class TestProjDataFileNames(unittest.TestCase):
def setUp(self):
"""Testing IpamCallFilenames()"""
self.ipam_data_filenames_cls = DataFileNames()
def test_create_IpamCallFilenames_intance(self):
self.ipam_data_filenames_cls = DataFileNames()
def test_type_extensible_attributes_filename(self):
self.assertIsInstance(
self.ipam_data_filenames_cls.extensible_attributes_filename(), str)
def test_eq_extensible_attributes_filename(self):
self.assertEqual(
'extensible_attributes.pkl',
self.ipam_data_filenames_cls.extensible_attributes_filename())
def test_type_extensible_attributes_list_values_filename(self):
self.assertIsInstance(
self.ipam_data_filenames_cls.
extensible_attributes_list_values_filename(), str)
def test_eq_extensible_attributes_list_values_filename(self):
self.assertEqual(
'extensible_attributes_list_values.pkl',
self.ipam_data_filenames_cls.
extensible_attributes_list_values_filename())
def test_type_network_views_filename(self):
self.assertIsInstance(
self.ipam_data_filenames_cls.network_views_filename(), str)
def test_eq_network_views_filename(self):
self.assertEqual('network_views.pkl',
self.ipam_data_filenames_cls.network_views_filename())
def test_type_networks_filename(self):
self.assertIsInstance(self.ipam_data_filenames_cls.networks_filename(),
str)
def test_eq_networks_filename(self):
self.assertEqual('networks.pkl',
self.ipam_data_filenames_cls.networks_filename())
def test_type_networkcontainers_filename(self):
self.assertIsInstance(
self.ipam_data_filenames_cls.networkcontainers_filename(), str)
def test_eq_networkcontainers_filename(self):
self.assertEqual(
'networkcontainers.pkl',
self.ipam_data_filenames_cls.networkcontainers_filename())
class IpamDataInterim:
"""Class to read in networks and networkcontainers to then be smashed,
mangled, and spit out into a .pickle and .xlsx file
"""
def __init__(self):
self.dir_cls = DirectoryValues()
self.ipam_filenames_cls = DataFileNames()
self.reader_cls = Reader()
# Load Networks Pickled Data
self.networks = self.reader_cls.read_from_pkl(
self.dir_cls.raw_dir(),
self.ipam_filenames_cls.networks_filename())
self.networkcontainers = self.reader_cls.read_from_pkl(
self.dir_cls.raw_dir(),
self.ipam_filenames_cls.networkcontainers_filename())
self.all_nets = self.networks + self.networkcontainers
def run_ipam_interim(self, xlsx, pickle):
flattened_dict_data = self.flatten_data(self.all_nets)
output_data = self.panda_processing_of_flattened_data(
flattened_dict_data)
output_data.to_excel(self.dir_cls.interim_dir() + '\\' + xlsx)
output_data.to_pickle(self.dir_cls.interim_dir() + '\\' + pickle)
def _convert_flatten(self, data, parent_key='', sep='_'):
"""Method to convert input of nested dict's to a flattened dict
default seperater '_'
"""
items = []
for k, v in data.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, MutableMapping):
items.extend(
self._convert_flatten(v, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
def flatten_data(self, data_to_be_flattened):
"""Method to flatten the requested data."""
return [
self._convert_flatten(data_to_be_flattened[i])
for i in range(len(data_to_be_flattened))
]
def panda_processing_of_flattened_data(self, all_nets):
"""Turns the returned flat dict into a panda dataframe.
Further processing:
1. Add 'net_type' column.
2. Add oct1, oct2, oct3, oct4, and /Cidr columns.
3. Add oct1, oct2, oct3, oct4, and /Cidr columns.
4. Sorting Data via line 2 values.
5. Return Indexed data starting at 10000
"""
net_flat_df = pd.DataFrame.from_dict(self.flatten_data(all_nets))
# Further processing Line 1.
net_flat_df['net_type'] = net_flat_df['_ref'].str.split('/',
expand=True)[0]
# Further processing Line 2.
oct_list = ['oct1', 'oct2', 'oct3', 'oct4', '/Cidr']
network_col = net_flat_df['network'].str.split(".", expand=True)
net_flat_df[oct_list[0]] = network_col[0].astype(str).astype(int)
net_flat_df[oct_list[1]] = network_col[1].astype(str).astype(int)
net_flat_df[oct_list[2]] = network_col[2].astype(str).astype(int)
split_third_value = network_col[3].str.split("/", expand=True)
net_flat_df[oct_list[3]] = split_third_value[0].astype(str).astype(int)
net_flat_df[oct_list[4]] = split_third_value[1].astype(str).astype(int)
# Further processesing Line 3.
network_col = net_flat_df['network'].str.split("/", expand=True)
net_flat_df['IP Subnet'] = network_col[0].astype(str)
net_flat_df['IP Cidr'] = network_col[1].astype(str).astype(int)
# Further processing Line 4.
net_flat_df = net_flat_df.sort_values(
oct_list, ascending=[True, True, True, True, True])
# Further processing Line 5.
net_flat_df.index = range(len(net_flat_df.index))
net_flat_df.index += 10000
return net_flat_df