def scan_commands(self):
self._commands = []
if self.mod == None:
return
dic = self.mod.__dict__
if '__root__' in dic:
root = dic['__root__']
else:
root = []
for k in dic:
if k.startswith('_') or k in root:
continue
item = dic[k]
if type(item) == types.FunctionType:
bisect.insort(self._commands, method.Method(item, k, self))
elif type(item) == types.ModuleType and utils.is_commander_module(
item):
mod = Module(k, item, self)
bisect.insort(self._commands, mod)
# Insert root functions into this module
for r in mod.roots():
bisect.insert(self._commands, r)
def betUnderdog(data, fair=True, margin="basic", odds=None, bet_choices=3, asian=False,results = None):
generating = method.Method(data, bet_choices=bet_choices)
if results is not None:
generating.results = results
if odds is None:
odds = find_correct_odds(fair, margin, generating)
underdog = np.argmax(odds, axis=1)
return generating.evaluate(bets=underdog, odds=odds, asian=asian)
def add_default_init(self):
init = method.Method(clazz=self,
mods=[C.mod.PB],
typ=self._name,
name=self._name)
if self._sup and self._sup != C.J.OBJ:
init.body = st.to_statements(init, u"super();")
self._mtds.append(init)
return init
def bet(self, odds):
generation = method.Method(self.data, bet_choices=3)
np_results = pandas.DataFrame.to_numpy(self.data['results'])
count = 0
for i in range(self.iterations):
count += 1
my_bet = np.random.choice(generation.bet_choices)
bet_amount = self.fraction * self.bankroll
self.bankroll = (1 - self.fraction) * self.bankroll
if my_bet == np_results[i]:
self.bankroll = self.bankroll + bet_amount * odds[i, my_bet]
if self.bankroll < self.threshold:
self.bankroll = 0
break
return count
def devide_bet(data=None, margin="basic", prob=None, bet_choices=3,results = None,odds = None):
results = pandas.Series.to_numpy(results, dtype=np.int)
generating = method.Method(data, bet_choices=bet_choices)
if odds is None:
odds = generating.find_fair_odds(margin)
if prob is None:
prob = 1 / bet_choices * np.ones(
odds.shape) # prob = 1 / 3 * np.ones(odds.shape) # prob = generating.get_probabilities() # prob = 1/odds
if results is None:
n_bet = data['results'].count()
else:
n_bet = np.size(results)
eval = np.zeros(prob.shape)
for i in range(n_bet):
eval[i, results[i]] = odds[i, results[i]] * prob[i, results[i]]
return eval
def roots(self): if self._roots == None: if not self.mod: return [] dic = self.mod.__dict__ if '__root__' in dic: root = dic['__root__'] else: root = [] root = filter(lambda x: x in dic and type(dic[x]) == types.FunctionType, root) self._roots = map(lambda x: method.Method(dic[x], x, self.mod), root) return self._roots
def add_fld_init(self):
if not self.mutable_flds: return None
i_flds = list(enumerate(self.mutable_flds))
params = map(lambda (i, fld): (fld.typ, u"p{}".format(i)), i_flds)
init = method.Method(clazz=self,
mods=[C.mod.PB],
typ=self._name,
name=self._name,
params=params)
def init_fld((i, fld)):
return fld.name + u" = p{};".format(i)
body = '\n'.join(map(init_fld, i_flds))
init.body = st.to_statements(init, body)
self._mtds.append(init)
return init
from pydantic import BaseModel
import configparser
import json
import database_handler
import method
app = FastAPI()
config = configparser.ConfigParser()
config.read('db.conf')
info = config['DEFAULT']
dbh = database_handler.DatabaseHandler(db_name=info['db_name'],
check_same_thread=False)
m = method.Method(conf_file='db.conf')
class Schedule(BaseModel):
sid: str # ID
name: str # 名称
content: str # 内容
category: str # 分类
level: int # 重要程度, 0: 未定义 1: 低 2: 中 3: 高
status: float # 当前进度, 0 <= status <= 1
creation_time: str # 创建时间
start_time: str # 开始时间
end_time: str # 结束时间
@app.get('/')
def get_or_declare_clinit(self):
if hasattr(self, C.J.CLINIT): return getattr(self, C.J.CLINIT)
clinit = method.Method(clazz=self, mods=[C.mod.ST], name=C.J.CLINIT)
self._mtds.append(clinit)
setattr(self, C.J.CLINIT, clinit)
return clinit
