def to_dict(self) -> dict:
result: dict = {}
result["decisionStrategy"] = is_none(self.decisionStrategy,
"UNANIMOUS")
result["name"] = self.name
result["type"] = is_none(self.type, "role")
result["roles"] = self.roles
result["logic"] = is_none(self.logic, "POSITIVE")
return result
def from_dict(obj) -> 'ScopeRoleRepresentation':
assert isinstance(obj, dict)
obj = key_dict_to_camel_case(obj)
id = obj.get("id")
name = obj.get("name")
containerId = obj.get("containerId")
composite = is_none(obj.get("composite"), False)
clientRole = is_none(obj.get("clientRole"), True)
return ScopeRoleRepresentation(id, name, containerId, composite,
clientRole)
def from_dict(obj) -> 'PolicieRoleRepresentation':
assert isinstance(obj, dict)
obj = key_dict_to_camel_case(obj)
decisionStrategy = is_none(obj.get("decisionStrategy"), "UNANIMOUS")
name = obj.get("name")
type = is_none(obj.get("type"), "role")
roles = obj.get("roles")
logic = is_none(obj.get("logic"), "POSITIVE")
return PolicieRoleRepresentation(name, roles, type, decisionStrategy,
logic)
def from_dict(obj: dict) -> 'GroupRepresentation':
assert isinstance(obj, dict)
obj = key_dict_to_camel_case(obj)
id = obj.get("id")
name = obj.get("name")
path = obj.get("path")
attributes = is_none(obj.get("attributes"), {})
realmRoles = is_none(obj.get("realmRoles"), [])
subGroups = is_none(obj.get("subGroups"), [])
return GroupRepresentation(id, name, path, attributes, realmRoles,
subGroups)
def __init__(self, sentence_tokenizer='normal', raw_reviews_path=None):
"""
Sentence Tokenizers: normal, punkt
"""
self.sent_tokenize = sent_tokenize
if sentence_tokenizer == 'normal':
pass
elif sentence_tokenizer == 'punkt':
self.sent_tokenize = nltk.data.load(
'tokenizers/punkt/english.pickle').tokenize
self.tokenizer = utils.get_tokenizer()
if utils.is_none(raw_reviews_path):
self.raw_reviews = utils.get_raw_test_reviews(review='tizi')
else:
with open(raw_reviews_path, 'r') as fi:
self.raw_reviews = [line.rstrip() for line in fi]
self.data = []
for _ in range(6):
self.data.append([])
self.categories = ['food', 'service', 'price', 'place']
self.conjunctions = [
"tetapi sayangnya", "namun", "tetapi", "walaupun", "akan tetapi",
"sayangnya", "hanya sayang", "sayang", "meski", "walau", "but"
]
def from_dict(obj) -> 'RealmRepresentation':
assert isinstance(obj, dict)
obj = key_dict_to_camel_case(obj)
enabled = is_none(obj.get("enabled"), True)
id = obj.get("id")
realm = obj.get("realm")
return RealmRepresentation(enabled, id, realm)
class Temperature(webapp2.RequestHandler):
def get(self):
request, response = self.request, self.response
mode, qty_str, jsonp_callback = request.get('mode'), request.get(
'q'), request.get('callback')
try:
qty = float(qty_str)
except Exception, e:
qty = None
converted = None
if not utils.is_none(qty):
converted = convert_temperature(mode, qty)
if not utils.is_none(converted):
result = {"mode": mode, "qty": qty, "v": converted}
else:
result = invalid_request
utils.write_jsonp_output(response, result, jsonp_callback)
##################
# Pipeline setup #
##################
# Read in parameter file
P.get_parameters("config_rna.yml")
# Small edits to config to enable cluster usage
P.PARAMS["cluster_queue_manager"] = P.PARAMS.get("pipeline_cluster_queue_manager")
P.PARAMS["conda_env"] = os.path.basename(os.environ["CONDA_PREFIX"])
# Make sure that params dict is typed correctly
for key in P.PARAMS:
if is_none(P.PARAMS[key]):
P.PARAMS[key] = None
elif is_on(P.PARAMS):
P.PARAMS[key] = True
# Global variables
CREATE_BIGWIGS = P.PARAMS.get("run_options_bigwigs")
CREATE_HUB = P.PARAMS.get("run_options_hub")
#############
# Pipeline #
#############
@follows(mkdir("statistics"), mkdir("statistics/fastqc"))
@transform("*.fastq.gz", regex(r"(.*).fastq.gz"), r"statistics/fastqc/\1_fastqc.zip")
def _set(cls, lvl, func):
assert is_none(cls.contents.get(
str(lvl))), "ambiguity in levels hierarchy"
cls.contents[str(lvl)] = func
