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f6fe11f76e
* [bugfix] convert metrics to numeric in dataframe It appears sometimes the dbapi driver and pandas's read_sql fail at returning the proper numeric types for metrics and they show up as `object` in the dataframe. This results in "No numeric types to aggregate" errors when trying to perform aggregations or pivoting in pandas. This PR looks for metrics in dataframes that are typed as "object" and uses pandas' to_numeric to convert. * Fix tests * Remove all iteritems
639 lines
25 KiB
Python
639 lines
25 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from datetime import datetime
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import unittest
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from mock import Mock, patch
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import pandas as pd
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from superset.utils import DTTM_ALIAS
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import superset.viz as viz
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class BaseVizTestCase(unittest.TestCase):
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def test_constructor_exception_no_datasource(self):
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form_data = {}
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datasource = None
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with self.assertRaises(Exception):
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viz.BaseViz(datasource, form_data)
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def test_get_fillna_returns_default_on_null_columns(self):
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form_data = {
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'viz_type': 'table',
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'token': '12345',
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}
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datasource = {'type': 'table'}
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test_viz = viz.BaseViz(datasource, form_data)
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self.assertEqual(
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test_viz.default_fillna,
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test_viz.get_fillna_for_columns(),
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)
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def test_get_df_returns_empty_df(self):
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datasource = Mock()
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datasource.type = 'table'
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mock_dttm_col = Mock()
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mock_dttm_col.python_date_format = Mock()
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datasource.get_col = Mock(return_value=mock_dttm_col)
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form_data = {'dummy': 123}
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query_obj = {'granularity': 'day'}
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results = Mock()
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results.query = Mock()
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results.status = Mock()
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results.error_message = None
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results.df = Mock()
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results.df.empty = True
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datasource.query = Mock(return_value=results)
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test_viz = viz.BaseViz(datasource, form_data)
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result = test_viz.get_df(query_obj)
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self.assertEqual(type(result), pd.DataFrame)
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self.assertTrue(result.empty)
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def test_get_df_handles_dttm_col(self):
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datasource = Mock()
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datasource.type = 'table'
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datasource.offset = 1
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mock_dttm_col = Mock()
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mock_dttm_col.python_date_format = 'epoch_ms'
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datasource.get_col = Mock(return_value=mock_dttm_col)
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form_data = {'dummy': 123}
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query_obj = {'granularity': 'day'}
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results = Mock()
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results.query = Mock()
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results.status = Mock()
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results.error_message = Mock()
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df = Mock()
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df.columns = [DTTM_ALIAS]
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f_datetime = datetime(1960, 1, 1, 5, 0)
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df.__getitem__ = Mock(return_value=pd.Series([f_datetime]))
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df.__setitem__ = Mock()
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df.replace = Mock()
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df.fillna = Mock()
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results.df = df
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results.df.empty = False
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datasource.query = Mock(return_value=results)
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test_viz = viz.BaseViz(datasource, form_data)
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test_viz.df_metrics_to_num = Mock()
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test_viz.get_fillna_for_columns = Mock(return_value=0)
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test_viz.get_df(query_obj)
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mock_call = df.__setitem__.mock_calls[0]
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self.assertEqual(mock_call[1][0], DTTM_ALIAS)
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self.assertFalse(mock_call[1][1].empty)
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self.assertEqual(mock_call[1][1][0], f_datetime)
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mock_call = df.__setitem__.mock_calls[1]
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self.assertEqual(mock_call[1][0], DTTM_ALIAS)
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self.assertEqual(mock_call[1][1][0].hour, 6)
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self.assertEqual(mock_call[1][1].dtype, 'datetime64[ns]')
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mock_dttm_col.python_date_format = 'utc'
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test_viz.get_df(query_obj)
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mock_call = df.__setitem__.mock_calls[2]
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self.assertEqual(mock_call[1][0], DTTM_ALIAS)
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self.assertFalse(mock_call[1][1].empty)
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self.assertEqual(mock_call[1][1][0].hour, 7)
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mock_call = df.__setitem__.mock_calls[3]
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self.assertEqual(mock_call[1][0], DTTM_ALIAS)
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self.assertEqual(mock_call[1][1][0].hour, 6)
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self.assertEqual(mock_call[1][1].dtype, 'datetime64[ns]')
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mock_call = df.__setitem__.mock_calls[4]
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self.assertEqual(mock_call[1][0], DTTM_ALIAS)
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self.assertEqual(mock_call[1][1][0].hour, 7)
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self.assertEqual(mock_call[1][1].dtype, 'datetime64[ns]')
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def test_cache_timeout(self):
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datasource = Mock()
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datasource.cache_timeout = 156
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test_viz = viz.BaseViz(datasource, form_data={})
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self.assertEqual(156, test_viz.cache_timeout)
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datasource.cache_timeout = None
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datasource.database = Mock()
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datasource.database.cache_timeout = 1666
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self.assertEqual(1666, test_viz.cache_timeout)
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class TableVizTestCase(unittest.TestCase):
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def test_get_data_applies_percentage(self):
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form_data = {
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'percent_metrics': ['sum__A', 'avg__B'],
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'metrics': ['sum__A', 'count', 'avg__C'],
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}
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datasource = Mock()
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raw = {}
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raw['sum__A'] = [15, 20, 25, 40]
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raw['avg__B'] = [10, 20, 5, 15]
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raw['avg__C'] = [11, 22, 33, 44]
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raw['count'] = [6, 7, 8, 9]
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raw['groupA'] = ['A', 'B', 'C', 'C']
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raw['groupB'] = ['x', 'x', 'y', 'z']
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df = pd.DataFrame(raw)
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test_viz = viz.TableViz(datasource, form_data)
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data = test_viz.get_data(df)
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# Check method correctly transforms data and computes percents
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self.assertEqual(set([
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'groupA', 'groupB', 'count',
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'sum__A', 'avg__C',
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'%sum__A', '%avg__B',
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]), set(data['columns']))
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expected = [
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{
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'groupA': 'A', 'groupB': 'x',
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'count': 6, 'sum__A': 15, 'avg__C': 11,
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'%sum__A': 0.15, '%avg__B': 0.2,
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},
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{
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'groupA': 'B', 'groupB': 'x',
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'count': 7, 'sum__A': 20, 'avg__C': 22,
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'%sum__A': 0.2, '%avg__B': 0.4,
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},
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{
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'groupA': 'C', 'groupB': 'y',
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'count': 8, 'sum__A': 25, 'avg__C': 33,
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'%sum__A': 0.25, '%avg__B': 0.1,
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},
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{
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'groupA': 'C', 'groupB': 'z',
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'count': 9, 'sum__A': 40, 'avg__C': 44,
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'%sum__A': 0.40, '%avg__B': 0.3,
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},
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]
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self.assertEqual(expected, data['records'])
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@patch('superset.viz.BaseViz.query_obj')
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def test_query_obj_merges_percent_metrics(self, super_query_obj):
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datasource = Mock()
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form_data = {
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'percent_metrics': ['sum__A', 'avg__B', 'max__Y'],
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'metrics': ['sum__A', 'count', 'avg__C'],
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}
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test_viz = viz.TableViz(datasource, form_data)
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f_query_obj = {
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'metrics': form_data['metrics'],
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}
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super_query_obj.return_value = f_query_obj
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query_obj = test_viz.query_obj()
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self.assertEqual([
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'sum__A', 'count', 'avg__C',
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'avg__B', 'max__Y',
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], query_obj['metrics'])
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@patch('superset.viz.BaseViz.query_obj')
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def test_query_obj_throws_columns_and_metrics(self, super_query_obj):
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datasource = Mock()
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form_data = {
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'all_columns': ['A', 'B'],
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'metrics': ['x', 'y'],
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}
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super_query_obj.return_value = {}
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test_viz = viz.TableViz(datasource, form_data)
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with self.assertRaises(Exception):
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test_viz.query_obj()
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del form_data['metrics']
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form_data['groupby'] = ['B', 'C']
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test_viz = viz.TableViz(datasource, form_data)
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with self.assertRaises(Exception):
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test_viz.query_obj()
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@patch('superset.viz.BaseViz.query_obj')
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def test_query_obj_merges_all_columns(self, super_query_obj):
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datasource = Mock()
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form_data = {
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'all_columns': ['colA', 'colB', 'colC'],
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'order_by_cols': ['["colA", "colB"]', '["colC"]'],
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}
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super_query_obj.return_value = {
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'columns': ['colD', 'colC'],
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'groupby': ['colA', 'colB'],
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}
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test_viz = viz.TableViz(datasource, form_data)
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query_obj = test_viz.query_obj()
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self.assertEqual(form_data['all_columns'], query_obj['columns'])
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self.assertEqual([], query_obj['groupby'])
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self.assertEqual([['colA', 'colB'], ['colC']], query_obj['orderby'])
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@patch('superset.viz.BaseViz.query_obj')
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def test_query_obj_uses_sortby(self, super_query_obj):
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datasource = Mock()
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form_data = {
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'timeseries_limit_metric': '__time__',
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'order_desc': False,
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}
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super_query_obj.return_value = {
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'metrics': ['colA', 'colB'],
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}
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test_viz = viz.TableViz(datasource, form_data)
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query_obj = test_viz.query_obj()
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self.assertEqual([
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'colA', 'colB', '__time__',
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], query_obj['metrics'])
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self.assertEqual([(
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'__time__', True,
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)], query_obj['orderby'])
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def test_should_be_timeseries_raises_when_no_granularity(self):
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datasource = Mock()
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form_data = {'include_time': True}
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test_viz = viz.TableViz(datasource, form_data)
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with self.assertRaises(Exception):
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test_viz.should_be_timeseries()
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class PairedTTestTestCase(unittest.TestCase):
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def test_get_data_transforms_dataframe(self):
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form_data = {
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'groupby': ['groupA', 'groupB', 'groupC'],
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'metrics': ['metric1', 'metric2', 'metric3'],
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}
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datasource = {'type': 'table'}
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# Test data
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raw = {}
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raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
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raw['groupA'] = ['a1', 'a1', 'a1', 'b1', 'b1', 'b1', 'c1', 'c1', 'c1']
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raw['groupB'] = ['a2', 'a2', 'a2', 'b2', 'b2', 'b2', 'c2', 'c2', 'c2']
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raw['groupC'] = ['a3', 'a3', 'a3', 'b3', 'b3', 'b3', 'c3', 'c3', 'c3']
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raw['metric1'] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
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raw['metric2'] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
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raw['metric3'] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
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df = pd.DataFrame(raw)
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pairedTTestViz = viz.viz_types['paired_ttest'](datasource, form_data)
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data = pairedTTestViz.get_data(df)
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# Check method correctly transforms data
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expected = {
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'metric1': [
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{
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'values': [
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{'x': 100, 'y': 1},
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{'x': 200, 'y': 2},
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{'x': 300, 'y': 3}],
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'group': ('a1', 'a2', 'a3'),
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},
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{
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'values': [
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{'x': 100, 'y': 4},
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{'x': 200, 'y': 5},
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{'x': 300, 'y': 6}],
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'group': ('b1', 'b2', 'b3'),
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},
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{
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'values': [
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{'x': 100, 'y': 7},
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{'x': 200, 'y': 8},
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{'x': 300, 'y': 9}],
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'group': ('c1', 'c2', 'c3'),
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},
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],
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'metric2': [
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{
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'values': [
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{'x': 100, 'y': 10},
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{'x': 200, 'y': 20},
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{'x': 300, 'y': 30}],
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'group': ('a1', 'a2', 'a3'),
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},
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{
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'values': [
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{'x': 100, 'y': 40},
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{'x': 200, 'y': 50},
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{'x': 300, 'y': 60}],
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'group': ('b1', 'b2', 'b3'),
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},
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{
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'values': [
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{'x': 100, 'y': 70},
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{'x': 200, 'y': 80},
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{'x': 300, 'y': 90}],
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'group': ('c1', 'c2', 'c3'),
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},
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],
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'metric3': [
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{
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'values': [
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{'x': 100, 'y': 100},
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{'x': 200, 'y': 200},
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{'x': 300, 'y': 300}],
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'group': ('a1', 'a2', 'a3'),
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},
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{
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'values': [
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{'x': 100, 'y': 400},
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{'x': 200, 'y': 500},
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{'x': 300, 'y': 600}],
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'group': ('b1', 'b2', 'b3'),
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},
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{
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'values': [
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{'x': 100, 'y': 700},
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{'x': 200, 'y': 800},
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{'x': 300, 'y': 900}],
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'group': ('c1', 'c2', 'c3'),
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},
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],
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}
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self.assertEqual(data, expected)
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def test_get_data_empty_null_keys(self):
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form_data = {
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'groupby': [],
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'metrics': ['', None],
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}
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datasource = {'type': 'table'}
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# Test data
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raw = {}
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raw[DTTM_ALIAS] = [100, 200, 300]
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raw[''] = [1, 2, 3]
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raw[None] = [10, 20, 30]
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df = pd.DataFrame(raw)
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pairedTTestViz = viz.viz_types['paired_ttest'](datasource, form_data)
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data = pairedTTestViz.get_data(df)
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# Check method correctly transforms data
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expected = {
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'N/A': [
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{
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'values': [
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{'x': 100, 'y': 1},
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{'x': 200, 'y': 2},
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{'x': 300, 'y': 3}],
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'group': 'All',
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},
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],
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'NULL': [
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{
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'values': [
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{'x': 100, 'y': 10},
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{'x': 200, 'y': 20},
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{'x': 300, 'y': 30}],
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'group': 'All',
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},
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],
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}
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self.assertEqual(data, expected)
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class PartitionVizTestCase(unittest.TestCase):
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@patch('superset.viz.BaseViz.query_obj')
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def test_query_obj_time_series_option(self, super_query_obj):
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datasource = Mock()
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form_data = {}
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test_viz = viz.PartitionViz(datasource, form_data)
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super_query_obj.return_value = {}
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query_obj = test_viz.query_obj()
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self.assertFalse(query_obj['is_timeseries'])
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test_viz.form_data['time_series_option'] = 'agg_sum'
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query_obj = test_viz.query_obj()
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self.assertTrue(query_obj['is_timeseries'])
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def test_levels_for_computes_levels(self):
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raw = {}
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raw[DTTM_ALIAS] = [100, 200, 300, 100, 200, 300, 100, 200, 300]
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raw['groupA'] = ['a1', 'a1', 'a1', 'b1', 'b1', 'b1', 'c1', 'c1', 'c1']
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raw['groupB'] = ['a2', 'a2', 'a2', 'b2', 'b2', 'b2', 'c2', 'c2', 'c2']
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raw['groupC'] = ['a3', 'a3', 'a3', 'b3', 'b3', 'b3', 'c3', 'c3', 'c3']
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raw['metric1'] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
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raw['metric2'] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
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raw['metric3'] = [100, 200, 300, 400, 500, 600, 700, 800, 900]
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df = pd.DataFrame(raw)
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groups = ['groupA', 'groupB', 'groupC']
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time_op = 'agg_sum'
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test_viz = viz.PartitionViz(Mock(), {})
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levels = test_viz.levels_for(time_op, groups, df)
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self.assertEqual(4, len(levels))
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expected = {
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DTTM_ALIAS: 1800,
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'metric1': 45,
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'metric2': 450,
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'metric3': 4500,
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}
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self.assertEqual(expected, levels[0].to_dict())
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expected = {
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DTTM_ALIAS: {'a1': 600, 'b1': 600, 'c1': 600},
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'metric1': {'a1': 6, 'b1': 15, 'c1': 24},
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'metric2': {'a1': 60, 'b1': 150, 'c1': 240},
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'metric3': {'a1': 600, 'b1': 1500, 'c1': 2400},
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}
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self.assertEqual(expected, levels[1].to_dict())
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self.assertEqual(['groupA', 'groupB'], levels[2].index.names)
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self.assertEqual(
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['groupA', 'groupB', 'groupC'],
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levels[3].index.names,
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)
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time_op = 'agg_mean'
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levels = test_viz.levels_for(time_op, groups, df)
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self.assertEqual(4, len(levels))
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expected = {
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DTTM_ALIAS: 200.0,
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'metric1': 5.0,
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'metric2': 50.0,
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'metric3': 500.0,
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}
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self.assertEqual(expected, levels[0].to_dict())
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expected = {
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DTTM_ALIAS: {'a1': 200, 'c1': 200, 'b1': 200},
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'metric1': {'a1': 2, 'b1': 5, 'c1': 8},
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'metric2': {'a1': 20, 'b1': 50, 'c1': 80},
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'metric3': {'a1': 200, 'b1': 500, 'c1': 800},
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}
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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)
|