def test__get_oc_filter(self):
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
data = pd.DataFrame([{
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Crest',
'category': 'Oral Care',
'product_fk': 252,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 1,
'shelf_number': 2,
'facing_sequence_number': 3,
'sub_brand_name': 'sb1'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Oral-B',
'category': 'Oral Care',
'product_fk': 132,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 1,
'shelf_number': 2,
'facing_sequence_number': 3,
'sub_brand_name': 'sb2'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Oral-B',
'category': 'Oral Care',
'product_fk': 999,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 1,
'shelf_number': 2,
'facing_sequence_number': 3,
'sub_brand_name': ''
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Crest',
'category': 'Oral Care',
'product_fk': 152,
'stacking_layer': 1,
'category_fk': 102,
'bay_number': 2,
'shelf_number': 2,
'facing_sequence_number': 1,
'sub_category': 'Bodywash'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Safeguard',
'category': 'Personal Cleaning Care',
'product_fk': 172,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 2,
'shelf_number': 2,
'facing_sequence_number': 3,
'sub_category': 'Handwash'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'hola',
'category': 'Personal Cleaning Care',
'product_fk': 173,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 2,
'shelf_number': 2,
'facing_sequence_number': 2,
'sub_category': 'HOLA'
}])
filters = scene_tool_box._get_category_specific_filters(
'Oral Care', data)
self.assertEqual(len(filters), 5, 'expects 5 filter entry created.')
def test_calculate_sequence_eye_level(self):
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
entity_df = pd.DataFrame([{
'entity_fk': 17,
'entity_name': u'SFG Handwash',
'entity_type_fk': 1002,
'entity_type_name': u'eye_level_fragments'
}, {
'entity_fk': 18,
'entity_name': u'SFG Bodywash',
'entity_type_fk': 1002,
'entity_type_name': u'eye_level_fragments'
}, {
'entity_fk': 27,
'entity_name': u'Competitor Other',
'entity_type_fk': 1002,
'entity_type_name': u'eye_level_fragments'
}])
data = pd.DataFrame([{
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Safeguard',
'category': 'Personal Cleaning Care',
'product_fk': 252,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 1,
'shelf_number': 2,
'facing_sequence_number': 3,
'sub_category': 'Handwash'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Safeguard',
'category': 'something',
'product_fk': 132,
'stacking_layer': 2,
'category_fk': 101,
'bay_number': 1,
'shelf_number': 2,
'facing_sequence_number': 3,
'sub_category': 'Handwash'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Safeguard',
'category': 'Personal Cleaning Care',
'product_fk': 152,
'stacking_layer': 1,
'category_fk': 102,
'bay_number': 2,
'shelf_number': 2,
'facing_sequence_number': 1,
'sub_category': 'Bodywash'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'Safeguard',
'category': 'Personal Cleaning Care',
'product_fk': 172,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 2,
'shelf_number': 2,
'facing_sequence_number': 3,
'sub_category': 'Handwash'
}, {
'scene_fk': 16588190,
'manufacturer_name': 'P&G\xe5\xae\x9d\xe6\xb4\x81',
'brand_name': 'hola',
'category': 'Personal Cleaning Care',
'product_fk': 173,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 2,
'shelf_number': 2,
'facing_sequence_number': 2,
'sub_category': 'HOLA'
}])
scene_tool_box._get_category_specific_filters = MagicMock(
return_value=pd.DataFrame(scene_tool_box.PCC_FILTERS))
scene_tool_box.get_filterd_matches = MagicMock(
return_value=pd.DataFrame(data))
scene_tool_box.common.write_to_db_result = MagicMock()
scene_tool_box.calculate_sequence_eye_level(entity_df, data,
'Personal Cleaning Care')
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls
if kpi_results:
self.assertEqual(len(kpi_results), 3,
'expects to write 3 parameters to db')
self.assertEqual(
kpi_results[2][2]['numerator_id'], 17,
"numerator_id !=17, sequence written isn't correct")
else:
raise Exception('No results were saved')
