| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # isort:skip_file |
| import uuid |
| from datetime import date, datetime, timezone |
| import logging |
| from math import nan |
| from unittest.mock import Mock, patch |
| from typing import Any, Dict, List, Set |
| |
| import numpy as np |
| import pandas as pd |
| import pytest |
| |
| import tests.test_app |
| import superset.viz as viz |
| from superset import app |
| from superset.constants import NULL_STRING |
| from superset.exceptions import QueryObjectValidationError, SpatialException |
| from superset.utils.core import DTTM_ALIAS |
| |
| from .base_tests import SupersetTestCase |
| from .utils import load_fixture |
| |
| logger = logging.getLogger(__name__) |
| |
| |
| class TestBaseViz(SupersetTestCase): |
| def test_constructor_exception_no_datasource(self): |
| form_data = {} |
| datasource = None |
| with self.assertRaises(Exception): |
| viz.BaseViz(datasource, form_data) |
| |
| def test_process_metrics(self): |
| # test TableViz metrics in correct order |
| form_data = { |
| "url_params": {}, |
| "row_limit": 500, |
| "metric": "sum__SP_POP_TOTL", |
| "entity": "country_code", |
| "secondary_metric": "sum__SP_POP_TOTL", |
| "granularity_sqla": "year", |
| "page_length": 0, |
| "all_columns": [], |
| "viz_type": "table", |
| "since": "2014-01-01", |
| "until": "2014-01-02", |
| "metrics": ["sum__SP_POP_TOTL", "SUM(SE_PRM_NENR_MA)", "SUM(SP_URB_TOTL)"], |
| "country_fieldtype": "cca3", |
| "percent_metrics": ["count"], |
| "slice_id": 74, |
| "time_grain_sqla": None, |
| "order_by_cols": [], |
| "groupby": ["country_name"], |
| "compare_lag": "10", |
| "limit": "25", |
| "datasource": "2__table", |
| "table_timestamp_format": "%Y-%m-%d %H:%M:%S", |
| "markup_type": "markdown", |
| "where": "", |
| "compare_suffix": "o10Y", |
| } |
| datasource = Mock() |
| datasource.type = "table" |
| test_viz = viz.BaseViz(datasource, form_data) |
| expect_metric_labels = [ |
| u"sum__SP_POP_TOTL", |
| u"SUM(SE_PRM_NENR_MA)", |
| u"SUM(SP_URB_TOTL)", |
| u"count", |
| ] |
| self.assertEqual(test_viz.metric_labels, expect_metric_labels) |
| self.assertEqual(test_viz.all_metrics, expect_metric_labels) |
| |
| def test_get_df_returns_empty_df(self): |
| form_data = {"dummy": 123} |
| query_obj = {"granularity": "day"} |
| datasource = self.get_datasource_mock() |
| test_viz = viz.BaseViz(datasource, form_data) |
| result = test_viz.get_df(query_obj) |
| self.assertEqual(type(result), pd.DataFrame) |
| self.assertTrue(result.empty) |
| |
| def test_get_df_handles_dttm_col(self): |
| form_data = {"dummy": 123} |
| query_obj = {"granularity": "day"} |
| results = Mock() |
| results.query = Mock() |
| results.status = Mock() |
| results.error_message = Mock() |
| datasource = Mock() |
| datasource.type = "table" |
| datasource.query = Mock(return_value=results) |
| mock_dttm_col = Mock() |
| datasource.get_column = Mock(return_value=mock_dttm_col) |
| |
| test_viz = viz.BaseViz(datasource, form_data) |
| test_viz.df_metrics_to_num = Mock() |
| test_viz.get_fillna_for_columns = Mock(return_value=0) |
| |
| results.df = pd.DataFrame(data={DTTM_ALIAS: ["1960-01-01 05:00:00"]}) |
| datasource.offset = 0 |
| mock_dttm_col = Mock() |
| datasource.get_column = Mock(return_value=mock_dttm_col) |
| mock_dttm_col.python_date_format = "epoch_ms" |
| result = test_viz.get_df(query_obj) |
| import logging |
| |
| logger.info(result) |
| pd.testing.assert_series_equal( |
| result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 5, 0)], name=DTTM_ALIAS) |
| ) |
| |
| mock_dttm_col.python_date_format = None |
| result = test_viz.get_df(query_obj) |
| pd.testing.assert_series_equal( |
| result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 5, 0)], name=DTTM_ALIAS) |
| ) |
| |
| datasource.offset = 1 |
| result = test_viz.get_df(query_obj) |
| pd.testing.assert_series_equal( |
| result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 6, 0)], name=DTTM_ALIAS) |
| ) |
| |
| datasource.offset = 0 |
| results.df = pd.DataFrame(data={DTTM_ALIAS: ["1960-01-01"]}) |
| mock_dttm_col.python_date_format = "%Y-%m-%d" |
| result = test_viz.get_df(query_obj) |
| pd.testing.assert_series_equal( |
| result[DTTM_ALIAS], pd.Series([datetime(1960, 1, 1, 0, 0)], name=DTTM_ALIAS) |
| ) |
| |
| def test_cache_timeout(self): |
| datasource = self.get_datasource_mock() |
| datasource.cache_timeout = 0 |
| test_viz = viz.BaseViz(datasource, form_data={}) |
| self.assertEqual(0, test_viz.cache_timeout) |
| |
| datasource.cache_timeout = 156 |
| test_viz = viz.BaseViz(datasource, form_data={}) |
| self.assertEqual(156, test_viz.cache_timeout) |
| |
| datasource.cache_timeout = None |
| datasource.database.cache_timeout = 0 |
| self.assertEqual(0, test_viz.cache_timeout) |
| |
| datasource.database.cache_timeout = 1666 |
| self.assertEqual(1666, test_viz.cache_timeout) |
| |
| datasource.database.cache_timeout = None |
| test_viz = viz.BaseViz(datasource, form_data={}) |
| self.assertEqual( |
| app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"], |
| test_viz.cache_timeout, |
| ) |
| |
| data_cache_timeout = app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"] |
| app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"] = None |
| datasource.database.cache_timeout = None |
| test_viz = viz.BaseViz(datasource, form_data={}) |
| self.assertEqual(app.config["CACHE_DEFAULT_TIMEOUT"], test_viz.cache_timeout) |
| # restore DATA_CACHE_CONFIG timeout |
| app.config["DATA_CACHE_CONFIG"]["CACHE_DEFAULT_TIMEOUT"] = data_cache_timeout |
| |
| |
| class TestTableViz(SupersetTestCase): |
| def test_get_data_applies_percentage(self): |
| form_data = { |
| "groupby": ["groupA", "groupB"], |
| "metrics": [ |
| { |
| "expressionType": "SIMPLE", |
| "aggregate": "SUM", |
| "label": "SUM(value1)", |
| "column": {"column_name": "value1", "type": "DOUBLE"}, |
| }, |
| "count", |
| "avg__C", |
| ], |
| "percent_metrics": [ |
| { |
| "expressionType": "SIMPLE", |
| "aggregate": "SUM", |
| "label": "SUM(value1)", |
| "column": {"column_name": "value1", "type": "DOUBLE"}, |
| }, |
| "avg__B", |
| ], |
| } |
| datasource = self.get_datasource_mock() |
| |
| df = pd.DataFrame( |
| { |
| "SUM(value1)": [15, 20, 25, 40], |
| "avg__B": [10, 20, 5, 15], |
| "avg__C": [11, 22, 33, 44], |
| "count": [6, 7, 8, 9], |
| "groupA": ["A", "B", "C", "C"], |
| "groupB": ["x", "x", "y", "z"], |
| } |
| ) |
| |
| test_viz = viz.TableViz(datasource, form_data) |
| data = test_viz.get_data(df) |
| # Check method correctly transforms data and computes percents |
| self.assertEqual( |
| [ |
| "groupA", |
| "groupB", |
| "SUM(value1)", |
| "count", |
| "avg__C", |
| "%SUM(value1)", |
| "%avg__B", |
| ], |
| list(data["columns"]), |
| ) |
| expected = [ |
| { |
| "groupA": "A", |
| "groupB": "x", |
| "SUM(value1)": 15, |
| "count": 6, |
| "avg__C": 11, |
| "%SUM(value1)": 0.15, |
| "%avg__B": 0.2, |
| }, |
| { |
| "groupA": "B", |
| "groupB": "x", |
| "SUM(value1)": 20, |
| "count": 7, |
| "avg__C": 22, |
| "%SUM(value1)": 0.2, |
| "%avg__B": 0.4, |
| }, |
| { |
| "groupA": "C", |
| "groupB": "y", |
| "SUM(value1)": 25, |
| "count": 8, |
| "avg__C": 33, |
| "%SUM(value1)": 0.25, |
| "%avg__B": 0.1, |
| }, |
| { |
| "groupA": "C", |
| "groupB": "z", |
| "SUM(value1)": 40, |
| "count": 9, |
| "avg__C": 44, |
| "%SUM(value1)": 0.4, |
| "%avg__B": 0.3, |
| }, |
| ] |
| self.assertEqual(expected, data["records"]) |
| |
| def test_parse_adhoc_filters(self): |
| form_data = { |
| "metrics": [ |
| { |
| "expressionType": "SIMPLE", |
| "aggregate": "SUM", |
| "label": "SUM(value1)", |
| "column": {"column_name": "value1", "type": "DOUBLE"}, |
| } |
| ], |
| "adhoc_filters": [ |
| { |
| "expressionType": "SIMPLE", |
| "clause": "WHERE", |
| "subject": "value2", |
| "operator": ">", |
| "comparator": "100", |
| }, |
| { |
| "expressionType": "SIMPLE", |
| "clause": "HAVING", |
| "subject": "SUM(value1)", |
| "operator": "<", |
| "comparator": "10", |
| }, |
| { |
| "expressionType": "SQL", |
| "clause": "HAVING", |
| "sqlExpression": "SUM(value1) > 5", |
| }, |
| { |
| "expressionType": "SQL", |
| "clause": "WHERE", |
| "sqlExpression": "value3 in ('North America')", |
| }, |
| ], |
| } |
| datasource = self.get_datasource_mock() |
| test_viz = viz.TableViz(datasource, form_data) |
| query_obj = test_viz.query_obj() |
| self.assertEqual( |
| [{"col": "value2", "val": "100", "op": ">"}], query_obj["filter"] |
| ) |
| self.assertEqual( |
| [{"op": "<", "val": "10", "col": "SUM(value1)"}], |
| query_obj["extras"]["having_druid"], |
| ) |
| self.assertEqual("(value3 in ('North America'))", query_obj["extras"]["where"]) |
| self.assertEqual("(SUM(value1) > 5)", query_obj["extras"]["having"]) |
| |
| def test_adhoc_filters_overwrite_legacy_filters(self): |
| form_data = { |
| "metrics": [ |
| { |
| "expressionType": "SIMPLE", |
| "aggregate": "SUM", |
| "label": "SUM(value1)", |
| "column": {"column_name": "value1", "type": "DOUBLE"}, |
| } |
| ], |
| "adhoc_filters": [ |
| { |
| "expressionType": "SIMPLE", |
| "clause": "WHERE", |
| "subject": "value2", |
| "operator": ">", |
| "comparator": "100", |
| }, |
| { |
| "expressionType": "SQL", |
| "clause": "WHERE", |
| "sqlExpression": "value3 in ('North America')", |
| }, |
| ], |
| "having": "SUM(value1) > 5", |
| } |
| datasource = self.get_datasource_mock() |
| test_viz = viz.TableViz(datasource, form_data) |
| query_obj = test_viz.query_obj() |
| self.assertEqual( |
| [{"col": "value2", "val": "100", "op": ">"}], query_obj["filter"] |
| ) |
| self.assertEqual([], query_obj["extras"]["having_druid"]) |
| self.assertEqual("(value3 in ('North America'))", query_obj["extras"]["where"]) |
| self.assertEqual("", query_obj["extras"]["having"]) |
| |
| def test_query_obj_merges_percent_metrics(self): |
| datasource = self.get_datasource_mock() |
| form_data = { |
| "metrics": ["sum__A", "count", "avg__C"], |
| "percent_metrics": ["sum__A", "avg__B", "max__Y"], |
| } |
| test_viz = viz.TableViz(datasource, form_data) |
| query_obj = test_viz.query_obj() |
| self.assertEqual( |
| ["sum__A", "count", "avg__C", "avg__B", "max__Y"], query_obj["metrics"] |
| ) |
| |
| def test_query_obj_throws_columns_and_metrics(self): |
| datasource = self.get_datasource_mock() |
| form_data = {"all_columns": ["A", "B"], "metrics": ["x", "y"]} |
| with self.assertRaises(Exception): |
| test_viz = viz.TableViz(datasource, form_data) |
| test_viz.query_obj() |
| del form_data["metrics"] |
| form_data["groupby"] = ["B", "C"] |
| with self.assertRaises(Exception): |
| test_viz = viz.TableViz(datasource, form_data) |
| test_viz.query_obj() |
| |
| @patch("superset.viz.BaseViz.query_obj") |
| def test_query_obj_merges_all_columns(self, super_query_obj): |
| datasource = self.get_datasource_mock() |
| form_data = { |
| "all_columns": ["colA", "colB", "colC"], |
| "order_by_cols": ['["colA", "colB"]', '["colC"]'], |
| } |
| super_query_obj.return_value = { |
| "columns": ["colD", "colC"], |
| "groupby": ["colA", "colB"], |
| } |
| test_viz = viz.TableViz(datasource, form_data) |
| query_obj = test_viz.query_obj() |
| self.assertEqual(form_data["all_columns"], query_obj["columns"]) |
| self.assertEqual([], query_obj["groupby"]) |
| self.assertEqual([["colA", "colB"], ["colC"]], query_obj["orderby"]) |
| |
| def test_query_obj_uses_sortby(self): |
| datasource = self.get_datasource_mock() |
| form_data = { |
| "metrics": ["colA", "colB"], |
| "order_desc": False, |
| } |
| |
| def run_test(metric): |
| form_data["timeseries_limit_metric"] = metric |
| test_viz = viz.TableViz(datasource, form_data) |
| query_obj = test_viz.query_obj() |
| self.assertEqual(["colA", "colB", metric], query_obj["metrics"]) |
| self.assertEqual([(metric, True)], query_obj["orderby"]) |
| |
| run_test("simple_metric") |
| run_test( |
| { |
| "label": "adhoc_metric", |
| "expressionType": "SIMPLE", |
| "aggregate": "SUM", |
| "column": {"column_name": "sort_column",}, |
| } |
| ) |
| |
| def test_should_be_timeseries_raises_when_no_granularity(self): |
| datasource = self.get_datasource_mock() |
| form_data = {"include_time": True} |
| with self.assertRaises(Exception): |
| test_viz = viz.TableViz(datasource, form_data) |
| test_viz.should_be_timeseries() |
| |
| def test_adhoc_metric_with_sortby(self): |
| metrics = [ |
| { |
| "expressionType": "SIMPLE", |
| "aggregate": "SUM", |
| "label": "sum_value", |
| "column": {"column_name": "value1", "type": "DOUBLE"}, |
| } |
| ] |
| form_data = { |
| "metrics": metrics, |
| "timeseries_limit_metric": { |
| "expressionType": "SIMPLE", |
| "aggregate": "SUM", |
| "label": "SUM(value1)", |
| "column": {"column_name": "value1", "type": "DOUBLE"}, |
| }, |
| "order_desc": False, |
| } |
| |
| df = pd.DataFrame({"SUM(value1)": [15], "sum_value": [15]}) |
| datasource = self.get_datasource_mock() |
| test_viz = viz.TableViz(datasource, form_data) |
| data = test_viz.get_data(df) |
| self.assertEqual(["sum_value"], data["columns"]) |
| |
| |
| class TestDistBarViz(SupersetTestCase): |
| def test_groupby_nulls(self): |
| form_data = { |
| "metrics": ["votes"], |
| "adhoc_filters": [], |
| "groupby": ["toppings"], |
| "columns": [], |
| } |
| datasource = self.get_datasource_mock() |
| df = pd.DataFrame( |
| { |
| "toppings": ["cheese", "pepperoni", "anchovies", None], |
| "votes": [3, 5, 1, 2], |
| } |
| ) |
| test_viz = viz.DistributionBarViz(datasource, form_data) |
| data = test_viz.get_data(df)[0] |
| self.assertEqual("votes", data["key"]) |
| expected_values = [ |
| {"x": "pepperoni", "y": 5}, |
| {"x": "cheese", "y": 3}, |
| {"x": NULL_STRING, "y": 2}, |
| {"x": "anchovies", "y": 1}, |
| ] |
| self.assertEqual(expected_values, data["values"]) |
| |
| def test_groupby_nans(self): |
| form_data = { |
| "metrics": ["count"], |
| "adhoc_filters": [], |
| "groupby": ["beds"], |
| "columns": [], |
| } |
| datasource = self.get_datasource_mock() |
| df = pd.DataFrame({"beds": [0, 1, nan, 2], "count": [30, 42, 3, 29]}) |
| test_viz = viz.DistributionBarViz(datasource, form_data) |
| data = test_viz.get_data(df)[0] |
| self.assertEqual("count", data["key"]) |
| expected_values = [ |
| {"x": "1.0", "y": 42}, |
| {"x": "0.0", "y": 30}, |
| {"x": "2.0", "y": 29}, |
| {"x": NULL_STRING, "y": 3}, |
| ] |
| self.assertEqual(expected_values, data["values"]) |
| |
| def test_column_nulls(self): |
| form_data = { |
| "metrics": ["votes"], |
| "adhoc_filters": [], |
| "groupby": ["toppings"], |
| "columns": ["role"], |
| } |
| datasource = self.get_datasource_mock() |
| df = pd.DataFrame( |
| { |
| "toppings": ["cheese", "pepperoni", "cheese", "pepperoni"], |
| "role": ["engineer", "engineer", None, None], |
| "votes": [3, 5, 1, 2], |
| } |
| ) |
| test_viz = viz.DistributionBarViz(datasource, form_data) |
| data = test_viz.get_data(df) |
| expected = [ |
| { |
| "key": NULL_STRING, |
| "values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}], |
| }, |
| { |
| "key": "engineer", |
| "values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}], |
| }, |
| ] |
| self.assertEqual(expected, data) |
| |
| |
| class TestPairedTTest(SupersetTestCase): |
| def test_get_data_transforms_dataframe(self): |
| form_data = { |
| "groupby": ["groupA", "groupB", "groupC"], |
| "metrics": ["metric1", "metric2", "metric3"], |
| } |
| datasource = self.get_datasource_mock() |
| # Test data |
| 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) |
| pairedTTestViz = viz.viz_types["paired_ttest"](datasource, form_data) |
| data = pairedTTestViz.get_data(df) |
| # Check method correctly transforms data |
| expected = { |
| "metric1": [ |
| { |
| "values": [ |
| {"x": 100, "y": 1}, |
| {"x": 200, "y": 2}, |
| {"x": 300, "y": 3}, |
| ], |
| "group": ("a1", "a2", "a3"), |
| }, |
| { |
| "values": [ |
| {"x": 100, "y": 4}, |
| {"x": 200, "y": 5}, |
| {"x": 300, "y": 6}, |
| ], |
| "group": ("b1", "b2", "b3"), |
| }, |
| { |
| "values": [ |
| {"x": 100, "y": 7}, |
| {"x": 200, "y": 8}, |
| {"x": 300, "y": 9}, |
| ], |
| "group": ("c1", "c2", "c3"), |
| }, |
| ], |
| "metric2": [ |
| { |
| "values": [ |
| {"x": 100, "y": 10}, |
| {"x": 200, "y": 20}, |
| {"x": 300, "y": 30}, |
| ], |
| "group": ("a1", "a2", "a3"), |
| }, |
| { |
| "values": [ |
| {"x": 100, "y": 40}, |
| {"x": 200, "y": 50}, |
| {"x": 300, "y": 60}, |
| ], |
| "group": ("b1", "b2", "b3"), |
| }, |
| { |
| "values": [ |
| {"x": 100, "y": 70}, |
| {"x": 200, "y": 80}, |
| {"x": 300, "y": 90}, |
| ], |
| "group": ("c1", "c2", "c3"), |
| }, |
| ], |
| "metric3": [ |
| { |
| "values": [ |
| {"x": 100, "y": 100}, |
| {"x": 200, "y": 200}, |
| {"x": 300, "y": 300}, |
| ], |
| "group": ("a1", "a2", "a3"), |
| }, |
| { |
| "values": [ |
| {"x": 100, "y": 400}, |
| {"x": 200, "y": 500}, |
| {"x": 300, "y": 600}, |
| ], |
| "group": ("b1", "b2", "b3"), |
| }, |
| { |
| "values": [ |
| {"x": 100, "y": 700}, |
| {"x": 200, "y": 800}, |
| {"x": 300, "y": 900}, |
| ], |
| "group": ("c1", "c2", "c3"), |
| }, |
| ], |
| } |
| self.assertEqual(data, expected) |
| |
| def test_get_data_empty_null_keys(self): |
| form_data = {"groupby": [], "metrics": ["", None]} |
| datasource = self.get_datasource_mock() |
| # Test data |
| raw = {} |
| raw[DTTM_ALIAS] = [100, 200, 300] |
| raw[""] = [1, 2, 3] |
| raw[None] = [10, 20, 30] |
| |
| df = pd.DataFrame(raw) |
| pairedTTestViz = viz.viz_types["paired_ttest"](datasource, form_data) |
| data = pairedTTestViz.get_data(df) |
| # Check method correctly transforms data |
| expected = { |
| "N/A": [ |
| { |
| "values": [ |
| {"x": 100, "y": 1}, |
| {"x": 200, "y": 2}, |
| {"x": 300, "y": 3}, |
| ], |
| "group": "All", |
| } |
| ], |
| "NULL": [ |
| { |
| "values": [ |
| {"x": 100, "y": 10}, |
| {"x": 200, "y": 20}, |
| {"x": 300, "y": 30}, |
| ], |
| "group": "All", |
| } |
| ], |
| } |
| self.assertEqual(data, expected) |
| |
| |
| 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:], 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(set(["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(set(["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() |
| |
| |
| 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") |
| |
| @patch("superset.utils.core.uuid.uuid4") |
| def test_filter_nulls(self, mock_uuid4): |
| mock_uuid4.return_value = uuid.UUID("12345678123456781234567812345678") |
| 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": "12345678-1234-5678-1234-567812345678", |
| "comparator": "", |
| "operator": "IS NOT NULL", |
| "subject": "lat", |
| "isExtra": False, |
| }, |
| { |
| "clause": "WHERE", |
| "expressionType": "SIMPLE", |
| "filterOptionName": "12345678-1234-5678-1234-567812345678", |
| "comparator": "", |
| "operator": "IS NOT NULL", |
| "subject": "lon", |
| "isExtra": False, |
| }, |
| ], |
| "delimited_key": [ |
| { |
| "clause": "WHERE", |
| "expressionType": "SIMPLE", |
| "filterOptionName": "12345678-1234-5678-1234-567812345678", |
| "comparator": "", |
| "operator": "IS NOT NULL", |
| "subject": "lonlat", |
| "isExtra": False, |
| } |
| ], |
| "geohash_key": [ |
| { |
| "clause": "WHERE", |
| "expressionType": "SIMPLE", |
| "filterOptionName": "12345678-1234-5678-1234-567812345678", |
| "comparator": "", |
| "operator": "IS NOT NULL", |
| "subject": "geo", |
| "isExtra": False, |
| } |
| ], |
| } |
| 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 = [ |
| { |
| u"values": [ |
| {u"y": 4, u"x": u"2018-02-20T00:00:00"}, |
| {u"y": 4, u"x": u"2018-03-09T00:00:00"}, |
| ], |
| u"key": (u"Real Madrid Basket",), |
| }, |
| { |
| u"values": [ |
| {u"y": 2, u"x": u"2018-02-20T00:00:00"}, |
| {u"y": 2, u"x": u"2018-03-09T00:00:00"}, |
| ], |
| u"key": (u"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.BigNumberViz( |
| 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.BigNumberViz( |
| 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.BigNumberViz( |
| 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.BigNumberViz( |
| datasource, |
| { |
| "metrics": ["y"], |
| "rolling_type": "cumsum", |
| "rolling_periods": 4, |
| "min_periods": 4, |
| }, |
| ) |
| with pytest.raises(QueryObjectValidationError): |
| test_viz.apply_rolling(df) |
| |
| |
| class TestBigNumberViz(SupersetTestCase): |
| def test_get_data(self): |
| datasource = self.get_datasource_mock() |
| df = pd.DataFrame( |
| data={ |
| DTTM_ALIAS: pd.to_datetime( |
| ["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"] |
| ), |
| "y": [1.0, 2.0, 3.0, 4.0], |
| } |
| ) |
| data = viz.BigNumberViz(datasource, {"metrics": ["y"]}).get_data(df) |
| self.assertEqual(data[2], {DTTM_ALIAS: pd.Timestamp("2019-01-05"), "y": 3}) |
| |
| def test_get_data_with_none(self): |
| datasource = self.get_datasource_mock() |
| df = pd.DataFrame( |
| data={ |
| DTTM_ALIAS: pd.to_datetime( |
| ["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"] |
| ), |
| "y": [1.0, 2.0, None, 4.0], |
| } |
| ) |
| data = viz.BigNumberViz(datasource, {"metrics": ["y"]}).get_data(df) |
| assert np.isnan(data[2]["y"]) |
| |
| |
| class TestPivotTableViz(SupersetTestCase): |
| df = pd.DataFrame( |
| data={ |
| "intcol": [1, 2, 3, None], |
| "floatcol": [0.1, 0.2, 0.3, None], |
| "strcol": ["a", "b", "c", None], |
| } |
| ) |
| |
| def test_get_aggfunc_numeric(self): |
| # is a sum function |
| func = viz.PivotTableViz.get_aggfunc("intcol", self.df, {}) |
| assert hasattr(func, "__call__") |
| assert func(self.df["intcol"]) == 6 |
| |
| assert ( |
| viz.PivotTableViz.get_aggfunc("intcol", self.df, {"pandas_aggfunc": "min"}) |
| == "min" |
| ) |
| assert ( |
| viz.PivotTableViz.get_aggfunc( |
| "floatcol", self.df, {"pandas_aggfunc": "max"} |
| ) |
| == "max" |
| ) |
| |
| def test_get_aggfunc_non_numeric(self): |
| assert viz.PivotTableViz.get_aggfunc("strcol", self.df, {}) == "max" |
| assert ( |
| viz.PivotTableViz.get_aggfunc("strcol", self.df, {"pandas_aggfunc": "sum"}) |
| == "max" |
| ) |
| assert ( |
| viz.PivotTableViz.get_aggfunc("strcol", self.df, {"pandas_aggfunc": "min"}) |
| == "min" |
| ) |
| |
| def test_format_datetime_from_pd_timestamp(self): |
| tstamp = pd.Timestamp(datetime(2020, 9, 3, tzinfo=timezone.utc)) |
| assert ( |
| viz.PivotTableViz._format_datetime(tstamp) == "__timestamp:1599091200000.0" |
| ) |
| |
| def test_format_datetime_from_datetime(self): |
| tstamp = datetime(2020, 9, 3, tzinfo=timezone.utc) |
| assert ( |
| viz.PivotTableViz._format_datetime(tstamp) == "__timestamp:1599091200000.0" |
| ) |
| |
| def test_format_datetime_from_date(self): |
| tstamp = date(2020, 9, 3) |
| assert ( |
| viz.PivotTableViz._format_datetime(tstamp) == "__timestamp:1599091200000.0" |
| ) |
| |
| def test_format_datetime_from_string(self): |
| tstamp = "2020-09-03T00:00:00" |
| assert ( |
| viz.PivotTableViz._format_datetime(tstamp) == "__timestamp:1599091200000.0" |
| ) |
| |
| def test_format_datetime_from_invalid_string(self): |
| tstamp = "abracadabra" |
| assert viz.PivotTableViz._format_datetime(tstamp) == tstamp |
| |
| def test_format_datetime_from_int(self): |
| assert viz.PivotTableViz._format_datetime(123) == 123 |
| assert viz.PivotTableViz._format_datetime(123.0) == 123.0 |
