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194 lines
6.2 KiB
Python
194 lines
6.2 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 random
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import pandas as pd
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import pytest
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from sqlalchemy import column, Float, String
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from superset import db
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from superset.connectors.sqla.models import SqlaTable, SqlMetric
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from superset.models.slice import Slice
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from superset.utils.core import get_example_default_schema
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from superset.utils.database import get_example_database
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from tests.integration_tests.dashboard_utils import create_slice, create_table_metadata
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from tests.integration_tests.test_app import app
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misc_dash_slices: set[str] = set()
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ENERGY_USAGE_TBL_NAME = "energy_usage"
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@pytest.fixture(scope="session")
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def load_energy_table_data():
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with app.app_context():
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database = get_example_database()
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with database.get_sqla_engine_with_context() as engine:
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df = _get_dataframe()
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df.to_sql(
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ENERGY_USAGE_TBL_NAME,
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engine,
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if_exists="replace",
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chunksize=500,
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index=False,
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dtype={"source": String(255), "target": String(255), "value": Float()},
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method="multi",
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schema=get_example_default_schema(),
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)
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yield
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with app.app_context():
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with get_example_database().get_sqla_engine_with_context() as engine:
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engine.execute("DROP TABLE IF EXISTS energy_usage")
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@pytest.fixture()
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def load_energy_table_with_slice(load_energy_table_data):
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with app.app_context():
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slices = _create_energy_table()
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yield slices
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_cleanup()
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def _get_dataframe():
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data = _get_energy_data()
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return pd.DataFrame.from_dict(data)
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def _create_energy_table() -> list[Slice]:
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table = create_table_metadata(
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table_name=ENERGY_USAGE_TBL_NAME,
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database=get_example_database(),
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table_description="Energy consumption",
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)
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table.fetch_metadata()
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if not any(col.metric_name == "sum__value" for col in table.metrics):
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col = str(column("value").compile(db.engine))
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table.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(table)
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db.session.commit()
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table.fetch_metadata()
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slices = []
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for slice_data in _get_energy_slices():
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slice = _create_and_commit_energy_slice(
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table,
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slice_data["slice_title"],
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slice_data["viz_type"],
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slice_data["params"],
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)
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slices.append(slice)
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return slices
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def _create_and_commit_energy_slice(
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table: SqlaTable, title: str, viz_type: str, param: dict[str, str]
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):
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slice = create_slice(title, viz_type, table, param)
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existing_slice = (
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db.session.query(Slice).filter_by(slice_name=slice.slice_name).first()
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)
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if existing_slice:
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db.session.delete(existing_slice)
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db.session.add(slice)
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db.session.commit()
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return slice
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def _cleanup() -> None:
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for slice_data in _get_energy_slices():
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slice = (
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db.session.query(Slice)
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.filter_by(slice_name=slice_data["slice_title"])
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.first()
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)
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db.session.delete(slice)
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metric = (
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db.session.query(SqlMetric).filter_by(metric_name="sum__value").one_or_none()
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)
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if metric:
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db.session.delete(metric)
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db.session.commit()
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def _get_energy_data():
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data = []
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for i in range(85):
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data.append(
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{
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"source": f"energy_source{i}",
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"target": f"energy_target{i}",
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"value": random.uniform(0.1, 11.0),
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}
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)
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return data
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def _get_energy_slices():
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return [
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{
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"slice_title": "Energy Sankey",
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"viz_type": "sankey",
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"params": {
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"collapsed_fieldsets": "",
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"groupby": ["source", "target"],
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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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},
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},
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{
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"slice_title": "Energy Force Layout",
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"viz_type": "graph_chart",
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"params": {
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"source": "source",
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"target": "target",
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"edgeLength": 400,
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"repulsion": 1000,
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"layout": "force",
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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": "graph_chart",
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},
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},
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{
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"slice_title": "Heatmap",
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"viz_type": "heatmap",
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"params": {
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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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"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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"xscale_interval": "1",
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"yscale_interval": "1",
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},
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"query_context": '{"datasource":{"id":12,"type":"table"},"force":false,"queries":[{"time_range":" : ","filters":[],"extras":{"time_grain_sqla":null,"having":"","where":""},"applied_time_extras":{},"columns":[],"metrics":[],"annotation_layers":[],"row_limit":5000,"timeseries_limit":0,"order_desc":true,"url_params":{},"custom_params":{},"custom_form_data":{}}],"result_format":"json","result_type":"full"}',
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},
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]
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