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7b3095d6ff
* Fix examples charts/dashboards and refactor * pylinting * Fix pylint * Lint the refactor * Rebased
68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
import gzip
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import os
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import pandas as pd
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from sqlalchemy import DateTime
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from superset import db
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from superset.utils import core as utils
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from .helpers import (
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config,
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DATA_FOLDER,
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get_slice_json,
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merge_slice,
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Slice,
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TBL,
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)
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def load_random_time_series_data():
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"""Loading random time series data from a zip file in the repo"""
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with gzip.open(os.path.join(DATA_FOLDER, 'random_time_series.json.gz')) as f:
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pdf = pd.read_json(f)
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pdf.ds = pd.to_datetime(pdf.ds, unit='s')
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pdf.to_sql(
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'random_time_series',
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db.engine,
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if_exists='replace',
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chunksize=500,
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dtype={
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'ds': DateTime,
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},
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index=False)
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print('Done loading table!')
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print('-' * 80)
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print('Creating table [random_time_series] reference')
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obj = db.session.query(TBL).filter_by(table_name='random_time_series').first()
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if not obj:
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obj = TBL(table_name='random_time_series')
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obj.main_dttm_col = 'ds'
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obj.database = utils.get_or_create_main_db()
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db.session.merge(obj)
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db.session.commit()
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obj.fetch_metadata()
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tbl = obj
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slice_data = {
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'granularity_sqla': 'day',
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'row_limit': config.get('ROW_LIMIT'),
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'since': '1 year ago',
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'until': 'now',
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'metric': 'count',
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'where': '',
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'viz_type': 'cal_heatmap',
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'domain_granularity': 'month',
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'subdomain_granularity': 'day',
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}
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print('Creating a slice')
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slc = Slice(
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slice_name='Calendar Heatmap',
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viz_type='cal_heatmap',
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datasource_type='table',
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datasource_id=tbl.id,
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params=get_slice_json(slice_data),
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)
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merge_slice(slc)
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