mirror of https://github.com/apache/superset.git
1108 lines
42 KiB
Python
1108 lines
42 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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# isort:skip_file
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from datetime import datetime
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import logging
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from math import nan
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from unittest.mock import Mock, patch
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import numpy as np
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import pandas as pd
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import pytest
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import tests.integration_tests.test_app
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import superset.viz as viz
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from superset import app
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from superset.constants import NULL_STRING
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from superset.exceptions import QueryObjectValidationError, SpatialException
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from superset.utils.core import DTTM_ALIAS
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from .base_tests import SupersetTestCase
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from .utils import load_fixture
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logger = logging.getLogger(__name__)
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class TestBaseViz(SupersetTestCase):
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def test_constructor_exception_no_datasource(self):
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form_data = {}
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datasource = None
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with self.assertRaises(Exception):
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viz.BaseViz(datasource, form_data)
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def test_process_metrics(self):
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# test TimeTableViz metrics in correct order
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form_data = {
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"url_params": {},
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"row_limit": 500,
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"metric": "sum__SP_POP_TOTL",
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"entity": "country_code",
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"secondary_metric": "sum__SP_POP_TOTL",
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"granularity_sqla": "year",
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"page_length": 0,
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"all_columns": [],
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"viz_type": "time_table",
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"since": "2014-01-01",
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"until": "2014-01-02",
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"metrics": ["sum__SP_POP_TOTL", "SUM(SE_PRM_NENR_MA)", "SUM(SP_URB_TOTL)"],
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"country_fieldtype": "cca3",
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"percent_metrics": ["count"],
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"slice_id": 74,
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"time_grain_sqla": None,
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"order_by_cols": [],
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"groupby": ["country_name"],
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"compare_lag": "10",
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"limit": "25",
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"datasource": "2__table",
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"table_timestamp_format": "%Y-%m-%d %H:%M:%S",
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"markup_type": "markdown",
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"where": "",
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"compare_suffix": "o10Y",
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}
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datasource = Mock()
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datasource.type = "table"
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test_viz = viz.BaseViz(datasource, form_data)
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expect_metric_labels = [
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"sum__SP_POP_TOTL",
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"SUM(SE_PRM_NENR_MA)",
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"SUM(SP_URB_TOTL)",
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"count",
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]
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self.assertEqual(test_viz.metric_labels, expect_metric_labels)
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self.assertEqual(test_viz.all_metrics, expect_metric_labels)
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def test_get_df_returns_empty_df(self):
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form_data = {"dummy": 123}
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query_obj = {"granularity": "day"}
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datasource = self.get_datasource_mock()
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test_viz = viz.BaseViz(datasource, form_data)
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result = test_viz.get_df(query_obj)
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self.assertEqual(type(result), pd.DataFrame)
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self.assertTrue(result.empty)
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def test_get_df_handles_dttm_col(self):
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form_data = {"dummy": 123}
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query_obj = {"granularity": "day"}
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results = Mock()
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results.query = Mock()
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results.status = Mock()
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results.error_message = Mock()
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datasource = Mock()
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datasource.type = "table"
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datasource.query = Mock(return_value=results)
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mock_dttm_col = Mock()
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datasource.get_column = Mock(return_value=mock_dttm_col)
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test_viz = viz.BaseViz(datasource, form_data)
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test_viz.df_metrics_to_num = Mock()
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test_viz.get_fillna_for_columns = Mock(return_value=0)
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results.df = pd.DataFrame(data={DTTM_ALIAS: ["1960-01-01 05:00:00"]})
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datasource.offset = 0
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mock_dttm_col = Mock()
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datasource.get_column = Mock(return_value=mock_dttm_col)
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mock_dttm_col.python_date_format = "epoch_ms"
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result = test_viz.get_df(query_obj)
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import logging
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logger.info(result)
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pd.testing.assert_series_equal(
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result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 5, 0)], name=DTTM_ALIAS)
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)
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mock_dttm_col.python_date_format = None
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result = test_viz.get_df(query_obj)
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pd.testing.assert_series_equal(
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result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 5, 0)], name=DTTM_ALIAS)
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)
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datasource.offset = 1
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result = test_viz.get_df(query_obj)
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pd.testing.assert_series_equal(
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result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 6, 0)], name=DTTM_ALIAS)
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)
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datasource.offset = 0
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results.df = pd.DataFrame(data={DTTM_ALIAS: ["1960-01-01"]})
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mock_dttm_col.python_date_format = "%Y-%m-%d"
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result = test_viz.get_df(query_obj)
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pd.testing.assert_series_equal(
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result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 0, 0)], name=DTTM_ALIAS)
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)
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def test_cache_timeout(self):
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datasource = self.get_datasource_mock()
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datasource.cache_timeout = 0
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test_viz = viz.BaseViz(datasource, form_data={})
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self.assertEqual(0, test_viz.cache_timeout)
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datasource.cache_timeout = 156
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test_viz = viz.BaseViz(datasource, form_data={})
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self.assertEqual(156, test_viz.cache_timeout)
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datasource.cache_timeout = None
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datasource.database.cache_timeout = 0
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self.assertEqual(0, test_viz.cache_timeout)
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datasource.database.cache_timeout = 1666
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self.assertEqual(1666, test_viz.cache_timeout)
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datasource.database.cache_timeout = None
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test_viz = viz.BaseViz(datasource, form_data={})
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self.assertEqual(
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app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"],
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test_viz.cache_timeout,
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)
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data_cache_timeout = app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"]
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app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"] = None
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datasource.database.cache_timeout = None
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test_viz = viz.BaseViz(datasource, form_data={})
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self.assertEqual(app.config["CACHE_DEFAULT_TIMEOUT"], test_viz.cache_timeout)
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# restore DATA_CACHE_CONFIG timeout
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app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"] = data_cache_timeout
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class TestDistBarViz(SupersetTestCase):
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def test_groupby_nulls(self):
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form_data = {
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"metrics": ["votes"],
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"adhoc_filters": [],
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"groupby": ["toppings"],
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"columns": [],
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"order_desc": True,
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}
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datasource = self.get_datasource_mock()
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df = pd.DataFrame(
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{
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"toppings": ["cheese", "pepperoni", "anchovies", None],
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"votes": [3, 5, 1, 2],
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}
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)
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test_viz = viz.DistributionBarViz(datasource, form_data)
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data = test_viz.get_data(df)[0]
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self.assertEqual("votes", data["key"])
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expected_values = [
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{"x": "pepperoni", "y": 5},
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{"x": "cheese", "y": 3},
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{"x": NULL_STRING, "y": 2},
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{"x": "anchovies", "y": 1},
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]
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self.assertEqual(expected_values, data["values"])
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def test_groupby_nans(self):
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form_data = {
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"metrics": ["count"],
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"adhoc_filters": [],
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"groupby": ["beds"],
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"columns": [],
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"order_desc": True,
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}
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datasource = self.get_datasource_mock()
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df = pd.DataFrame({"beds": [0, 1, nan, 2], "count": [30, 42, 3, 29]})
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test_viz = viz.DistributionBarViz(datasource, form_data)
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data = test_viz.get_data(df)[0]
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self.assertEqual("count", data["key"])
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expected_values = [
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{"x": "1.0", "y": 42},
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{"x": "0.0", "y": 30},
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{"x": "2.0", "y": 29},
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{"x": NULL_STRING, "y": 3},
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]
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self.assertEqual(expected_values, data["values"])
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def test_column_nulls(self):
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form_data = {
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"metrics": ["votes"],
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"adhoc_filters": [],
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"groupby": ["toppings"],
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"columns": ["role"],
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"order_desc": True,
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}
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datasource = self.get_datasource_mock()
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df = pd.DataFrame(
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{
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"toppings": ["cheese", "pepperoni", "cheese", "pepperoni"],
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"role": ["engineer", "engineer", None, None],
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"votes": [3, 5, 1, 2],
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}
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)
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test_viz = viz.DistributionBarViz(datasource, form_data)
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data = test_viz.get_data(df)
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expected = [
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{
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"key": NULL_STRING,
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"values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}],
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},
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{
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"key": "engineer",
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"values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}],
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},
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]
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self.assertEqual(expected, data)
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def test_column_metrics_in_order(self):
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form_data = {
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"metrics": ["z_column", "votes", "a_column"],
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"adhoc_filters": [],
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"groupby": ["toppings"],
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"columns": [],
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"order_desc": True,
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}
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datasource = self.get_datasource_mock()
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df = pd.DataFrame(
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{
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"toppings": ["cheese", "pepperoni", "cheese", "pepperoni"],
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"role": ["engineer", "engineer", None, None],
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"votes": [3, 5, 1, 2],
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"a_column": [3, 5, 1, 2],
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"z_column": [3, 5, 1, 2],
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}
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)
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test_viz = viz.DistributionBarViz(datasource, form_data)
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data = test_viz.get_data(df)
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expected = [
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{
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"key": "z_column",
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"values": [{"x": "pepperoni", "y": 3.5}, {"x": "cheese", "y": 2.0}],
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},
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{
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"key": "votes",
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"values": [{"x": "pepperoni", "y": 3.5}, {"x": "cheese", "y": 2.0}],
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},
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{
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"key": "a_column",
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"values": [{"x": "pepperoni", "y": 3.5}, {"x": "cheese", "y": 2.0}],
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},
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]
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self.assertEqual(expected, data)
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def test_column_metrics_in_order_with_breakdowns(self):
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form_data = {
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"metrics": ["z_column", "votes", "a_column"],
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"adhoc_filters": [],
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"groupby": ["toppings"],
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"columns": ["role"],
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"order_desc": True,
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}
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datasource = self.get_datasource_mock()
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df = pd.DataFrame(
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{
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"toppings": ["cheese", "pepperoni", "cheese", "pepperoni"],
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"role": ["engineer", "engineer", None, None],
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"votes": [3, 5, 1, 2],
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"a_column": [3, 5, 1, 2],
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"z_column": [3, 5, 1, 2],
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}
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)
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test_viz = viz.DistributionBarViz(datasource, form_data)
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data = test_viz.get_data(df)
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expected = [
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{
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"key": f"z_column, {NULL_STRING}",
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"values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}],
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},
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{
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"key": "z_column, engineer",
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"values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}],
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},
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{
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"key": f"votes, {NULL_STRING}",
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"values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}],
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},
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{
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"key": "votes, engineer",
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"values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}],
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},
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{
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"key": f"a_column, {NULL_STRING}",
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"values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}],
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},
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{
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"key": "a_column, engineer",
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"values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}],
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},
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]
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self.assertEqual(expected, data)
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class TestPairedTTest(SupersetTestCase):
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def test_get_data_transforms_dataframe(self):
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form_data = {
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"groupby": ["groupA", "groupB", "groupC"],
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"metrics": ["metric1", "metric2", "metric3"],
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}
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datasource = self.get_datasource_mock()
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# Test data
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raw = {}
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raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
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raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
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raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
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raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
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raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
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raw["metric2"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
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raw["metric3"] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
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df = pd.DataFrame(raw)
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pairedTTestViz = viz.viz_types["paired_ttest"](datasource, form_data)
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data = pairedTTestViz.get_data(df)
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# Check method correctly transforms data
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expected = {
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"metric1": [
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{
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"values": [
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{"x": 100, "y": 1},
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{"x": 200, "y": 2},
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{"x": 300, "y": 3},
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],
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"group": ("a1", "a2", "a3"),
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},
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{
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"values": [
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{"x": 100, "y": 4},
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{"x": 200, "y": 5},
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{"x": 300, "y": 6},
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],
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"group": ("b1", "b2", "b3"),
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},
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{
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"values": [
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{"x": 100, "y": 7},
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{"x": 200, "y": 8},
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{"x": 300, "y": 9},
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],
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"group": ("c1", "c2", "c3"),
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},
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],
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"metric2": [
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{
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"values": [
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{"x": 100, "y": 10},
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{"x": 200, "y": 20},
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{"x": 300, "y": 30},
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],
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"group": ("a1", "a2", "a3"),
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},
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{
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"values": [
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{"x": 100, "y": 40},
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{"x": 200, "y": 50},
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{"x": 300, "y": 60},
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],
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"group": ("b1", "b2", "b3"),
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},
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{
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"values": [
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{"x": 100, "y": 70},
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{"x": 200, "y": 80},
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{"x": 300, "y": 90},
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],
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"group": ("c1", "c2", "c3"),
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},
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],
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"metric3": [
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{
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"values": [
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{"x": 100, "y": 100},
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{"x": 200, "y": 200},
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{"x": 300, "y": 300},
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],
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"group": ("a1", "a2", "a3"),
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},
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{
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"values": [
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{"x": 100, "y": 400},
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{"x": 200, "y": 500},
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{"x": 300, "y": 600},
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],
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"group": ("b1", "b2", "b3"),
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},
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{
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"values": [
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{"x": 100, "y": 700},
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{"x": 200, "y": 800},
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{"x": 300, "y": 900},
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],
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"group": ("c1", "c2", "c3"),
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},
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],
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}
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self.assertEqual(data, expected)
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def test_get_data_empty_null_keys(self):
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form_data = {"groupby": [], "metrics": [""]}
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datasource = self.get_datasource_mock()
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# Test data
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raw = {}
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raw[DTTM_ALIAS] = [100, 200, 300]
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raw[""] = [1, 2, 3]
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raw[None] = [10, 20, 30]
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df = pd.DataFrame(raw)
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pairedTTestViz = viz.viz_types["paired_ttest"](datasource, form_data)
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data = pairedTTestViz.get_data(df)
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# Check method correctly transforms data
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expected = {
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"N/A": [
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{
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"values": [
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{"x": 100, "y": 1},
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{"x": 200, "y": 2},
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{"x": 300, "y": 3},
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],
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"group": "All",
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}
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],
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}
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self.assertEqual(data, expected)
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form_data = {"groupby": [], "metrics": [None]}
|
|
with self.assertRaises(ValueError):
|
|
viz.viz_types["paired_ttest"](datasource, form_data)
|
|
|
|
|
|
class TestPartitionViz(SupersetTestCase):
|
|
@patch("superset.viz.BaseViz.query_obj")
|
|
def test_query_obj_time_series_option(self, super_query_obj):
|
|
datasource = self.get_datasource_mock()
|
|
form_data = {}
|
|
test_viz = viz.PartitionViz(datasource, form_data)
|
|
super_query_obj.return_value = {}
|
|
query_obj = test_viz.query_obj()
|
|
self.assertFalse(query_obj["is_timeseries"])
|
|
test_viz.form_data["time_series_option"] = "agg_sum"
|
|
query_obj = test_viz.query_obj()
|
|
self.assertTrue(query_obj["is_timeseries"])
|
|
|
|
def test_levels_for_computes_levels(self):
|
|
raw = {}
|
|
raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
|
|
raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
|
|
raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
|
|
raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
|
|
raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
raw["metric2"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
raw["metric3"] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
|
|
df = pd.DataFrame(raw)
|
|
groups = ["groupA", "groupB", "groupC"]
|
|
time_op = "agg_sum"
|
|
test_viz = viz.PartitionViz(Mock(), {})
|
|
levels = test_viz.levels_for(time_op, groups, df)
|
|
self.assertEqual(4, len(levels))
|
|
expected = {DTTM_ALIAS: 1800, "metric1": 45, "metric2": 450, "metric3": 4500}
|
|
self.assertEqual(expected, levels[0].to_dict())
|
|
expected = {
|
|
DTTM_ALIAS: {"a1": 600, "b1": 600, "c1": 600},
|
|
"metric1": {"a1": 6, "b1": 15, "c1": 24},
|
|
"metric2": {"a1": 60, "b1": 150, "c1": 240},
|
|
"metric3": {"a1": 600, "b1": 1500, "c1": 2400},
|
|
}
|
|
self.assertEqual(expected, levels[1].to_dict())
|
|
self.assertEqual(["groupA", "groupB"], levels[2].index.names)
|
|
self.assertEqual(["groupA", "groupB", "groupC"], levels[3].index.names)
|
|
time_op = "agg_mean"
|
|
levels = test_viz.levels_for(time_op, groups, df)
|
|
self.assertEqual(4, len(levels))
|
|
expected = {
|
|
DTTM_ALIAS: 200.0,
|
|
"metric1": 5.0,
|
|
"metric2": 50.0,
|
|
"metric3": 500.0,
|
|
}
|
|
self.assertEqual(expected, levels[0].to_dict())
|
|
expected = {
|
|
DTTM_ALIAS: {"a1": 200, "c1": 200, "b1": 200},
|
|
"metric1": {"a1": 2, "b1": 5, "c1": 8},
|
|
"metric2": {"a1": 20, "b1": 50, "c1": 80},
|
|
"metric3": {"a1": 200, "b1": 500, "c1": 800},
|
|
}
|
|
self.assertEqual(expected, levels[1].to_dict())
|
|
self.assertEqual(["groupA", "groupB"], levels[2].index.names)
|
|
self.assertEqual(["groupA", "groupB", "groupC"], levels[3].index.names)
|
|
|
|
def test_levels_for_diff_computes_difference(self):
|
|
raw = {}
|
|
raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
|
|
raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
|
|
raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
|
|
raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
|
|
raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
raw["metric2"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
raw["metric3"] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
|
|
df = pd.DataFrame(raw)
|
|
groups = ["groupA", "groupB", "groupC"]
|
|
test_viz = viz.PartitionViz(Mock(), {})
|
|
time_op = "point_diff"
|
|
levels = test_viz.levels_for_diff(time_op, groups, df)
|
|
expected = {"metric1": 6, "metric2": 60, "metric3": 600}
|
|
self.assertEqual(expected, levels[0].to_dict())
|
|
expected = {
|
|
"metric1": {"a1": 2, "b1": 2, "c1": 2},
|
|
"metric2": {"a1": 20, "b1": 20, "c1": 20},
|
|
"metric3": {"a1": 200, "b1": 200, "c1": 200},
|
|
}
|
|
self.assertEqual(expected, levels[1].to_dict())
|
|
self.assertEqual(4, len(levels))
|
|
self.assertEqual(["groupA", "groupB", "groupC"], levels[3].index.names)
|
|
|
|
def test_levels_for_time_calls_process_data_and_drops_cols(self):
|
|
raw = {}
|
|
raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
|
|
raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
|
|
raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
|
|
raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
|
|
raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
raw["metric2"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
raw["metric3"] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
|
|
df = pd.DataFrame(raw)
|
|
groups = ["groupA", "groupB", "groupC"]
|
|
test_viz = viz.PartitionViz(Mock(), {"groupby": groups})
|
|
|
|
def return_args(df_drop, aggregate):
|
|
return df_drop
|
|
|
|
test_viz.process_data = Mock(side_effect=return_args)
|
|
levels = test_viz.levels_for_time(groups, df)
|
|
self.assertEqual(4, len(levels))
|
|
cols = [DTTM_ALIAS, "metric1", "metric2", "metric3"]
|
|
self.assertEqual(sorted(cols), sorted(levels[0].columns.tolist()))
|
|
cols += ["groupA"]
|
|
self.assertEqual(sorted(cols), sorted(levels[1].columns.tolist()))
|
|
cols += ["groupB"]
|
|
self.assertEqual(sorted(cols), sorted(levels[2].columns.tolist()))
|
|
cols += ["groupC"]
|
|
self.assertEqual(sorted(cols), sorted(levels[3].columns.tolist()))
|
|
self.assertEqual(4, len(test_viz.process_data.mock_calls))
|
|
|
|
def test_nest_values_returns_hierarchy(self):
|
|
raw = {}
|
|
raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
|
|
raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
|
|
raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
|
|
raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
raw["metric2"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
raw["metric3"] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
|
|
df = pd.DataFrame(raw)
|
|
test_viz = viz.PartitionViz(Mock(), {})
|
|
groups = ["groupA", "groupB", "groupC"]
|
|
levels = test_viz.levels_for("agg_sum", groups, df)
|
|
nest = test_viz.nest_values(levels)
|
|
self.assertEqual(3, len(nest))
|
|
for i in range(0, 3):
|
|
self.assertEqual("metric" + str(i + 1), nest[i]["name"])
|
|
self.assertEqual(3, len(nest[0]["children"]))
|
|
self.assertEqual(1, len(nest[0]["children"][0]["children"]))
|
|
self.assertEqual(1, len(nest[0]["children"][0]["children"][0]["children"]))
|
|
|
|
def test_nest_procs_returns_hierarchy(self):
|
|
raw = {}
|
|
raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
|
|
raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
|
|
raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
|
|
raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
|
|
raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
raw["metric2"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
raw["metric3"] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
|
|
df = pd.DataFrame(raw)
|
|
test_viz = viz.PartitionViz(Mock(), {})
|
|
groups = ["groupA", "groupB", "groupC"]
|
|
metrics = ["metric1", "metric2", "metric3"]
|
|
procs = {}
|
|
for i in range(0, 4):
|
|
df_drop = df.drop(groups[i:], axis=1)
|
|
pivot = df_drop.pivot_table(
|
|
index=DTTM_ALIAS, columns=groups[:i], values=metrics
|
|
)
|
|
procs[i] = pivot
|
|
nest = test_viz.nest_procs(procs)
|
|
self.assertEqual(3, len(nest))
|
|
for i in range(0, 3):
|
|
self.assertEqual("metric" + str(i + 1), nest[i]["name"])
|
|
self.assertEqual(None, nest[i].get("val"))
|
|
self.assertEqual(3, len(nest[0]["children"]))
|
|
self.assertEqual(3, len(nest[0]["children"][0]["children"]))
|
|
self.assertEqual(1, len(nest[0]["children"][0]["children"][0]["children"]))
|
|
self.assertEqual(
|
|
1, len(nest[0]["children"][0]["children"][0]["children"][0]["children"])
|
|
)
|
|
|
|
def test_get_data_calls_correct_method(self):
|
|
raw = {}
|
|
raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
|
|
raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
|
|
raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
|
|
raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
|
|
raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
raw["metric2"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
|
|
raw["metric3"] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
|
|
df = pd.DataFrame(raw)
|
|
test_viz = viz.PartitionViz(Mock(), {})
|
|
with self.assertRaises(ValueError):
|
|
test_viz.get_data(df)
|
|
test_viz.levels_for = Mock(return_value=1)
|
|
test_viz.nest_values = Mock(return_value=1)
|
|
test_viz.form_data["groupby"] = ["groups"]
|
|
test_viz.form_data["time_series_option"] = "not_time"
|
|
test_viz.get_data(df)
|
|
self.assertEqual("agg_sum", test_viz.levels_for.mock_calls[0][1][0])
|
|
test_viz.form_data["time_series_option"] = "agg_sum"
|
|
test_viz.get_data(df)
|
|
self.assertEqual("agg_sum", test_viz.levels_for.mock_calls[1][1][0])
|
|
test_viz.form_data["time_series_option"] = "agg_mean"
|
|
test_viz.get_data(df)
|
|
self.assertEqual("agg_mean", test_viz.levels_for.mock_calls[2][1][0])
|
|
test_viz.form_data["time_series_option"] = "point_diff"
|
|
test_viz.levels_for_diff = Mock(return_value=1)
|
|
test_viz.get_data(df)
|
|
self.assertEqual("point_diff", test_viz.levels_for_diff.mock_calls[0][1][0])
|
|
test_viz.form_data["time_series_option"] = "point_percent"
|
|
test_viz.get_data(df)
|
|
self.assertEqual("point_percent", test_viz.levels_for_diff.mock_calls[1][1][0])
|
|
test_viz.form_data["time_series_option"] = "point_factor"
|
|
test_viz.get_data(df)
|
|
self.assertEqual("point_factor", test_viz.levels_for_diff.mock_calls[2][1][0])
|
|
test_viz.levels_for_time = Mock(return_value=1)
|
|
test_viz.nest_procs = Mock(return_value=1)
|
|
test_viz.form_data["time_series_option"] = "adv_anal"
|
|
test_viz.get_data(df)
|
|
self.assertEqual(1, len(test_viz.levels_for_time.mock_calls))
|
|
self.assertEqual(1, len(test_viz.nest_procs.mock_calls))
|
|
test_viz.form_data["time_series_option"] = "time_series"
|
|
test_viz.get_data(df)
|
|
self.assertEqual("agg_sum", test_viz.levels_for.mock_calls[3][1][0])
|
|
self.assertEqual(7, len(test_viz.nest_values.mock_calls))
|
|
|
|
|
|
class TestRoseVis(SupersetTestCase):
|
|
def test_rose_vis_get_data(self):
|
|
raw = {}
|
|
t1 = pd.Timestamp("2000")
|
|
t2 = pd.Timestamp("2002")
|
|
t3 = pd.Timestamp("2004")
|
|
raw[DTTM_ALIAS] = [t1, t2, t3, t1, t2, t3, t1, t2, t3]
|
|
raw["groupA"] = ["a1", "a1", "a1", "b1", "b1", "b1", "c1", "c1", "c1"]
|
|
raw["groupB"] = ["a2", "a2", "a2", "b2", "b2", "b2", "c2", "c2", "c2"]
|
|
raw["groupC"] = ["a3", "a3", "a3", "b3", "b3", "b3", "c3", "c3", "c3"]
|
|
raw["metric1"] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
df = pd.DataFrame(raw)
|
|
fd = {"metrics": ["metric1"], "groupby": ["groupA"]}
|
|
test_viz = viz.RoseViz(Mock(), fd)
|
|
test_viz.metrics = fd["metrics"]
|
|
res = test_viz.get_data(df)
|
|
expected = {
|
|
946684800000000000: [
|
|
{"time": t1, "value": 1, "key": ("a1",), "name": ("a1",)},
|
|
{"time": t1, "value": 4, "key": ("b1",), "name": ("b1",)},
|
|
{"time": t1, "value": 7, "key": ("c1",), "name": ("c1",)},
|
|
],
|
|
1009843200000000000: [
|
|
{"time": t2, "value": 2, "key": ("a1",), "name": ("a1",)},
|
|
{"time": t2, "value": 5, "key": ("b1",), "name": ("b1",)},
|
|
{"time": t2, "value": 8, "key": ("c1",), "name": ("c1",)},
|
|
],
|
|
1072915200000000000: [
|
|
{"time": t3, "value": 3, "key": ("a1",), "name": ("a1",)},
|
|
{"time": t3, "value": 6, "key": ("b1",), "name": ("b1",)},
|
|
{"time": t3, "value": 9, "key": ("c1",), "name": ("c1",)},
|
|
],
|
|
}
|
|
self.assertEqual(expected, res)
|
|
|
|
|
|
class TestTimeSeriesTableViz(SupersetTestCase):
|
|
def test_get_data_metrics(self):
|
|
form_data = {"metrics": ["sum__A", "count"], "groupby": []}
|
|
datasource = self.get_datasource_mock()
|
|
raw = {}
|
|
t1 = pd.Timestamp("2000")
|
|
t2 = pd.Timestamp("2002")
|
|
raw[DTTM_ALIAS] = [t1, t2]
|
|
raw["sum__A"] = [15, 20]
|
|
raw["count"] = [6, 7]
|
|
df = pd.DataFrame(raw)
|
|
test_viz = viz.TimeTableViz(datasource, form_data)
|
|
data = test_viz.get_data(df)
|
|
# Check method correctly transforms data
|
|
self.assertEqual({"count", "sum__A"}, set(data["columns"]))
|
|
time_format = "%Y-%m-%d %H:%M:%S"
|
|
expected = {
|
|
t1.strftime(time_format): {"sum__A": 15, "count": 6},
|
|
t2.strftime(time_format): {"sum__A": 20, "count": 7},
|
|
}
|
|
self.assertEqual(expected, data["records"])
|
|
|
|
def test_get_data_group_by(self):
|
|
form_data = {"metrics": ["sum__A"], "groupby": ["groupby1"]}
|
|
datasource = self.get_datasource_mock()
|
|
raw = {}
|
|
t1 = pd.Timestamp("2000")
|
|
t2 = pd.Timestamp("2002")
|
|
raw[DTTM_ALIAS] = [t1, t1, t1, t2, t2, t2]
|
|
raw["sum__A"] = [15, 20, 25, 30, 35, 40]
|
|
raw["groupby1"] = ["a1", "a2", "a3", "a1", "a2", "a3"]
|
|
df = pd.DataFrame(raw)
|
|
test_viz = viz.TimeTableViz(datasource, form_data)
|
|
data = test_viz.get_data(df)
|
|
# Check method correctly transforms data
|
|
self.assertEqual({"a1", "a2", "a3"}, set(data["columns"]))
|
|
time_format = "%Y-%m-%d %H:%M:%S"
|
|
expected = {
|
|
t1.strftime(time_format): {"a1": 15, "a2": 20, "a3": 25},
|
|
t2.strftime(time_format): {"a1": 30, "a2": 35, "a3": 40},
|
|
}
|
|
self.assertEqual(expected, data["records"])
|
|
|
|
@patch("superset.viz.BaseViz.query_obj")
|
|
def test_query_obj_throws_metrics_and_groupby(self, super_query_obj):
|
|
datasource = self.get_datasource_mock()
|
|
form_data = {"groupby": ["a"]}
|
|
super_query_obj.return_value = {}
|
|
test_viz = viz.TimeTableViz(datasource, form_data)
|
|
with self.assertRaises(Exception):
|
|
test_viz.query_obj()
|
|
form_data["metrics"] = ["x", "y"]
|
|
test_viz = viz.TimeTableViz(datasource, form_data)
|
|
with self.assertRaises(Exception):
|
|
test_viz.query_obj()
|
|
|
|
def test_query_obj_order_by(self):
|
|
test_viz = viz.TimeTableViz(
|
|
self.get_datasource_mock(), {"metrics": ["sum__A", "count"], "groupby": []}
|
|
)
|
|
query_obj = test_viz.query_obj()
|
|
self.assertEqual(query_obj["orderby"], [("sum__A", False)])
|
|
|
|
|
|
class TestBaseDeckGLViz(SupersetTestCase):
|
|
def test_get_metrics(self):
|
|
form_data = load_fixture("deck_path_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, form_data)
|
|
result = test_viz_deckgl.get_metrics()
|
|
assert result == [form_data.get("size")]
|
|
|
|
form_data = {}
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, form_data)
|
|
result = test_viz_deckgl.get_metrics()
|
|
assert result == []
|
|
|
|
def test_scatterviz_get_metrics(self):
|
|
form_data = load_fixture("deck_path_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
|
|
form_data = {}
|
|
test_viz_deckgl = viz.DeckScatterViz(datasource, form_data)
|
|
test_viz_deckgl.point_radius_fixed = {"type": "metric", "value": "int"}
|
|
result = test_viz_deckgl.get_metrics()
|
|
assert result == ["int"]
|
|
|
|
form_data = {}
|
|
test_viz_deckgl = viz.DeckScatterViz(datasource, form_data)
|
|
test_viz_deckgl.point_radius_fixed = {}
|
|
result = test_viz_deckgl.get_metrics()
|
|
assert result == []
|
|
|
|
def test_get_js_columns(self):
|
|
form_data = load_fixture("deck_path_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
mock_d = {"a": "dummy1", "b": "dummy2", "c": "dummy3"}
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, form_data)
|
|
result = test_viz_deckgl.get_js_columns(mock_d)
|
|
|
|
assert result == {"color": None}
|
|
|
|
def test_get_properties(self):
|
|
mock_d = {}
|
|
form_data = load_fixture("deck_path_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, form_data)
|
|
|
|
with self.assertRaises(NotImplementedError) as context:
|
|
test_viz_deckgl.get_properties(mock_d)
|
|
|
|
self.assertTrue("" in str(context.exception))
|
|
|
|
def test_process_spatial_query_obj(self):
|
|
form_data = load_fixture("deck_path_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
mock_key = "spatial_key"
|
|
mock_gb = []
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, form_data)
|
|
|
|
with self.assertRaises(ValueError) as context:
|
|
test_viz_deckgl.process_spatial_query_obj(mock_key, mock_gb)
|
|
|
|
self.assertTrue("Bad spatial key" in str(context.exception))
|
|
|
|
test_form_data = {
|
|
"latlong_key": {"type": "latlong", "lonCol": "lon", "latCol": "lat"},
|
|
"delimited_key": {"type": "delimited", "lonlatCol": "lonlat"},
|
|
"geohash_key": {"type": "geohash", "geohashCol": "geo"},
|
|
}
|
|
|
|
datasource = self.get_datasource_mock()
|
|
expected_results = {
|
|
"latlong_key": ["lon", "lat"],
|
|
"delimited_key": ["lonlat"],
|
|
"geohash_key": ["geo"],
|
|
}
|
|
for mock_key in ["latlong_key", "delimited_key", "geohash_key"]:
|
|
mock_gb = []
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, test_form_data)
|
|
test_viz_deckgl.process_spatial_query_obj(mock_key, mock_gb)
|
|
assert expected_results.get(mock_key) == mock_gb
|
|
|
|
def test_geojson_query_obj(self):
|
|
form_data = load_fixture("deck_geojson_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
test_viz_deckgl = viz.DeckGeoJson(datasource, form_data)
|
|
results = test_viz_deckgl.query_obj()
|
|
|
|
assert results["metrics"] == []
|
|
assert results["groupby"] == []
|
|
assert results["columns"] == ["test_col"]
|
|
|
|
def test_parse_coordinates(self):
|
|
form_data = load_fixture("deck_path_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
viz_instance = viz.BaseDeckGLViz(datasource, form_data)
|
|
|
|
coord = viz_instance.parse_coordinates("1.23, 3.21")
|
|
self.assertEqual(coord, (1.23, 3.21))
|
|
|
|
coord = viz_instance.parse_coordinates("1.23 3.21")
|
|
self.assertEqual(coord, (1.23, 3.21))
|
|
|
|
self.assertEqual(viz_instance.parse_coordinates(None), None)
|
|
|
|
self.assertEqual(viz_instance.parse_coordinates(""), None)
|
|
|
|
def test_parse_coordinates_raises(self):
|
|
form_data = load_fixture("deck_path_form_data.json")
|
|
datasource = self.get_datasource_mock()
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, form_data)
|
|
|
|
with self.assertRaises(SpatialException):
|
|
test_viz_deckgl.parse_coordinates("NULL")
|
|
|
|
with self.assertRaises(SpatialException):
|
|
test_viz_deckgl.parse_coordinates("fldkjsalkj,fdlaskjfjadlksj")
|
|
|
|
def test_filter_nulls(self):
|
|
test_form_data = {
|
|
"latlong_key": {"type": "latlong", "lonCol": "lon", "latCol": "lat"},
|
|
"delimited_key": {"type": "delimited", "lonlatCol": "lonlat"},
|
|
"geohash_key": {"type": "geohash", "geohashCol": "geo"},
|
|
}
|
|
|
|
datasource = self.get_datasource_mock()
|
|
expected_results = {
|
|
"latlong_key": [
|
|
{
|
|
"clause": "WHERE",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "c7f171cf3204bcbf456acfeac5cd9afd",
|
|
"comparator": "",
|
|
"operator": "IS NOT NULL",
|
|
"subject": "lat",
|
|
},
|
|
{
|
|
"clause": "WHERE",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "52634073fbb8ae0a3aa59ad48abac55e",
|
|
"comparator": "",
|
|
"operator": "IS NOT NULL",
|
|
"subject": "lon",
|
|
},
|
|
],
|
|
"delimited_key": [
|
|
{
|
|
"clause": "WHERE",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "cae5c925c140593743da08499e6fb207",
|
|
"comparator": "",
|
|
"operator": "IS NOT NULL",
|
|
"subject": "lonlat",
|
|
}
|
|
],
|
|
"geohash_key": [
|
|
{
|
|
"clause": "WHERE",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "d84f55222d8e414e888fa5f990b341d2",
|
|
"comparator": "",
|
|
"operator": "IS NOT NULL",
|
|
"subject": "geo",
|
|
}
|
|
],
|
|
}
|
|
for mock_key in ["latlong_key", "delimited_key", "geohash_key"]:
|
|
test_viz_deckgl = viz.BaseDeckGLViz(datasource, test_form_data.copy())
|
|
test_viz_deckgl.spatial_control_keys = [mock_key]
|
|
test_viz_deckgl.add_null_filters()
|
|
adhoc_filters = test_viz_deckgl.form_data["adhoc_filters"]
|
|
assert expected_results.get(mock_key) == adhoc_filters
|
|
|
|
|
|
class TestTimeSeriesViz(SupersetTestCase):
|
|
def test_timeseries_unicode_data(self):
|
|
datasource = self.get_datasource_mock()
|
|
form_data = {"groupby": ["name"], "metrics": ["sum__payout"]}
|
|
raw = {}
|
|
raw["name"] = [
|
|
"Real Madrid C.F.🇺🇸🇬🇧",
|
|
"Real Madrid C.F.🇺🇸🇬🇧",
|
|
"Real Madrid Basket",
|
|
"Real Madrid Basket",
|
|
]
|
|
raw["__timestamp"] = [
|
|
"2018-02-20T00:00:00",
|
|
"2018-03-09T00:00:00",
|
|
"2018-02-20T00:00:00",
|
|
"2018-03-09T00:00:00",
|
|
]
|
|
raw["sum__payout"] = [2, 2, 4, 4]
|
|
df = pd.DataFrame(raw)
|
|
|
|
test_viz = viz.NVD3TimeSeriesViz(datasource, form_data)
|
|
viz_data = {}
|
|
viz_data = test_viz.get_data(df)
|
|
expected = [
|
|
{
|
|
"values": [
|
|
{"y": 4, "x": "2018-02-20T00:00:00"},
|
|
{"y": 4, "x": "2018-03-09T00:00:00"},
|
|
],
|
|
"key": ("Real Madrid Basket",),
|
|
},
|
|
{
|
|
"values": [
|
|
{"y": 2, "x": "2018-02-20T00:00:00"},
|
|
{"y": 2, "x": "2018-03-09T00:00:00"},
|
|
],
|
|
"key": ("Real Madrid C.F.\U0001f1fa\U0001f1f8\U0001f1ec\U0001f1e7",),
|
|
},
|
|
]
|
|
self.assertEqual(expected, viz_data)
|
|
|
|
def test_process_data_resample(self):
|
|
datasource = self.get_datasource_mock()
|
|
|
|
df = pd.DataFrame(
|
|
{
|
|
"__timestamp": pd.to_datetime(
|
|
["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]
|
|
),
|
|
"y": [1.0, 2.0, 5.0, 7.0],
|
|
}
|
|
)
|
|
|
|
self.assertEqual(
|
|
viz.NVD3TimeSeriesViz(
|
|
datasource,
|
|
{"metrics": ["y"], "resample_method": "sum", "resample_rule": "1D"},
|
|
)
|
|
.process_data(df)["y"]
|
|
.tolist(),
|
|
[1.0, 2.0, 0.0, 0.0, 5.0, 0.0, 7.0],
|
|
)
|
|
|
|
np.testing.assert_equal(
|
|
viz.NVD3TimeSeriesViz(
|
|
datasource,
|
|
{"metrics": ["y"], "resample_method": "asfreq", "resample_rule": "1D"},
|
|
)
|
|
.process_data(df)["y"]
|
|
.tolist(),
|
|
[1.0, 2.0, np.nan, np.nan, 5.0, np.nan, 7.0],
|
|
)
|
|
|
|
def test_apply_rolling(self):
|
|
datasource = self.get_datasource_mock()
|
|
df = pd.DataFrame(
|
|
index=pd.to_datetime(
|
|
["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]
|
|
),
|
|
data={"y": [1.0, 2.0, 3.0, 4.0]},
|
|
)
|
|
self.assertEqual(
|
|
viz.NVD3TimeSeriesViz(
|
|
datasource,
|
|
{
|
|
"metrics": ["y"],
|
|
"rolling_type": "cumsum",
|
|
"rolling_periods": 0,
|
|
"min_periods": 0,
|
|
},
|
|
)
|
|
.apply_rolling(df)["y"]
|
|
.tolist(),
|
|
[1.0, 3.0, 6.0, 10.0],
|
|
)
|
|
self.assertEqual(
|
|
viz.NVD3TimeSeriesViz(
|
|
datasource,
|
|
{
|
|
"metrics": ["y"],
|
|
"rolling_type": "sum",
|
|
"rolling_periods": 2,
|
|
"min_periods": 0,
|
|
},
|
|
)
|
|
.apply_rolling(df)["y"]
|
|
.tolist(),
|
|
[1.0, 3.0, 5.0, 7.0],
|
|
)
|
|
self.assertEqual(
|
|
viz.NVD3TimeSeriesViz(
|
|
datasource,
|
|
{
|
|
"metrics": ["y"],
|
|
"rolling_type": "mean",
|
|
"rolling_periods": 10,
|
|
"min_periods": 0,
|
|
},
|
|
)
|
|
.apply_rolling(df)["y"]
|
|
.tolist(),
|
|
[1.0, 1.5, 2.0, 2.5],
|
|
)
|
|
|
|
def test_apply_rolling_without_data(self):
|
|
datasource = self.get_datasource_mock()
|
|
df = pd.DataFrame(
|
|
index=pd.to_datetime(
|
|
["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]
|
|
),
|
|
data={"y": [1.0, 2.0, 3.0, 4.0]},
|
|
)
|
|
test_viz = viz.NVD3TimeSeriesViz(
|
|
datasource,
|
|
{
|
|
"metrics": ["y"],
|
|
"rolling_type": "cumsum",
|
|
"rolling_periods": 4,
|
|
"min_periods": 4,
|
|
},
|
|
)
|
|
with pytest.raises(QueryObjectValidationError):
|
|
test_viz.apply_rolling(df)
|