def main(path):
datarecord_batch = []
save_interval = 1000
# read in the two columns of the meta sheet to a dict that defines a DataSet
# TODO: Need a transaction, in case loading fails!
logger.info('read metadata...')
# book = xlrd.open_workbook(path) #open our xls file, there's lots of extra default options in this call, for logging etc. take a look at the docs
# sheetname = 'Meta'
# worksheet = book.sheet_by_name(sheetname) #we can pull by name
metadata = read_metadata(path)
dataset = None
try:
metadata = read_metadata(path)
try:
extant_dataset = DataSet.objects.get(facility_id=metadata['facility_id'])
logger.info(str(('extent_dataset',extant_dataset)))
if(extant_dataset):
logger.warn(str(('deleting extant dataset for facility id: ', metadata['facility_id'])))
extant_dataset.delete()
except Exception,e:
logger.info(str(('on trying to delete',e)))
# raise e
dataset = DataSet(**metadata)
dataset.save()
logger.info(str(('dataset created: ', dataset)))
def build_schema(self):
fields = get_detail_schema(
DataSet(), 'dataset', lambda x: x.show_in_detail )
fields['datapointFile'] = get_schema_fieldinformation(
'datapoint_file','')
fields['safVersion'] = get_schema_fieldinformation('saf_version','')
fields['screeningFacility'] = get_schema_fieldinformation(
'screening_facility','')
schema['fields'] = OrderedDict(sorted(
fields.items(), key=lambda x: x[0]))
return schema
def main(path):
datarecord_batch = []
save_interval = 1000
# read in the two columns of the meta sheet to a dict that defines a DataSet
# TODO: Need a transaction, in case loading fails!
logger.debug('read metadata...')
metadata = read_metadata(path)
dataset = None
try:
metadata = read_metadata(path)
try:
extant_dataset = DataSet.objects.get(
facility_id=metadata['facility_id'])
logger.debug(str(('extent_dataset',extant_dataset)))
if(extant_dataset):
logger.warn(str(('deleting extant dataset for facility id: ',
metadata['facility_id'])))
extant_dataset.delete()
except Exception,e:
logger.debug(str(('on trying to delete',e)))
dataset = DataSet(**metadata)
dataset.save()
logger.debug(str(('dataset created: ', dataset)))
metadata = read_metadata(book.sheet_by_name('Meta'))
try:
extant_dataset = DataSet.objects.get(
facility_id=metadata['facility_id'])
if (extant_dataset):
logger.warn('deleting extant dataset for facility id: %r' %
metadata['facility_id'])
extant_dataset.delete()
except ObjectDoesNotExist, e:
pass
except Exception, e:
logger.exception('on delete of extant dataset: %r' %
metadata['facility_id'])
raise
dataset = DataSet(**metadata)
logger.info('dataset to save %s' % dataset)
dataset.save()
logger.debug('read data columns...')
col_to_definitions = read_datacolumns(book)
small_molecule_col = None
col_to_dc_map = {}
for i, dc_definition in enumerate(col_to_definitions):
dc_definition['dataset'] = dataset
if (not 'display_order' in dc_definition
or dc_definition['display_order'] == None):
dc_definition['display_order'] = i
datacolumn = DataColumn(**dc_definition)
datacolumn.save()
metadata = read_metadata(book.sheet_by_name('Meta'))
try:
extant_dataset = DataSet.objects.get( facility_id=metadata['facility_id'] )
if(extant_dataset):
logger.warn(
'deleting extant dataset for facility id: %r'
% metadata['facility_id'] )
extant_dataset.delete()
except ObjectDoesNotExist, e:
pass
except Exception,e:
logger.exception(
'on delete of extant dataset: %r' % metadata['facility_id'])
raise
dataset = DataSet(**metadata)
logger.info('dataset to save %s' % dataset)
dataset.save()
read_datacolumns_and_data(book, dataset)
read_explicit_reagents(book, dataset)
dataset.save()
def read_metadata(meta_sheet):
properties = ('model_field', 'required', 'default', 'converter')
field_definitions = {
'Lead Screener First': 'lead_screener_firstname',
'Lead Screener Last': 'lead_screener_lastname',
