def ConcurrencyPay(context, *args):
def pay(order_id):
print("支付：")
url = preData["eatojoy_url"] + "/user/payment/pay"
headers = {"token": preData["eatojoy_user_token"]}
print("请求头：")
print(headers)
print("请求地址：" + url)
data = {
"order_id": order_id,
"source_id": preData["eatojoy_source_id"]
}
print("请求参数：")
print(decode_str(data))
ret = requests.post(url, json=data, headers=headers)
print("返回：")
timeX = time.clock()
print(timeX)
print(ret.json())
try:
time1 = time.clock()
thread_count = len(preData["eatojoy_order_id"])
with pool(max_workers=100) as executor:
future_tasks = [
executor.submit(pay, order_id)
for order_id in preData["eatojoy_order_id"]
]
# for order_id in eatojoy.preData["eatojoy_order_id"]:
# executor.submit(pay(order_id))
time2 = time.clock()
times = time2 - time1
print(times / thread_count)
return True
# try:
# time1 = time.clock()
# thread_count = len(eatojoy.preData["eatojoy_order_id"])
# for order_id in eatojoy.preData["eatojoy_order_id"]:
# threading.Thread(target = pay(order_id))
# time2 = time.clock()
# times = time2 - time1
# print(times / thread_count)
# return True
except Exception as e:
print(e)
# captura o horario que começou para calcular o tempo que gastou
start = datetime.now()
# lista arquivos do diretório
lst_arq = LerSubDir("./")
# verifica quantos arquivos tem o diretório
qtd_arq = len(lst_arq)
# caputura a qtd total de nucleos de processadores disponíveis e subtrai 1
n_cpu = os.cpu_count() - 1
print('arquivos encontrados', qtd_arq)
print('Processadores utilizados', n_cpu)
# declara o executor para concurrent futures
executor = pool(max_workers=n_cpu)
# executa o processamento paralelo
final = executor.map(lambda txt: os.rename(txt, txt[0:-3] + 'txt'),
lst_arq)
# deleta da memória a lista de arquivos
del lst_arq
# captura o horario que começou para calcular o tempo que gastou
stop = datetime.now()
# tempo gasto de execução
print('Tempo gasto:', stop - start)
def __init__(self, instance_num=5):
self.instance_num = instance_num
self.glob_pool = pool(cpu_count())
self.avalible_instance = cycle([x for x in range(instance_num)])
self.start_traci()
def ConcurrencyCartAdd(context, *args):
def checkout():
print("加入购物车：")
url = preData["eatojoy_url"] + "/user/cart/add"
headers = {"token": preData["eatojoy_user_token"]}
print("请求头：")
print(headers)
print("请求地址：" + url)
data = {
"product_id": "1",
"vendor_id": "2",
"product_num": 1,
"product_msg": "产品备注"
}
print("请求参数：")
print(decode_str(data))
ret = requests.post(url, json=data, headers=headers)
print("返回：")
print(ret.json())
print("购物车列表：")
url = preData["eatojoy_url"] + "/user/cart/list"
headers = {"token": preData["eatojoy_user_token"]}
print("请求头：")
print(headers)
print("请求地址：" + url)
data = {"vendor_id": "2", "lon": "114.082846", "lat": "22.4167417"}
print("请求参数：")
print(decode_str(data))
ret = requests.get(url, json=data, headers=headers)
print(ret.json())
id = ret.json()["data"]["product_list"][0]["id"]
product_num = ret.json()["data"]["product_list"][0]["product_num"]
order_grand_total = ret.json()["data"]["price_total"]
print("返回：")
print(ret.json())
print("下单：")
url = preData["eatojoy_url"] + "/user/order/checkout"
headers = {"token": preData["eatojoy_user_token"]}
print("请求头：")
print(headers)
print("请求地址：" + url)
data = {
"product_id": id,
"merchants_id": "2",
"order_product_counts": product_num,
"order_grand_total": order_grand_total,
"take_food_time": preData["eatojoy_take_food_time"],
"package_fee": 10,
"coupons_fee": 0
}
print("请求参数：")
print(decode_str(data))
ret = requests.post(url, json=data, headers=headers)
print(ret.json())
preData["eatojoy_order_id"].append(get_value("data", ret.json()))
try:
time1 = time.clock()
with pool(max_workers=100) as executor:
future_tasks = [
executor.submit(checkout) for i in range(1, 100)
]
time2 = time.clock()
times = time2 - time1
print(times)
print(preData["eatojoy_order_id"])
return True
except Exception as e:
print(e)
from ChangeDetectors.netlist_txt_detect import MonitorNetlistChanges
from ChangeDetectors.components_txt_detect import MonitorComponentsChanges
from os import terminal_size
from InternalWorkingScripts import createComponents_txt
from InternalWorkingScripts import _createNetlist_txt
from ChangeDetectors import MonitorComponentsChanges
from ChangeDetectors import MonitorNetlistChanges
from concurrent.futures import ProcessPoolExecutor as pool
from multiprocessing import Process
import time
if __name__ == '__main__':
createComponents_txt()
_createNetlist_txt()
with pool() as p:
p.submit(MonitorComponentsChanges)
p.submit(MonitorNetlistChanges)
def variance(X, P):
mu = weighted_mean(X, P)
with pool() as p:
w2 = p.map(lambda x, p: p * x * x, X, P)
return reduce(add, w2) - mu**2
def weighted_mean(X, P):
with pool() as p:
w1 = p.map(mul, X, P)
return reduce(add, w1)
from concurrent.futures import ProcessPoolExecutor as pool
def sentence_mapper(text):
sentences = list()
textSoup = BeautifulSoup(text, "lxml")
paragraphs = textSoup.find_all("p", attrs={"class": None})
prepared = ("".join([p.text.strip().lower() for p in paragraphs[1:-1]]))
for t in prepared.split("."):
part = "".join([c for c in t if c.isalpha() or c.isspace()])
sentences.append(part.strip())
return sentences
# parallel map
with pool(4) as p:
mapped_sentences = p.map(sentence_mapper, texts)
# reduce
sentences = reduce(add, mapped_sentences)
# print first and last sentence to check the results
print(sentences[0])
print(sentences[-1])
# + {"slideshow": {"slide_type": "slide"}, "cell_type": "markdown"}
# ## References
#
# - [Using Conditional Random Fields and Python for Latin word segmentation](https://medium.com/@felixmohr/using-python-and-conditional-random-fields-for-latin-word-segmentation-416ca7a9e513)
dataframes[i_.strip("\n")] = element_lists
"""注意此处的缩进程度需到此处，继续缩进存在无法更新过快"""
for i in condition:
element_list[title] = 0
dataframe[i.strip("\n")] = element_list
#pprint.pprint(dataframe)
"""字典更新"""
dataframe_ = dict(dataframe, **dataframes)
#pprint.pprint("更新后的字典 ：")
#pprint.pprint(dataframe_)
"""excel写入"""
dataframes = pd.DataFrame(dataframe_)
writer = pd.ExcelWriter(
r"F:\360下载\提取后的excel文件(非分词结果）\{name}.xls".format(name=title))
dataframes.to_excel(writer, sheet_name='一', index=False)
writer.save()
print("写入成功--------------------------------")
#no_jieba_get(input_path)
if __name__ == "__main__":
_path = glob.glob(os.path.join(input_path, "*"))
#print(index,_path.replace('\u2022',"#").replace("\u30fb","##").replace("\u4dae","###").replace("\u4722","####"))
#或者更改项目编码
pool_path = [_path[i:i + 1] for i in range(0, len(_path), 1)]
#print(pool_path)
pools = pool(100)
for i in pool_path:
pools.submit(no_jieba_get, i)
"""为什么21个就直接结束，因为内部线程没有运行完毕"""