def do_one_flow(driver, path):
try:
with open(path, encoding='utf-8') as f:
filename = f.name
logger.info(Messages.Start_Flow % filename)
open_file(driver, path, do_flow)
logger.info(Messages.End_Flow % filename)
except Exception as e:
logger.error(Messages.Flow_Handle_Failed % e)
def __process_pgn_list(self, file_list, output_file=None):
"""
This takes a PGN file and creates two output files
1. First file contains the game information
2. Second file containing the moves
"""
log.info("Starting process..")
result = Result.get_empty_result()
file_name_games = output_file + '_game_info.csv'
file_name_moves = output_file + '_moves.csv'
file_games = open_file(file_name_games)
file_moves = open_file(file_name_moves)
if file_games is None or file_moves is None:
log.info("No data exported!")
return result
add_headers = True
for file in file_list:
process = Process(file, file_games, file_moves, self._engine_path,
self._depth)
process.parse_file(add_headers)
add_headers = False
file_games.close()
file_moves.close()
# return a result object to indicate outcome
result = self.__get_result_of_output_files(file_name_games,
file_name_moves)
log.info("ending process..")
return result
font_name = font_manager.FontProperties(fname="c:/Windows/Fonts/malgun.ttf").get_name()
rc('font', family=font_name)
import os
import numpy as np
import collections as clt
import pandas as pd
import random as rd
# Step 2. 경마정보조회를 위한 요청변수 및 요청주소 등을 구성합니다.
# Step 2-1. Looging을 위하여 공통 모듈에서 로거를 정의합니다.
f = cm.pre_logging() # 등록마 조회 결과 파일을 로딩
# Step 2-2. '4-1'에서 저장한 파일을 이용하여 해당 데이터를 로딩합니다.
c = []
fn = os.getcwd() + '/data/' + 'RegisteredHorse.da' # url = 'http://data.kra.co.kr/publicdata/service/hrReg/getHrReg' # 등록마 조회 (meet)
c.append(cm.open_file(fn, True))
cm.logF(f, c)
# Step 3. item의 구조를 분석하고, 분석하고자 하는 값들을 추출하기 위한 데이터를 전처리합니다.
pp = pp.PrettyPrinter(indent=4)
data = []
for i in range(0, len(c)):
try:
# 응답값이 XML일 경우
_djson = cm.x2j(c[i])
item = _djson['response']['body']['items']['item']
# pp.pprint(item)
cm.logF(f, item)
if (isinstance(item, dict)):
data.append(item)
def do_flow(driver, flowdata):
if flowdata:
# 获取流程节点类型
t = flowdata.get(FlowNodeProp.Type.value)
# 流程-打开目标网址
if t == FlowNodeType.Open.value:
param = flowdata.get(FlowNodeProp.Params.value)
if param:
target_url = param
else:
# 获取目标网站网址
target_url = get_item(flowdata, FlowNodeProp.TargetURL.value)
# 如果存在常量值执行替换
target_url = const.get_const_val(target_url)
driver.get(target_url)
# 流程-处理读取数据文件
elif t == FlowNodeType.Read.value:
# 读取方式
findmethod = get_item(flowdata, FlowNodeProp.FindMethod.value)
# 读取文件路径
target_url = get_item(flowdata, FlowNodeProp.TargetURL.value)
# 读取数据文件
with open(target_url, encoding='utf-8') as f:
mock_data = json.load(f)
# 根据数据文件，页面自动赋值
if mock_data:
# 获取常量值
mock_data = const.repalce_const_val(mock_data)
recursive_set_data(driver, By.__dict__[findmethod], mock_data)
# 流程-处理单击
elif t == FlowNodeType.Click.value or t == FlowNodeType.DbClick.value:
handle_click(driver, flowdata, t)
# 流程-处理弹出框
elif t == FlowNodeType.Alert.value:
# 等待弹出框出现
wait = WebDriverWait(driver, 10)
wait.until(EC.alert_is_present())
# 获取弹出框
alert = driver.switch_to.alert
# 弹出框确认（默认执行弹出框确认操作）
alert.accept()
# 流程-剪切板操作(复制)
elif t == FlowNodeType.Copy.value:
if flowdata.get(FlowNodeProp.FindMethod.value) != None:
# 获取目标元素
element = get_element_by_flow(driver, flowdata)
if element.text:
copy_val = element.text
elif element.get_attribute('value'):
copy_val = element.get_attribute('value')
else:
# 如果存在常量值优先替换
flowdata = repalce_const_val(flowdata)
flowdata = repalce_dynamic_val(flowdata)
copy_val = get_item(flowdata, FlowNodeProp.ItemVal.value)
# 复制到ClipBoard
pyperclip.copy(copy_val)
# 流程-剪切板操作(粘贴)
elif t == FlowNodeType.Paste.value:
# 获取目标元素
element = get_element_by_flow(driver, flowdata)
# 复制到ClipBoard
setval = pyperclip.paste()
if setval:
element.clear()
# 首选使用常量值
val = const.get_const_val(setval)
element.send_keys(val)
# 流程-添加缓存值
elif t == FlowNodeType.Cache.value:
# 获取想要设置的缓存值
val = get_item(flowdata, FlowNodeProp.ItemVal.value)
if isinstance(val, str):
# 首选使用常量值
val = const.get_const_val(val)
setval = val
elif isinstance(val, object):
setval = json.dumps(val)
# 获取缓存键
cachekey = get_item(flowdata, FlowNodeProp.CacheKey.value)
# 添加浏览器缓存
driver.add_cookie({'name': cachekey, 'value': setval})
# 流程-画面要素設定
elif t == FlowNodeType.SetVal.value:
# 如果存在常量值优先替换
flowdata = repalce_const_val(flowdata)
# 获取动态生成的项目設定値
flowdata = repalce_dynamic_val(flowdata)
# 目标元素
target = get_item(flowdata, FlowNodeProp.Target.value)
# 获取目标元素方式
findmethod = get_item(flowdata, FlowNodeProp.FindMethod.value)
# 获取动态生成的项目設定値
itemval = get_item(flowdata, FlowNodeProp.ItemVal.value)
# 指定元素设置缓存值
set_element_val(driver, By.__dict__[findmethod], itemval, target)
# 流程-循环操作
elif t == FlowNodeType.For.value:
# 子操作流程节点集合
child_flows = get_item(flowdata, FlowNodeProp.Flow.value)
if FlowNodeProp.StartIdx.value in flowdata and FlowNodeProp.EndIdx.value in flowdata:
# 开始索引
startIdx = get_item(flowdata, FlowNodeProp.StartIdx.value)
# 结束索引
endIdx = get_item(flowdata, FlowNodeProp.EndIdx.value)
# 循环指定索引范围
for idx in range(int(startIdx), int(endIdx) + 1):
handle_for_childflow(driver, child_flows, idx)
elif FlowNodeProp.TargetURL.value in flowdata:
# 读取文件路径
target_url = get_item(flowdata, FlowNodeProp.TargetURL.value)
with open(target_url) as csvfile:
for row in csv.reader(csvfile):
handle_for_childflow(driver, child_flows, row[0])
else:
# 获取目标元素列表
elements = get_elements_by_flow(driver, flowdata)
# 循环处理元素列表
for element in elements:
handle_for_childflow(driver, child_flows, element)
# 流程-加载既存流程文件
elif t == FlowNodeType.FlowFile.value:
# 读取文件路径
file_url = get_item(flowdata, FlowNodeProp.TargetURL.value)
param = flowdata.get(FlowNodeProp.Params.value)
open_file(driver, file_url, do_flow, param)
# 流程-鼠标键盘操作
elif t == FlowNodeType.KeyBoard.value:
# 读取鼠标键盘操作
itemval = get_item(flowdata, FlowNodeProp.ItemVal.value)
# 获取操作次数
count = flowdata.get(FlowNodeProp.Count.value, 1)
source = None
if FlowNodeProp.Target.value in flowdata and FlowNodeProp.FindMethod.value in flowdata:
# 读取指定元素
source = get_element_by_flow(driver, flowdata)
else:
source = driver
for i in range(count):
source.send_keys(Keys.__dict__[itemval])
# ActionChains(source).send_keys(Keys.__dict__[itemval]).perform()
# 获取节点可选操作配置
option = flowdata.get(FlowNodeProp.Option.value)
if option:
# 处理可选操作
handle_option(driver, option)