| # 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 |
| from datetime import datetime |
| import logging |
| from math import nan |
| from unittest.mock import Mock, patch |
| |
| import numpy as np |
| import pandas as pd |
| import pytest |
| |
| import tests.integration_tests.test_app # noqa: F401 |
| 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): # noqa: B017, PT027 |
| viz.BaseViz(datasource, form_data) |
| |
| def test_process_metrics(self): |
| # test TimeTableViz 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": "time_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 = [ |
| "sum__SP_POP_TOTL", |
| "SUM(SE_PRM_NENR_MA)", |
| "SUM(SP_URB_TOTL)", |
| "count", |
| ] |
| assert test_viz.metric_labels == expect_metric_labels |
| assert 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) |
| assert isinstance(result, pd.DataFrame) |
| assert 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 # noqa: F401 |
| |
| 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={}) |
| assert 0 == test_viz.cache_timeout |
| |
| datasource.cache_timeout = 156 |
| test_viz = viz.BaseViz(datasource, form_data={}) |
| assert 156 == test_viz.cache_timeout |
| |
| datasource.cache_timeout = None |
| datasource.database.cache_timeout = 0 |
| assert 0 == test_viz.cache_timeout |
| |
| datasource.database.cache_timeout = 1666 |
| assert 1666 == test_viz.cache_timeout |
| |
| datasource.database.cache_timeout = None |
| test_viz = viz.BaseViz(datasource, form_data={}) |
| assert ( |
| 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={}) |
| assert 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 TestDistBarViz(SupersetTestCase): |
| def test_groupby_nulls(self): |
| form_data = { |
| "metrics": ["votes"], |
| "adhoc_filters": [], |
| "groupby": ["toppings"], |
| "columns": [], |
| "order_desc": True, |
| } |
| 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] |
| assert "votes" == data["key"] |
| expected_values = [ |
| {"x": "pepperoni", "y": 5}, |
| {"x": "cheese", "y": 3}, |
| {"x": NULL_STRING, "y": 2}, |
| {"x": "anchovies", "y": 1}, |
| ] |
| assert expected_values == data["values"] |
| |
| def test_groupby_nans(self): |
| form_data = { |
| "metrics": ["count"], |
| "adhoc_filters": [], |
| "groupby": ["beds"], |
| "columns": [], |
| "order_desc": True, |
| } |
| 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] |
| assert "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}, |
| ] |
| |
| assert expected_values == data["values"] |
| |
| def test_column_nulls(self): |
| form_data = { |
| "metrics": ["votes"], |
| "adhoc_filters": [], |
| "groupby": ["toppings"], |
| "columns": ["role"], |
| "order_desc": True, |
| } |
| 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}], |
| }, |
| ] |
| assert expected == data |
| |
| def test_column_metrics_in_order(self): |
| form_data = { |
| "metrics": ["z_column", "votes", "a_column"], |
| "adhoc_filters": [], |
| "groupby": ["toppings"], |
| "columns": [], |
| "order_desc": True, |
| } |
| datasource = self.get_datasource_mock() |
| df = pd.DataFrame( |
| { |
| "toppings": ["cheese", "pepperoni", "cheese", "pepperoni"], |
| "role": ["engineer", "engineer", None, None], |
| "votes": [3, 5, 1, 2], |
| "a_column": [3, 5, 1, 2], |
| "z_column": [3, 5, 1, 2], |
| } |
| ) |
| test_viz = viz.DistributionBarViz(datasource, form_data) |
| data = test_viz.get_data(df) |
| |
| expected = [ |
| { |
| "key": "z_column", |
| "values": [{"x": "pepperoni", "y": 3.5}, {"x": "cheese", "y": 2.0}], |
| }, |
| { |
| "key": "votes", |
| "values": [{"x": "pepperoni", "y": 3.5}, {"x": "cheese", "y": 2.0}], |
| }, |
| { |
| "key": "a_column", |
| "values": [{"x": "pepperoni", "y": 3.5}, {"x": "cheese", "y": 2.0}], |
| }, |
| ] |
| |
| assert expected == data |
| |
| def test_column_metrics_in_order_with_breakdowns(self): |
| form_data = { |
| "metrics": ["z_column", "votes", "a_column"], |
| "adhoc_filters": [], |
| "groupby": ["toppings"], |
| "columns": ["role"], |
| "order_desc": True, |
| } |
| datasource = self.get_datasource_mock() |
| df = pd.DataFrame( |
| { |
| "toppings": ["cheese", "pepperoni", "cheese", "pepperoni"], |
| "role": ["engineer", "engineer", None, None], |
| "votes": [3, 5, 1, 2], |
| "a_column": [3, 5, 1, 2], |
| "z_column": [3, 5, 1, 2], |
| } |
| ) |
| test_viz = viz.DistributionBarViz(datasource, form_data) |
| data = test_viz.get_data(df) |
| |
| expected = [ |
| { |
| "key": f"z_column, {NULL_STRING}", |
| "values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}], |
| }, |
| { |
| "key": "z_column, engineer", |
| "values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}], |
| }, |
| { |
| "key": f"votes, {NULL_STRING}", |
| "values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}], |
| }, |
| { |
| "key": "votes, engineer", |
| "values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}], |
| }, |
| { |
| "key": f"a_column, {NULL_STRING}", |
| "values": [{"x": "pepperoni", "y": 2}, {"x": "cheese", "y": 1}], |
| }, |
| { |
| "key": "a_column, engineer", |
| "values": [{"x": "pepperoni", "y": 5}, {"x": "cheese", "y": 3}], |
| }, |
| ] |
| |
| assert 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) # noqa: N806 |
| 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"), |
| }, |
| ], |
| } |
| assert data == expected |
| |
| def test_get_data_empty_null_keys(self): |
| form_data = {"groupby": [], "metrics": [""]} |
| 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) # noqa: N806 |
| 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", |
| } |
| ], |
| } |
| assert data == expected |
| |
| form_data = {"groupby": [], "metrics": [None]} |
| with self.assertRaises(ValueError): # noqa: PT027 |
| 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() |
| assert not query_obj["is_timeseries"] |
| test_viz.form_data["time_series_option"] = "agg_sum" |
| query_obj = test_viz.query_obj() |
| assert 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) |
| assert 4 == len(levels) |
| expected = {DTTM_ALIAS: 1800, "metric1": 45, "metric2": 450, "metric3": 4500} |
| assert 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}, |
| } |
| assert expected == levels[1].to_dict() |
| assert ["groupA", "groupB"] == levels[2].index.names |
| assert ["groupA", "groupB", "groupC"] == levels[3].index.names |
| time_op = "agg_mean" |
| levels = test_viz.levels_for(time_op, groups, df) |
| assert 4 == len(levels) |
| expected = { |
| DTTM_ALIAS: 200.0, |
| "metric1": 5.0, |
| "metric2": 50.0, |
| "metric3": 500.0, |
| } |
| assert 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}, |
| } |
| assert expected == levels[1].to_dict() |
| assert ["groupA", "groupB"] == levels[2].index.names |
| assert ["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} |
| assert 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}, |
| } |
| assert expected == levels[1].to_dict() |
| assert 4 == len(levels) |
| assert ["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) |
| assert 4 == len(levels) |
| cols = [DTTM_ALIAS, "metric1", "metric2", "metric3"] |
| assert sorted(cols) == sorted(levels[0].columns.tolist()) |
| cols += ["groupA"] |
| assert sorted(cols) == sorted(levels[1].columns.tolist()) |
| cols += ["groupB"] |
| assert sorted(cols) == sorted(levels[2].columns.tolist()) |
| cols += ["groupC"] |
| assert sorted(cols) == sorted(levels[3].columns.tolist()) |
| assert 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) |
| assert 3 == len(nest) |
| for i in range(0, 3): |
| assert "metric" + str(i + 1) == nest[i]["name"] |
| assert 3 == len(nest[0]["children"]) |
| assert 1 == len(nest[0]["children"][0]["children"]) |
| assert 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) |
| assert 3 == len(nest) |
| for i in range(0, 3): |
| assert "metric" + str(i + 1) == nest[i]["name"] |
| assert None is nest[i].get("val") |
| assert 3 == len(nest[0]["children"]) |
| assert 3 == len(nest[0]["children"][0]["children"]) |
| assert 1 == len(nest[0]["children"][0]["children"][0]["children"]) |
| assert 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): # noqa: PT027 |
| 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) |
| assert "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) |
| assert "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) |
| assert "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) |
| assert "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) |
| assert "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) |
| assert "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) |
| assert 1 == len(test_viz.levels_for_time.mock_calls) |
| assert 1 == len(test_viz.nest_procs.mock_calls) |
| test_viz.form_data["time_series_option"] = "time_series" |
| test_viz.get_data(df) |
| assert "agg_sum" == test_viz.levels_for.mock_calls[3][1][0] |
| assert 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",)}, |
| ], |
| } |
| assert 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 |
| assert {"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}, |
| } |
| assert 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 |
| assert {"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}, |
| } |
| assert 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): # noqa: B017, PT027 |
| test_viz.query_obj() |
| form_data["metrics"] = ["x", "y"] |
| test_viz = viz.TimeTableViz(datasource, form_data) |
| with self.assertRaises(Exception): # noqa: B017, PT027 |
| 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() |
| assert 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: # noqa: PT027 |
| test_viz_deckgl.get_properties(mock_d) |
| |
| assert "" 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: # noqa: PT027 |
| test_viz_deckgl.process_spatial_query_obj(mock_key, mock_gb) |
| |
| assert "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") |
| assert coord == (1.23, 3.21) |
| |
| coord = viz_instance.parse_coordinates("1.23 3.21") |
| assert coord == (1.23, 3.21) |
| |
| assert viz_instance.parse_coordinates(None) is None |
| |
| assert viz_instance.parse_coordinates("") is 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): # noqa: PT027 |
| test_viz_deckgl.parse_coordinates("NULL") |
| |
| with self.assertRaises(SpatialException): # noqa: PT027 |
| 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",), |
| }, |
| ] |
| assert 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], |
| } |
| ) |
| |
| assert 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]}, |
| ) |
| assert 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] |
| assert 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] |
| assert 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) |
