def test_stringToDate(self):
self.assertEqual(util.stringToDate("01.02.2017"),
UtilTest.localizeTime(datetime.datetime(2017, 2, 1)))
self.assertEqual(util.stringToDate("01.03.2017"),
UtilTest.localizeTime(datetime.datetime(2017, 3, 1)))
self.assertEqual(util.stringToDate("01.05.2017"),
UtilTest.localizeTime(datetime.datetime(2017, 5, 1)))
self.assertEqual(util.stringToDate("01.06.2017"),
UtilTest.localizeTime(datetime.datetime(2017, 6, 1)))
def __init__(self, date, flows, granularity=0):
self.flows = flows
self.flow = flows[0]
self.date = date
self.granularity = granularity
self.dates = SimilarPastDaysFinder(self.flow).findSimilarPastDays(date)
country = MediationConfig.getCountryByName(self.flow["country"])
self.options = self.flow["options"]
self.adjustment = None
lazyDayDifference = self.flow["options"].get("lazyDayDifference", None)
if lazyDayDifference != None:
for lazyDayStr in country["lazyDays"]:
lazyDay = util.stringToDate(lazyDayStr)
if lazyDay.date() == date.date():
self.adjustment = lazyDayDifference
break
(1, 1),
(17, 4),
(14, 4),
(1, 5),
(8, 5),
(5, 7),
(6, 7),
(28, 9),
(28, 10),
(17, 11),
(24, 12),
(25, 12),
(26, 12),
]
INITIAL_DATE = util.stringToDate("01.08.2016")
IGNORE_DAYS = [(24, 12), (31, 12), (1, 1)]
def _getPastHolidays(lobName, date, days):
resultDays = []
i = 1
dayToTest = date
while len(resultDays) <= days and dayToTest > INITIAL_DATE:
dayToTest -= datetime.timedelta(days=1)
if _isHoliday(dayToTest) and _isUsualDay(dayToTest):
resultDays.append(dayToTest)
i += 1
return resultDays
def dataQueryV2():
"""
Endpoint for getting traffic data.
POST body:
{
"from":"01.02.2017",
"to":"15.02.2017",
"country":"CZ",
"lobName":"ACI",
"flowName":["GSM"],
"forwards":[],
"granularity":0
}
Response:
{
"data": [
{
"GSM": 188385532,
"_id": "2017-02-01T00:00:00+01:00",
"dayAverage": 1162595297.6666667,
"dayDifference": 1.023,
"expected": 161627916,
"status": "OK",
"tickDifference": 1.166
},
...
],
"metadata": {
"flowName": "GSM",
"granularity": 480,
"metrics": {
"GSM": {
"type": "traffic"
},
"dayAverage": {
"type": "traffic"
},
"dayDifference": {
"type": "difference"
},
"expected": {
"type": "traffic"
},
"status": {
"type": "other"
},
"tickDifference": {
"type": "difference"
}
}
}
"""
searchParam = request.get_json()
fromDate = util.stringToDate(searchParam["from"])
toDate = util.stringToDate(searchParam["to"])
country = searchParam["country"]
lobName = searchParam["lobName"]
lobConfig = MediationConfig.getLobWithCountry(country, lobName)
flows = []
granularity = searchParam.get("granularity", 0)
flows.append(lobConfig["flows"][searchParam["flowName"]])
response = {}
# Query the traffic data and add to metric list
mongoQuery = data_query.DateRangeGroupQuery(fromDate,
toDate,
flows,
granularity=granularity)
data = mongoQuery.execute()
metrics = {}
metricsList = []
flowName = mongoQuery.metrics[0]
metricsList.append(flowName)
metadata = mongoQuery.metadata
if len(flows) == 1:
# Run outage detection analysis
metric = metricsList[0]
flowLevelQuery = data_query.FlowLevelDateRangeQuery(
fromDate, toDate, flows, metadata["granularity"], data)
flowLevelData = flowLevelQuery.execute()
data = util.merge2DateLists(flowLevelData, None, data, None)
metricsList.extend(flowLevelQuery.metrics)
outageQuery = data_query.OutageDateRangeQuery(fromDate, toDate,
flows[0],
metadata["granularity"])
outageQuery.setPrecomputedData(data, metric)
outageList = outageQuery.execute()
data = util.merge2DateLists(outageList, [outageQuery.metric], data,
None)
metricsList.append(outageQuery.metric)
# Create metadata infor
for metric in metricsList:
if metric == flowName or metric == "dayAverage" or metric == "expected":
type = "traffic"
elif "Difference" in metric:
type = "difference"
else:
type = "other"
metrics[metric] = {"type": type}
response["data"] = data
response["metadata"] = {
**{
"metrics": metrics
},
**metadata, "flowName": flowName
}
return jsonify(response)
dates = list(
map(lambda x: x.strftime("%d.%m %H:%M"),
util.dateDataListToList(data, "_id")))
data1 = util.dateDataListToList(data, flowName)
expected = util.dateDataListToList(data, "expected")
dataTicks = []
dataTickPos = []
for i in range(0, len(dates)):
if True:
dataTicks.append(dates[i])
dataTickPos.append(i)
return data1, expected, dataTicks, dataTickPos
fromDate = util.stringToDate("30.01.2017")
toDate = util.stringToDate("12.02.2017")
# lobName = "ICG"
# flowName = "CENTREX01"
lobName = "GSM"
flowName = "MSSBRN1B"
lob = MediationConfig.getLobWithCountry("CZ", lobName)
flow = lob["flows"][flowName]
flows = [flow]
granularity = 240
data = []
expected = []
ticks = []
pos = []
d, e, t, p = getData(flows, util.stringToDate("28.09.2016"),
# lob1Data = util.dateDataListToList(q1.execute(), flowName)
# lob2Data = util.dateDataListToList(q2.execute(), flowName)
# lob3Data = util.dateDataListToList(q3.execute(), flowName)
#
# ticks = util.dateDataListToList(q1.execute(), "_id")
# plt.figure(figsize=(12, 6))
# plt.plot(lob2Data, color="blue", label="21.10")
# plt.plot(lob1Data, color="red", label="27.10")
# plt.plot(lob3Data, color="green", label="17.11")
#
# #plt.xticks(ticks, rotation='vertical')
# plt.title(flowName)
# plt.legend(loc='upper left')
# plt.show()
fromDate = util.stringToDate("02.03.2017")
toDate = util.stringToDate("03.03.2017")
lobName = "GSM"
flowName = "MSSBRN1A"
lob = MediationConfig.getLobWithCountry("CZ", lobName)
flow = lob["flows"][flowName]
flows = [flow]
granularity = 5
response = {}
mongoQuery = data_query.DateRangeGroupQuery(fromDate,
toDate,
flows,
granularity=granularity)
data = mongoQuery.execute()
metrics = {}
import matplotlib.pyplot as plt
from common import util
from mediation import MediationConfig
from mediation import data_query
fromDate = util.stringToDate("21.12.2016")
toDate = util.stringToDate("30.12.2016")
lobName = "GSM"
flowName = "MSSBRN1A"
lob = MediationConfig.getLobWithCountry("CZ", lobName)
flow = lob["flows"][flowName]
flows = [flow]
granularity = 60
response = {}
mongoQuery = data_query.DateRangeGroupQuery(fromDate,
toDate,
flows,
granularity=granularity)
data = mongoQuery.execute()
metrics = {}
metricsList = mongoQuery.metrics
metadata = mongoQuery.metadata
if len(flows) == 1:
metric = metricsList[0]
flowLevelQuery = data_query.FlowLevelDateRangeQuery(
fromDate, toDate, flows, metadata["granularity"], data)
flowLevelData = flowLevelQuery.execute()
data = util.merge2DateLists(flowLevelData, None, data, None)
metricsList.extend(flowLevelQuery.metrics)