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)
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_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.PNGCN_PROD.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_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.PNGCN_PROD.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')
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_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.PNGCN_PROD.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_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 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_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 __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_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_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__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_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_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")
# try blade & razor template
blade_template_fk = 144
self.data_provider_mock['templates'] = pd.DataFrame([{'template_fk': 144}])
scene_tool_box = PngcnSceneKpis(self.ProjectConnector_mock,
self.common_mock, 16588190,
self.data_provider_mock)
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, psdataprovider=self.psdataprovider, template_fk=blade_template_fk)
self.assertEqual(len(kpi_results[kpi_results['bay_number'] == 3]), 2,
'expects to have only 2 lines with bay number 3 since the eye level for blade is [2, 3]')
self.assertEqual(len(kpi_results[kpi_results['bay_number'] == 2]), 1,
'expects to have only 1 line with bay number 2 since the eye level for blade is [2, 3]')
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_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_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.PNGCN_PROD.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)
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_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_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')
