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()
if not small_molecule_col and datacolumn.data_type == 'small_molecule':
small_molecule_col = datacolumn
logger.debug('datacolumn created: %r' % datacolumn)
if datacolumn.worksheet_column:
col_to_dc_map[int_for_col(
datacolumn.worksheet_column)] = datacolumn
logger.debug('final data columns: %s' % col_to_dc_map)
logger.debug('read the Data sheet')
data_sheet = book.sheet_by_name('Data')
for i, label in enumerate(data_sheet.row_values(0)):
logger.debug('find datasheet label %r:%r' % (colname(i), label))
if label in meta_columns:
meta_columns[label] = i
raise e
# Read in the data columns sheet to an array of dict's,
# each dict defines a DataColumn.
logger.debug('read data columns...')
dataColumnDefinitions = readDataColumns(path)
# Now that the array of DataColumn dicts is created,
# use them to create the DataColumn instances.
dataColumns = {}
for i,dc in enumerate(dataColumnDefinitions):
dc['dataset'] = dataset
if(not 'display_order' in dc or dc['display_order']==None):
dc['display_order']=i
dataColumn = DataColumn(**dc)
dataColumn.save()
logger.debug(str(('====datacolumn created:', dataColumn)))
dataColumns[dataColumn.name] = dataColumn
logger.debug('read the Data sheet')
sheetname = 'Data'
dataSheet = iu.readtable([path, sheetname])
# First, map the sheet column indices to the DataColumns that were created.
dataColumnList = {}
# Follows are optional columns
# Meta columns contain forensic information, all are optional
metaColumnDict = {'Control Type':-1, 'batch_id':-1}
# What is being studied - at least one is required
mappingColumnDict = {
'Small Molecule Batch':-1, 'Plate':-1, 'Well':-1,
if not small_mol_col:
dc_definitions_found.append(default_reagent_columns['Small Molecule Batch'])
logger.info('data column definitions found: %s'
% [x['display_name'] for x in dc_definitions_found])
col_to_dc_map = {}
first_small_molecule_column = None
for i,dc_definition in enumerate(dc_definitions_found):
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()
if not first_small_molecule_column and datacolumn.data_type == 'small_molecule':
first_small_molecule_column = datacolumn
logger.debug('datacolumn created: %r' % datacolumn)
if datacolumn.worksheet_column:
col_to_dc_map[int_for_col(datacolumn.worksheet_column)] = datacolumn
logger.debug('final data columns: %s' % col_to_dc_map)
read_data(book, col_to_dc_map, first_small_molecule_column, dataset)
def read_explicit_reagents(book, dataset):
try:
reagents_sheet = book.sheet_by_name('Reagents')
for row in range(1,reagents_sheet.nrows):
facility_batch_id = read_string(reagents_sheet.cell(row,0))
