def main(mora_base: str, use_ad: bool, output_file_path: str) -> None:
mh = MoraHelper(hostname=mora_base, export_ansi=True)
employees: List[ExportUser] = create_mapping(mh, use_ad)
employee_dicts: List[Dict] = list(map(methodcaller("dict"), employees))
fields = ["cpr", "mo_uuid", "ad_guid", "sam_account_name"]
mh._write_csv(fields, employee_dicts, output_file_path)
def export_engagement(self, mh: MoraHelper, filename, lc, lc_historic):
rows = []
logger.info("Reading users")
if lc:
employees = list(map(lambda x: x[0], lc.users.values()))
else:
employees = mh.read_all_users()
logger.info("Reading engagements")
# Todo: This O(#employees x #engagments), a pre-sorting of engagements would
# make it O(#employees + #engagments) - consider if this is worth the effort
for employee in employees:
logger.info("employee: %r", employee)
if lc:
for row in self._gen_from_loracache(employee, lc, lc_historic):
rows.append(row)
else:
for row in self._gen_from_mo(employee, mh):
rows.append(row)
mh._write_csv(self.fieldnames, rows, filename)
def main(speedup, dry_run=None):
t = time.time()
mh = MoraHelper(hostname=SETTINGS['mora.base'], export_ansi=False)
dest_folder = pathlib.Path(SETTINGS['mora.folder.query_export'])
root_unit = SETTINGS['exporters.plan2learn.root_unit']
if speedup:
# Here we should activate read-only mode, actual state and
# full history dumps needs to be in sync.
# Full history does not calculate derived data, we must
# fetch both kinds.
lc = LoraCache(resolve_dar=True, full_history=False)
lc.populate_cache(dry_run=dry_run, skip_associations=True)
lc.calculate_derived_unit_data()
lc.calculate_primary_engagements()
lc_historic = LoraCache(resolve_dar=False,
full_history=True,
skip_past=True)
lc_historic.populate_cache(dry_run=dry_run, skip_associations=True)
# Here we should de-activate read-only mode
else:
lc = None
lc_historic = None
# Todo: We need the nodes structure to keep a consistent output,
# consider if the 70 seconds is worth the implementation time of
# reading this from cache.
nodes = mh.read_ou_tree(root_unit)
brugere_rows = export_bruger(mh, nodes, lc, lc_historic)
print('Bruger: {}s'.format(time.time() - t))
logger.info('Bruger: {}s'.format(time.time() - t))
filename = str(dest_folder / 'plan2learn_organisation.csv')
eksporterede_afdelinger = export_organisation(mh, nodes, filename, lc)
print('Organisation: {}s'.format(time.time() - t))
logger.info('Organisation: {}s'.format(time.time() - t))
filename = str(dest_folder / 'plan2learn_engagement.csv')
brugere_rows = export_engagement(mh, filename, eksporterede_afdelinger,
brugere_rows, lc, lc_historic)
print('Engagement: {}s'.format(time.time() - t))
logger.info('Engagement: {}s'.format(time.time() - t))
filename = str(dest_folder / 'plan2learn_stillingskode.csv')
export_stillingskode(mh, nodes, filename)
print('Stillingskode: {}s'.format(time.time() - t))
logger.info('Stillingskode: {}s'.format(time.time() - t))
filename = str(dest_folder / 'plan2learn_leder.csv')
export_leder(mh, nodes, filename, eksporterede_afdelinger)
print('Leder: {}s'.format(time.time() - t))
logger.info('Leder: {}s'.format(time.time() - t))
# Now exported the now fully populated brugere.csv
filename = str(dest_folder / 'plan2learn_bruger.csv')
brugere_fieldnames = [
'BrugerId', 'CPR', 'Navn', 'E-mail', 'Mobil', 'Stilling'
]
mh._write_csv(brugere_fieldnames, brugere_rows, filename)
print('Export completed')
logger.info('Export completed')
