def execute(ctx, run_as_daemon):
logger = Logger("Execute")
if run_as_daemon and daemon is None:
raise ImportError(
f"You are trying to run in daemon mode, "
"but the package 'daemon' hasn't been installed. "
"pip install python-daemon. \n"
"If you are running on a Windows machine, this package is not supported"
)
if run_as_daemon and daemon is not None:
stderr = ctx.obj['log']
stdout = f'{stderr}.out'
logger.info(f"running as a daemon process, logging to {stderr}")
pidfile = TimeoutPIDLockFile('etl.pid', -1)
logger.info(f"process_id in {pidfile}")
with open(stdout, 'w+') as stdout_handle, open(stderr,
'w+') as stderr_handle:
d_ctx = daemon.DaemonContext(working_directory=os.getcwd(),
stdout=stdout_handle,
stderr=stderr_handle,
pidfile=TimeoutPIDLockFile(
'etl.pid', -1))
with d_ctx:
_process_data(ctx)
else:
_process_data(ctx)
def check_tables(ctx):
bclink_helpers = ctx['bclink_helpers']
logger = Logger("check_tables")
retval = {}
logger.info("printing to see if tables exist")
for bclink_table in bclink_helpers.table_map.values():
retval[bclink_table] = bclink_helpers.check_table_exists(bclink_table)
if bclink_helpers.global_ids:
retval[bclink_helpers.global_ids] = bclink_helpers.check_table_exists(
bclink_helpers.global_ids)
logger.info(json.dumps(retval, indent=6))
return retval
def delete_data(ctx):
bclink_helpers = ctx['bclink_helpers']
logger = Logger("delete_data")
logger.info("deleting data...")
data = ctx['data']
input_data = data['input']
output_data = data['output']
folders = coconnect.tools.get_subfolders(output_data)
options = [
inquirer.Checkbox('folders',
message="Which data-dump do you want to remove?",
choices=list(folders.values())),
]
selected_folders = inquirer.prompt(options)
if selected_folders == None:
os.kill(os.getpid(), signal.SIGINT)
selected_folders = selected_folders["folders"]
for selected_folder in selected_folders:
files = coconnect.tools.get_files(selected_folder, type='tsv')
options = [
inquirer.Checkbox(
'files',
message="Confirm the removal of the following tsv files.. ",
choices=files,
default=files),
]
selected_files = inquirer.prompt(options)
if selected_files == None:
os.kill(os.getpid(), signal.SIGINT)
selected_files = selected_files["files"]
for f in selected_files:
bclink_helpers.remove_table(f)
click.echo(f"Deleting {f}")
os.remove(f)
def remove_missing_sources_from_rules(rules,tables):
logger = Logger("remove_missing_sources_from_rules")
tables = [os.path.basename(x) for x in tables]
rules_copy = copy.deepcopy(rules)
for destination_table,cdm_table in rules['cdm'].items():
for table_name,sub_table in cdm_table.items():
first = list(sub_table.keys())[0]
source_table = sub_table[first]['source_table']
if source_table not in tables:
rules_copy['cdm'][destination_table].pop(table_name)
logger.debug(f"removed {table_name} from rules")
if not rules_copy['cdm'][destination_table]:
rules_copy['cdm'].pop(destination_table)
logger.debug(f"removed cdm table '{destination_table}' from rules")
return rules_copy
def diff_csv(file1,file2,separator=None,nrows=None):
logger = Logger("CSV File Diff")
if separator == None:
sep1 = get_separator_from_filename(file1)
sep2 = get_separator_from_filename(file2)
else:
sep1 = separator
sep2 = separator
df1 = pd.read_csv(file1,sep=sep1,nrows=nrows)
df2 = pd.read_csv(file2,sep=sep2,nrows=nrows)
exact_match = df1.equals(df2)
if exact_match:
return
df = pd.concat([df1,df2]).drop_duplicates(keep=False)
if len(df) > 0:
logger.error(" ======== Differing Rows ========== ")
logger.error(df)
m = df1.merge(df2, on=df.columns[0], how='outer', suffixes=['', '_'], indicator=True)[['_merge']]
m = m[~m['_merge'].str.contains('both')]
file1 = file1.split('/')[-1]
file2 = file2.split('/')[-1]
m['_merge'] = m['_merge'].map({'left_only':file1,'right_only':file2})
m = m.rename(columns={'_merge':'Only Contained Within'})
m.index.name = 'Row Number'
logger.error(m.reset_index().to_dict(orient='records'))
raise DifferingRows("Something not right with the rows, changes detected.")
elif len(df1.columns) != len(df2.columns):
raise DifferingColumns('in df1 but not df2',list(set(df1.columns) - set(df2.columns)),'\n',
'in df2 but not df1',list(set(df2.columns) - set(df1.columns)))
else:
logger.error(" ======= Rows are likely in a different order ====== ")
for i in range(len(df1)):
if not (df1.iloc[i] == df2.iloc[i]).any():
print ('Row',i,'is in a different location')
raise Exception("differences detected")
def coconnect(ctx, version, log_level, cprofile):
if ctx.invoked_subcommand == None:
if version:
click.echo(cc.__version__)
else:
click.echo(ctx.get_help())
return
cc.params['debug_level'] = int(log_level)
log = Logger("coconnect")
if cprofile:
import cProfile
log.info("Profiling...")
pr = cProfile.Profile()
pr.enable()
def callback():
pr.disable()
log.info("Profiling completed")
pr.dump_stats('coconnect.prof')
ctx.call_on_close(callback)
def _load(ctx, output_folder, cdm_tables, global_ids, bclink_helpers):
if not 'load' in ctx.obj['steps']:
return
logger = Logger("load")
logger.info("starting loading data processes")
logger.info("starting loading global ids")
if global_ids:
bclink_helpers.load_global_ids(output_folder)
logger.info("starting loading cdm tables")
bclink_helpers.load_tables(output_folder, cdm_tables)
def drop_duplicates(ctx):
bclink_helpers = ctx['bclink_helpers']
logger = Logger("drop_duplicates")
retval = {}
logger.info("printing to see if tables exist")
for cdm_table, bclink_table in bclink_helpers.table_map.items():
#dont do this for person table
#a person with the same sex and date of birth isnt a duplicate
if cdm_table == "person":
continue
logger.info(f"Looking for duplicates in {cdm_table} ({bclink_table})")
#if the table hasnt been created, skip
exists = bclink_helpers.check_table_exists(bclink_table)
if not exists:
continue
#find out what the primary key is
droped_duplicates = bclink_helpers.drop_duplicates(bclink_table)
if len(droped_duplicates) > 0:
logger.warning(
f"Found and dropped {len(droped_duplicates)} duplicates in {bclink_table}"
)
def clean_tables(ctx, skip_local_folder, data=None):
bclink_helpers = ctx['bclink_helpers']
interactive = ctx['interactive']
logger = Logger("clean_tables")
if data is None:
data = ctx['data']
if isinstance(data, dict):
output_folders = [data['output']]
else:
output_folders = [x['output'] for x in data]
if interactive:
tables = list(bclink_helpers.table_map.values())
choices = [(f"{v} ({k})", v)
for k, v in bclink_helpers.table_map.items()]
tables.append(bclink_helpers.global_ids)
choices.append((f'{bclink_helpers.global_ids} (global_ids)',
bclink_helpers.global_ids))
questions = [
inquirer.Checkbox(
'clean_tables',
message=f"Clean all-rows in which BCLink tables?",
choices=choices,
default=tables)
]
answers = inquirer.prompt(questions)
if answers == None:
os.kill(os.getpid(), signal.SIGINT)
tables = answers['clean_tables']
bclink_helpers.clean_tables(tables)
else:
bclink_helpers.clean_tables()
if skip_local_folder:
return
if interactive:
questions = [
inquirer.Checkbox('output_folders',
message=f"Clean the following output folders?",
choices=output_folders,
default=output_folders)
]
answers = inquirer.prompt(questions)
if answers == None:
os.kill(os.getpid(), signal.SIGINT)
output_folders = answers['output_folders']
for output_folder in output_folders:
if not os.path.exists(output_folder):
logger.info(f"No folder to remove at '{output_folder}'")
continue
if os.path.exists(output_folder) and os.path.isdir(output_folder):
logger.info(f"removing {output_folder}")
shutil.rmtree(output_folder)
def load_json_delta(f_in,original):
logger = Logger("load_json_delta")
logger.info(f"loading a json from '{f_in}' as a delta")
data = load_json(f_in)
original_date = original['metadata']['date_created']
data_date = data['metadata']['date_created']
logger.info(f"Original JSON date: {original_date}")
logger.info(f"New JSON date: {data_date}")
_data = copy.deepcopy(data)
for destination_table,rule_set in data['cdm'].items():
for name,rules in rule_set.items():
exists_in_original_rules = None
if destination_table in original['cdm']:
if name in original['cdm'][destination_table]:
exists_in_original_rules = original['cdm'][destination_table][name]
if exists_in_original_rules:
_data['cdm'][destination_table].pop(name)
else:
logger.info(f"Detected a new rule for {name}")
logger.debug(json.dumps(rules,indent=6))
if not _data['cdm'][destination_table]:
_data['cdm'].pop(destination_table)
logger.info(json.dumps(_data,indent=6))
return _data
def manual(ctx, rules, inputs, output_folder, clean, table_map, gui_user, user,
database, dry_run):
_rules = coconnect.tools.load_json(rules)
destination_tables = list(_rules['cdm'].keys())
data = {'input': list(inputs), 'output': output_folder}
table_map = _get_table_map(table_map, destination_tables)
bclink_settings = {
'user': user,
'gui_user': gui_user,
'database': database,
'dry_run': dry_run,
'tables': table_map,
}
logger = Logger("Manual")
logger.info(f'Rules: {rules}')
logger.info(f'Inputs: {data["input"]}')
logger.info(f'Output: {data["output"]}')
logger.info(f'Clean Tables: {clean}')
logger.info(f'Processing {destination_tables}')
logger.info(f'BCLink settings:')
logger.info(json.dumps(bclink_settings, indent=6))
bclink_helpers = BCLinkHelpers(**bclink_settings)
_execute(ctx, rules, data, clean, bclink_helpers)
def _execute(ctx, rules=None, data=None, clean=None, bclink_helpers=None):
if data == None:
data = ctx.obj['data']
if clean == None:
clean = ctx.obj['clean']
if rules == None:
rules = ctx.obj['rules']
if bclink_helpers == None:
bclink_helpers = ctx.obj['bclink_helpers']
interactive = ctx.obj['interactive']
steps = ctx.obj['steps']
ctx.obj['listen_for_changes'] = all(
[step in steps for step in ['extract', 'transform', 'load']])
check_and_drop_duplicates = 'drop_duplicates' in steps
logger = Logger("execute")
logger.info(f"Executing steps {steps}")
if clean and 'clean' in steps:
logger.info(f"cleaning existing bclink tables")
ctx.invoke(clean_tables, data=data)
tables = list(rules['cdm'].keys())
if interactive and ('extract' in steps or 'transform' in steps):
choices = []
#location = f"{output_folder}/{name}"
for table in tables:
source_tables = [
f"{data['input']}/{x}"
for x in coconnect.tools.get_source_tables_from_rules(
rules, table)
]
choices.append((f"{table} ({source_tables})", table))
questions = [
inquirer.Checkbox('tables',
message=f"Confirm executing ETL for ... ",
choices=choices,
default=tables)
]
answers = inquirer.prompt(questions)
if answers == None:
os.kill(os.getpid(), signal.SIGINT)
tables = answers['tables']
if len(tables) == 0:
logger.info("no tables selected, skipping..")
return
rules = coconnect.tools.filter_rules_by_destination_tables(
rules, tables)
logger.info(f'cdm tables: {tables}')
logger.info(f"Executing ETL...")
#call any extracting of data
#----------------------------------
extract_data = _extract(ctx, data, rules, bclink_helpers)
indexer = extract_data.get('indexer')
existing_global_ids = extract_data.get('existing_global_ids')
data = extract_data.get('data')
#----------------------------------
inputs = data['input']
output_folder = data['output']
#call transform
#----------------------------------
_transform(ctx, rules, inputs, output_folder, indexer, existing_global_ids)
#----------------------------------
#remove this lookup file once done with it
if existing_global_ids and os.path.exists(existing_global_ids):
os.remove(existing_global_ids)
if 'load' not in steps:
logger.info("done!")
return
cdm_tables = coconnect.tools.get_files(output_folder, type='tsv')
if interactive:
choices = []
for x in cdm_tables:
tab = os.path.splitext(os.path.basename(x))[0]
bctab = bclink_helpers.get_bclink_table(tab)
text = f"{x} --> {bctab} ({tab})"
choices.append((text, x))
options = [
inquirer.Checkbox('cdm_tables',
message="Choose which CDM tables to load..",
choices=choices,
default=cdm_tables),
]
answers = inquirer.prompt(options)
if answers == None:
os.kill(os.getpid(), signal.SIGINT)
tables_to_load = answers['cdm_tables']
cdm_tables = tables_to_load
if len(cdm_tables) == 0:
logger.info("No tables chosen to be loaded..")
return
else:
logger.info("Chosen to load...")
logger.warning(cdm_tables)
cdm_tables = [os.path.splitext(os.path.basename(x))[0] for x in cdm_tables]
try:
idx_global_ids = cdm_tables.index('global_ids')
global_ids = cdm_tables.pop(idx_global_ids)
except ValueError:
global_ids = None
#call load
#----------------------------------
_load(ctx, output_folder, cdm_tables, global_ids, bclink_helpers)
if check_and_drop_duplicates:
#final check for duplicates
logger.info(f"looking for duplicates and deleting any")
ctx.invoke(drop_duplicates)
bclink_helpers.print_report()
logger.info("done!")
def _transform(ctx, rules, inputs, output_folder, indexer,
existing_global_ids):
if not 'transform' in ctx.obj['steps']:
return
logger = Logger("transform")
logger.info("starting data transform processes")
if isinstance(inputs, str):
inputs = [inputs]
logger.info(f"inputs: {inputs}")
logger.info(f"output_folder: {output_folder}")
logger.info(f"indexer: {indexer}")
logger.info(f"existing_global_ids: {existing_global_ids}")
ctx.invoke(run,
rules=rules,
inputs=inputs,
output_folder=output_folder,
indexing_conf=indexer,
person_id_map=existing_global_ids)
def _process_dict_data(ctx):
logger = Logger("_process_dict_data")
logger.info("ETL process has begun")
interactive = ctx.obj['interactive']
data = ctx.obj['data']
clean = ctx.obj['clean']
rules = ctx.obj['rules']
bclink_helpers = ctx.obj['bclink_helpers']
bclink_helpers.print_summary()
#calculate the amount of time to wait before checking for changes
tdelta = None
if 'watch' in data:
watch = data['watch']
tdelta = datetime.timedelta(**watch)
#get the input folder to watch
input_folder = data['input']
#get the root output folder
output_folder = data['output']
i = 0
while True:
#find subfolders containing data dumps
subfolders = coconnect.tools.get_subfolders(input_folder)
# if len(subfolders)>0:
# logger.info(f"Found {len(subfolders)} subfolders at path '{input_folder}'")
# if interactive and len(subfolders)>0:
# questions = [
# inquirer.Checkbox('folders',
# message="Confirm processing the following subfolders.. ",
# choices=subfolders,
# default=subfolders
# )
# ]
# answers = inquirer.prompt(questions)
# if answers == None:
# os.kill(os.getpid(), signal.SIGINT)
# subfolders = {k:v for k,v in subfolders.items() if k in answers['folders']}
# logger.info(f"selected {subfolders}")
logger.debug(
f"Found and checking {len(subfolders.values())} subfolders")
logger.debug(list(subfolders.values()))
if len(subfolders.values()) > 0:
logger.debug(f"{list(subfolders.values())}")
njobs = 0
#print (reversed(sorted(subfolders.items(),key=lambda x: os.path.getmtime(x[1]))))
for name, path in sorted(subfolders.items(),
key=lambda x: os.path.getmtime(x[1])):
output_folder_exists = os.path.exists(f"{output_folder}/{name}")
inputs = coconnect.tools.get_files(path, type='csv')
filtered_rules = coconnect.tools.remove_missing_sources_from_rules(
rules, inputs)
if output_folder_exists:
output_tables = [
os.path.splitext(os.path.basename(x))[0]
for x in coconnect.tools.get_files(
f"{output_folder}/{name}", type='tsv')
]
expected_outputs = list(filtered_rules['cdm'].keys())
to_process = list(set(expected_outputs) - set(output_tables))
if len(to_process) == 0:
continue
filtered_rules = coconnect.tools.filter_rules_by_destination_tables(
filtered_rules, to_process)
logger.debug(f"New data found!")
logger.info(f"Creating a new task for processing {path}")
if len(inputs) == 0:
logger.critical(f"Subfolder contains no .csv files!")
continue
tables = list(filtered_rules['cdm'].keys())
logger.debug(f'inputs: {inputs}')
logger.info(f'cdm tables: {tables}')
_data = copy.deepcopy(data)
_data['input'] = inputs
_data['output'] = f"{output_folder}/{name}"
_execute(ctx,
data=_data,
rules=filtered_rules,
clean=clean if (i == 0 and njobs == 0) else False)
njobs += 1
if tdelta is None:
break
if njobs > 0 or i == 0:
logger.info(
f"Refreshing {input_folder} every {tdelta} to look for new subfolders...."
)
if len(subfolders.values()) == 0:
logger.warning("No subfolders for data dumps yet found...")
i += 1
time.sleep(tdelta.total_seconds())
def load_csv(_map,chunksize=None,dtype=str,nrows=None,lower_col_names=False,load_path="",rules=None,sep=',',na_values=['']):
if isinstance(_map,list):
_map = {
os.path.basename(x):x
for x in _map
}
logger = Logger("coconnect.tools.load_csv")
if isinstance(_map,list) or isinstance(_map,tuple):
_map = { x:x for x in _map}
elif isinstance(_map,str):
_map = { _map:_map }
if rules is not None:
logger.debug("rules .json file supplied")
if not isinstance(rules,dict):
rules = load_json(rules)
source_map = get_mapped_fields_from_rules(rules)
inputs_from_json = list(source_map.keys())
inputs_from_cli = list(_map.keys())
if len(inputs_from_cli) == 0:
raise MissingInputFiles (f"You haven't loaded any input files!")
logger.debug(f"{len(inputs_from_cli)} input files loaded")
logger.debug(f"{inputs_from_cli}")
missing_inputs = list(set(inputs_from_json) - set(inputs_from_cli))
if len(missing_inputs) > 0 :
raise MissingInputFiles (f"Found the following files {missing_inputs} in the json file, that are not in the loaded file list... {inputs_from_cli}")
#reduce the mapping of inputs, if we dont need them all
_map = {
k: {
'file':v,
'fields':source_map[k]
}
for k,v in _map.items()
if k in source_map
}
if not nrows is None:
chunksize = nrows if chunksize is None else chunksize
retval = local.LocalDataCollection(chunksize=chunksize)
for key,obj in _map.items():
fields = None
if isinstance(obj,str):
fname = obj
else:
fname = obj['file']
fields = obj['fields']
df = pd.read_csv(load_path+fname,
chunksize=chunksize,
#iterator=True,
nrows=nrows,
sep=sep,
keep_default_na=False,
na_values=na_values,
dtype=dtype,
usecols=fields)
df.attrs = {'original_file':load_path+fname}
if isinstance(df,pd.DataFrame):
#this should be removed
if lower_col_names:
df.columns = df.columns.str.lower()
retval[key] = local.DataBrick(df,name=key)
return retval
def load(ctx):
logger = Logger("Load")
logger.info("doing load only")
ctx.obj['steps'] = ['load']
ctx.invoke(execute)
def transform(ctx):
logger = Logger("Transform")
logger.info("doing transform only")
ctx.obj['steps'] = ['transform']
ctx.invoke(execute)
def extract(ctx):
logger = Logger("Extract")
logger.info("doing extract only")
ctx.obj['steps'] = ['extract']
ctx.invoke(execute)
def _extract(ctx, data, rules, bclink_helpers):
if not 'extract' in ctx.obj['steps']:
return {'data': data}
logger = Logger("extract")
logger.info(f"starting extraction processes")
inputs = data['input']
if isinstance(inputs, str):
if not os.path.exists(inputs):
raise Exception(f"{inputs} is not an existing path")
if not os.path.isdir(inputs):
raise Exception(f"{inputs} is not a dir!")
inputs = coconnect.tools.get_files(inputs)
if len(inputs) == 0:
raise Exception(f"No .csv files found in {inputs}")
do_pseudonymise = False
_pseudonymise = {}
if 'pseudonymise' in data:
_pseudonymise = data['pseudonymise']
do_pseudonymise = True
if 'do' in _pseudonymise:
do_pseudonymise = _pseudonymise['do']
if do_pseudonymise:
chunksize = 1000
if 'chunksize' in _pseudonymise:
chunksize = _pseudonymise['chunksize']
output = "./pseudonymised_input_data/"
if 'output' in _pseudonymise:
output = _pseudonymise['output']
if 'salt' not in _pseudonymise:
raise Exception("To use pseudonymise a salt must be provided!")
salt = _pseudonymise['salt']
logger.info(f"Called do_pseudonymisation on input data {data} ")
if not isinstance(rules, dict):
rules = coconnect.tools.load_json(rules)
person_id_map = coconnect.tools.get_person_ids(rules)
input_map = {os.path.basename(x): x for x in inputs}
inputs = []
for table, person_id in person_id_map.items():
if table not in input_map:
logger.warning(f"Could not find table {table} in input_map")
logger.warning(input_map)
continue
fin = input_map[table]
print(fin)
fout = ctx.invoke(pseudonymise,
input=fin,
output_folder=output,
chunksize=chunksize,
salt=salt,
person_id=person_id)
inputs.append(fout)
data.pop('pseudonymise')
data['input'] = inputs
_dir = data['output']
f_global_ids = f"{_dir}/existing_global_ids.tsv"
f_global_ids = bclink_helpers.get_global_ids(f_global_ids)
indexer = bclink_helpers.get_indicies()
return {
'indexer': indexer,
'data': data,
'existing_global_ids': f_global_ids
}
def _process_list_data(ctx):
logger = Logger("_process_list_data")
logger.info("ETL process has begun")
interactive = ctx.obj['interactive']
data = []
clean = ctx.obj['clean']
rules = ctx.obj['rules']
bclink_helpers = ctx.obj['bclink_helpers']
config_file = ctx.obj['conf']
conf = _load_config(config_file)
rules_file = conf['rules']
rules_file_last_modified = os.path.getmtime(rules_file)
bclink_helpers.print_summary()
display_msg = True
_clean = clean
while True:
re_execute = False
try:
conf = _load_config(config_file)
except Exception as e:
if not display_msg:
logger.critical(e)
logger.error(
f"You've misconfigured your file '{config_file}'!! Please fix!"
)
time.sleep(5)
display_msg = True
continue
current_rules_file = conf['rules']
new_rules_file = rules_file != current_rules_file
if new_rules_file:
#if there's a new rules file
logger.info(
f"Detected a new rules file.. old was '{rules_file}' and new is '{current_rules_file}'"
)
rules_file = current_rules_file
rules = coconnect.tools.load_json_delta(rules_file, rules)
rules_file_last_modified = os.path.getmtime(rules_file)
re_execute = True
else:
#otherwise check for changes in the existing file
new_rules_file_last_modified = os.path.getmtime(current_rules_file)
change_in_rules = rules_file_last_modified != new_rules_file_last_modified
if change_in_rules:
logger.info(
f"Detected a change/update in the rules file '{rules_file}'"
)
rules = coconnect.tools.load_json_delta(
current_rules_file, rules)
re_execute = True
current_data = conf['data']
if not data == current_data:
logger.debug(f"old {data}")
logger.debug(f"new {current_data}")
new_data = [obj for obj in current_data if obj not in data]
logger.info(f"New data found! {new_data}")
re_execute = True
else:
new_data = data
logger.debug(f"re-execute {re_execute}")
if re_execute:
current_data = copy.deepcopy(new_data)
#loop over any new data
for item in new_data:
if isinstance(item['input'], list):
inputs = item['input']
else:
input_folder = item['input']
if not os.path.isdir(input_folder):
raise Exception(
f"{input_folder} is not a directory containing files!"
)
inputs = coconnect.tools.get_files(input_folder,
type='csv')
filtered_rules = coconnect.tools.remove_missing_sources_from_rules(
rules, inputs)
_execute(ctx, data=item, rules=filtered_rules, clean=_clean)
_clean = False
data += [x for x in current_data if x not in data]
display_msg = True
if new_rules_file or change_in_rules:
#if there's a new rules file or rules delta,
#need to pick up the full rules for the next loop
#incase we insert new data
# --> we dont want to just apply the delta to the new data
rules = coconnect.tools.load_json(current_rules_file)
if ctx.obj['listen_for_changes'] == False:
break
if display_msg:
logger.info(
f"Finished!... Listening for changes to data in {config_file}")
if display_msg:
display_msg = False
time.sleep(5)