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88964b7dfc
* [WiP] deprecate auto-generated metrics & fix examples Picking up some leftover pieces not needed anymore since the MetricsControl * db merge * db merge * fix migration * Creating metrics required for tests
109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import gzip
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import os
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import pandas as pd
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from sqlalchemy import BigInteger, Date, DateTime, String
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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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misc_dash_slices,
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Slice,
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TBL,
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)
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def load_multiformat_time_series():
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"""Loading time series data from a zip file in the repo"""
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with gzip.open(os.path.join(DATA_FOLDER, 'multiformat_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.ds2 = pd.to_datetime(pdf.ds2, unit='s')
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pdf.to_sql(
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'multiformat_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': Date,
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'ds2': DateTime,
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'epoch_s': BigInteger,
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'epoch_ms': BigInteger,
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'string0': String(100),
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'string1': String(100),
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'string2': String(100),
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'string3': String(100),
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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 [multiformat_time_series] reference')
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obj = db.session.query(TBL).filter_by(table_name='multiformat_time_series').first()
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if not obj:
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obj = TBL(table_name='multiformat_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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dttm_and_expr_dict = {
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'ds': [None, None],
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'ds2': [None, None],
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'epoch_s': ['epoch_s', None],
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'epoch_ms': ['epoch_ms', None],
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'string2': ['%Y%m%d-%H%M%S', None],
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'string1': ['%Y-%m-%d^%H:%M:%S', None],
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'string0': ['%Y-%m-%d %H:%M:%S.%f', None],
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'string3': ['%Y/%m/%d%H:%M:%S.%f', None],
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}
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for col in obj.columns:
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dttm_and_expr = dttm_and_expr_dict[col.column_name]
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col.python_date_format = dttm_and_expr[0]
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col.dbatabase_expr = dttm_and_expr[1]
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col.is_dttm = True
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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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print('Creating Heatmap charts')
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for i, col in enumerate(tbl.columns):
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slice_data = {
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'metrics': ['count'],
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'granularity_sqla': col.column_name,
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'row_limit': config.get('ROW_LIMIT'),
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'since': '2015',
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'until': '2016',
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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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slc = Slice(
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slice_name=f'Calendar Heatmap multiformat {i}',
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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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misc_dash_slices.add('Calendar Heatmap multiformat 0')
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