerate(dsfilenames):
if dsfn[-4:] == '.txt':
print(dsfn)
mds.add_dataset(
dsfn_ind,
_io.read_dataset(dsfn,
collision_action='keepseparate',
record_zero_counts=False,
ignore_zero_count_lines=False,
verbosity=verbosity))
elif dsfn[-4:] == '.pkl':
if verbosity > 0:
print(" - Loading DataSet from pickle file...", end='')
with open(dsfn, 'rb') as f:
mds.add_dataset(dsfn_ind, _pickle.load(f))
if verbosity > 0:
print("complete.")
else:
raise ValueError("File must end in .pkl or .txt!")
# # If it isn't a string, we assume that `dsfilenames` is a DataSet.
# else:
# ds = dsfilenames
if verbosity > 0:
print(" - Extracting metadata from the DataSet...", end='')
# To store the aux information about the RB experiments.
all_spec_filenames = []
# circuits_for_specfile = {}
# outdslist = []
# We go through the dataset and extract all the necessary auxillary information.
for circ in mds[mds.keys()[0]].keys():
# The spec filename or names for this circuits
specfns_forcirc = mds.auxInfo[circ]['spec']
# The RB length for this circuit
# try:
# l = mds.auxInfo[circ]['depth']
# except:
# l = mds.auxInfo[circ]['length']
# The target bitstring for this circuit.
# target = mds.auxInfo[circ]['target']
# This can be a string (a single spec filename) or a list, so make always a list.
if isinstance(specfns_forcirc, str):
specfns_forcirc = [
specfns_forcirc,
]
for sfn_forcirc in specfns_forcirc:
# If this is the first instance of seeing this filename then...
if sfn_forcirc not in all_spec_filenames:
# ... we store it in the list of all spec filenames to import later.
all_spec_filenames.append(sfn_forcirc)
# And it won't yet be a key in the circuits_for_specfile dict, so we add it.
# circuits_for_specfile[sfn_forcirc] = {}
# # If we've not yet had this length for that spec filename, we add that as a key.
# if l not in circuits_for_specfile[sfn_forcirc].keys():
# circuits_for_specfile[sfn_forcirc][l] = []
# # We add the circuit and target output to the dict for the corresponding spec files.
# circuits_for_specfile[sfn_forcirc][l].append((circ, target))
# circ_specindices = []
# for sfn_forcirc in specfns_forcirc:
# circ_specindices.append(all_spec_filenames.index(sfn_forcirc))
if verbosity > 0:
print("complete.")
print(" - Reading in the metadata from the extracted filenames...",
end='')
# We put RB specs that we create via file import (and the circuits above) into this dict
rbspecdict = {}
# We look for spec files in the same directory as the datafiles, so we find what that is.
# THIS REQUIRES ALL THE FILES TO BE IN THE SAME DIRECTORY
directory = dsfilenames[0].split('/')
directory = '/'.join(directory[:-1])
if len(directory) > 0:
directory += '/'
for specfilename in all_spec_filenames:
# Import the RB spec file.
rbspec = load_benchmarkspec(directory + specfilename)
# Add in the circuits that correspond to each spec, extracted from the dataset.
# rbspec.add_circuits(circuits_for_specfile[specfilename])
# Record the spec in a list, to be given to an RBAnalyzer object.
rbspecdict[specfilename] = rbspec
if verbosity > 0:
print("complete.")
print(" - Recording all of the data in a Benchmarker...", end='')
# Put everything into an RBAnalyzer object, which is a container for RB data, and return this.
benchmarker = _benchmarker.Benchmarker(rbspecdict,
ds=mds,
summary_data=None)
if verbosity > 0: print("complete.")
return benchmarker
elif (summarydatasets_filenames is not None) or (summarydatasets_folder
is not None):
rbspecdict = {}
# If a dict, its just the keys of the dict that are the rbspec file names.
if summarydatasets_filenames is not None:
specfiles = list(summarydatasets_filenames.keys())
# If a folder, we look for files in that folder with the standard name format.
elif summarydatasets_folder is not None:
specfiles = []
specfilefound = True
i = 0
while specfilefound:
try:
filename = summarydatasets_folder + "/spec{}.txt".format(i)
with open(filename, 'r') as f:
if verbosity > 0:
print(filename + " found")
specfiles.append(filename)
i += 1
except:
specfilefound = False
if verbosity > 0:
print(filename +
" not found so terminating spec file search.")
for sfn_ind, specfilename in enumerate(specfiles):
rbspec = load_benchmarkspec(specfilename)
rbspecdict[sfn_ind] = rbspec
summary_data = {}
predicted_summary_data = {
pkey: {}
for pkey in predicted_summarydatasets_folders.keys()
}
for i, (specfilename,
rbspec) in enumerate(zip(specfiles, rbspecdict.values())):
structure = rbspec.get_structure()
summary_data[i] = {}
for pkey in predicted_summarydatasets_folders.keys():
predicted_summary_data[pkey][i] = {}
if summarydatasets_filenames is not None:
sds_filenames = summarydatasets_filenames[specfilename]
elif summarydatasets_folder is not None:
sds_filenames = [
summarydatasets_folder + '/{}-{}.txt'.format(i, j)
for j in range(len(structure))
]
predsds_filenames_dict = {}
for pkey, pfolder in predicted_summarydatasets_folders.items():
predsds_filenames_dict[pkey] = [
pfolder + '/{}-{}.txt'.format(i, j)
for j in range(len(structure))
]
for sdsfn, qubits in zip(sds_filenames, structure):
summary_data[i][qubits] = import_rb_summary_data(
sdsfn, len(qubits), verbosity=verbosity)
for pkey, predsds_filenames in predsds_filenames_dict.items():
for sdsfn, qubits in zip(predsds_filenames, structure):
predicted_summary_data[pkey][i][
qubits] = import_rb_summary_data(sdsfn,
len(qubits),
verbosity=verbosity)
benchmarker = _benchmarker.Benchmarker(
rbspecdict,
ds=None,
summary_data=summary_data,
predicted_summary_data=predicted_summary_data)
return benchmarker
else:
raise ValueError(
"Either a filename for a DataSet or filenames for a set of RBSpecs "
+ "and RBSummaryDatasets must be provided!")
def load_benchmarker(directory, load_datasets=True, verbosity=1):
"""
"""
with open(directory + '/global.txt', 'r') as f:
globaldict = _json.load(f)
numpasses = globaldict['numpasses']
speckeys = globaldict['speckeys']
success_key = globaldict['success_key']
success_outcome = globaldict['success_outcome']
dscomparator = globaldict['dscomparator']
if load_datasets:
dskeys = [
dskey.name for dskey in _os.scandir(directory + '/data')
if dskey.is_dir()
]
multidsdict = {dskey: _mds.MultiDataSet() for dskey in dskeys}
for dskey in dskeys:
for passnum in range(numpasses):
dsfn = directory + '/data/{}/ds{}.txt'.format(dskey, passnum)
ds = _io.read_dataset(dsfn,
collision_action='keepseparate',
record_zero_counts=False,
ignore_zero_count_lines=False,
verbosity=verbosity)
multidsdict[dskey].add_dataset(passnum, ds)
else:
multidsdict = None
specs = {}
for i, speckey in enumerate(speckeys):
specs[speckey] = load_benchmarkspec(directory +
'/specs/{}.txt'.format(i))
summary_data = {'global': {}, 'pass': {}, 'aux': {}}
predictionkeys = [
pkey.name for pkey in _os.scandir(directory + '/predictions')
if pkey.is_dir()
]
predicted_summary_data = {pkey: {} for pkey in predictionkeys}
for i, spec in enumerate(specs.values()):
summary_data['pass'][i] = {}
summary_data['global'][i] = {}
summary_data['aux'][i] = {}
for pkey in predictionkeys:
predicted_summary_data[pkey][i] = {}
structure = spec.get_structure()
for j, qubits in enumerate(structure):
# Import the summary data for that spec and qubit subset
with open(directory + '/summarydata/{}-{}.txt'.format(i, j),
'r') as f:
sd = _json.load(f)
summary_data['pass'][i][qubits] = {}
for dtype, data in sd['pass'].items():
summary_data['pass'][i][qubits][dtype] = {
int(key): value
for (key, value) in data.items()
}
summary_data['global'][i][qubits] = {}
for dtype, data in sd['global'].items():
summary_data['global'][i][qubits][dtype] = {
int(key): value
for (key, value) in data.items()
}
# Import the auxillary data
with open(directory + '/aux/{}-{}.txt'.format(i, j), 'r') as f:
aux = _json.load(f)
summary_data['aux'][i][qubits] = {}
for dtype, data in aux.items():
summary_data['aux'][i][qubits][dtype] = {
int(key): value
for (key, value) in data.items()
}
# Import the predicted summary data for that spec and qubit subset
for pkey in predictionkeys:
with open(
directory +
'/predictions/{}/summarydata/{}-{}.txt'.format(
pkey, i, j), 'r') as f:
psd = _json.load(f)
predicted_summary_data[pkey][i][qubits] = {}
for dtype, data in psd.items():
predicted_summary_data[pkey][i][qubits][dtype] = {
int(key): value
for (key, value) in data.items()
}
benchmarker = _benchmarker.Benchmarker(
specs,
ds=multidsdict,
summary_data=summary_data,
predicted_summary_data=predicted_summary_data,
dstype='dict',
success_outcome=success_outcome,
success_key=success_key,
dscomparator=dscomparator)
return benchmarker
