def test_calculate_presize_linear_length(self):
"""
test that the function returns 0 (finished as expected)
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
self.assertEquals(scene_tool_box.calculate_presize_linear_length(), 0)
def __init__(self, data_provider, output=None):
self.data_provider = data_provider
self.output = output
self.project_name = data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.scene_id = self.data_provider.scene_id
self.common = Common(data_provider)
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.scene_tool_box = PngcnSceneKpis(self.rds_conn, self.common, self.scene_id, self.data_provider)
def test_calculate_variant_block(self):
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
scene_tool_box.common.write_to_db_result = MagicMock()
data = {
"a": [
pd.Series({
'cluster': (16, 13),
'scene_fk': 19625867,
'facing_percentage': 1,
'is_block': True,
'number_of_facings': 4,
'category': ['Personal Cleaning Care'],
'sub_brand_name': 'a',
'x': 1000,
'y': 1500,
'brand_name': 'SFG'
})
],
"b": [
pd.Series({
'cluster': (15),
'scene_fk': 19625867,
'facing_percentage': 0.7,
'is_block': True,
'number_of_facings': 8,
'category': ['Personal Cleaning Care'],
'sub_brand_name': 'b',
'x': 200,
'y': 2000,
'brand_name': 'SFG'
}),
pd.Series({
'cluster': (14),
'scene_fk': 19625867,
'facing_percentage': 0.8,
'is_block': True,
'number_of_facings': 10,
'category': ['Personal Cleaning Care'],
'sub_brand_name': 'b',
'x': 500,
'y': 100,
'brand_name': 'SFG'
})
]
}
kpi_results = scene_tool_box.reorder_all_blocks_results(data)
if kpi_results:
self.assertEqual(len(kpi_results), 3, 'expects to get 3 blocks')
self.assertEqual(kpi_results[1]['seq_x'], 1,
"the x seq isn't 1 like expected")
self.assertEqual(kpi_results[1]['seq_y'], 3,
"the y seq isn't 3 like expected")
else:
raise Exception('No results were returned')
def test__get_filterd_matches_test_type(self):
"""
test that the result is a DF
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
# test that the result is a DF
df_type = scene_tool_box.get_filterd_matches()
self.assertIsInstance(df_type, type(pd.DataFrame()))
def test__get_display_size_of_product_in_scene(self):
"""
test that the result is a DF
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
self.assertIsInstance(
scene_tool_box._get_display_size_of_product_in_scene(),
type(pd.DataFrame()))
def test_get_png_manufacturer_fk(self):
"""
test the result type
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
result = type(scene_tool_box.get_png_manufacturer_fk())
expected_result = int
self.assertEqual(expected_result, result)
def test__get_filterd_matches_test_manufacturer_size(self):
"""
test that there is only one manufacturer fk in the return DF
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
# test that the result is a DF
df_type = scene_tool_box.get_filterd_matches()
# test that there is only one manufacturer fk in the return DF
unique_fk = len(df_type['manufacturer_fk'].unique())
self.assertEqual(unique_fk, 1)
def test_calculate_result_type(self):
"""
test that the result is float
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
# test that the result is float
numerator, denominator = 12, 15
result = type(scene_tool_box.calculate_result(numerator, denominator))
expected_result = float
self.assertEqual(expected_result, result)
def test_calculate_result_sanity(self):
"""
test that function returns zero for denominator=0
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
# test that function returns zero for denominator=0
numerator, denominator = 12, 0
result = scene_tool_box.calculate_result(numerator, denominator)
expected_result = 0
self.assertEqual(expected_result, result)
def test_save_nlsos_as_kpi_results(self):
"""
test that the score and result are as expected
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
data = [{
'gross_len_split_stack_new': 13,
'product_type': 'Irrelevant',
'product_fk': None,
'rlv_sos_sc': 0,
'gross_len_split_stack': 0.15
}, {
'gross_len_split_stack_new': 65,
'product_type': '',
'product_fk': 252,
'rlv_sos_sc': 1,
'gross_len_split_stack': 1.23
}, {
'gross_len_split_stack_new': 35,
'product_type': '',
'product_fk': 253,
'rlv_sos_sc': 1,
'gross_len_split_stack': 1.23
}, {
'gross_len_split_stack_new': 121,
'product_type': 'Irrelevant',
'product_fk': 132,
'rlv_sos_sc': 0,
'gross_len_split_stack': 0.99
}, {
'gross_len_split_stack_new': 201,
'product_type': 'Irrelevant',
'product_fk': 132,
'rlv_sos_sc': 0,
'gross_len_split_stack': 0.75
}, {
'gross_len_split_stack_new': 13,
'product_type': '',
'product_fk': 272,
'rlv_sos_sc': 0,
'gross_len_split_stack': 0.15
}]
scene_tool_box.common.write_to_db_result = MagicMock()
scene_tool_box.save_nlsos_as_kpi_results(pd.DataFrame(data))
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls
result = kpi_results[0][2]['score']
expected_result = 65.0
self.assertEqual(result, expected_result)
def test__get_filterd_matches_test_manufacturer_fk(self):
"""
test that the only manufacturer fk in DF is Png
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
# test that the result is a DF
df_type = scene_tool_box.get_filterd_matches()
# test that the only manufacturer fk in DF is Png
png_fk = df_type['manufacturer_fk'].unique()[0]
self.assertEqual(png_fk, 4)
def test_get_eye_level_shelves(self):
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
data = pd.DataFrame([{
'bay_number': 1,
'shelf_number': 3
}, {
'bay_number': 1,
'shelf_number': 3
}, {
'bay_number': 1,
'shelf_number': 6
}, {
'bay_number': 1,
'shelf_number': 6
}, {
'bay_number': 2,
'shelf_number': 2
}, {
'bay_number': 2,
'shelf_number': 4
}, {
'bay_number': 2,
'shelf_number': 10
}, {
'bay_number': 2,
'shelf_number': 9
}, {
'bay_number': 3,
'shelf_number': 1
}, {
'bay_number': 3,
'shelf_number': 1
}, {
'bay_number': 3,
'shelf_number': 2
}, {
'bay_number': 3,
'shelf_number': 2
}])
scene_tool_box.get_filterd_matches = MagicMock(
return_value=pd.DataFrame(data))
scene_tool_box.common.write_to_db_result = MagicMock()
kpi_results = scene_tool_box.get_eye_level_shelves(data)
self.assertEqual(len(kpi_results[kpi_results['bay_number'] == 3]), 4,
'expects to have 4 lines with bay number 3')
self.assertTrue(kpi_results[kpi_results['bay_number'] == 1].empty,
"Expected to have an empty df where bay number =1")
def test_calculate_display_size_empty_df(self):
"""
test that we don't return any thing when the used df is empty
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
scene_tool_box.mock_df_products_size = \
self.mock_object('_get_display_size_of_product_in_scene',
path='Projects.PNGCNTEST.SceneKpis.KPISceneToolBox.PngcnSceneKpis')
# test that we don't return any thing when the used df is empty
scene_tool_box.mock_df_products_size.return_value = pd.DataFrame({})
result = scene_tool_box.calculate_display_size()
expected_result = None
self.assertEqual(expected_result, result)
def test_insert_data_into_custom_scif(self):
"""
test type delete qury type.
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
scene_tool_box.data_provider.session_id = 'ebebc629-6b82-4be8-a872-0caa248ea248'
new_scif = pd.DataFrame(r.new_scif)
scene_tool_box.common.execute_custom_query()
scene_tool_box.insert_data_into_custom_scif(new_scif)
delete_query = scene_tool_box.common.execute_custom_query.mock_calls[
1][1][0]
insert_query = scene_tool_box.common.execute_custom_query.mock_calls[
2][1][0]
self.assertIsInstance(delete_query, str)
self.assertIsInstance(insert_query, str)
def test_insert_into_kpi_scene_results(self):
"""
test that the score are as expected
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
data = [{
'pk': 101,
'display_group': 5,
'product_fk': 252,
'facings': 0.84,
'product_size': 1.23
}, {
'pk': 121,
'display_group': 4,
'product_fk': 132,
'facings': 0.80,
'product_size': 0.99
}, {
'pk': 201,
'display_group': 4,
'product_fk': 132,
'facings': 0.28,
'product_size': 0.75
}, {
'pk': 151,
'display_group': 5,
'product_fk': 252,
'facings': 0.95,
'product_size': 0.15
}]
scene_tool_box.get_display_group = MagicMock()
scene_tool_box.get_display_group.return_value = 1
scene_tool_box.common.write_to_db_result = MagicMock()
scene_tool_box.insert_into_kpi_scene_results(data)
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls
result = kpi_results[1][2]['result']
expected_result = 1.23 + 0.15
score = kpi_results[1][2]['score']
expected_score = 0.84 + 0.95
self.assertEqual(result, expected_result)
self.assertEqual(score, expected_score)
class SceneGenerator:
def __init__(self, data_provider, output=None):
self.data_provider = data_provider
self.output = output
self.project_name = data_provider.project_name
self.session_uid = self.data_provider.session_uid
self.scene_id = self.data_provider.scene_id
self.common = Common(data_provider)
self.rds_conn = PSProjectConnector(self.project_name, DbUsers.CalculationEng)
self.scene_tool_box = PngcnSceneKpis(self.rds_conn, self.common, self.scene_id, self.data_provider)
@log_runtime('Total Calculations', log_start=True)
def PngcnSceneKpis(self):
"""
This is the main KPI calculation function.
It calculates the score for every KPI set and saves it to the DB.
"""
self.scene_tool_box.process_scene()
def test_calculate_linear_or_presize_linear_length_test_results_sanity(
self):
"""
test if the numerator is greater then denominator (if the subgroup is greater then containing group)
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
data = [{
'scene_fk': 101,
'manufacturer_fk': 2,
'product_fk': 252,
'width_mm': 0.84,
'width_mm_advance': 1.23
}, {
'scene_fk': 121,
'manufacturer_fk': 4,
'product_fk': 132,
'width_mm': 0.80,
'width_mm_advance': 0.99
}, {
'scene_fk': 201,
'manufacturer_fk': 4,
'product_fk': 152,
'width_mm': 0.28,
'width_mm_advance': 0.75
}, {
'scene_fk': 151,
'manufacturer_fk': 5,
'product_fk': 172,
'width_mm': 0.95,
'width_mm_advance': 0.15
}]
scene_tool_box.get_filterd_matches = MagicMock(
return_value=pd.DataFrame(data))
scene_tool_box.png_manufacturer_fk = 4
scene_tool_box.common.write_to_db_result = MagicMock()
width = random.choice(['width_mm', 'width_mm_advance'])
scene_tool_box.calculate_linear_or_presize_linear_length(width)
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls[0][2]
if kpi_results:
numerator = kpi_results['numerator_result']
denominator = kpi_results['denominator_result']
self.assertGreaterEqual(
denominator, numerator,
'the numerator cant be greater then denominator')
def test_calculate_linear_or_presize_linear_length_test_results_length(
self):
"""
test that we write 8 fields to DB
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
data = [{
'scene_fk': 101,
'manufacturer_fk': 2,
'product_fk': 252,
'width_mm': 0.84,
'width_mm_advance': 1.23
}, {
'scene_fk': 121,
'manufacturer_fk': 4,
'product_fk': 132,
'width_mm': 0.80,
'width_mm_advance': 0.99
}, {
'scene_fk': 201,
'manufacturer_fk': 4,
'product_fk': 152,
'width_mm': 0.28,
'width_mm_advance': 0.75
}, {
'scene_fk': 151,
'manufacturer_fk': 5,
'product_fk': 172,
'width_mm': 0.95,
'width_mm_advance': 0.15
}]
scene_tool_box.get_filterd_matches = MagicMock(
return_value=pd.DataFrame(data))
scene_tool_box.png_manufacturer_fk = 4
scene_tool_box.common.write_to_db_result = MagicMock()
width = random.choice(['width_mm', 'width_mm_advance'])
scene_tool_box.calculate_linear_or_presize_linear_length(width)
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls[0][2]
if kpi_results:
self.assertEqual(len(kpi_results), 8,
'expects to write 8 parameters to db')
def test_calculate_display_size_correct_results_type(self):
"""
test that the type of the numerator and denominator is float
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
mock_df_products_size = self.mock_object(
'_get_display_size_of_product_in_scene',
path='Projects.PNGCNTEST.SceneKpis.KPISceneToolBox.PngcnSceneKpis')
# test that we don't return any thing when the used df is empty
mock_df_products_size.return_value = pd.DataFrame([{
'item_id': 2,
'scene_id': 3,
'product_size': 0.25
}])
# test that we write the correct results to DB
data_scif = [{
u'scene_id': 16588190,
u'item_id': 123,
u'manufacturer_fk': 4,
u'rlv_sos_sc': 1,
u'status': 1
}, {
u'scene_id': 16588190,
u'item_id': 125,
u'manufacturer_fk': 3,
u'rlv_sos_sc': 1,
u'status': 1
}, {
u'scene_id': 16588190,
u'item_id': 136,
u'manufacturer_fk': 3,
u'rlv_sos_sc': 1,
u'status': 1
}]
scene_tool_box.scif = pd.DataFrame(data_scif)
data_df_products_size = [{
'item_id': 123,
'scene_id': 16588190,
'product_size': 1.245
}, {
'item_id': 124,
'scene_id': 16588190,
'product_size': 0.285
}, {
'item_id': 125,
'scene_id': 16588190,
'product_size': 1.225
}, {
'item_id': 126,
'scene_id': 16588190,
'product_size': 0.232
}, {
'item_id': 136,
'scene_id': 16588190,
'product_size': 0
}]
mock_df_products_size.return_value = pd.DataFrame(
data_df_products_size)
scene_tool_box.common.write_to_db_result = MagicMock()
scene_tool_box.calculate_display_size()
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls[0][2]
if kpi_results:
numerator = kpi_results['numerator_result']
denominator = kpi_results['denominator_result']
# test that the type of the numerator and denominator is float
self.assertIsInstance(denominator, float)
self.assertIsInstance(numerator, float)
def test_calculate_facing_eye_level(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': 'Safeguard',
'category': 'Personal Cleaning Care',
'product_fk': 152,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 1,
'shelf_number': 1,
'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': 152,
'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': 152,
'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': 152,
'stacking_layer': 1,
'category_fk': 101,
'bay_number': 2,
'shelf_number': 2,
'facing_sequence_number': 2,
'sub_category': 'HOLA'
}])
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_facing_eye_level(data, 2)
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls
if kpi_results:
self.assertEqual(len(kpi_results), 2,
'expects to write 2 parameters to db')
self.assertEqual(
kpi_results[1][2]['result'], 4,
"result isn't 4 although there are 4 facings in shelf 2")
else:
raise Exception('No results were saved')
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_display_size_correct_results_length(self):
"""
test result length
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
mock_df_products_size = self.mock_object(
'_get_display_size_of_product_in_scene',
path='Projects.PNGCNTEST.SceneKpis.KPISceneToolBox.PngcnSceneKpis')
# test that we don't return any thing when the used df is empty
mock_df_products_size.return_value = pd.DataFrame([{
'item_id': 2,
'scene_id': 3,
'product_size': 0.25
}])
# test that we write the correct results to DB
data_scif = [{
u'scene_id': 16588190,
u'item_id': 123,
u'manufacturer_fk': 4,
u'rlv_sos_sc': 1,
u'status': 1
}, {
u'scene_id': 16588190,
u'item_id': 125,
u'manufacturer_fk': 3,
u'rlv_sos_sc': 1,
u'status': 1
}, {
u'scene_id': 16588190,
u'item_id': 136,
u'manufacturer_fk': 3,
u'rlv_sos_sc': 1,
u'status': 1
}]
scene_tool_box.scif = pd.DataFrame(data_scif)
data_df_products_size = [{
'item_id': 123,
'scene_id': 16588190,
'product_size': 1.245
}, {
'item_id': 124,
'scene_id': 16588190,
'product_size': 0.285
}, {
'item_id': 125,
'scene_id': 16588190,
'product_size': 1.225
}, {
'item_id': 126,
'scene_id': 16588190,
'product_size': 0.232
}, {
'item_id': 136,
'scene_id': 16588190,
'product_size': 0
}]
mock_df_products_size.return_value = pd.DataFrame(
data_df_products_size)
scene_tool_box.common.write_to_db_result = MagicMock()
# scene_tool_box.png_manufacturer_fk = 4
scene_tool_box.calculate_display_size()
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls[0][2]
if kpi_results:
# test that we write 8 fields to DB
self.assertEqual(len(kpi_results), 8,
'expects to write 8 parameters to db')
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')
def test_calculate_display_size_correct_results_sanity(self):
"""
test if the numerator is greater then denominator (if the subgroup is greater then containing group)
"""
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
mock_df_products_size = self.mock_object(
'_get_display_size_of_product_in_scene',
path='Projects.PNGCNTEST.SceneKpis.KPISceneToolBox.PngcnSceneKpis')
# test that we write the correct results to DB
data_scif = [{
u'scene_id': 16588190,
u'item_id': 123,
u'manufacturer_fk': 4,
u'rlv_sos_sc': 1,
u'status': 1
}, {
u'scene_id': 16588190,
u'item_id': 125,
u'manufacturer_fk': 3,
u'rlv_sos_sc': 1,
u'status': 1
}, {
u'scene_id': 16588190,
u'item_id': 136,
u'manufacturer_fk': 3,
u'rlv_sos_sc': 1,
u'status': 1
}]
scene_tool_box.scif = pd.DataFrame(data_scif)
data_df_products_size = [{
'item_id': 123,
'scene_id': 16588190,
'product_size': 1.245
}, {
'item_id': 124,
'scene_id': 16588190,
'product_size': 0.285
}, {
'item_id': 125,
'scene_id': 16588190,
'product_size': 1.225
}, {
'item_id': 126,
'scene_id': 16588190,
'product_size': 0.232
}, {
'item_id': 136,
'scene_id': 16588190,
'product_size': 0
}]
mock_df_products_size.return_value = pd.DataFrame(
data_df_products_size)
scene_tool_box.common.write_to_db_result = MagicMock()
scene_tool_box.calculate_display_size()
kpi_results = scene_tool_box.common.write_to_db_result.mock_calls[0][2]
if kpi_results:
numerator = kpi_results['numerator_result']
denominator = kpi_results['denominator_result']
# test if the numerator is greater then denominator (if the subgroup is greater then containing group)
self.assertGreaterEqual(
denominator, numerator,
'the numerator cant be greater then denominator')
