def handleResultByDays(res, subtype, start, end, granule):
""" Get the data of the last week of which the last day is end
data of 7 days is needed
args:
res: results to handle
start: begin date in seconds
end: end date in seconds
return:
new results
"""
# 返回的结果:
# {
# 'biz_model': 'satisfaction',
# 'subtypes': {'serv_count': 4, 'serv_dura': 16, 'sec_op': 1}
# 'time_granule': 'week',
# 1:{
# 'total': 30,
# 'key': [
# 1433116800,
# ...
# ],
# 'value': [
# 7,
# ...
# ],
# 'size': 6
# }，
# 4:{
# ...
# }
# }
#
handledRes = {'total': 0, 'key': list(), 'value': list()}
if start > end:
pass
# 转化开始时间和结束时间，把时间的 timestamp 全部转为 00:00:00
realStart = getDateStamp(str2timestamp(params['start_date']))
realEnd = getDateStamp(str2timestamp(params['end_date']))
realStart = getStartDatePoint(realStart, granule)
# 在不连续的结果中填充数据
def fillData(start, end, granule, handler=None):
""" Fill the time gap between 'start' and 'end', and handle
the date number by handler
"""
if handler is None:
handler = lambda x: x
if start > end:
pass
fillDate = start
while fillDate <= end:
handledRes['key'].append(handler(fillDate))
handledRes['value'].append(0)
if granule in ['day', 'week']:
fillDate += SECONDS_IN_TIME[granule]
else:
fillDate = getStartDatePoint(fillDate, granule, 1)
# [{u'_id': 1428624000.0, u'value': 23.0}, {u'_id': 1433980800.0, u'value': 1.0}]
# 或者
# [{u'_id': 1434412800.0, u'value': {u'count': 1.0, u'rating': 4.0, u'dura': 124502400.0}}]
def handleCount(handler = None):
lastAdd, curKey, curData = 0, 0, 0
if handler is None:
handler = lambda val: (int(val['_id']), int(val['value']))
for record in res:
key, value = handler(record)
if realStart <= key <= realEnd:
# 在开始和结束之间的时间，肯定又用的，需要做处理
# 先算出 key 值
key2Add = getStartDatePoint(key, granule)
if key2Add > curKey:
# 出现了一个新 key，把上一个添加到结果中，初始化新的数据点
if not curKey == 0:
if lastAdd == 0:
# 第一次添加，填充这个时期之前的所有时间，如果时间范围不满足填充条件，则不会填充
fillData (realStart,
getStartDatePoint(curKey, granule, -1),
granule)
else:
# 如果当前的时间点和上一次添加的时间点之间有一个以上的空隙则需要填充
fillData(getStartDatePoint(lastAdd, granule, 1),
getStartDatePoint(curKey, granule, -1),
granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(curData)
lastAdd = curKey
curKey, curData = key2Add, value
else:
curData += value
# 跳出循环，在后面补充 0
if lastAdd == 0:
# 一次也没有添加过，但是要处理最后一次的数据
if curKey > 0:
fillData(realStart, getStartDatePoint(curKey, granule, -1), granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(curData)
else:
fillData(realStart, realEnd, granule)
elif end > lastAdd:
if curKey <> lastAdd:
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(curData)
lastAdd = curKey
fillData(getStartDatePoint(lastAdd, granule, 1), realEnd, granule)
handledRes['size'] = len(handledRes['key'])
def handleMean(handler = None):
lastAdd, totalCount, curKey, curData, curCount = 0, 0, 0, 0, 0
if handler is None:
handler = lambda val: \
(int(val['_id']), int(val['value']), int(val['count']))
for record in res:
key, value, count = handler(record)
if realStart <= key <= realEnd:
# 在开始和结束之间的时间，肯定又用的，需要做处理
# 先算出 key 值
key2Add = getStartDatePoint(key, granule)
if key2Add > curKey:
# 出现了一个新 key，把上一个添加到结果中，初始化新的数据点
if not curKey == 0:
if lastAdd == 0:
# 第一次添加，填充这个时期之前的所有时间，如果时间范围不满足填充条件，则不会填充
fillData (realStart,
getStartDatePoint(curKey, granule, -1),
granule)
else:
# 如果当前的时间点和上一次添加的时间点之间有一个以上的空隙则需要填充
fillData(getStartDatePoint(lastAdd, granule, 1),
getStartDatePoint(curKey, granule, -1),
granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(round(curData / curCount, 1))
totalCount += curCount
lastAdd = curKey
# 新的 key，需要初始化
curKey = key2Add
curData = value
curCount = count
else:
curData += value
curCount += count
# 跳出循环，在后面补充 0
if lastAdd == 0:
# 一次也没有添加过
if curKey > 0:
fillData(realStart, getStartDatePoint(curKey, granule, -1), granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(round(curData / curCount, 1))
totalCount += curCount
else:
fillData(realStart, realEnd, granule)
elif end > lastAdd:
if curKey <> lastAdd:
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(round(curData / curCount, 1))
totalCount += curCount
lastAdd = curKey
fillData(getStartDatePoint(lastAdd, granule, 1), realEnd, granule)
handledRes['total'] = round((handledRes['total'] / (1 if totalCount == 0 else totalCount)), 1)
handledRes['size'] = len(handledRes['key'])
# desk 结果的结构，三个结果在一起:
# 而且所有的数字都被处理成了 float
# results:[
# {
# '_id': 1433635200。0,
# 'value': {'count': 1。0, 'rating': 4。0, dura: 123724800.0}
# },
# ...
#]
kvHandler = None
if subtype =='serv_count':
kvHandler = lambda val : (int(val['_id']), int(val['value']['count']))
elif subtype == 'serv_dura':
kvHandler = lambda val : (int(val['_id']), int(val['value']['dura']), int(val['value']['count']))
elif subtype == 'serv_rating':
kvHandler = lambda val : (int(val['_id']), val['value']['rating'], int(val['value']['count']))
# 开始处理数据
if subtype in ['sec_op', 'serv_count']:
handleCount(kvHandler)
if subtype in ['serv_dura', 'serv_rating']:
handleMean(kvHandler)
new_result[getSubTypeID(modelType, subtype)] = handledRes
print('subtype: %s, key len: %d, value len: %d' % (subtype, len(handledRes['key']), len(handledRes['value'])))
def handleResults(modelType, res, params):
""" Handle results according to the params
args:
res: dict of results
params: params to handle the results
example:
resultParams = {
'db_conn': conn, # not used
'time_granule': day,
'start_date': start_date,
'end_date': end_date
'apps': {
'salesforce': ['sec_op'],
'desk': ['serv_count', 'serv_dura']
}
}
return:
new results
"""
# parse params
print ('Handling results...')
granule = params['time_granule']
if granule not in TIME_GRANULE:
raise Exception ('Time granule is not supported!')
# 组成新结果
new_result = {
'biz_model': modelType,
'subtypes': dict(),
'time_granule': granule
}
for k, v in params['apps'].items():
for sub in v:
new_result['subtypes'][sub] = getSubTypeID(modelType, sub)
def handleResultByDays(res, subtype, start, end, granule):
""" Get the data of the last week of which the last day is end
data of 7 days is needed
args:
res: results to handle
start: begin date in seconds
end: end date in seconds
return:
new results
"""
# 返回的结果:
# {
# 'biz_model': 'satisfaction',
# 'subtypes': {'serv_count': 4, 'serv_dura': 16, 'sec_op': 1}
# 'time_granule': 'week',
# 1:{
# 'total': 30,
# 'key': [
# 1433116800,
# ...
# ],
# 'value': [
# 7,
# ...
# ],
# 'size': 6
# }，
# 4:{
# ...
# }
# }
#
handledRes = {'total': 0, 'key': list(), 'value': list()}
if start > end:
pass
# 转化开始时间和结束时间，把时间的 timestamp 全部转为 00:00:00
realStart = getDateStamp(str2timestamp(params['start_date']))
realEnd = getDateStamp(str2timestamp(params['end_date']))
realStart = getStartDatePoint(realStart, granule)
# 在不连续的结果中填充数据
def fillData(start, end, granule, handler=None):
""" Fill the time gap between 'start' and 'end', and handle
the date number by handler
"""
if handler is None:
handler = lambda x: x
if start > end:
pass
fillDate = start
while fillDate <= end:
handledRes['key'].append(handler(fillDate))
handledRes['value'].append(0)
if granule in ['day', 'week']:
fillDate += SECONDS_IN_TIME[granule]
else:
fillDate = getStartDatePoint(fillDate, granule, 1)
# [{u'_id': 1428624000.0, u'value': 23.0}, {u'_id': 1433980800.0, u'value': 1.0}]
# 或者
# [{u'_id': 1434412800.0, u'value': {u'count': 1.0, u'rating': 4.0, u'dura': 124502400.0}}]
def handleCount(handler = None):
lastAdd, curKey, curData = 0, 0, 0
if handler is None:
handler = lambda val: (int(val['_id']), int(val['value']))
for record in res:
key, value = handler(record)
if realStart <= key <= realEnd:
# 在开始和结束之间的时间，肯定又用的，需要做处理
# 先算出 key 值
key2Add = getStartDatePoint(key, granule)
if key2Add > curKey:
# 出现了一个新 key，把上一个添加到结果中，初始化新的数据点
if not curKey == 0:
if lastAdd == 0:
# 第一次添加，填充这个时期之前的所有时间，如果时间范围不满足填充条件，则不会填充
fillData (realStart,
getStartDatePoint(curKey, granule, -1),
granule)
else:
# 如果当前的时间点和上一次添加的时间点之间有一个以上的空隙则需要填充
fillData(getStartDatePoint(lastAdd, granule, 1),
getStartDatePoint(curKey, granule, -1),
granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(curData)
lastAdd = curKey
curKey, curData = key2Add, value
else:
curData += value
# 跳出循环，在后面补充 0
if lastAdd == 0:
# 一次也没有添加过，但是要处理最后一次的数据
if curKey > 0:
fillData(realStart, getStartDatePoint(curKey, granule, -1), granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(curData)
else:
fillData(realStart, realEnd, granule)
elif end > lastAdd:
if curKey <> lastAdd:
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(curData)
lastAdd = curKey
fillData(getStartDatePoint(lastAdd, granule, 1), realEnd, granule)
handledRes['size'] = len(handledRes['key'])
def handleMean(handler = None):
lastAdd, totalCount, curKey, curData, curCount = 0, 0, 0, 0, 0
if handler is None:
handler = lambda val: \
(int(val['_id']), int(val['value']), int(val['count']))
for record in res:
key, value, count = handler(record)
if realStart <= key <= realEnd:
# 在开始和结束之间的时间，肯定又用的，需要做处理
# 先算出 key 值
key2Add = getStartDatePoint(key, granule)
if key2Add > curKey:
# 出现了一个新 key，把上一个添加到结果中，初始化新的数据点
if not curKey == 0:
if lastAdd == 0:
# 第一次添加，填充这个时期之前的所有时间，如果时间范围不满足填充条件，则不会填充
fillData (realStart,
getStartDatePoint(curKey, granule, -1),
granule)
else:
# 如果当前的时间点和上一次添加的时间点之间有一个以上的空隙则需要填充
fillData(getStartDatePoint(lastAdd, granule, 1),
getStartDatePoint(curKey, granule, -1),
granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(round(curData / curCount, 1))
totalCount += curCount
lastAdd = curKey
# 新的 key，需要初始化
curKey = key2Add
curData = value
curCount = count
else:
curData += value
curCount += count
# 跳出循环，在后面补充 0
if lastAdd == 0:
# 一次也没有添加过
if curKey > 0:
fillData(realStart, getStartDatePoint(curKey, granule, -1), granule)
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(round(curData / curCount, 1))
totalCount += curCount
else:
fillData(realStart, realEnd, granule)
elif end > lastAdd:
if curKey <> lastAdd:
handledRes['total'] += curData
handledRes['key'].append(curKey)
handledRes['value'].append(round(curData / curCount, 1))
totalCount += curCount
lastAdd = curKey
fillData(getStartDatePoint(lastAdd, granule, 1), realEnd, granule)
handledRes['total'] = round((handledRes['total'] / (1 if totalCount == 0 else totalCount)), 1)
handledRes['size'] = len(handledRes['key'])
# desk 结果的结构，三个结果在一起:
# 而且所有的数字都被处理成了 float
# results:[
# {
# '_id': 1433635200。0,
# 'value': {'count': 1。0, 'rating': 4。0, dura: 123724800.0}
# },
# ...
#]
kvHandler = None
if subtype =='serv_count':
kvHandler = lambda val : (int(val['_id']), int(val['value']['count']))
elif subtype == 'serv_dura':
kvHandler = lambda val : (int(val['_id']), int(val['value']['dura']), int(val['value']['count']))
elif subtype == 'serv_rating':
kvHandler = lambda val : (int(val['_id']), val['value']['rating'], int(val['value']['count']))
# 开始处理数据
if subtype in ['sec_op', 'serv_count']:
handleCount(kvHandler)
if subtype in ['serv_dura', 'serv_rating']:
handleMean(kvHandler)
new_result[getSubTypeID(modelType, subtype)] = handledRes
print('subtype: %s, key len: %d, value len: %d' % (subtype, len(handledRes['key']), len(handledRes['value'])))
#for res in handledRes['key']:
# print(timestamp2str(res))
# 在结果中指需要取 start 以后的数据，当前是数据结果都是按天算的
# 没有带时间，所以如果存在的话能够匹配到
for k, v in params['apps'].items():
for sub in v:
handleResultByDays(res[k]['results'], sub,
str2timestamp(params['start_date']),
str2timestamp(params['end_date']),
granule)
print ('------------ %f ------------' % time.clock())
print ('Done!')
return new_result
