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_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_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_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__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_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')
