def connection_statistics_report(connection_statistics_df, max_title):
"""Function to create report table out of connection_statistics_df DataFrame"""
translate_dct = dct_from_columns(
'customer_report',
max_title,
'Статистика_подключения_устройств_перевод_eng',
'Статистика_подключения_устройств_перевод_ru',
init_file='san_automation_info.xlsx')
connection_statistics_report_df = connection_statistics_df.copy()
# translate notes
columns = [
column for column in connection_statistics_df.columns
if 'note' in column and connection_statistics_df[column].notna().any()
]
columns.append('Fabric_name')
connection_statistics_report_df = translate_values(
connection_statistics_report_df,
translate_dct,
translate_columns=columns)
# translate column names
connection_statistics_report_df.rename(columns=translate_dct, inplace=True)
# drop empty columns
connection_statistics_report_df.dropna(axis=1, how='all', inplace=True)
return connection_statistics_report_df
def sensor_report(sensor_aggregated_df, data_names, report_columns_usage_dct,
max_title):
"""Function to create report Datafrmae from sensor_aggregated_df
(slice and reorder columns, translate values in columns)"""
# loading values to translate
translate_dct = dct_from_columns('customer_report',
max_title,
'Датчики_перевод_eng',
'Датчики_перевод_ru',
init_file='san_automation_info.xlsx')
sensor_report_df = translate_values(
sensor_aggregated_df,
translate_dct=translate_dct,
translate_columns=['Type', 'Status', 'Vlaue', 'Unit'])
# translate_values(sensor_aggregated_df, translate_dct, max_title)
sensor_report_df, = dataframe_segmentation(sensor_aggregated_df,
data_names[1:],
report_columns_usage_dct,
max_title)
return sensor_report_df
# def translate_values(translated_df, translate_dct, max_title):
# """Function to translate values in corresponding columns"""
# # columns which values need to be translated
# translate_columns = ['Type', 'Status', 'Vlaue', 'Unit']
# # translate values in column if column in DataFrame
# for column in translate_columns:
# if column in translated_df.columns:
# translated_df[column] = translated_df[column].replace(to_replace=translate_dct)
# return translated_df
def create_report_tables(portshow_aggregated_df,
storage_connection_statistics_df,
device_connection_statistics_df, data_names,
report_columns_usage_dct, max_title):
"""Function to create required report DataFrames out of aggregated DataFrame"""
add_columns_lst = [
'FW_Recommeneded', 'Driver_Recommeneded', 'FW_Supported',
'HW_Supported'
]
portshow_aggregated_df = portshow_aggregated_df.reindex(
columns=[*portshow_aggregated_df.columns.tolist(), *add_columns_lst])
# partition aggregated DataFrame to required tables
# pylint: disable=unbalanced-tuple-unpacking
servers_report_df, storage_report_df, library_report_df, hba_report_df, \
storage_connection_df, library_connection_df, server_connection_df, npiv_report_df = \
dataframe_segmentation(portshow_aggregated_df, data_names, report_columns_usage_dct, max_title)
# clean and sort DataFrames
# device report
servers_report_df = _clean_dataframe(servers_report_df, 'srv')
hba_report_df = _clean_dataframe(hba_report_df,
'srv',
duplicates=['Идентификатор порта WWPN'])
storage_report_df = _clean_dataframe(storage_report_df, 'stor')
storage_report_df = _multi_fabric(storage_report_df,
report_columns_usage_dct)
library_report_df = _clean_dataframe(library_report_df, 'lib')
# device connection reports
storage_connection_df = _clean_dataframe(storage_connection_df,
'stor',
clean=True)
storage_connection_df = translate_values(storage_connection_df)
library_connection_df = _clean_dataframe(library_connection_df,
'lib',
clean=True)
server_connection_df = _clean_dataframe(server_connection_df,
'srv',
clean=True)
npiv_report_df = _clean_dataframe(npiv_report_df,
'npiv',
duplicates=None,
clean=True)
# device connection statistics reports
storage_connection_statistics_report_df = connection_statistics_report(
storage_connection_statistics_df, max_title)
device_connection_statistics_report_df = connection_statistics_report(
device_connection_statistics_df, max_title)
return 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
def statistics_report(statistics_df, data_name, translate_dct, max_title):
"""Function to convert zonemember_statistics_df, alias_statistics_df and cfg_statistics_df
to report DataFrames (traslate column names and All)"""
# create statitics report DataFrame
statistics_report_df = statistics_df.copy()
if data_name == 'Статистика_зон':
possible_allna_columns = [
'zone_duplicated', 'Target_Initiator_note',
'Effective_cfg_usage_note'
]
statistics_report_df = drop_all_na(statistics_report_df,
possible_allna_columns)
# drop 'Wwnn_to_Wwnp_number_unpacked' column if all values are zero
statistics_report_df = drop_all_identical(
statistics_report_df,
columns_values={'Wwnn_to_Wwnp_number_unpacked': 0},
dropna=True)
# rename values in columns
if data_name == 'Статистика_зон':
translate_columns = [
'Fabric_name', 'Fabric_device_status', 'Target_Initiator_note',
'Target_model_note', 'Effective_cfg_usage_note', 'Pair_zone_note',
'All_devices_multiple_fabric_label_connection',
'Zone_and_Pairzone_names_related', 'Zone_name_device_names_related'
]
else:
translate_columns = ['Fabric_name']
statistics_report_df = \
translate_values(statistics_report_df, translate_dct, translate_columns)
# column titles used to create dictionary to traslate column names
statistic_columns_lst = [data_name + '_eng', data_name + '_ru']
# dictionary used to translate column names
statistic_columns_dct = dct_from_columns('customer_report', max_title, *statistic_columns_lst, \
init_file = 'san_automation_info.xlsx')
# translate columns in fabric_statistics_report and statistics_subtotal_df DataFrames
statistics_report_df.rename(columns=statistic_columns_dct, inplace=True)
return statistics_report_df
def create_report(aggregated_df, data_name, translate_dct,
report_columns_usage_dct, max_title):
"""
Auxiliary function to remove unnecessary columns from aggregated DataFrame and
extract required columns and create report dataframe
"""
# pylint: disable=unbalanced-tuple-unpacking
cleaned_df = drop_excessive_columns(aggregated_df,
report_columns_usage_dct)
translate_columns = [
'Fabric_device_status', 'Target_Initiator_note', 'Target_model_note',
'Effective_cfg_usage_note', 'Pair_zone_note',
'Multiple_fabric_label_connection', 'Zone_and_Pairzone_names_related',
'Zone_name_device_names_related'
]
cleaned_df = translate_values(cleaned_df, translate_dct, translate_columns)
# take required data from aggregated DataFrame to create report
report_df, = dataframe_segmentation(cleaned_df, data_name,
report_columns_usage_dct, max_title)
return report_df
def absent_device(zoning_aggregated_df, data_name, translate_dct,
report_columns_usage_dct, max_title):
"""Function to create table with absent and unavailable remote devices in zoning configuration"""
mask_absent = zoning_aggregated_df.Fabric_device_status.isin(
['absent', 'remote_na'])
absent_columns = [
'Fabric_name', 'Fabric_label', 'cfg', 'cfg_type', 'zone_member',
'alias_member', 'Fabric_device_status', 'zonemember_Fabric_name',
'zonemember_Fabric_label', 'zone'
]
absent_device_df = zoning_aggregated_df.loc[mask_absent, absent_columns]
absent_device_df = absent_device_df.groupby(absent_columns[:-1],
as_index=False,
dropna=False).agg(
{'zone': ', '.join})
absent_device_df = translate_values(absent_device_df, translate_dct,
['Fabric_device_status'])
zoning_absent_device_report_df, = dataframe_segmentation(
absent_device_df, data_name, report_columns_usage_dct, max_title)
return zoning_absent_device_report_df