def getseries(q):
pkl = "observation_" + q["route"] + "_" + q["link"] + "_" + q["direction"]
ts = mem.get(pkl)
# existising query stored
if ts:
print("cached trips")
else:
result = db.observation.find(q).sort("_id", -1)
data = []
dates = []
for res in result:
d = res["item"]
for res2 in d:
data.append(res2["stt"])
dates.append(res2['date'])
ts = TimeSeries(data,dates)
#put_pickle(pkl, ts)
result = ts.resample('10min', how="mean",convention='end',fill_method='pad')
r3 = (result["2014-01-06":"2014-04-13"].dropna().values)
t3 = (result["2014-01-06":"2014-04-13"].dropna().index.values)
r2 = (result["2013-05-01":"2013-08-04"].dropna().values)
t2 = (result["2013-05-01":"2013-08-04"].dropna().index.values)
r1 = (result["2012-09-03":"2012-10-21"].dropna().values)
t1 = (result["2012-09-03":"2012-10-21"].dropna().index.values)
stt = np.append(r1, np.append(r2,r3))
idx = np.append(t1, np.append(t2,t3))
ts = TimeSeries(stt,idx)
return ts
def getseriesweather():
for ws in stations():
result = db.weather.find({"location":ws}).sort("_id", -1)
dailyrainMM = []
TemperatureC = []
dates = []
print(">>>> location >>> ",ws)
for res in (result):
for res2 in (res['item']):
dailyrainMM.append(float(res2["dailyrainMM"]))
TemperatureC.append(float(res2["TemperatureC"]))
dates.append(datetime.strptime(res2['Time'],'%Y-%m-%d %H:%M:%S'))
ts = TimeSeries(dailyrainMM,dates)
ts2 = TimeSeries(TemperatureC,dates)
result = ts.resample('10min', how="mean",convention='end',fill_method="pad")
result2 = ts2.resample('10min', how="mean",convention='end',fill_method="pad")
weatherstore[ws] = {"dailyrainMM":result,"TemperatureC":result2}
return weatherstore
def getseriesweather(attr,bool,location):
result = db.weather.find({"location":location}).sort("_id", -1)
weather = []
dates = []
for res in (result):
for res2 in (res['item']):
if set((attr,'Time')).issubset(res2.keys()):
weather.append(float(res2[attr]))
dates.append(datetime.strptime(res2['Time'],'%Y-%m-%d %H:%M:%S'))
ts = TimeSeries(weather,dates)
result = ts.resample('10min', how=bool,convention='end',fill_method="pad")
return result
def getseries(q):
pkl = "observation_" + q["route"] + "_" + q["link"] + "_" + q["direction"]
result = db.observation.find(q).sort("_id", -1)
data = []
dates = []
for res in result:
d = res["item"]
for res2 in d:
data.append(res2["stt"])
dates.append(res2['date'])
ts = TimeSeries(data,dates)
result = ts.resample('10min', how="mean",convention='end',fill_method="pad")
return result
def getseries(q):
pkl = "observation_" + q["route"] + "_" + q["link"] + "_" + q["direction"]
print(q)
result = db.observation.find(q).sort("_id", -1)
data = []
dates = []
for res in result:
d = res["item"]
for res2 in d:
data.append(res2["stt"])
dates.append(res2['date'])
ts = TimeSeries(data,dates)
result = ts.resample('10min', how="mean",convention='end',fill_method='pad')
stt = (result["2014-01-13":"2014-04-21"].dropna().values)
idx = (result["2014-01-13":"2014-04-21"].dropna().index.values)
ts = TimeSeries(stt,idx)
return ts
def getseries(q):
pkl = "observation_" + q["route"] + "_" + q["link"] + "_" + q["direction"]
ts = mem.get(pkl)
# existising query stored
if ts:
print("cached trips")
else:
result = db.observation.find(q).sort("_id", -1)
data = []
dates = []
for res in result:
d = res["item"]
for res2 in d:
data.append(res2["stt"])
dates.append(res2['date'])
ts = TimeSeries(data,dates)
#put_pickle(pkl, ts)
result = ts.resample('10min', how="mean",convention='end',fill_method='pad')
return result
