class TestChainObject(unittest.TestCase): def setUp(self): self.path = './tests/' # self.path = '' project_name = 'Example Data (A)' # Load Example Data (A) data and meta into self name_data = '%s.csv' % (project_name) path_data = '%s%s' % (self.path, name_data) self.example_data_A_data = pd.DataFrame.from_csv(path_data) name_meta = '%s.json' % (project_name) path_meta = '%s%s' % (self.path, name_meta) self.example_data_A_meta = load_json(path_meta) # The minimum list of variables required to populate a stack with all single*delimited set variations self.minimum = ['q2b', 'Wave', 'q2', 'q3', 'q5_1'] self.setup_stack_Example_Data_A() self.setup_chains_Example_Data_A() def test_save_chain(self): self.setup_chains_Example_Data_A() for chain in self.chains: chain.save(path=self.path) loaded_chain = Chain.load('%s%s.chain' % (self.path, chain.name)) # Create a dictionary with the attribute structure of the chain chain_attributes = test_helper.create_attribute_dict(chain) # Create a dictionary with the attribute structure of the chain loaded_chain_attributes = test_helper.create_attribute_dict(loaded_chain) # Ensure that we are not comparing the same variable (in memory) self.assertNotEqual(id(chain), id(loaded_chain)) # Make sure that this is working by altering the loaded_stack_attributes # and comparing the result. (It should fail) # Change a 'value' in the dict loaded_chain_attributes['__dict__']['name'] = 'SomeOtherName' with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # reset the value loaded_chain_attributes['__dict__']['name'] = chain_attributes['__dict__']['name'] self.assertEqual(chain_attributes, loaded_chain_attributes) # Change a 'key' in the dict del loaded_chain_attributes['__dict__']['name'] loaded_chain_attributes['__dict__']['new_name'] = chain_attributes['__dict__']['name'] with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # reset the value del loaded_chain_attributes['__dict__']['new_name'] loaded_chain_attributes['__dict__']['name'] = chain_attributes['__dict__']['name'] self.assertEqual(chain_attributes, loaded_chain_attributes) # Remove a key/value pair del loaded_chain_attributes['__dict__']['name'] with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # Cleanup if os.path.exists('./tests/{0}.chain'.format(chain.name)): os.remove('./tests/{0}.chain'.format(chain.name)) def test_validate_x_y_combination(self): fk = 'no_filter' xk = self.minimum yk = ['@'] + self.minimum views = ['cbase', 'counts', 'c%'] # check the correct error message is returned, irrespective of orientation... # error #1 expected_message = "If the number of keys for both x and y are greater than 1, whether or not you have specified the x and y values, orient_on must be either 'x' or 'y'." with self.assertRaises(ValueError) as error_message: _ = self.stack.get_chain( name='y', data_keys=self.stack.name, filters=fk, x=xk, y=yk, views=views, post_process=True ) self.assertEqual(error_message.exception[0], expected_message) def test_lazy_name(self): fk = 'no_filter' xk = self.minimum yk = ['@'] + self.minimum views = ['cbase', 'counts', 'c%'] # get chain but do not name - y orientation chain_y = self.stack.get_chain( data_keys=self.stack.name, filters=fk, x=xk, y=yk[0], views=views, post_process=False ) # get chain but do not name - x orientation chain_x = self.stack.get_chain( data_keys=self.stack.name, filters=fk, x=xk[0], y=yk, views=views, post_process=False ) # check lazy_name is working as it should be self.assertEqual(chain_y.name, '[email protected]_1.cbase.counts.c%') self.assertEqual(chain_x.name, '[email protected]_1.cbase.counts.c%') def test_dervie_attributes(self): # check chain attributes self.assertEqual(self.chains[0].name, '@') self.assertEqual(self.chains[0].orientation, 'y') self.assertEqual(self.chains[0].source_name, '@') self.assertEqual(self.chains[0].len_of_axis, 5) self.assertEqual(self.chains[0].content_of_axis, ['q2b', 'Wave', 'q2', 'q3', 'q5_1']) self.assertEqual(self.chains[0].views, ['x|frequency|x:y|||cbase', 'x|frequency||||counts', 'x|frequency||y||c%']) self.assertEqual(self.chains[0].data_key, 'Example Data (A)') self.assertEqual(self.chains[0].filter, 'no_filter') self.assertEqual(self.chains[0].source_type, None) self.assertEqual(self.chains[-1].name, 'q5_1') self.assertEqual(self.chains[-1].orientation, 'x') self.assertEqual(self.chains[-1].source_name, 'q5_1') self.assertEqual(self.chains[-1].len_of_axis, 6) self.assertEqual(self.chains[-1].content_of_axis, ['@', 'q2b', 'Wave', 'q2', 'q3', 'q5_1']) self.assertEqual(self.chains[-1].views, ['x|frequency|x:y|||cbase', 'x|frequency||||counts', 'x|frequency||y||c%']) self.assertEqual(self.chains[-1].data_key, 'Example Data (A)') self.assertEqual(self.chains[-1].filter, 'no_filter') self.assertEqual(self.chains[-1].source_type, None) def test_post_process_shapes(self): # check chain attributes after post_processing self.assertEqual(self.chains[0].x_new_order, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual(self.chains[0].x_hidden_codes, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual(self.chains[0].y_new_order, None) self.assertEqual(self.chains[0].y_hidden_codes, None) self.assertEqual(self.chains[0].props_tests, []) self.assertEqual(self.chains[0].props_tests_levels, []) self.assertEqual(self.chains[0].has_props_tests, False) self.assertEqual(self.chains[0].means_tests, []) self.assertEqual(self.chains[0].means_tests_levels, []) self.assertEqual(self.chains[0].has_means_tests, False) self.assertEqual(self.chains[0].view_sizes, [[(1, 1), (3, 1), (3, 1)], [(1, 1), (5, 1), (5, 1)], [(1, 1), (8, 1), (8, 1)], [(1, 1), (9, 1), (9, 1)], [(1, 1), (7, 1), (7, 1)]]) self.assertEqual(self.chains[0].view_lengths, [[1, 3, 3], [1, 5, 5], [1, 8, 8], [1, 9, 9], [1, 7, 7]]) self.assertEqual(self.chains[0].source_length, 1) self.assertEqual(self.chains[-1].x_new_order, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual(self.chains[-1].x_hidden_codes, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual(self.chains[-1].y_new_order, None) self.assertEqual(self.chains[-1].y_hidden_codes, None) self.assertEqual(self.chains[-1].props_tests, []) self.assertEqual(self.chains[-1].props_tests_levels, []) self.assertEqual(self.chains[-1].has_props_tests, False) self.assertEqual(self.chains[-1].means_tests, []) self.assertEqual(self.chains[-1].means_tests_levels, []) self.assertEqual(self.chains[-1].has_means_tests, False) self.assertEqual(self.chains[-1].view_sizes, [[(1, 1), (7, 1), (7, 1)], [(1, 3), (7, 3), (7, 3)], [(1, 5), (7, 5), (7, 5)], [(1, 8), (7, 8), (7, 8)], [(1, 9), (7, 9), (7, 9)], [(1, 7), (7, 7), (7, 7)]]) self.assertEqual(self.chains[-1].view_lengths, [[1, 7, 7], [1, 7, 7], [1, 7, 7], [1, 7, 7], [1, 7, 7], [1, 7, 7]]) self.assertEqual(self.chains[-1].source_length, 9) def test_describe(self): fk = 'no_filter' for chain in self.chains: chain_described = chain.describe() #test describe() returns a dataframe self.assertIsInstance(chain_described, pd.DataFrame) #test descibe() returns the expected dataframe - *no args* if chain.orientation == 'y': keys = chain[self.stack.name][fk].keys() views = chain[self.stack.name][fk][keys[0]][chain.source_name].keys() data = [self.stack.name]*(len(keys)*len(views)) filters = [fk]*(len(keys)*len(views)) x = [] for key in keys: x.extend([key]*len(views)) y = [chain.source_name]*(len(keys)*len(views)) view = [v for v in views]*len(keys) ones = [1]*(len(keys)*len(views)) df = pd.DataFrame({'data': data, 'filter': filters, 'x': x, 'y': y, 'view': view, '#': ones}) df = df[chain_described.columns.tolist()] assert_frame_equal(chain_described, df) elif chain.orientation == 'x': keys = chain[self.stack.name][fk][chain.source_name].keys() views = chain[self.stack.name][fk][chain.source_name][keys[0]].keys() data = [self.stack.name]*(len(keys)*len(views)) filters = [fk]*(len(keys)*len(views)) y = [] for key in keys: y.extend([key]*len(views)) x = [chain.source_name]*(len(keys)*len(views)) view = [v for v in views]*len(keys) ones = [1]*(len(keys)*len(views)) df = pd.DataFrame({'data': data, 'filter': filters, 'x': x, 'y': y, 'view': view, '#': ones}) df = df[chain_described.columns.tolist()] assert_frame_equal(chain_described, df) @classmethod def tearDownClass(self): self.stack = Stack("StackName") filepath ='./tests/'+self.stack.name+'.stack' if os.path.exists(filepath): os.remove(filepath) def is_empty(self, any_structure): if any_structure: #print('Structure is not empty.') return False else: #print('Structure is empty.') return True def create_key_stack(self, branch_pos="data"): """ Creates a dictionary that has the structure of the keys in the Stack It is used to loop through the stack without affecting it. """ key_stack = {} for data_key in self.stack: key_stack[data_key] = {} for the_filter in self.stack[data_key][branch_pos]: key_stack[data_key][the_filter] = {} for x in self.stack[data_key][branch_pos][the_filter]: key_stack[data_key][the_filter][x] = [] for y in self.stack[data_key][branch_pos][the_filter][x]: link = self.stack[data_key][branch_pos][the_filter][x][y] if not isinstance(link, Link): continue key_stack[data_key][the_filter][x].append(y) return key_stack def setup_stack_Example_Data_A(self, fk=None, xk=None, yk=None, views=None, weights=None): if fk is None: fk = 'no_filter' if xk is None: xk = self.minimum if yk is None: yk = ['@'] + self.minimum if views is None: views = ['default', 'cbase', 'counts', 'c%'] if not isinstance(weights, list): weights = [weights] self.stack = Stack(name="Example Data (A)") self.stack.add_data( data_key=self.stack.name, meta=self.example_data_A_meta, data=self.example_data_A_data ) for weight in weights: self.stack.add_link( data_keys=self.stack.name, filters=fk, x=xk, y=yk, views=QuantipyViews(views), weights=weight ) def setup_chains_Example_Data_A(self, fk=None, xk=None, yk=None, views=None, orient_on=None): if fk is None: fk = 'no_filter' if xk is None: xk = self.minimum if yk is None: yk = ['@'] + self.minimum if views is None: views = [ 'x|frequency|x:y|||cbase', 'x|frequency||||counts', 'x|frequency||y||c%' ] self.chains = [] for y in yk: self.chains.append( self.stack.get_chain( name=y, data_keys=self.stack.name, filters='no_filter', x=xk, y=y, views=views, post_process=True ) ) for x in xk: self.chains.append( self.stack.get_chain( name=x, data_keys=self.stack.name, filters='no_filter', x=x, y=yk, views=views, post_process=True ) )
class TestChainObject(unittest.TestCase): def setUp(self): self.path = './tests/' self.path_chain = './temp.chain'.format(self.path) # self.path = '' project_name = 'Example Data (A)' # Load Example Data (A) data and meta into self name_data = '%s.csv' % (project_name) path_data = '%s%s' % (self.path, name_data) self.example_data_A_data = pd.DataFrame.from_csv(path_data) name_meta = '%s.json' % (project_name) path_meta = '%s%s' % (self.path, name_meta) self.example_data_A_meta = load_json(path_meta) # The minimum list of variables required to populate a stack with all single*delimited set variations self.minimum = ['q2b', 'Wave', 'q2', 'q3', 'q5_1'] self.setup_stack_Example_Data_A() self.setup_chains_Example_Data_A() def test_save_chain(self): self.setup_chains_Example_Data_A() for chain in self.chains: # Create a dictionary with the attribute structure of the chain chain_attributes = chain.__dict__ chain_described = chain.describe() # Save and then load a copy of the chain chain.save(path=self.path_chain) loaded_chain = Chain.load(self.path_chain) # Ensure that we are not comparing the same variable (in memory) self.assertNotEqual(id(chain), id(loaded_chain)) # Create a dictionary with the attribute structure of the chain loaded_chain_attributes = loaded_chain.__dict__ loaded_chain_described = loaded_chain.describe() # Confirm that the chains contain the same views sort_order = ['data', 'filter', 'x', 'y', 'view'] actual = chain_described.sort(sort_order).values.tolist() expected = loaded_chain_described.sort(sort_order).values.tolist() self.assertSequenceEqual(actual, expected) # Make sure that this is working by altering the loaded_stack_attributes # and comparing the result. (It should fail) # Change a 'value' in the dict loaded_chain_attributes['name'] = 'SomeOtherName' with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # reset the value loaded_chain_attributes['name'] = chain_attributes['name'] self.assertEqual(chain_attributes, loaded_chain_attributes) # Change a 'key' in the dict del loaded_chain_attributes['name'] loaded_chain_attributes['new_name'] = chain_attributes['name'] with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # reset the value del loaded_chain_attributes['new_name'] loaded_chain_attributes['name'] = chain_attributes['name'] self.assertEqual(chain_attributes, loaded_chain_attributes) # Remove a key/value pair del loaded_chain_attributes['name'] with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # Cleanup if os.path.exists('./tests/{0}.chain'.format(chain.name)): os.remove('./tests/{0}.chain'.format(chain.name)) def test_auto_orientation(self): fk = 'no_filter' xk = self.minimum yk = ['@'] + self.minimum views = ['cbase', 'counts', 'c%'] # If multiple x and y keys are given without orient_on # x-orientation chains are assumed. chain = self.stack.get_chain(name='y', data_keys=self.stack.name, filters=fk, x=xk, y=yk, views=views) self.assertTrue(chain.orientation == 'x') def test_lazy_name(self): fk = 'no_filter' xk = self.minimum yk = ['@'] + self.minimum views = ['cbase', 'counts', 'c%'] # get chain but do not name - y orientation chain_y = self.stack.get_chain(data_keys=self.stack.name, filters=fk, x=xk, y=yk[0], views=views) # get chain but do not name - x orientation chain_x = self.stack.get_chain(data_keys=self.stack.name, filters=fk, x=xk[0], y=yk, views=views) # check lazy_name is working as it should be self.assertEqual(chain_y.name, '[email protected]_1.cbase.counts.c%') self.assertEqual(chain_x.name, '[email protected]_1.cbase.counts.c%') def test_dervie_attributes(self): # check chain attributes self.assertEqual(self.chains[0].name, '@') self.assertEqual(self.chains[0].orientation, 'y') self.assertEqual(self.chains[0].source_name, '@') self.assertEqual(self.chains[0].len_of_axis, 5) self.assertEqual(self.chains[0].content_of_axis, ['q2b', 'Wave', 'q2', 'q3', 'q5_1']) self.assertEqual(self.chains[0].views, ['x|f|x:|||cbase', 'x|f|:|||counts', 'x|f|:|y||c%']) self.assertEqual(self.chains[0].data_key, 'Example Data (A)') self.assertEqual(self.chains[0].filter, 'no_filter') self.assertEqual(self.chains[0].source_type, None) self.assertEqual(self.chains[-1].name, 'q5_1') self.assertEqual(self.chains[-1].orientation, 'x') self.assertEqual(self.chains[-1].source_name, 'q5_1') self.assertEqual(self.chains[-1].len_of_axis, 6) self.assertEqual(self.chains[-1].content_of_axis, ['@', 'q2b', 'Wave', 'q2', 'q3', 'q5_1']) self.assertEqual(self.chains[-1].views, ['x|f|x:|||cbase', 'x|f|:|||counts', 'x|f|:|y||c%']) self.assertEqual(self.chains[-1].data_key, 'Example Data (A)') self.assertEqual(self.chains[-1].filter, 'no_filter') self.assertEqual(self.chains[-1].source_type, None) def test_describe(self): fk = 'no_filter' for chain in self.chains: chain_described = chain.describe() #test describe() returns a dataframe self.assertIsInstance(chain_described, pd.DataFrame) #test descibe() returns the expected dataframe - *no args* if chain.orientation == 'y': keys = chain[self.stack.name][fk].keys() views = chain[self.stack.name][fk][keys[0]][ chain.source_name].keys() data = [self.stack.name] * (len(keys) * len(views)) filters = [fk] * (len(keys) * len(views)) x = [] for key in keys: x.extend([key] * len(views)) y = [chain.source_name] * (len(keys) * len(views)) view = [v for v in views] * len(keys) ones = [1] * (len(keys) * len(views)) df = pd.DataFrame({ 'data': data, 'filter': filters, 'x': x, 'y': y, 'view': view, '#': ones }) df = df[chain_described.columns.tolist()] assert_frame_equal(chain_described, df) elif chain.orientation == 'x': keys = chain[self.stack.name][fk][chain.source_name].keys() views = chain[self.stack.name][fk][chain.source_name][ keys[0]].keys() data = [self.stack.name] * (len(keys) * len(views)) filters = [fk] * (len(keys) * len(views)) y = [] for key in keys: y.extend([key] * len(views)) x = [chain.source_name] * (len(keys) * len(views)) view = [v for v in views] * len(keys) ones = [1] * (len(keys) * len(views)) df = pd.DataFrame({ 'data': data, 'filter': filters, 'x': x, 'y': y, 'view': view, '#': ones }) df = df[chain_described.columns.tolist()] assert_frame_equal(chain_described, df) @classmethod def tearDownClass(self): self.stack = Stack("StackName") filepath = './tests/' + self.stack.name + '.stack' if os.path.exists(filepath): os.remove(filepath) def is_empty(self, any_structure): if any_structure: #print('Structure is not empty.') return False else: #print('Structure is empty.') return True def create_key_stack(self, branch_pos="data"): """ Creates a dictionary that has the structure of the keys in the Stack It is used to loop through the stack without affecting it. """ key_stack = {} for data_key in self.stack: key_stack[data_key] = {} for the_filter in self.stack[data_key][branch_pos]: key_stack[data_key][the_filter] = {} for x in self.stack[data_key][branch_pos][the_filter]: key_stack[data_key][the_filter][x] = [] for y in self.stack[data_key][branch_pos][the_filter][x]: link = self.stack[data_key][branch_pos][the_filter][x][ y] if not isinstance(link, Link): continue key_stack[data_key][the_filter][x].append(y) return key_stack def setup_stack_Example_Data_A(self, fk=None, xk=None, yk=None, views=None, weights=None): if fk is None: fk = 'no_filter' if xk is None: xk = self.minimum if yk is None: yk = ['@'] + self.minimum if views is None: views = ['default', 'cbase', 'counts', 'c%'] if not isinstance(weights, list): weights = [weights] self.stack = Stack(name="Example Data (A)") self.stack.add_data(data_key=self.stack.name, meta=self.example_data_A_meta, data=self.example_data_A_data) for weight in weights: self.stack.add_link(data_keys=self.stack.name, filters=fk, x=xk, y=yk, views=QuantipyViews(views), weights=weight) def setup_chains_Example_Data_A(self, fk=None, xk=None, yk=None, views=None, orient_on=None): if fk is None: fk = 'no_filter' if xk is None: xk = self.minimum if yk is None: yk = ['@'] + self.minimum if views is None: views = ['x|f|x:|||cbase', 'x|f|:|||counts', 'x|f|:|y||c%'] self.chains = [] for y in yk: self.chains.append( self.stack.get_chain(name=y, data_keys=self.stack.name, filters='no_filter', x=xk, y=y, views=views)) for x in xk: self.chains.append( self.stack.get_chain(name=x, data_keys=self.stack.name, filters='no_filter', x=x, y=yk, views=views))
class TestChainObject(unittest.TestCase): def setUp(self): self.path = './tests/' # self.path = '' project_name = 'Example Data (A)' # Load Example Data (A) data and meta into self name_data = '%s.csv' % (project_name) path_data = '%s%s' % (self.path, name_data) self.example_data_A_data = pd.DataFrame.from_csv(path_data) name_meta = '%s.json' % (project_name) path_meta = '%s%s' % (self.path, name_meta) self.example_data_A_meta = load_json(path_meta) # The minimum list of variables required to populate a stack with all single*delimited set variations self.minimum = ['q2b', 'Wave', 'q2', 'q3', 'q5_1'] self.setup_stack_Example_Data_A() self.setup_chains_Example_Data_A() def test_save_chain(self): self.setup_chains_Example_Data_A() for chain in self.chains: chain.save(path=self.path) loaded_chain = Chain.load('%s%s.chain' % (self.path, chain.name)) # Create a dictionary with the attribute structure of the chain chain_attributes = test_helper.create_attribute_dict(chain) # Create a dictionary with the attribute structure of the chain loaded_chain_attributes = test_helper.create_attribute_dict( loaded_chain) # Ensure that we are not comparing the same variable (in memory) self.assertNotEqual(id(chain), id(loaded_chain)) # Make sure that this is working by altering the loaded_stack_attributes # and comparing the result. (It should fail) # Change a 'value' in the dict loaded_chain_attributes['__dict__']['name'] = 'SomeOtherName' with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # reset the value loaded_chain_attributes['__dict__']['name'] = chain_attributes[ '__dict__']['name'] self.assertEqual(chain_attributes, loaded_chain_attributes) # Change a 'key' in the dict del loaded_chain_attributes['__dict__']['name'] loaded_chain_attributes['__dict__']['new_name'] = chain_attributes[ '__dict__']['name'] with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # reset the value del loaded_chain_attributes['__dict__']['new_name'] loaded_chain_attributes['__dict__']['name'] = chain_attributes[ '__dict__']['name'] self.assertEqual(chain_attributes, loaded_chain_attributes) # Remove a key/value pair del loaded_chain_attributes['__dict__']['name'] with self.assertRaises(AssertionError): self.assertEqual(chain_attributes, loaded_chain_attributes) # Cleanup if os.path.exists('./tests/{0}.chain'.format(chain.name)): os.remove('./tests/{0}.chain'.format(chain.name)) def test_validate_x_y_combination(self): fk = 'no_filter' xk = self.minimum yk = ['@'] + self.minimum views = ['cbase', 'counts', 'c%'] # check the correct error message is returned, irrespective of orientation... # error #1 expected_message = "If the number of keys for both x and y are greater than 1, whether or not you have specified the x and y values, orient_on must be either 'x' or 'y'." with self.assertRaises(ValueError) as error_message: _ = self.stack.get_chain(name='y', data_keys=self.stack.name, filters=fk, x=xk, y=yk, views=views, post_process=True) self.assertEqual(error_message.exception[0], expected_message) def test_lazy_name(self): fk = 'no_filter' xk = self.minimum yk = ['@'] + self.minimum views = ['cbase', 'counts', 'c%'] # get chain but do not name - y orientation chain_y = self.stack.get_chain(data_keys=self.stack.name, filters=fk, x=xk, y=yk[0], views=views, post_process=False) # get chain but do not name - x orientation chain_x = self.stack.get_chain(data_keys=self.stack.name, filters=fk, x=xk[0], y=yk, views=views, post_process=False) # check lazy_name is working as it should be self.assertEqual(chain_y.name, '[email protected]_1.cbase.counts.c%') self.assertEqual(chain_x.name, '[email protected]_1.cbase.counts.c%') def test_dervie_attributes(self): # check chain attributes self.assertEqual(self.chains[0].name, '@') self.assertEqual(self.chains[0].orientation, 'y') self.assertEqual(self.chains[0].source_name, '@') self.assertEqual(self.chains[0].len_of_axis, 5) self.assertEqual(self.chains[0].content_of_axis, ['q2b', 'Wave', 'q2', 'q3', 'q5_1']) self.assertEqual(self.chains[0].views, [ 'x|frequency|x:y|||cbase', 'x|frequency||||counts', 'x|frequency||y||c%' ]) self.assertEqual(self.chains[0].data_key, 'Example Data (A)') self.assertEqual(self.chains[0].filter, 'no_filter') self.assertEqual(self.chains[0].source_type, None) self.assertEqual(self.chains[-1].name, 'q5_1') self.assertEqual(self.chains[-1].orientation, 'x') self.assertEqual(self.chains[-1].source_name, 'q5_1') self.assertEqual(self.chains[-1].len_of_axis, 6) self.assertEqual(self.chains[-1].content_of_axis, ['@', 'q2b', 'Wave', 'q2', 'q3', 'q5_1']) self.assertEqual(self.chains[-1].views, [ 'x|frequency|x:y|||cbase', 'x|frequency||||counts', 'x|frequency||y||c%' ]) self.assertEqual(self.chains[-1].data_key, 'Example Data (A)') self.assertEqual(self.chains[-1].filter, 'no_filter') self.assertEqual(self.chains[-1].source_type, None) def test_post_process_shapes(self): # check chain attributes after post_processing self.assertEqual( self.chains[0].x_new_order, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual( self.chains[0].x_hidden_codes, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual(self.chains[0].y_new_order, None) self.assertEqual(self.chains[0].y_hidden_codes, None) self.assertEqual(self.chains[0].props_tests, []) self.assertEqual(self.chains[0].props_tests_levels, []) self.assertEqual(self.chains[0].has_props_tests, False) self.assertEqual(self.chains[0].means_tests, []) self.assertEqual(self.chains[0].means_tests_levels, []) self.assertEqual(self.chains[0].has_means_tests, False) self.assertEqual( self.chains[0].view_sizes, [[(1, 1), (3, 1), (3, 1)], [(1, 1), (5, 1), (5, 1)], [(1, 1), (8, 1), (8, 1)], [(1, 1), (9, 1), (9, 1)], [(1, 1), (7, 1), (7, 1)]]) self.assertEqual( self.chains[0].view_lengths, [[1, 3, 3], [1, 5, 5], [1, 8, 8], [1, 9, 9], [1, 7, 7]]) self.assertEqual(self.chains[0].source_length, 1) self.assertEqual(self.chains[-1].x_new_order, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual(self.chains[-1].x_hidden_codes, [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]) self.assertEqual(self.chains[-1].y_new_order, None) self.assertEqual(self.chains[-1].y_hidden_codes, None) self.assertEqual(self.chains[-1].props_tests, []) self.assertEqual(self.chains[-1].props_tests_levels, []) self.assertEqual(self.chains[-1].has_props_tests, False) self.assertEqual(self.chains[-1].means_tests, []) self.assertEqual(self.chains[-1].means_tests_levels, []) self.assertEqual(self.chains[-1].has_means_tests, False) self.assertEqual(self.chains[-1].view_sizes, [[(1, 1), (7, 1), (7, 1)], [(1, 3), (7, 3), (7, 3)], [(1, 5), (7, 5), (7, 5)], [(1, 8), (7, 8), (7, 8)], [(1, 9), (7, 9), (7, 9)], [(1, 7), (7, 7), (7, 7)]]) self.assertEqual( self.chains[-1].view_lengths, [[1, 7, 7], [1, 7, 7], [1, 7, 7], [1, 7, 7], [1, 7, 7], [1, 7, 7]]) self.assertEqual(self.chains[-1].source_length, 9) def test_describe(self): fk = 'no_filter' for chain in self.chains: chain_described = chain.describe() #test describe() returns a dataframe self.assertIsInstance(chain_described, pd.DataFrame) #test descibe() returns the expected dataframe - *no args* if chain.orientation == 'y': keys = chain[self.stack.name][fk].keys() views = chain[self.stack.name][fk][keys[0]][ chain.source_name].keys() data = [self.stack.name] * (len(keys) * len(views)) filters = [fk] * (len(keys) * len(views)) x = [] for key in keys: x.extend([key] * len(views)) y = [chain.source_name] * (len(keys) * len(views)) view = [v for v in views] * len(keys) ones = [1] * (len(keys) * len(views)) df = pd.DataFrame({ 'data': data, 'filter': filters, 'x': x, 'y': y, 'view': view, '#': ones }) df = df[chain_described.columns.tolist()] assert_frame_equal(chain_described, df) elif chain.orientation == 'x': keys = chain[self.stack.name][fk][chain.source_name].keys() views = chain[self.stack.name][fk][chain.source_name][ keys[0]].keys() data = [self.stack.name] * (len(keys) * len(views)) filters = [fk] * (len(keys) * len(views)) y = [] for key in keys: y.extend([key] * len(views)) x = [chain.source_name] * (len(keys) * len(views)) view = [v for v in views] * len(keys) ones = [1] * (len(keys) * len(views)) df = pd.DataFrame({ 'data': data, 'filter': filters, 'x': x, 'y': y, 'view': view, '#': ones }) df = df[chain_described.columns.tolist()] assert_frame_equal(chain_described, df) @classmethod def tearDownClass(self): self.stack = Stack("StackName") filepath = './tests/' + self.stack.name + '.stack' if os.path.exists(filepath): os.remove(filepath) def is_empty(self, any_structure): if any_structure: #print('Structure is not empty.') return False else: #print('Structure is empty.') return True def create_key_stack(self, branch_pos="data"): """ Creates a dictionary that has the structure of the keys in the Stack It is used to loop through the stack without affecting it. """ key_stack = {} for data_key in self.stack: key_stack[data_key] = {} for the_filter in self.stack[data_key][branch_pos]: key_stack[data_key][the_filter] = {} for x in self.stack[data_key][branch_pos][the_filter]: key_stack[data_key][the_filter][x] = [] for y in self.stack[data_key][branch_pos][the_filter][x]: link = self.stack[data_key][branch_pos][the_filter][x][ y] if not isinstance(link, Link): continue key_stack[data_key][the_filter][x].append(y) return key_stack def setup_stack_Example_Data_A(self, fk=None, xk=None, yk=None, views=None, weights=None): if fk is None: fk = 'no_filter' if xk is None: xk = self.minimum if yk is None: yk = ['@'] + self.minimum if views is None: views = ['default', 'cbase', 'counts', 'c%'] if not isinstance(weights, list): weights = [weights] self.stack = Stack(name="Example Data (A)") self.stack.add_data(data_key=self.stack.name, meta=self.example_data_A_meta, data=self.example_data_A_data) for weight in weights: self.stack.add_link(data_keys=self.stack.name, filters=fk, x=xk, y=yk, views=QuantipyViews(views), weights=weight) def setup_chains_Example_Data_A(self, fk=None, xk=None, yk=None, views=None, orient_on=None): if fk is None: fk = 'no_filter' if xk is None: xk = self.minimum if yk is None: yk = ['@'] + self.minimum if views is None: views = [ 'x|frequency|x:y|||cbase', 'x|frequency||||counts', 'x|frequency||y||c%' ] self.chains = [] for y in yk: self.chains.append( self.stack.get_chain(name=y, data_keys=self.stack.name, filters='no_filter', x=xk, y=y, views=views, post_process=True)) for x in xk: self.chains.append( self.stack.get_chain(name=x, data_keys=self.stack.name, filters='no_filter', x=x, y=yk, views=views, post_process=True))