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- from util import getQuery, pickleQuery, getkwh
- import pandas as p
- import matplotlib.pyplot as plt
- import seaborn as sns
-
- # query = """
- # SELECT comb.icp_id, comb.read_time, COALESCE(kwh_tot, 0) AS kwh_tot
- # FROM
- # (
- # SELECT read_time, icp_id
- # FROM
- # (
- # SELECT read_time
- # FROM GENERATE_SERIES('2017-01-01 00:30:00'::timestamp, '2017-02-01 00:00:00'::timestamp,
- # '30 minutes'::interval) read_time
- # ) AS tsdata CROSS JOIN
- # (
- # SELECT *
- # FROM
- # (
- # SELECT icp_id, COUNT(DISTINCT read_date) AS data_days
- # FROM coup_prd.coupdatamaster
- # WHERE read_date >= to_date('01/01/2017','dd/mm/yyyy')
- # AND read_date < to_date('01/01/2018','dd/mm/yyyy')
- # AND content_code = 'UN'
- # AND icp_id LIKE '%%19'
- # GROUP BY icp_id
- # ) AS cir
- # WHERE data_days >= 360
- # ) AS qual_icp
- # ) AS comb
- # LEFT JOIN
- # (
- # SELECT *, read_date + CONCAT(period / 2, ':', period %% 2 * 30, ':00')::time AS read_time
- # FROM public.coup_tall_jan
- # ) AS tall_timestamp
- # ON comb.read_time = tall_timestamp.read_time AND comb.icp_id = tall_timestamp.icp_id;
- # """
- #
- # pickleQuery(query, "../data/jan19s.pkl")
-
- kwhdata = getkwh('2018-01-01', '2018-04-01', '2018-01-01 00:30:00', '2018-04-01 00:00:00', '%%1')
-
- print(kwhdata.info())
-
- print("Pickling")
- kwhdata.to_pickle("../data/2018-proj-sample.pkl")
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