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3d08266714
* [load_examples] download data at runtime When running `superset load_examples` to load example data sets, Superset used to load from the local package. This created a few issues notably around licensing (what are these datasets licensed as?) and around package size. For now, I moved the data sets here: https://github.com/apache-superset/examples-data Altered the logic to download the data from where it is stored. * flakes
111 lines
3.5 KiB
Python
111 lines
3.5 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 datetime
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import random
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import geohash
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import pandas as pd
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from sqlalchemy import DateTime, Float, 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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get_example_data,
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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_long_lat_data():
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"""Loading lat/long data from a csv file in the repo"""
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data = get_example_data('san_francisco.csv.gz', make_bytes=True)
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pdf = pd.read_csv(data, encoding='utf-8')
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start = datetime.datetime.now().replace(
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hour=0, minute=0, second=0, microsecond=0)
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pdf['datetime'] = [
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start + datetime.timedelta(hours=i * 24 / (len(pdf) - 1))
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for i in range(len(pdf))
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]
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pdf['occupancy'] = [random.randint(1, 6) for _ in range(len(pdf))]
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pdf['radius_miles'] = [random.uniform(1, 3) for _ in range(len(pdf))]
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pdf['geohash'] = pdf[['LAT', 'LON']].apply(
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lambda x: geohash.encode(*x), axis=1)
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pdf['delimited'] = pdf['LAT'].map(str).str.cat(pdf['LON'].map(str), sep=',')
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pdf.to_sql( # pylint: disable=no-member
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'long_lat',
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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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'longitude': Float(),
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'latitude': Float(),
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'number': Float(),
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'street': String(100),
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'unit': String(10),
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'city': String(50),
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'district': String(50),
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'region': String(50),
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'postcode': Float(),
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'id': String(100),
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'datetime': DateTime(),
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'occupancy': Float(),
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'radius_miles': Float(),
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'geohash': String(12),
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'delimited': String(60),
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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 reference')
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obj = db.session.query(TBL).filter_by(table_name='long_lat').first()
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if not obj:
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obj = TBL(table_name='long_lat')
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obj.main_dttm_col = 'datetime'
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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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'since': '2014-01-01',
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'until': 'now',
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'where': '',
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'viz_type': 'mapbox',
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'all_columns_x': 'LON',
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'all_columns_y': 'LAT',
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'mapbox_style': 'mapbox://styles/mapbox/light-v9',
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'all_columns': ['occupancy'],
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'row_limit': 500000,
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}
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print('Creating a slice')
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slc = Slice(
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slice_name='Mapbox Long/Lat',
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viz_type='mapbox',
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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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misc_dash_slices.add(slc.slice_name)
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merge_slice(slc)
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