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data = pd.read_csv("./day.csv")
for i in range(data.shape[0]):
    contents = [list(zip(data.columns, map(lambda x: str(x), tuple(data.loc[i,:]))))]
    put_logs(client, project, logstore, contents)

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e_set("timestamp",dt_totimestamp(dt_parse(v("dteday"))))
e_if(
	e_search("weathersit==1"), e_set("good", 1, "normal", 0, "bad", 0, "terrible", 0),
	e_search("weathersit==2"), e_set("good", 0, "normal", 1, "bad", 0, "terrible", 0),
	e_search("weathersit==3"), e_set("good", 0, "normal", 0, "bad", 1, "terrible", 0),
	e_search("weathersit==4"), e_set("good", 0, "normal", 0, "bad", 0, "terrible", 1)
)
e_if(
	e_search("weekday==0"), e_set("sunday", 0, "monday", 0, "tuesday", 0, "wednesday", 0, "thursday", 0, "friday", 0, "saturday", 0),
	e_search("weekday==1"), e_set("sunday", 0, "monday", 1, "tuesday", 0, "wednesday", 0, "thursday", 0, "friday", 0, "saturday", 0),

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select
    timestamp,
    value
where
    timestamp > ( to_unixtime(localtimestamp) - 3600)
order by timestamp asc

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* | select
    preds[1] as unixtime,
    preds[3] as predict
from (
    select
        ts_predicate_ar(timestamp, value, 2, 10) as p
    from log
), unnest(p) as t(preds)

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* | select
    unixtime,
    predict
from (
    select
        ts_predicate_ar(timestamp, value, 2, 10)
    from log
)

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* | select
    ts_cp_detect(timestamp, value, 50)
limit 1000

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ar_data = np.concatenate((
    generate(1.4, -0.48, 5, 0.5, 450),
    generate(1.4, -0.48, 10, 0.5, 100),
    generate(1.4, -0.48, 5, 0.5, 450)
))
timestamps = sorted(range(1000))
contents = []
for timestamp in timestamps:
    contents.append([
        ("timestamp", str(timestamp)),

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* | select
    ts_predicate_ar(timestamp, value, 2, 10)
limit 110