def is_not_yaml(check_obj):
check_obj.is_string()
try:
yaml.loads(check_obj._val)
raise CheckError('{} is valid YAML'.format(check_obj._val))
except ValueError:
return check_obj
def is_not_yaml(self):
self.is_string()
try:
yaml.loads(self._val)
raise CheckError('{} is valid YAML'.format(self._val))
except ValueError:
return self
def get_yaml(fh):
"""YAML to object with input type detection"""
import yaml
if isinstance(fh, file):
return yaml.load(fh)
elif isinstance(fh, str):
return yaml.loads(fh)
def load_config_file(self, config_file):
if not os.path.exists(config_file):
raise Exception("Path to config file do not exists on disk...")
with open(config_file, "r") as stream:
data = stream.read()
if not data:
raise Exception("No data in config file : {}".format(config_file))
# Try first with yaml as that is default config lagn
# If yaml loading failed then try json loading
try:
data_tree = yaml.loads(data)
except Exception:
try:
data_tree = json.loads(data)
except Exception:
raise Exception(
"Unable to load data as yaml or json from config file : {}"
.format(config_file))
log.debug("Loading default data from default config file : {}".format(
config_file))
# If data was loaded into python datastructure then load it into the config tree
self.merge_data_tree(data_tree)
def to_yaml_string(self, pb_msg, project_ns=False):
""" Serialize a protobuf message into YAML
Arguments:
pb_msg - A GI-protobuf object of type provided into constructor
project_ns - Need the desc in project namespace, required for
posting to Restconf as part of onboarding
Returns:
A YAML string representing the protobuf message
Raises:
SerializationError - Message could not be serialized
TypeError - Incorrect protobuf type provided
"""
self._log.debug("Convert desc to yaml (ns:{}): {}".format(
project_ns, pb_msg.as_dict()))
try:
desc_msg = self.to_desc_msg(pb_msg, not project_ns)
yaml_str = desc_msg.to_yaml(self.model)
if project_ns:
# Remove rw-project:project top level element
dic = yaml.loads(yaml_str)
ystr = yaml.dump(dic[NS_PROJECT][0])
else:
ystr = yaml_str
except Exception as e:
self._log.exception("Exception converting to yaml: {}".format(e))
raise SerializationError(e)
return ystr
def yaml2dict(content):
try:
return yaml.loads(content)
except yaml.YAMLError as e:
msg_sche = 'your content {} is not in the right yaml ' \
'style and details are {}'
error(__file__, msg_sche.format(content, e))
def testCreateAPIsMapFile(self):
config = yaml.loads(
textwrap.dedent("""\
orange:
v1:
discovery: organge_v1.json
default: True
v2:
discovery: organge_v2.json
banana:
v2beta:
discovery: banana_v2beta.json
v2_staging:
version: v2
discovery: banana_v2_staging.json
default: True
pear:
v7_test:
discovery: pear_v7_test.json
"""))
with files.TemporaryDirectory() as tmp_dir:
regen.GenerateApiMap(tmp_dir, 'fruits', config)
content = self.GetFileContent(os.path.join(tmp_dir, 'api_map.py'))
self.assertEquals(
self.GetFileContent(
os.path.join(os.path.dirname(__file__), 'testdata',
'api_map_sample.txt')), content)
def svc_id(svc_recs, rec):
_id = None
for key, val in svc_recs.items():
if loads(key).items() <= rec.items():
assert _id is None
_id = val
return _id
def load_config_file(self, config_file):
if not os.path.exists(config_file):
raise Exception("Path to config file do not exists on disk...")
with open(config_file, "r") as stream:
data = stream.read()
if not data:
raise Exception("No data in config file : {}".format(config_file))
# Try first with yaml as that is default config lagn
# If yaml loading failed then try json loading
try:
data_tree = yaml.loads(data)
except Exception:
try:
data_tree = json.loads(data)
except Exception:
raise Exception("Unable to load data as yaml or json from"
" config file : {}".format(config_file))
Log.debug("Loading default data from default config file : {}".format(config_file))
# If data was loaded into python datastructure then load it into the
# config tree
self.merge_data_tree(data_tree)
def testCreateAPIsMapFile(self):
config = yaml.loads(
textwrap.dedent(
"""\
orange:
v1:
discovery: organge_v1.json
default: True
v2:
discovery: organge_v2.json
banana:
v2beta:
discovery: banana_v2beta.json
v2_staging:
version: v2
discovery: banana_v2_staging.json
default: True
pear:
v7_test:
discovery: pear_v7_test.json
"""
)
)
with files.TemporaryDirectory() as tmp_dir:
regen.GenerateApiMap(tmp_dir, "fruits", config)
content = self.GetFileContent(os.path.join(tmp_dir, "api_map.py"))
self.assertEquals(
self.GetFileContent(os.path.join(os.path.dirname(__file__), "testdata", "api_map_sample.txt")), content
)
def loadResultDefinition(fname):
'''Deserialize `fname` appropriate to extension. Return the result.'''
ext = fname.split('.')[-1]
fstr = open(fname).read()
if ext == 'json':
return json.loads(fstr)
elif ext == 'yaml':
return yaml.loads(fstr)
def _load_schema_url(url):
import urllib2
try:
res = urllib2.urlopen(url)
tables = res.read()
except urllib2.URLError:
raise SchemingException("Could not load %s" % url)
return yaml.loads(tables, url)
def test_read_cities_data():
config = yaml.loads('src/configs/config.yaml')
result = io.read_cities_data('br', config)
assert isinstance(result, dict)
for v in result.values():
assert isinstance(v, dict)
def decode_content_data(message):
'''
Validate message and decode data from content according to mime-type.
Parameters
----------
message : dict
One of the message types defined in :data:`MESSAGE_SCHEMA`.
Returns
-------
object
Return deserialized object from ``content['data']`` field of message.
Raises
------
RuntimeError
If ``content['error']`` field is set.
'''
validate(message)
error = message['content'].get('error', None)
if error is not None:
raise RuntimeError(error)
mime_type = 'application/python-pickle'
transfer_encoding = 'BASE64'
metadata = message['content'].get('metadata', None)
if metadata is not None:
mime_type = metadata.get('mime_type', mime_type)
transfer_encoding = metadata.get('transfer_encoding',
transfer_encoding)
data = message['content'].get('data', None)
if data is None:
return None
# If content data was base64 encoded, decode it.
#
# [1]: https://www.w3.org/Protocols/rfc1341/5_Content-Transfer-Encoding.html
if transfer_encoding == 'BASE64':
data = base64.b64decode(data)
if mime_type == 'application/python-pickle':
# Pickle object.
return pickle.loads(data)
elif mime_type == 'application/x-yaml':
return yaml.loads(data)
elif mime_type == 'application/json':
return json.loads(data)
elif mime_type in ('application/octet-stream', 'text/plain'):
return data
else:
raise ValueError('Unrecognized mime-type: %s' % mime_type)
def decode_content_data(message):
'''
Validate message and decode data from content according to mime-type.
Parameters
----------
message : dict
One of the message types defined in :data:`MESSAGE_SCHEMA`.
Returns
-------
object
Return deserialized object from ``content['data']`` field of message.
Raises
------
RuntimeError
If ``content['error']`` field is set.
'''
validate(message)
error = message['content'].get('error', None)
if error is not None:
raise RuntimeError(error)
mime_type = 'application/python-pickle'
transfer_encoding = 'BASE64'
metadata = message['content'].get('metadata', None)
if metadata is not None:
mime_type = metadata.get('mime_type', mime_type)
transfer_encoding = metadata.get('transfer_encoding',
transfer_encoding)
data = message['content'].get('data', None)
if data is None:
return None
# If content data was base64 encoded, decode it.
#
# [1]: https://www.w3.org/Protocols/rfc1341/5_Content-Transfer-Encoding.html
if transfer_encoding == 'BASE64':
data = base64.b64decode(data)
if mime_type == 'application/python-pickle':
# Pickle object.
return pickle.loads(data)
elif mime_type == 'application/x-yaml':
return yaml.loads(data)
elif mime_type == 'application/json':
return json.loads(data)
elif mime_type in ('application/octet-stream', 'text/plain'):
return data
else:
raise ValueError('Unrecognized mime-type: %s' % mime_type)
def query(ctx, input, output, context, query_file):
""" Run an MQL query and store the results. """
graph = ctx.obj['GRAPH']
for uri in input:
load_dump(graph, ensure_uri(uri))
if query_file is not None:
query = read_yaml_uri(ensure_uri(query_file))
else:
query = yaml.loads(sys.stdin)
save_query_json(graph, query, output, context_id=context)
def query(ctx, input, output, context, query_file):
""" Run an MQL query and store the results. """
graph = ctx.obj['GRAPH']
for uri in input:
load_dump(graph, ensure_uri(uri))
if query_file is not None:
query = read_yaml_uri(ensure_uri(query_file))
else:
query = yaml.loads(sys.stdin)
save_query_json(graph, query, output, context_id=context)
def handle(self, *args, **options):
for filename in args:
with open(filename) as f:
if filename.endswith('.json'):
bug_dicts = simplejson.load(f)
else:
# assume YAML
s = f.read()
bug_dicts = yaml.loads(s)
for bug_dict in bug_dicts:
mysite.customs.core_bugimporters.import_one_bug_item(bug_dict)
def testGetAPIsMap(self):
config = yaml.loads(
textwrap.dedent(
"""\
orange:
v1:
discovery: organge_v1.json
default: True
v2:
discovery: organge_v2.json
banana:
v2beta:
discovery: banana_v2beta.json
v2_staging:
version: v2
discovery: banana_v2_staging.json
default: True
pear:
v7_test:
discovery: pear_v7_test.json
"""
)
)
expected_map = {
"orange": {
"v1": api_def.APIDef(
"fruits.orange.v1.orange_v1_client.OrangeV1", "fruits.orange.v1.orange_v1_messages", True
),
"v2": api_def.APIDef(
"fruits.orange.v2.orange_v2_client.OrangeV2", "fruits.orange.v2.orange_v2_messages"
),
},
"banana": {
"v2beta": api_def.APIDef(
"fruits.banana.v2beta.banana_v2beta_client.BananaV2beta",
"fruits.banana.v2beta.banana_v2beta_messages",
),
"v2_staging": api_def.APIDef(
"fruits.banana.v2_staging.banana_v2_client.BananaV2",
"fruits.banana.v2_staging.banana_v2_messages",
True,
),
},
"pear": {
"v7_test": api_def.APIDef(
"fruits.pear.v7_test.pear_v7_test_client.PearV7Test",
"fruits.pear.v7_test.pear_v7_test_messages",
True,
)
},
}
actual_map = regen._MakeApiMap("fruits", config)
self.assertEquals(expected_map, actual_map)
def _make_call(self, call):
resp = requests.request('GET', call)
if resp.status_code != 200:
return None
else:
data = resp.text
if self.style == 'json':
return json.loads(data)
elif self.style == 'yaml':
return yaml.loads(data)
else:
return data
def handle(self, *args, **options):
for filename in args:
with open(filename) as f:
if filename.endswith('.json'):
bug_dicts = json.load(f)
elif filename.endswith('.jsonlines'):
bug_dicts = jsonlines_decoder(f)
else:
# assume YAML
s = f.read()
bug_dicts = yaml.loads(s)
for bug_dict in bug_dicts:
_import_one(bug_dict)
def handle(self, *args, **options):
for filename in args:
with open(filename) as f:
if filename.endswith('.json'):
bug_dicts = simplejson.load(f)
elif filename.endswith('.jsonlines'):
bug_dicts = jsonlines_decoder(f)
else:
# assume YAML
s = f.read()
bug_dicts = yaml.loads(s)
for bug_dict in bug_dicts:
mysite.customs.core_bugimporters.import_one_bug_item(bug_dict)
def testGetAPIsMap(self):
config = yaml.loads(
textwrap.dedent("""\
orange:
v1:
discovery: organge_v1.json
default: True
v2:
discovery: organge_v2.json
banana:
v2beta:
discovery: banana_v2beta.json
v2_staging:
version: v2
discovery: banana_v2_staging.json
default: True
pear:
v7_test:
discovery: pear_v7_test.json
"""))
expected_map = {
'orange': {
'v1':
api_def.APIDef('fruits.orange.v1.orange_v1_client.OrangeV1',
'fruits.orange.v1.orange_v1_messages', True),
'v2':
api_def.APIDef('fruits.orange.v2.orange_v2_client.OrangeV2',
'fruits.orange.v2.orange_v2_messages')
},
'banana': {
'v2beta':
api_def.APIDef(
'fruits.banana.v2beta.banana_v2beta_client.BananaV2beta',
'fruits.banana.v2beta.banana_v2beta_messages'),
'v2_staging':
api_def.APIDef(
'fruits.banana.v2_staging.banana_v2_client.BananaV2',
'fruits.banana.v2_staging.banana_v2_messages', True)
},
'pear': {
'v7_test':
api_def.APIDef(
'fruits.pear.v7_test.pear_v7_test_client.PearV7Test',
'fruits.pear.v7_test.pear_v7_test_messages', True)
}
}
actual_map = regen._MakeApiMap('fruits', config)
self.assertEquals(expected_map, actual_map)
def testGetAPIsMapNoDefaultsClientsForAPIs(self):
config = yaml.loads(
textwrap.dedent("""\
orange:
v1:
discovery: organge_v1.json
v2:
discovery: organge_v2.json
"""))
with self.assertRaises(Exception) as ctx:
regen._MakeApiMap('fruits', config)
msg = str(ctx.exception)
self.assertEquals(msg,
'No default client versions found for [fig, lime]!')
def __init__(self, filename, title='Tk', prefer_tk=True, file_type='json'):
self.prefer_tk = prefer_tk
self.vars = {}
if file_type == 'json':
user_interface = json.load(open(filename)) if os.path.isfile(
filename) else json.loads(filename)
elif file_type == 'yaml':
user_interface = yaml.load(open(filename)) if os.path.isfile(
filename) else yaml.loads(filename)
else:
raise ValueError('Only json or yaml file definitions are'
'supported.')
self.create_widgets(self, user_interface)
def __init__(self, filename, title='Tk', preferTk=True):
"""
Initialize a Tk root and created the UI from a JSON file.
Returns the Tk root.
"""
# Needs to be done this way - because base class do not derive from
# object :-(
tkinter.Tk.__init__(self)
self.preferTk = preferTk
self.title(title)
user_interface = yaml.load(open(filename)) if os.path.isfile(
filename) else yaml.loads(filename)
self.create_widgets(self, user_interface)
super().__init__(filename, title, preferTk)
def getParsedSchemaFromYaml(model):
"""reads a JSON schema file in yaml format
and returns the parsed dictionary from it
Keyword arguments:
model -- file name and path to the model to load or model content from stdin
"""
if __isHttpLoadableModel(model):
with urllib.request.urlopen(model) as url:
return yaml.loads(url.read().decode(), Loader=yaml.FullLoader)
elif doesFileExist(model):
# model is treaten as file to input
with open(model) as json_schema:
return yaml.load(json_schema, Loader=yaml.FullLoader)
else:
# model is treaten as string content to get parsed
return yaml.load(model, Loader=yaml.FullLoader)
def testGetAPIsMapNoDefaultsClientsForAPIs(self):
config = yaml.loads(
textwrap.dedent(
"""\
orange:
v1:
discovery: organge_v1.json
v2:
discovery: organge_v2.json
"""
)
)
with self.assertRaises(Exception) as ctx:
regen._MakeApiMap("fruits", config)
msg = str(ctx.exception)
self.assertEquals(msg, "No default client versions found for [fig, lime]!")
def jq_all(query, data_str, *args, **kwargs):
"""
Queries the nested data and returns all results as a list.
Parameters
----------
data_str : str
Nested data in Python dict's representation format.
If must be loadable by ``yaml.safe_load()``.
Returns
-------
result : str
String representation of the result list.
"""
try:
return json.dumps(pyjq.all(query, yaml.loads(data_str)), *args, **kwargs)
except Exception as e:
return 'jq error: {} query: {!r} str: {!r}'.format(e, query, data_str)
def ucsdJsonParser(text):
jsondict = None
if text:
try:
jsondict = json.loads(text)
except: pass
# we're here because standard JSON parser failed to make sense of the input
# try interpreting as YAML:
try:
jsondict = yaml.loads(text)
except: pass
# we're here because standard YAML parser failed to make sense of the input
# try alternative JSON lib:
try:
jsondict = demjson.decode(text)
except: pass
# inconceivable!
return jsondict
def __init__(self, data_or_file, shape):
super(ZacSimulator, self).__init__()
if isinstance(data_or_file, file):
data = yaml.load(data_or_file)
else:
data = yaml.loads(data_or_file)
self.sets = data["sets"]
self.cells = self.sets.keys()
self.strings = data["strings"]
self.python_code = "# auto-generated code:\n" + data["python_code"]
self.cpp_code = data["cpp_code"]
if len(shape) == 1:
shape = (shape[0], 1)
self.shape = shape
self.possible_values = self.sets
self.stringy_subcells = [k for k, v in self.sets.iteritems() if isinstance(v[0], basestring)]
self.neighbourhood = ZacNeighbourhood(data["neighbourhood"])
self.acc = SubcellAccessor(self.sets.keys())
self.computation = PasteComputation(self.cpp_code, self.python_code)
self.border = BorderSizeEnsurer()
if len(shape) == 1:
self.loop = OneDimCellLoop()
else:
self.loop = TwoDimCellLoop()
for k in self.sets.keys():
nconf_arr = np.zeros(shape, dtype=int)
cconf_arr = np.zeros(shape, dtype=int)
setattr(self, "cconf_%s" % k, cconf_arr)
setattr(self, "nconf_%s" % k, nconf_arr)
if k in self.stringy_subcells:
val = [index for index, val in enumerate(self.strings) if val == self.sets[k][0]][0]
cconf_arr[:] = val
nconf_arr[:] = val
self.stepfunc = StepFunc(self, self.loop, self.acc, self.neighbourhood, self.border, visitors=[self.computation])
self.stepfunc.gen_code()
def load(self, filePath, formatType=DETECT):
self.config = {}
self.formatType = formatType
self.filePath = filePath
if os.path.isfile(filePath):
file = open(filePath).read()
if self.formatType == self.DETECT:
bname = os.path.basename(self.filePath)
extension = os.path.splitext(bname)[0]
try:
self.formatType = self.formats[extension]
except:
return
if self.formatType == self.JSON:
self.config = json.loads(content)
elif self.formatType == self.YAML:
self.fixYamlIndexes(content)
self.config = yaml.loads(content)
elif self.formatType == self.PROPERTIES:
self.config = Properties.loads(content)
def __init__(self, spec):
self._stateMachine = self
self._spec = spec
if isinstance(spec, dict): # for testing and calling as a library
self._stateMachineSpec = spec
else:
if self._spec.endswith("json"):
self._stateMachineSpec = json.loads(
open(self._spec, "r").read())
elif self._spec.endswith("yml"):
self._stateMachineSpec = yaml.loads(
open(self._spec, "r").read())
else:
self._stateMachineSpec = {} # for incremantal construction
self._input = {}
self._output = {}
self._state = {}
# make instances of all inputs, outputs, and states
for input in self._stateMachineSpec["Input"]:
self._input[input] = Input(
self._stateMachineSpec["Input"]
[input]) # construct an instance with the document node
for output in self._stateMachineSpec["Output"]:
self._output[output] = Output(
self._stateMachineSpec["Output"]
[output]) # construct an instance with the document node
for state in self._stateMachineSpec["State"]:
self._state[state] = State(
state, self._stateMachineSpec["State"][state],
self._stateMachine
) # construct an instance with the name, the document node, and the state machine instance
self._currentState = self._state[self._stateMachineSpec[
"CurrentState"]] # initialize the state machine to the provided state
self._currentTime = 0
self._intervalStartTime = self._currentTime
self._intervalTime = self._currentTime - self._intervalStartTime # wrap- and sign-safe interval compare
def load(self, file, configFormat = formats["detect"]):
self.config = {}
self.format = configFormat
self.file = file
content = file.read()
if self.format == self.formats["detect"]:
baseName = os.path.basename(self.file.name)
extension = baseName.rsplit(".", 1)[1]
if extension in self.formats:
self.format = self.formats[extension]
if self.format == self.formats["json"]:
self.config = json.loads(content)
elif self.format == self.formats["yaml"]:
self.fixYamlIndexes(content)
self.config = yaml.loads(content)
elif self.format == self.formats["properties"]:
self.config = Properties.loads(content)
elif self.format == self.formats["toml"]:
self.config = toml.loads(content)
elif self.format == self.formats["ini"]:
self.config = toml.loads(content)
def __init__(self, filename, title='Tk', prefer_tk=True, file_type='json'):
"""
Initialize a Tk root and created the UI from a JSON file.
Returns the Tk root.
"""
# Needs to be done this way - because base class do not derive from
# object :-(
tkinter.Tk.__init__(self)
self.prefer_tk = prefer_tk
self.title(title)
if file_type == 'json':
user_interface = json.load(open(filename)) if os.path.isfile(
filename) else json.loads(filename)
elif file_type == 'yaml':
user_interface = yaml.load(open(filename)) if os.path.isfile(
filename) else yaml.loads(filename)
else:
raise ValueError('Only json or yaml file definitions are'
'supported.')
self.create_widgets(self, user_interface)
import threading
import RPi.GPIO as GPIO
from cPickle import loads as load
from cPickle import dumps as dump
## Settings
with open('/etc/hydrobot/settings.yml','r') as f:
settings = yaml.load(f.read())
## Stored Procedures
procedures = {}
if os.path.isfile('/etc/hydrobot/procedures.yml'):
with open('/etc/hydrobot/procedures.yml','r') as f:
procedures = yaml.loads(f.read())
if os.path.isfile(settings['hub']['procedures file']):
with open(settings['hub']['procedures file'],'r') as f:
procedures = yaml.loads(f.read())
## Node Role
def node():
root_pattern = 'output::'+settings['node']['id']+'::'
r = redis.StrictRedis(host=settings['hub']['host'],port=settings['hub']['port'])
r.set('node::'+settings['node']['id'],dump(settings['node']))
pins = []
for i in settings['node']['pins']:
pins.append(int(i.keys()[0]))
for i in pins:
print root_pattern+str(i)
def comment_data(self, data, verified=None, code_review=None):
comment_message = yaml.loads(data)
self.remote.comment_change(self.number, self.patchset_number, comment_message, verified=verified, code_review=code_review)
if extra_columns:
logger.error("Your configuration names columns that don't exists in the database.")
logger.error("These columns are: %s" % (extra_columns,))
sys.exit(1)
def anonymize(config):
sql = []
sql.extend(get_truncates(config))
sql.extend(get_deletes(config))
sql.extend(get_updates(config))
print "SET FOREIGN_KEY_CHECKS=0;"
for stmt in sql:
print stmt + ";"
print "SET FOREIGN_KEY_CHECKS=1;"
if __name__ == "__main__":
db_file_names = sys.argv[1:]
if not db_file_names:
logger.error("Usage: %s config_file [config_file ...]", sys.argv[0])
for db_file_name in db_file_names:
logger.info("Processing %s", db_file_name)
with open(db_file_name) as db_file:
cfg = yaml.loads(db_file)
check_config(fg)
anonymize(cfg)
def loads(obj: str):
return loads(obj)
def load(file="testyaml.yaml"):
with open(file, 'r+') as fr:
return loads(file)
def loads(cls, s, *args, **kwargs): try: return yamllib.loads(s, *args, **kwargs) except Exception, e: raise TranscoderLoadException(e)
import json
import redis
import threading
import RPi.GPIO as GPIO
from cPickle import loads as load
from cPickle import dumps as dump
## Settings
with open('/etc/hydrobot/settings.yml', 'r') as f:
settings = yaml.load(f.read())
## Stored Procedures
procedures = {}
if os.path.isfile('/etc/hydrobot/procedures.yml'):
with open('/etc/hydrobot/procedures.yml', 'r') as f:
procedures = yaml.loads(f.read())
if os.path.isfile(settings['hub']['procedures file']):
with open(settings['hub']['procedures file'], 'r') as f:
procedures = yaml.loads(f.read())
## Node Role
def node():
root_pattern = 'output::' + settings['node']['id'] + '::'
r = redis.StrictRedis(host=settings['hub']['host'],
port=settings['hub']['port'])
r.set('node::' + settings['node']['id'], dump(settings['node']))
pins = []
for i in settings['node']['pins']:
pins.append(int(i.keys()[0]))
for i in pins:
def from_yaml(cls, yml):
if '\n' not in yml:
with open(os.path.expanduser(yml)) as f:
return cls.from_dict(yaml.load(f))
else:
return cls.from_dict(yaml.loads(yml))
def optimize_variational_circuit(
ansatz_specs,
backend_specs,
optimizer_specs,
cost_function_specs,
qubit_operator,
initial_parameters="None",
fixed_parameters="None",
noise_model="None",
device_connectivity="None",
parameter_grid="None",
parameter_values_list=None,
constraint_operator="None",
prior_expectation_values: Optional[str] = None,
keep_history=False,
thetas=None,
):
warnings.warn(
"optimize_variational_circuit will be depreciated in favor of optimize_ansatz_based_cost_function in steps/optimize.py in z-quantum-core.",
DeprecationWarning,
)
if initial_parameters != "None":
initial_params = load_array(initial_parameters)
else:
initial_params = None
if fixed_parameters != "None":
fixed_params = load_array(fixed_parameters)
else:
fixed_params = None
# Load qubit operator
operator = load_qubit_operator(qubit_operator)
if isinstance(ansatz_specs, str):
ansatz_specs_dict = yaml.load(ansatz_specs, Loader=yaml.SafeLoader)
else:
ansatz_specs_dict = ansatz_specs
if "WarmStartQAOAAnsatz" in ansatz_specs_dict["function_name"]:
thetas = np.array(load_list(thetas))
ansatz = create_object(ansatz_specs_dict,
cost_hamiltonian=operator,
thetas=thetas)
elif "QAOA" in ansatz_specs_dict["function_name"]:
ansatz = create_object(ansatz_specs_dict, cost_hamiltonian=operator)
else:
ansatz = create_object(ansatz_specs_dict)
# Load parameter grid
if parameter_grid != "None":
grid = load_parameter_grid(parameter_grid)
else:
grid = None
# Load parameter values list
if parameter_values_list is not None:
parameter_values_list = load_array(parameter_values_list)
# Load optimizer specs
if isinstance(optimizer_specs, str):
optimizer_specs_dict = yaml.load(optimizer_specs,
Loader=yaml.SafeLoader)
else:
optimizer_specs_dict = optimizer_specs
if (grid is not None and optimizer_specs_dict["function_name"]
== "GridSearchOptimizer"):
optimizer = create_object(optimizer_specs_dict, grid=grid)
elif (parameter_values_list is not None and
optimizer_specs_dict["function_name"] == "SearchPointsOptimizer"):
optimizer = create_object(optimizer_specs_dict,
parameter_values_list=parameter_values_list)
else:
optimizer = create_object(optimizer_specs_dict)
# Load backend specs
if isinstance(backend_specs, str):
backend_specs_dict = yaml.load(backend_specs, Loader=yaml.SafeLoader)
else:
backend_specs_dict = backend_specs
if noise_model != "None":
backend_specs_dict["noise_model"] = load_noise_model(noise_model)
if device_connectivity != "None":
backend_specs_dict["device_connectivity"] = load_circuit_connectivity(
device_connectivity)
backend = create_object(backend_specs_dict)
# Load cost function specs
if isinstance(cost_function_specs, str):
cost_function_specs_dict = yaml.load(cost_function_specs,
Loader=yaml.SafeLoader)
else:
cost_function_specs_dict = cost_function_specs
estimation_method_specs = cost_function_specs_dict.pop(
"estimation_method_specs", None)
if estimation_method_specs is not None:
if isinstance(estimation_method_specs, str):
estimation_method_specs = yaml.loads(estimation_method_specs)
estimation_method = create_object(estimation_method_specs)
else:
estimation_method = estimate_expectation_values_by_averaging
cost_function_specs_dict["estimation_method"] = estimation_method
estimation_preprocessors_specs_list = cost_function_specs_dict.pop(
"estimation_preprocessors_specs", None)
if estimation_preprocessors_specs_list is not None:
estimation_preprocessors = []
for estimation_preprocessor_specs in estimation_preprocessors_specs_list:
if isinstance(estimation_preprocessor_specs, str):
estimation_preprocessor_specs = yaml.loads(
estimation_preprocessor_specs)
estimation_preprocessors.append(
create_object(estimation_preprocessor_specs))
cost_function_specs_dict[
"estimation_preprocessors"] = estimation_preprocessors
cost_function_specs_dict["target_operator"] = operator
cost_function_specs_dict["ansatz"] = ansatz
cost_function_specs_dict["backend"] = backend
cost_function_specs_dict["fixed_parameters"] = fixed_params
cost_function = create_object(cost_function_specs_dict)
if prior_expectation_values is not None:
if isinstance(prior_expectation_values, str):
cost_function.estimator.prior_expectation_values = load_expectation_values(
prior_expectation_values)
if constraint_operator != "None":
constraint_op = load_qubit_operator(constraint_operator)
constraints_cost_function_specs = yaml.load(cost_function_specs,
Loader=yaml.SafeLoader)
constraints_estimator_specs = constraints_cost_function_specs.pop(
"estimator-specs", None)
if constraints_estimator_specs is not None:
constraints_cost_function_specs["estimator"] = create_object(
constraints_estimator_specs)
constraints_cost_function_specs["ansatz"] = ansatz
constraints_cost_function_specs["backend"] = backend
constraints_cost_function_specs["target_operator"] = constraint_op
constraint_cost_function = create_object(
constraints_cost_function_specs)
constraint_cost_function_wrapper = (
lambda params: constraint_cost_function.evaluate(params).value)
constraint_functions = ({
"type": "eq",
"fun": constraint_cost_function_wrapper
}, )
optimizer.constraints = constraint_functions
opt_results = optimizer.minimize(cost_function, initial_params,
keep_history)
save_optimization_results(opt_results, "optimization-results.json")
save_array(opt_results.opt_params, "optimized-parameters.json")
def validate(self):
try:
yaml.loads(self._content)
except ValueError as err:
return err
print(re.sub(r'\#(.*)', '_comment: $1', am))
print(re.sub(r'\#(.*)', '_comment: \\1', am))
print(re.sub(r'\#\s*(.*)', '_comment: \\1', am))
get_ipython().magic('cd')
get_ipython().magic('cd')
get_ipython().magic('cd Desktop/')
with open('print(re.sub(r'\#\s*(.*)', '_comment: \\1', am))
with open('automoderator.yaml', 'w') as f:
f.write(re.sub(r'\#\s*(.*)', '_comment: \\1', am))
with open('automoderator.yaml', 'w', encoding='utf8') as f:
f.write(re.sub(r'\#\s*(.*)', '_comment: \\1', am))
am_obj = yaml.loads(re.sub(r'\#\s*(.*)', '_comment: \\1', am))
import yaml
am_obj = yaml.loads(re.sub(r'\#\s*(.*)', '_comment: \\1', am))
am_obj = yaml.load_all(re.sub(r'\#\s*(.*)', '_comment: \\1', am))
am_obj
list(am_obj)
am_obj = yaml.load_all(re.sub(r'\#\s*(.*)', '_comment: "\\1"', am))
list(am_obj)
am_obj = yaml.load_all(re.sub(r'\#\s*(.*)', '_comment: \'\\1\'', am))
list(am_obj)
am_obj = list(yaml.load_all(re.sub(r'\#\s*(.*)', '_comment: \'\\1\'', am.replace('"', '\uff02'))))
am_obj = yaml.load_all(re.sub(r'\#\s*(.*)', make_comment_tag, am))
def make_comment_tag(matchobj):
print(matchobj)
am_obj = yaml.load_all(re.sub(r'\#\s*(.*)', make_comment_tag, am))
def make_comment_tag(matchobj):
return '_comment: ' + matchobj.group(1)
am_obj = yaml.load_all(re.sub(r'\#\s*(.*)', make_comment_tag, am))