def define_device_to_rename(portshow_aggregated_df, device_rename_df,
max_title, force_form_update_flag,
force_change_data_lst, report_data_lst):
"""
Function to define (create new, return previously saved or return empty)
device_rename_df DataFrame to apply device rename schema
"""
# if device_rename_df DataFrame doesn't exist (1st iteration)
# or force flag to change device_rename_df DataFrame is on
# or some related DataFrames was forcibly changed
if device_rename_df is None or force_form_update_flag:
print('\n')
if force_change_data_lst:
print(
f"Request to force change of {', '.join(force_change_data_lst)} data was received."
)
reply = reply_request(
'Do you want to change auto assigned device names? (y)es/(n)o: ')
if reply == 'y':
# if device_rename_df DataFrame doesn't exist (1st iteration)
if device_rename_df is None:
# create new device rename DataFrame
manual_device_rename_df = create_device_rename_form(
portshow_aggregated_df)
else:
# if any related DataFrames was forcibly changed ask if device rename form reset required
if force_change_data_lst:
reply = reply_request(
'Do you want to apply previously saved device rename schema? (y)es/(n)o: '
)
if reply == 'y':
print('\n')
return device_rename_df
else:
print('\n')
reply = reply_request(
'Do you want to reset device rename form? (y)es/(n)o: '
)
if reply == 'y':
# create new device rename DataFrame
manual_device_rename_df = create_device_rename_form(
portshow_aggregated_df)
else:
# use saved device rename DataFrame
manual_device_rename_df = device_rename_df.copy()
else:
# if no force change in related DataFrames but device_rename_df DataFrame
# change initiated use saved device rename DataFrame
manual_device_rename_df = device_rename_df.copy()
# save manual_device_rename_df DataFrame to excel file to use at as form to fill
sheet_title = 'device_rename_form'
file_path = save_xlsx_file(manual_device_rename_df,
sheet_title,
report_data_lst,
force_flag=True)
file_name = os.path.basename(file_path)
file_directory = os.path.dirname(file_path)
print(
f"\nTo rename devices put new names into the '{file_name}' file, '{sheet_title}' sheet in\n'{file_directory}' directory"
)
print('ATTN! CLOSE file after changes were made\n')
# complete the manual_device_rename_df form and import it
reply = reply_request("When finish enter 'yes': ", ['yes'])
if reply == 'y':
print('\n')
device_rename_df = dataframe_import(sheet_title,
max_title,
init_file=file_path,
header=2)
else:
# if don't change auto assigned names save empty device_rename_df DataFrame
device_rename_columns = [
'Fabric_name', 'Device_Host_Name', 'Group_Name', 'deviceType',
'deviceSubtype', 'Device_Host_Name_rename'
]
device_rename_df = pd.DataFrame(columns=device_rename_columns)
else:
# check loaded device_rename_df DataFrame (if it's empty)
device_rename_df = verify_data(report_data_lst, ['device_rename'],
device_rename_df,
show_status=False)
return device_rename_df
def switch_params_analysis_main(fabricshow_ag_labels_df, chassis_params_df,
switch_params_df, maps_params_df,
blade_module_loc_df, ag_principal_df,
report_data_lst):
"""Main function to create aggregated switch parameters table and report tables"""
# report_data_lst contains information:
# customer_name, dir_report, dir to save obtained data, max_title, report_steps_dct
*_, max_title, report_steps_dct = report_data_lst
# names to save data obtained after current module execution
data_names = [
'report_columns_usage', 'switch_params_aggregated', 'Коммутаторы',
'Фабрика', 'Параметры_коммутаторов', 'Лицензии',
'Глобальные_параметры_фабрики'
]
# service step information
print(f'\n\n{report_steps_dct[data_names[0]][3]}\n')
# load data if they were saved on previos program execution iteration
data_lst = load_data(report_data_lst, *data_names)
# unpacking DataFrames from the loaded list with data
# pylint: disable=unbalanced-tuple-unpacking
report_columns_usage_dct, switch_params_aggregated_df, switches_report_df, fabric_report_df, \
switches_parameters_report_df, licenses_report_df, global_fabric_parameters_report_df = data_lst
# list of data to analyze from report_info table
analyzed_data_names = [
'chassis_parameters', 'switch_parameters', 'switchshow_ports',
'maps_parameters', 'blade_interconnect', 'fabric_labels'
]
# clean fabricshow DataFrame from unneccessary data
fabric_clean_df = fabric_clean(fabricshow_ag_labels_df)
# force run when any data from data_lst was not saved (file not found) or
# procedure execution explicitly requested for output data or data used during fn execution
force_run = verify_force_run(data_names, data_lst, report_steps_dct,
max_title, analyzed_data_names)
if force_run:
# import data with switch models, firmware and etc
switch_models_df = dataframe_import('switch_models', max_title)
# current operation information string
info = f'Generating aggregated switch parameters table'
print(info, end=" ")
# create aggregated table by joining DataFrames
switch_params_aggregated_df, report_columns_usage_dct = \
fabric_aggregation(fabric_clean_df, chassis_params_df, \
switch_params_df, maps_params_df, switch_models_df, ag_principal_df)
# add 'Device_Location for Blade chassis switches
switch_params_aggregated_df = fill_device_location(
switch_params_aggregated_df, blade_module_loc_df)
# after finish display status
status_info('ok', max_title, len(info))
# check if switch config files missing
mask_fabric = switch_params_aggregated_df[[
'Fabric_name', 'Fabric_label'
]].notna().all(axis=1)
mask_no_config = switch_params_aggregated_df['chassis_name'].isna()
missing_configs_num = switch_params_aggregated_df.loc[mask_no_config][
'Fabric_name'].count()
if missing_configs_num:
info = f'{missing_configs_num} switch configuration{"s" if missing_configs_num > 1 else ""} MISSING'
print(info, end=" ")
status_info('warning', max_title, len(info))
switches_report_df, fabric_report_df, switches_parameters_report_df, \
licenses_report_df, global_fabric_parameters_report_df = \
switchs_params_report(switch_params_aggregated_df, data_names, report_columns_usage_dct, max_title)
# # partition aggregated DataFrame to required tables
# switches_report_df, fabric_report_df, \
# switches_parameters_report_df, licenses_report_df = \
# dataframe_segmentation(switch_params_aggregated_df, data_names[2:-1], \
# report_columns_usage_dct, max_title)
# # global parameters are equal for all switches in one fabric thus checking Principal switches only
# mask_principal = switch_params_aggregated_df['switchRole'] == 'Principal'
# switch_params_principal_df = switch_params_aggregated_df.loc[mask_principal].copy()
# global_fabric_parameters_report_df, = dataframe_segmentation(switch_params_principal_df, data_names[-1], \
# report_columns_usage_dct, max_title)
# # drop rows with empty switch names columns
# fabric_report_df.dropna(subset = ['Имя коммутатора'], inplace = True)
# switches_parameters_report_df.dropna(subset = ['Имя коммутатора'], inplace = True)
# licenses_report_df.dropna(subset = ['Имя коммутатора'], inplace = True)
# # drop fabric_id if all have same value
# if fabric_report_df['Fabric ID'].dropna().nunique() == 1:
# fabric_report_df.drop(columns=['Fabric ID'], inplace=True)
# # TO_REMOVE No need to drop duplicates coz Principal switches only used before
# # # parameters are equal for all switches in one fabric
# # if report_columns_usage_dct['fabric_name_usage']:
# # global_fabric_parameters_report_df.drop_duplicates(subset=['Фабрика', 'Подсеть'], inplace=True)
# # else:
# # global_fabric_parameters_report_df.drop_duplicates(subset=['Подсеть'], inplace=True)
# global_fabric_parameters_report_df.reset_index(inplace=True, drop=True)
# create list with partitioned DataFrames
data_lst = [
report_columns_usage_dct, switch_params_aggregated_df,
switches_report_df, fabric_report_df,
switches_parameters_report_df, licenses_report_df,
global_fabric_parameters_report_df
]
# saving data to json or csv file
save_data(report_data_lst, data_names, *data_lst)
# verify if loaded data is empty and replace information string with empty DataFrame
else:
report_columns_usage_dct, switch_params_aggregated_df, switches_report_df, fabric_report_df, \
switches_parameters_report_df, licenses_report_df, global_fabric_parameters_report_df = verify_data(report_data_lst, data_names, *data_lst)
data_lst = [
report_columns_usage_dct, switch_params_aggregated_df,
switches_report_df, fabric_report_df,
switches_parameters_report_df, licenses_report_df,
global_fabric_parameters_report_df
]
# save data to service file if it's required
for data_name, data_frame in zip(data_names[1:], data_lst[1:]):
save_xlsx_file(data_frame, data_name, report_data_lst)
return report_columns_usage_dct, switch_params_aggregated_df, fabric_clean_df
def portcmd_analysis_main(portshow_df, switchshow_ports_df, switch_params_df,
switch_params_aggregated_df, isl_aggregated_df,
nsshow_df, nscamshow_df, ag_principal_df,
porttrunkarea_df, alias_df, fdmi_df, blade_module_df,
blade_servers_df, blade_vc_df, synergy_module_df,
synergy_servers_df, system_3par_df, port_3par_df,
report_columns_usage_dct, report_data_lst):
"""Main function to add connected devices information to portshow DataFrame"""
# report_data_lst contains information:
# customer_name, dir_report, dir to save obtained data, max_title, report_steps_dct
*_, max_title, report_steps_dct = report_data_lst
# names to save data obtained after current module execution
data_names = [
'portshow_aggregated', 'storage_connection_statistics',
'device_connection_statistics', 'device_rename',
'report_columns_usage_upd', 'Серверы', 'Массивы', 'Библиотеки',
'Микрокоды_HBA', 'Подключение_массивов', 'Подключение_библиотек',
'Подключение_серверов', 'NPIV', 'Статистика_массивов',
'Статистика_устройств'
]
# service step information
print(f'\n\n{report_steps_dct[data_names[0]][3]}\n')
report_columns_usage_bckp = report_columns_usage_dct
# load data if they were saved on previos program execution iteration
data_lst = load_data(report_data_lst, *data_names)
# flag to forcible save portshow_aggregated_df if required
portshow_force_flag = False
# unpacking DataFrames from the loaded list with data
# pylint: disable=unbalanced-tuple-unpacking
portshow_aggregated_df, storage_connection_statistics_df, device_connection_statistics_df, \
device_rename_df, report_columns_usage_dct, \
servers_report_df, storage_report_df, library_report_df, hba_report_df, \
storage_connection_df, library_connection_df, server_connection_df, npiv_report_df, \
storage_connection_statistics_report_df, device_connection_statistics_report_df = data_lst
nsshow_unsplit_df = pd.DataFrame()
if not report_columns_usage_dct:
report_columns_usage_dct = report_columns_usage_bckp
# list of data to analyze from report_info table
analyzed_data_names = [
'portcmd', 'switchshow_ports', 'switch_params_aggregated',
'switch_parameters', 'chassis_parameters', 'fdmi', 'nscamshow',
'nsshow', 'alias', 'blade_servers', 'fabric_labels', 'isl', 'trunk',
'isl_aggregated', 'Параметры_SFP', 'portshow_sfp_aggregated'
]
# force run when any data from data_lst was not saved (file not found) or
# procedure execution explicitly requested for output data or data used during fn execution
force_run = verify_force_run(data_names, data_lst, report_steps_dct,
max_title, analyzed_data_names)
if force_run:
# import data with switch models, firmware and etc
switch_models_df = dataframe_import('switch_models', max_title)
# data imported from init file (regular expression patterns) to extract values from data columns
# re_pattern list contains comp_keys, match_keys, comp_dct
_, _, *re_pattern_lst = data_extract_objects('nameserver', max_title)
oui_df = dataframe_import('oui',
max_title,
columns=['Connected_oui', 'type', 'subtype'])
# current operation information string
info = f'Generating connected devices table'
print(info, end=" ")
portshow_aggregated_df, alias_wwnn_wwnp_df, nsshow_unsplit_df, expected_ag_links_df = \
portshow_aggregated(portshow_df, switchshow_ports_df, switch_params_df,
switch_params_aggregated_df, isl_aggregated_df, nsshow_df,
nscamshow_df, ag_principal_df, porttrunkarea_df, switch_models_df, alias_df,
oui_df, fdmi_df, blade_module_df, blade_servers_df, blade_vc_df,
synergy_module_df, synergy_servers_df, system_3par_df, port_3par_df,
re_pattern_lst, report_data_lst)
# after finish display status
status_info('ok', max_title, len(info))
# show warning if any UNKNOWN device class founded, if any PortSymb or NodeSymb is not parsed,
# if new switch founded
portshow_force_flag, nsshow_unsplit_force_flag, expected_ag_links_force_flag = \
warning_notification(portshow_aggregated_df, switch_params_aggregated_df,
nsshow_unsplit_df, expected_ag_links_df, report_data_lst)
# correct device names manually
portshow_aggregated_df, device_rename_df = \
devicename_correction_main(portshow_aggregated_df, device_rename_df,
report_columns_usage_dct, report_data_lst)
# count Device_Host_Name instances for fabric_label, label and total in fabric
portshow_aggregated_df = device_ports_per_group(portshow_aggregated_df)
# count device connection statistics
info = f'Counting device connection statistics'
print(info, end=" ")
storage_connection_statistics_df = storage_connection_statistics(
portshow_aggregated_df, re_pattern_lst)
device_connection_statistics_df = device_connection_statistics(
portshow_aggregated_df)
status_info('ok', max_title, len(info))
servers_report_df, storage_report_df, library_report_df, hba_report_df, \
storage_connection_df, library_connection_df, server_connection_df, npiv_report_df, \
storage_connection_statistics_report_df, device_connection_statistics_report_df = \
create_report_tables(portshow_aggregated_df, storage_connection_statistics_df,
device_connection_statistics_df, data_names[5:-2],
report_columns_usage_dct, max_title)
# create list with partitioned DataFrames
data_lst = [
portshow_aggregated_df, storage_connection_statistics_df,
device_connection_statistics_df, device_rename_df,
report_columns_usage_dct, servers_report_df, storage_report_df,
library_report_df, hba_report_df, storage_connection_df,
library_connection_df, server_connection_df, npiv_report_df,
storage_connection_statistics_report_df,
device_connection_statistics_report_df
]
# saving data to json or csv file
save_data(report_data_lst, data_names, *data_lst)
save_xlsx_file(nsshow_unsplit_df,
'nsshow_unsplit',
report_data_lst,
force_flag=nsshow_unsplit_force_flag)
save_xlsx_file(expected_ag_links_df,
'expected_ag_links',
report_data_lst,
force_flag=expected_ag_links_force_flag)
# verify if loaded data is empty and replace information string with empty DataFrame
else:
portshow_aggregated_df, storage_connection_statistics_df, device_connection_statistics_df, \
device_rename_df, report_columns_usage_dct, \
servers_report_df, storage_report_df, library_report_df, hba_report_df, \
storage_connection_df, library_connection_df, server_connection_df, npiv_report_df, \
storage_connection_statistics_report_df, device_connection_statistics_report_df \
= verify_data(report_data_lst, data_names, *data_lst)
data_lst = [
portshow_aggregated_df, storage_connection_statistics_df,
device_connection_statistics_df, device_rename_df,
report_columns_usage_dct, servers_report_df, storage_report_df,
library_report_df, hba_report_df, storage_connection_df,
library_connection_df, server_connection_df, npiv_report_df,
storage_connection_statistics_report_df,
device_connection_statistics_report_df
]
# save data to service file if it's required
for data_name, data_frame in zip(data_names, data_lst):
force_flag = False
if data_name == 'portshow_aggregated':
force_flag = portshow_force_flag
save_xlsx_file(data_frame,
data_name,
report_data_lst,
force_flag=force_flag)
return portshow_aggregated_df
def err_sfp_cfg_analysis_main(portshow_aggregated_df, sfpshow_df,
portcfgshow_df, report_columns_usage_dct,
report_data_lst):
"""Main function to add porterr, transceiver and portcfg information to portshow DataFrame"""
# report_data_lst contains information:
# customer_name, dir_report, dir to save obtained data, max_title, report_steps_dct
*_, max_title, report_steps_dct = report_data_lst
portshow_sfp_force_flag = False
portshow_sfp_export_flag, *_ = report_steps_dct['portshow_sfp_aggregated']
# names to save data obtained after current module execution
data_names = [
'portshow_sfp_aggregated', 'Ошибки', 'Параметры_SFP',
'Параметры_портов'
]
# service step information
print(f'\n\n{report_steps_dct[data_names[0]][3]}\n')
# load data if they were saved on previos program execution iteration
data_lst = load_data(report_data_lst, *data_names)
# unpacking DataFrames from the loaded list with data
# pylint: disable=unbalanced-tuple-unpacking
portshow_sfp_aggregated_df, error_report_df, sfp_report_df, portcfg_report_df = data_lst
# list of data to analyze from report_info table
analyzed_data_names = [
'portshow_aggregated', 'sfpshow', 'portcfgshow', 'portcmd',
'switchshow_ports', 'switch_params_aggregated', 'fdmi',
'device_rename', 'report_columns_usage_upd', 'nscamshow', 'nsshow',
'alias', 'blade_servers', 'fabric_labels'
]
# force run when any data from data_lst was not saved (file not found) or
# procedure execution explicitly requested for output data or data used during fn execution
force_run = verify_force_run(data_names, data_lst, report_steps_dct,
max_title, analyzed_data_names)
if force_run:
# import transeivers information from file
sfp_model_df = dataframe_import('sfp_models', max_title)
# current operation information string
info = f'Updating connected devices table'
print(info, end=" ")
# add sfpshow, transceiver information and portcfg to aggregated portcmd DataFrame
portshow_sfp_aggregated_df = port_complete(portshow_aggregated_df,
sfpshow_df, sfp_model_df,
portcfgshow_df)
# after finish display status
status_info('ok', max_title, len(info))
# warning if UKNOWN SFP present
if (portshow_sfp_aggregated_df['Transceiver_Supported'] ==
'Unknown SFP').any():
info_columns = [
'Fabric_name', 'Fabric_label', 'configname', 'chassis_name',
'chassis_wwn', 'slot', 'port', 'Transceiver_Supported'
]
portshow_sfp_info_df = portshow_sfp_aggregated_df.drop_duplicates(
subset=info_columns).copy()
unknown_count = len(portshow_sfp_info_df[
portshow_sfp_info_df['Transceiver_Supported'] ==
'Unknown SFP'])
info = f'{unknown_count} {"port" if unknown_count == 1 else "ports"} with UNKNOWN supported SFP tag found'
print(info, end=" ")
status_info('warning', max_title, len(info))
# ask if save portshow_aggregated_df
if not portshow_sfp_export_flag:
reply = reply_request(
"Do you want to save 'portshow_sfp_aggregated'? (y)es/(n)o: "
)
if reply == 'y':
portshow_sfp_force_flag = True
# create reaport tables from port_complete_df DataFrtame
error_report_df, sfp_report_df, portcfg_report_df = \
create_report_tables(portshow_sfp_aggregated_df, data_names[1:], report_columns_usage_dct, max_title)
# saving data to json or csv file
data_lst = [
portshow_sfp_aggregated_df, error_report_df, sfp_report_df,
portcfg_report_df
]
save_data(report_data_lst, data_names, *data_lst)
# verify if loaded data is empty and reset DataFrame if yes
else:
portshow_sfp_aggregated_df, error_report_df, sfp_report_df, portcfg_report_df \
= verify_data(report_data_lst, data_names, *data_lst)
data_lst = [
portshow_sfp_aggregated_df, error_report_df, sfp_report_df,
portcfg_report_df
]
# save data to excel file if it's required
for data_name, data_frame in zip(data_names, data_lst):
force_flag = False
if data_name == 'portshow_sfp_aggregated':
force_flag = portshow_sfp_force_flag
save_xlsx_file(data_frame,
data_name,
report_data_lst,
force_flag=force_flag)
return portshow_sfp_aggregated_df
def fabric_main(fabricshow_ag_labels_df, chassis_params_df, \
switch_params_df, maps_params_df, report_data_lst):
"""Main function to create tables"""
# report_data_lst contains information:
# customer_name, dir_report, dir to save obtained data, max_title, report_steps_dct
*_, max_title, report_steps_dct = report_data_lst
# names to save data obtained after current module execution
data_names = [
'Коммутаторы', 'Фабрика', 'Глобальные_параметры_фабрики',
'Параметры_коммутаторов', 'Лицензии'
]
# service step information
print(f'\n\n{report_steps_dct[data_names[0]][3]}\n')
# load data if they were saved on previos program execution iteration
data_lst = load_data(report_data_lst, *data_names)
# unpacking DataFrames from the loaded list with data
# pylint: disable=unbalanced-tuple-unpacking
switches_report_df, fabric_report_df, global_fabric_parameters_report_df, \
switches_parameters_report_df, licenses_report_df = data_lst
# data force extract check
# list of keys for each data from data_lst representing if it is required
# to re-collect or re-analyze data even they were obtained on previous iterations
force_extract_keys_lst = [
report_steps_dct[data_name][1] for data_name in data_names
]
# list with True (if data loaded) and/or False (if data was not found and None returned)
data_check = force_extract_check(data_names, data_lst,
force_extract_keys_lst, max_title)
# flag if fabrics labels was forced to be changed
fabric_labels_change = True if report_steps_dct['fabric_labels'][
1] else False
# initialization chassis information and farbric name columns usage
report_columns_usage_dct = {
'fabric_name_usage': True,
'chassis_info_usage': True
}
# import data with switch models, firmware and etc
switch_models_df = dataframe_import('switch_models', max_title)
# clean fabricshow DataFrame from unneccessary data
fabric_clean_df = fabric_clean(fabricshow_ag_labels_df)
# create aggregated table by joining DataFrames
switch_params_aggregated_df, report_columns_usage_dct = \
fabric_aggregation(fabric_clean_df, chassis_params_df, \
switch_params_df, maps_params_df, switch_models_df)
save_xlsx_file(switch_params_aggregated_df, 'switch_params_aggregated', \
report_data_lst, report_type = 'analysis')
# when no data saved or force extract flag is on or fabric labels have been changed than
# analyze extracted config data
if not all(data_check) or any(
force_extract_keys_lst) or fabric_labels_change:
# information string if fabric labels force changed was initiated
# and statistics recounting required
if fabric_labels_change and not any(force_extract_keys_lst) and all(
data_check):
info = f'Switch information force extract due to change in Fabrics labeling'
print(info, end=" ")
status_info('ok', max_title, len(info))
# partition aggregated DataFrame to required tables
switches_report_df, fabric_report_df, global_fabric_parameters_report_df, \
switches_parameters_report_df, licenses_report_df = \
dataframe_segmentation(switch_params_aggregated_df, data_names, \
report_columns_usage_dct, max_title)
# drop rows with empty switch names columns
fabric_report_df.dropna(subset=['Имя коммутатора'], inplace=True)
switches_parameters_report_df.dropna(subset=['Имя коммутатора'],
inplace=True)
licenses_report_df.dropna(subset=['Имя коммутатора'], inplace=True)
# parameters are equal for all switches in one fabric
if report_columns_usage_dct['fabric_name_usage']:
global_fabric_parameters_report_df.drop_duplicates(
subset=['Фабрика', 'Подсеть'], inplace=True)
else:
global_fabric_parameters_report_df.drop_duplicates(
subset=['Подсеть'], inplace=True)
global_fabric_parameters_report_df.reset_index(inplace=True, drop=True)
# create list with partitioned DataFrames
data_lst = [switches_report_df, fabric_report_df, global_fabric_parameters_report_df, \
switches_parameters_report_df, licenses_report_df]
# current operation information string
info = f'Generating Fabric and Switches tables'
print(info, end=" ")
# after finish display status
status_info('ok', max_title, len(info))
# saving DataFrames to csv file
save_data(report_data_lst, data_names, *data_lst)
# save_data(report_data_lst, data_auxillary_names, *data_auxillary_lst)
# save data to service file if it's required
for data_name, data_frame in zip(data_names, data_lst):
save_xlsx_file(data_frame, data_name, report_data_lst)
return switch_params_aggregated_df, report_columns_usage_dct, fabric_clean_df