def sort_data_by_duration(self, partition):
""" Sort the training or validation sets by (increasing) duration
"""
if partition == 'train':
self.train_audio_paths, self.train_durations, self.train_texts = sort_data(
self.train_audio_paths, self.train_durations, self.train_texts)
elif partition == 'valid':
self.valid_audio_paths, self.valid_durations, self.valid_texts = sort_data(
self.valid_audio_paths, self.valid_durations, self.valid_texts)
else:
raise Exception("Invalid partition. " "Must be train/validation")
def run(self):
"""
Main function to run program
:return:
"""
while True:
# load data to memory
if not self.all_data:
tmp_data = []
with open(self.filename, 'r') as f:
for line in f:
data = line.strip().split(',')
if validate(data[1]):
tmp_data.append([data[0], validate(data[1]), 0])
self.all_data = sort_data(tmp_data)
print('''Greetings user! Let's make an addressbook!
Would you like to:
1.) Add a New Contact
2.) List All Contacts
3.) Search Contacts
4.) Delete A Contact
5.) Make Call
6.) List top 5 Call
7.) Quit Program\n''')
choice = input("Select an option: ")
if choice == "1":
try:
self.create_contact()
except NotAPhoneNumberException:
print("The phone number entered is invalid, creation aborted!")
elif choice == "2":
self.list_all_contact()
elif choice == "3":
self.search_contact()
elif choice == "4":
self.delete_contact()
elif choice == "5":
self.make_call()
elif choice == "6":
self.list_top_contact()
elif choice == "7":
print("Ending Contact Book.\nHave a nice day!")
break
else:
print("Invalid Input! Try again.")
def group_by_parameter(aggregate_function, data):
"""
Returns dictionary with grouped data
:param aggregate_function: function providing key parameter
:param data: data to group
:return: grouped data
"""
def instantiate_list(group_tuple):
k, v = group_tuple
return k, list(v)
return dict(map(instantiate_list, groupby(sort_data(aggregate_function, data), key=aggregate_function)))
def list_top_contact(self):
"""
Function list top 5 contact
:return: list of top 5 contacts
"""
print('-------------------------------------------------------------------------')
print_contact({'name': 'NAME', 'phone': 'PHONE', 'outgoing': 'OUTGOING CALL'}, top=True)
print('-------------------------------------------------------------------------')
for item in sort_data(self.all_data, 2)[:5]:
print_contact({'name': item[0], 'phone': item[1], 'outgoing': item[2]}, top=True)
print('-------------------------------------------------------------------------')
def algo(context):
date_now = context.now.strftime('%Y-%m-%d')
date_previous = get_trading_date_from_now(date_now, -1,
ql.Days) # 前一个交易日，用于获取因子数据的日期
if date_now not in trading_date_list: # 非调仓日
pass # 预留非调仓日的微调空间
else: # 调仓日执行算法
print(date_now + '日回测程序执行中...')
try:
code_list = list_gm2wind(
list(
get_history_constituents(
INDEX,
start_date=date_previous,
end_date=date_previous)[0]['constituents'].keys()))
except IndexError:
code_list = w.wset("sectorconstituent", "date=" + date_previous +
";windcode=" + INDEX).Data[1]
I = trading_date_list.index(date_now)
trading_dates = trading_date_list[I - HISTORY_LENGTH:I + 1]
data_dfs = []
for i in range(len(trading_dates) - 1):
date_start = get_trading_date_from_now(
trading_dates[i], -1, ql.Days) # 计算因子值的日子，买入前一日的因子值
date_end = get_trading_date_from_now(trading_dates[i + 1], -1,
ql.Days) # 计算收益率到期的日子-收盘
factors_df = get_factor_from_wind(code_list, FACTOR_LIST,
date_start) # 获取因子
return_df = get_return_from_wind(code_list, date_start, date_end)
factors_df_and_return_df = pd.concat(
[factors_df, return_df], axis=1).dropna() # 去掉因子或者回报有空缺值的样本
factors_df_and_return_df = sort_data(
factors_df_and_return_df) # 使用排序数据作为输入
data_dfs.append(factors_df_and_return_df)
factors_return_df = pd.concat(data_dfs,
axis=0) # 获取的最终训练数据拼接，return为目标
# 根据data_df训练模型
model = OrdinaryLinearRegression()
model.fit(factors_return_df)
# 根据factor_date_previous选取股票
factor_date_previous_df = get_factor_from_wind(code_list, FACTOR_LIST,
date_previous).dropna()
sorted_codes = model.predict(
factor_date_previous_df) # 获取预测收益率从小到大排序的股票列表
sorted_codes = list_wind2jq(sorted_codes)
# 根据股票列表下单
stock_codes = sorted_codes[-STOCK_NUMBER:]
stock_now = {}
for stock_code in stock_codes: # 平均持仓持股
stock_now[stock_code] = 1. / STOCK_NUMBER
stock_dict[date_now] = stock_now
def create_contact(self):
"""
Function to create a contact
:return: none
"""
name = input("Enter name: ")
phone = input("Enter phone number: ")
if not validate(str(phone)):
raise NotAPhoneNumberException
self.all_data.append([name, validate(phone), 0])
self.all_data = sort_data(self.all_data)
def delete_contact(self):
"""
Function to delete contact
:return: none
"""
search = input("Please enter name or number: ")
new_data = []
for item in self.all_data:
if not fnmatch.fnmatch(str.lower(item[0]), '*{}*'.format(str.lower(search))) or fnmatch.fnmatch(
item[1], '*{}*'.format(search)):
new_data.append([item[0], item[1], item[2]])
self.all_data = sort_data(new_data)
return print("The contact was successfully deleted\n")
def get_data():
res = utils.get_data()
return utils.sort_data(res)
#last one hour time in epoch format
frm = utils.get_date(date=now, th=12)
#get query for reconnected
device=['Device_IPv4','Device_IPv6']
avg=[]
node_avg=[]
node_based_avg={}
files=[]
for ip in device:
query = utils.get_query(frm=frm,to=to, ip=ip)
#Get data from elasticsearch
result = utils.get_details(index="prod_push_cep-*", body=query)
#fetch details and store it in list
result = utils.fetch_hits(result=result, ip=ip)
data=utils.sort_data(result)
# x=utils.sort_node_based(data)
#print x
filename='/var/www/html/Report/'+ ip + '_'+datetime.now().strftime("%Y%m%d-%H%M")+'.csv'
header1=["UAID","IP","Host"]
temp=1
for v in data.itervalues():
if len(v) > temp:
temp=len(v)
for i in range(temp):
header1.extend(["Diff"])
utils.convert_csv(data=data, header=header1,filename=filename)
utils.sort_csv(filename=filename)