from datetime import datetime
import unittest

from mock import Mock, patch
import pandas as pd

import superset.utils as utils
from superset.utils import DTTM_ALIAS
import superset.viz as viz


class BaseVizTestCase(unittest.TestCase):
    def test_constructor_exception_no_datasource(self):
        form_data = {}
        datasource = None
        with self.assertRaises(Exception):
            viz.BaseViz(datasource, form_data)

    def test_get_fillna_returns_default_on_null_columns(self):
        form_data = {
            'viz_type': 'table',
            'token': '12345',
        }
        datasource = {'type': 'table'}
        test_viz = viz.BaseViz(datasource, form_data)
        self.assertEqual(
            test_viz.default_fillna,
            test_viz.get_fillna_for_columns(),
        )

    def test_get_df_returns_empty_df(self):
        datasource = Mock()
        datasource.type = 'table'
        mock_dttm_col = Mock()
        mock_dttm_col.python_date_format = Mock()
        datasource.get_col = Mock(return_value=mock_dttm_col)
        form_data = {'dummy': 123}
        query_obj = {'granularity': 'day'}
        results = Mock()
        results.query = Mock()
        results.status = Mock()
        results.error_message = None
        results.df = Mock()
        results.df.empty = True
        datasource.query = Mock(return_value=results)
        test_viz = viz.BaseViz(datasource, form_data)
        result = test_viz.get_df(query_obj)
        self.assertEqual(type(result), pd.DataFrame)
        self.assertTrue(result.empty)
        self.assertEqual(test_viz.error_message, 'No data.')
        self.assertEqual(test_viz.status, utils.QueryStatus.FAILED)

    def test_get_df_handles_dttm_col(self):
        datasource = Mock()
        datasource.type = 'table'
        datasource.offset = 1
        mock_dttm_col = Mock()
        mock_dttm_col.python_date_format = 'epoch_ms'
        datasource.get_col = Mock(return_value=mock_dttm_col)
        form_data = {'dummy': 123}
        query_obj = {'granularity': 'day'}
        results = Mock()
        results.query = Mock()
        results.status = Mock()
        results.error_message = Mock()
        df = Mock()
        df.columns = [DTTM_ALIAS]
        f_datetime = datetime(1960, 1, 1, 5, 0)
        df.__getitem__ = Mock(return_value=pd.Series([f_datetime]))
        df.__setitem__ = Mock()
        df.replace = Mock()
        df.fillna = Mock()
        results.df = df
        results.df.empty = False
        datasource.query = Mock(return_value=results)
        test_viz = viz.BaseViz(datasource, form_data)
        test_viz.get_fillna_for_columns = Mock(return_value=0)
        test_viz.get_df(query_obj)
        mock_call = df.__setitem__.mock_calls[0]
        self.assertEqual(mock_call[1][0], DTTM_ALIAS)
        self.assertFalse(mock_call[1][1].empty)
        self.assertEqual(mock_call[1][1][0], f_datetime)
        mock_call = df.__setitem__.mock_calls[1]
        self.assertEqual(mock_call[1][0], DTTM_ALIAS)
        self.assertEqual(mock_call[1][1][0].hour, 6)
        self.assertEqual(mock_call[1][1].dtype, 'datetime64[ns]')
        mock_dttm_col.python_date_format = 'utc'
        test_viz.get_df(query_obj)
        mock_call = df.__setitem__.mock_calls[2]
        self.assertEqual(mock_call[1][0], DTTM_ALIAS)
        self.assertFalse(mock_call[1][1].empty)
        self.assertEqual(mock_call[1][1][0].hour, 7)
        mock_call = df.__setitem__.mock_calls[3]
        self.assertEqual(mock_call[1][0], DTTM_ALIAS)
        self.assertEqual(mock_call[1][1][0].hour, 6)
        self.assertEqual(mock_call[1][1].dtype, 'datetime64[ns]')
        mock_call = df.__setitem__.mock_calls[4]
        self.assertEqual(mock_call[1][0], DTTM_ALIAS)
        self.assertEqual(mock_call[1][1][0].hour, 7)
        self.assertEqual(mock_call[1][1].dtype, 'datetime64[ns]')

    def test_cache_timeout(self):
        datasource = Mock()
        datasource.cache_timeout = 156
        test_viz = viz.BaseViz(datasource, form_data={})
        self.assertEqual(156, test_viz.cache_timeout)
        datasource.cache_timeout = None
        datasource.database = Mock()
        datasource.database.cache_timeout = 1666
        self.assertEqual(1666, test_viz.cache_timeout)


class TableVizTestCase(unittest.TestCase):
    def test_get_data_applies_percentage(self):
        form_data = {
            'percent_metrics': ['sum__A', 'avg__B'],
            'metrics': ['sum__A', 'count', 'avg__C'],
        }
        datasource = Mock()
        raw = {}
        raw['sum__A'] = [15, 20, 25, 40]
        raw['avg__B'] = [10, 20, 5, 15]
        raw['avg__C'] = [11, 22, 33, 44]
        raw['count'] = [6, 7, 8, 9]
        raw['groupA'] = ['A', 'B', 'C', 'C']
        raw['groupB'] = ['x', 'x', 'y', 'z']
        df = pd.DataFrame(raw)
        test_viz = viz.TableViz(datasource, form_data)
        data = test_viz.get_data(df)
        # Check method correctly transforms data and computes percents
        self.assertEqual(set([
            'groupA', 'groupB', 'count',
            'sum__A', 'avg__C',
            '%sum__A', '%avg__B',
        ]), set(data['columns']))
        expected = [
            {
                'groupA': 'A', 'groupB': 'x',
                'count': 6, 'sum__A': 15, 'avg__C': 11,
                '%sum__A': 0.15, '%avg__B': 0.2,
            },
            {
                'groupA': 'B', 'groupB': 'x',
                'count': 7, 'sum__A': 20, 'avg__C': 22,
                '%sum__A': 0.2, '%avg__B': 0.4,
            },
            {
                'groupA': 'C', 'groupB': 'y',
                'count': 8, 'sum__A': 25, 'avg__C': 33,
                '%sum__A': 0.25, '%avg__B': 0.1,
            },
            {
                'groupA': 'C', 'groupB': 'z',
                'count': 9, 'sum__A': 40, 'avg__C': 44,
                '%sum__A': 0.40, '%avg__B': 0.3,
            },
        ]
        self.assertEqual(expected, data['records'])

    @patch('superset.viz.BaseViz.query_obj')
    def test_query_obj_merges_percent_metrics(self, super_query_obj):
        datasource = Mock()
        form_data = {
            'percent_metrics': ['sum__A', 'avg__B', 'max__Y'],
            'metrics': ['sum__A', 'count', 'avg__C'],
        }
        test_viz = viz.TableViz(datasource, form_data)
        f_query_obj = {
            'metrics': form_data['metrics'],
        }
        super_query_obj.return_value = f_query_obj
        query_obj = test_viz.query_obj()
        self.assertEqual([
            'sum__A', 'count', 'avg__C',
            'avg__B', 'max__Y',
        ], query_obj['metrics'])

    @patch('superset.viz.BaseViz.query_obj')
    def test_query_obj_throws_columns_and_metrics(self, super_query_obj):
        datasource = Mock()
        form_data = {
            'all_columns': ['A', 'B'],
            'metrics': ['x', 'y'],
        }
        super_query_obj.return_value = {}
        test_viz = viz.TableViz(datasource, form_data)
        with self.assertRaises(Exception):
            test_viz.query_obj()
        del form_data['metrics']
        form_data['groupby'] = ['B', 'C']
        test_viz = viz.TableViz(datasource, form_data)
        with self.assertRaises(Exception):
            test_viz.query_obj()

    @patch('superset.viz.BaseViz.query_obj')
    def test_query_obj_merges_all_columns(self, super_query_obj):
        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'])

    @patch('superset.viz.BaseViz.query_obj')
    def test_query_obj_uses_sortby(self, super_query_obj):
        datasource = Mock()
        form_data = {
            'timeseries_limit_metric': '__time__',
            'order_desc': False,
        }
        super_query_obj.return_value = {
            'metrics': ['colA', 'colB'],
        }
        test_viz = viz.TableViz(datasource, form_data)
        query_obj = test_viz.query_obj()
        self.assertEqual([
            'colA', 'colB', '__time__',
        ], query_obj['metrics'])
        self.assertEqual([(
            '__time__', True,
        )], query_obj['orderby'])

    def test_should_be_timeseries_raises_when_no_granularity(self):
        datasource = Mock()
        form_data = {'include_time': True}
        test_viz = viz.TableViz(datasource, form_data)
        with self.assertRaises(Exception):
            test_viz.should_be_timeseries()


class PairedTTestTestCase(unittest.TestCase):
    def test_get_data_transforms_dataframe(self):
        form_data = {
            'groupby': ['groupA', 'groupB', 'groupC'],
            'metrics': ['metric1', 'metric2', 'metric3'],
        }
        datasource = {'type': 'table'}
        # 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 = {'type': 'table'}
        # 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 PartitionVizTestCase(unittest.TestCase):

    @patch('superset.viz.BaseViz.query_obj')
    def test_query_obj_time_series_option(self, super_query_obj):
        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):
        test_viz = viz.PartitionViz(Mock(), {})
        df = 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 RoseVisTestCase(unittest.TestCase):

    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)