code stringlengths 3 6.57k |
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ufactory.getPair(cusd, ceur) |
spell.setWhitelistLPTokens([lp], [True], {'from': admin}) |
bank.setWhitelistSpells([spell], [True], {'from': admin}) |
bank.setWhitelistTokens([cusd, ceur], [True, True], {'from': admin}) |
print('tx gas used', tx.gas_used) |
print('bank collateral size', bank.getPositionInfo(position_id) |
print('bank collateral value', bank.getCollateralCELOValue(position_id) |
print('bank borrow value', bank.getBorrowCELOValue(position_id) |
print('bank ceur', bank.getBankInfo(ceur) |
print('bank cusd', bank.getBankInfo(cusd) |
print('ceur Px', simple_oracle.getCELOPx(ceur) |
print('cusd Px', simple_oracle.getCELOPx(cusd) |
print('lp Px', uniswap_lp_oracle.getCELOPx(lp) |
decompressobj(-MAX_WBITS) |
_encode_bytes(text) |
isinstance(text, str) |
isinstance(text, unicode) |
unicode(text or '') |
text.encode('utf-8') |
make_compressed_frame(message, compressor) |
isinstance(message, (str, unicode) |
str(message) |
_encode_bytes(message) |
compressor.compress(message) |
Z_FULL_FLUSH (rather than Z_SYNC_FLUSH) |
compressor.flush(Z_FULL_FLUSH) |
message.endswith('\x00\x00\xff\xff') |
len(message) |
send_raw_frame(websocket, raw_message) |
websocket.raw_write(raw_message) |
websocket.current_app.on_close(MSG_SOCKET_DEAD) |
WebSocketError(MSG_SOCKET_DEAD) |
read_frame(websocket) |
Header.decode_header(websocket.stream) |
websocket.raw_read(header.length) |
WebSocketError('Could not read payload') |
len(payload) |
WebSocketError('Unexpected EOF reading frame payload') |
header.unmask_payload(payload) |
DECOMPRESSOR.decompress(payload) |
DECOMPRESSOR.decompress('\0\0\xff\xff') |
DECOMPRESSOR.flush() |
is_on_cloudfoundry(env: Environ=os.environ) |
load_cups_from_vcap_services(name: str, env: Environ=os.environ) |
service (CUPS) |
is_on_cloudfoundry(env) |
json.loads(env['VCAP_SERVICES']) |
vcap.get('user-provided', []) |
items() |
is_on_cloudfoundry(env) |
json.loads(env['VCAP_SERVICES']) |
Mongo(MongoClient) |
__init__(self, username, password, host, db='tags', collection='tweets_pipeline_v2') |
super(Mongo, self) |
self.get_default_database() |
pipelined(self, count=True) |
count_documents(query) |
find(query) |
feed(self, count=True) |
count_documents(query) |
find(query) |
search(self, count=True) |
count_documents(query) |
find(query) |
left_for_analysts(self, count=True) |
count_documents(query) |
find(query) |
removed_validators(self, count=True) |
count_documents(query) |
find(query) |
removed_analysts(self, count=True) |
count_documents(query) |
find(query) |
Mongo(_username, _password, mongodb_host) |
print(mongo_client.pipelined() |
print(mongo_client.search() |
print(mongo_client.feed() |
print(mongo_client.left_for_analysts() |
print(mongo_client.removed_validators() |
print(mongo_client.removed_analysts() |
lk.counting(6) |
lk.logdx(mod.__name__) |
lk.counting() |
dir(mod) |
name.startswith('test_') |
getattr(mod, name) |
lk.logax('testing', func.__name__) |
func() |
lk.logt('[I1117]', e) |
pd.read_csv('50_Startups.csv') |
LabelEncoder() |
labelencoder_X.fit_transform(X[:, 3]) |
OneHotEncoder(categorical_features = [3]) |
onehotencoder.fit_transform(X) |
toarray() |
ColumnTransformer([("State", OneHotEncoder() |
ct.fit_transform(X) |
train_test_split(X, y, test_size = 0.2, random_state = 0) |
StandardScaler() |
sc_X.fit_transform(X_train) |
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