class ProfileCombinations(TestCase):
def setUp(self):
normal_profile_selection = np.array([1000, 2000, 3000, 5000])
cutting_tolerance = 10
profiles_df = pd.read_excel("offerte_lilo_1.xlsx")
self.dataset = profiles_df.sort_values(profiles_df.columns[0],
ascending=False)
self.permutations = Permutations(self.dataset.copy(),
normal_profile_selection,
cutting_tolerance)
#self.depth = self.permutations.get_permutation_depth()
#self.permuted_dict = self.permutations.get_permuted_dataframes(self.dataset, self.depth)
def test_profile_get_combination_depth(self):
self.pr = cProfile.Profile()
self.pr.enable()
self.depth = self.permutations.get_permutation_depth()
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
print
"\n--->>>"
def test_profile_combination_generator(self):
self.pr = cProfile.Profile()
self.pr.enable()
self.depth = self.permutations.get_permutation_depth()
self.combinations_df = self.permutations.get_combinations_dataframe(
self.dataset)
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
print
"\n--->>>"
class TestPermutations(TestCase):
def setUp(self):
normal_profile_selection = np.array([1000, 2000, 3000, 5000])
cutting_tolerance = 10
profiles_df = pd.read_excel("profillängen.xlsx")
self.dataset = profiles_df.sort_values(profiles_df.columns[0],
ascending=False)
self.permutations = Permutations(self.dataset.copy(),
normal_profile_selection,
cutting_tolerance)
self.depth = self.permutations.get_permutation_depth()
self.permuted_dict = self.permutations.get_permuted_dataframes(
self.dataset, self.depth)
def test_permutation_depth_calculation(self):
"""
Tests if the calculated permutation depth is correct for the given dataset.
"""
self.assertEqual(5, self.depth)
def test_permuted_dataframes_sums(self):
"""
Tests if the dictionary, containing permuted dataframes for every permutation depth,
contains only permutations which sum() is smaller than the largest raw profile.
"""
for key in self.permuted_dict:
self.assertFalse(
(self.permuted_dict[key]["sum"].values > 5000).all())
def test_number_of_permutation_dataframes(self):
permutation_df_counter = 0
for _ in self.permuted_dict:
permutation_df_counter += 1
self.assertEqual(self.depth, permutation_df_counter)
def test_permutation_dataframe_merging(self):
permutations_df = self.permutations.merge_permutation_dataframes(
self.permuted_dict)
self.assertEqual(len(permutations_df.columns), self.depth + 2)
def test_get_combinations(self):
combinations_df = self.permutations.get_combinations_dataframe(
self.dataset)
self.assertEqual(len(combinations_df.columns), self.depth + 2)
#load profile lengths in pandas dataframe
profiles_df = pd.read_excel(root.filename)
profiles_df = profiles_df.sort_values(profiles_df.columns[0], ascending=False)
#check which profiles are available
result = messagebox.askyesno("Verfügbare Profile","Ist ein 5 m Profil verfügbar?")
if not result:
selection = no_five_profile_selection
#check for cutting tolerance
#check for permutation depth
permutations = Permutations(profiles_df.copy(), selection, cutting_tolerance)
permutation_depth = permutations.get_permutation_depth()
permutation_depth = 3
#create permutations of values
perm_df_dict = permutations.get_permuted_dataframes(profiles_df, permutation_depth)
#create work list of remaining profiles
profiles_array = profiles_df[profiles_df.columns[0]]
remaining_profiles_array = profiles_array.copy()
#help members
raw_profile_list = []
garbage_array = np.array([])
id_counter = 0
#Define initial profile
LastProfile = RawProfile("-1", 0)