147 lines
4.4 KiB
Python
147 lines
4.4 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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"""Loads datasets, dashboards and slices in a new superset instance"""
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# pylint: disable=C,R,W
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import textwrap
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import pandas as pd
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from sqlalchemy import Float, String
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from sqlalchemy.sql import column
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from superset import db
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from superset.connectors.sqla.models import SqlMetric
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from superset.utils import core as utils
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from .helpers import get_example_data, merge_slice, misc_dash_slices, Slice, TBL
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def load_energy(only_metadata=False, force=False):
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"""Loads an energy related dataset to use with sankey and graphs"""
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tbl_name = "energy_usage"
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database = utils.get_example_database()
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table_exists = database.has_table_by_name(tbl_name)
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if not only_metadata and (not table_exists or force):
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data = get_example_data("energy.json.gz")
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pdf = pd.read_json(data)
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pdf.to_sql(
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tbl_name,
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database.get_sqla_engine(),
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if_exists="replace",
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chunksize=500,
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dtype={"source": String(255), "target": String(255), "value": Float()},
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index=False,
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)
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print("Creating table [wb_health_population] reference")
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tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
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if not tbl:
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tbl = TBL(table_name=tbl_name)
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tbl.description = "Energy consumption"
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tbl.database = database
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if not any(col.metric_name == "sum__value" for col in tbl.metrics):
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col = str(column("value").compile(db.engine))
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tbl.metrics.append(
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SqlMetric(metric_name="sum__value", expression=f"SUM({col})")
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)
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db.session.merge(tbl)
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db.session.commit()
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tbl.fetch_metadata()
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slc = Slice(
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slice_name="Energy Sankey",
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viz_type="sankey",
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datasource_type="table",
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datasource_id=tbl.id,
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params=textwrap.dedent(
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"""\
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{
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"collapsed_fieldsets": "",
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"groupby": [
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"source",
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"target"
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],
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"having": "",
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"metric": "sum__value",
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"row_limit": "5000",
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"slice_name": "Energy Sankey",
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"viz_type": "sankey",
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"where": ""
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}
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"""
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),
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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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slc = Slice(
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slice_name="Energy Force Layout",
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viz_type="directed_force",
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datasource_type="table",
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datasource_id=tbl.id,
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params=textwrap.dedent(
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"""\
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{
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"charge": "-500",
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"collapsed_fieldsets": "",
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"groupby": [
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"source",
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"target"
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],
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"having": "",
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"link_length": "200",
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"metric": "sum__value",
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"row_limit": "5000",
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"slice_name": "Force",
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"viz_type": "directed_force",
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"where": ""
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}
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"""
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),
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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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slc = Slice(
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slice_name="Heatmap",
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viz_type="heatmap",
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datasource_type="table",
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datasource_id=tbl.id,
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params=textwrap.dedent(
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"""\
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{
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"all_columns_x": "source",
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"all_columns_y": "target",
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"canvas_image_rendering": "pixelated",
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"collapsed_fieldsets": "",
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"having": "",
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"linear_color_scheme": "blue_white_yellow",
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"metric": "sum__value",
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"normalize_across": "heatmap",
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"slice_name": "Heatmap",
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"viz_type": "heatmap",
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"where": "",
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"xscale_interval": "1",
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"yscale_interval": "1"
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}
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"""
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),
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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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