def readDataColumns(path):
# Read in the DataColumn Sheet
sheetname = 'Data Columns'
dataColumnSheet = iu.readtable([path, sheetname])
_fields = util.get_fields(DataColumn)
_typelookup = dict((f.name, iu.totype(f)) for f in _fields)
# TODO: Use the import_utils methods here
# TODO: compare and combine this with the fieldinformation entity
labels = {'Worksheet Column':'worksheet_column',
'Display Order':'display_order',
'Name':'name',
'Data Type':'data_type',
'Decimal Places':'precision',
'Description':'description',
'Replicate Number':'replicate',
'Time point':'time_point',
'Assay readout type':'readout_type',
'Comments':'comments'}
# create an array of dict's, each dict defines a DataColumn
dataColumnDefinitions = []
# first put the label row in (it contains the worksheet column, and its unique)
for v in dataColumnSheet.labels[1:]:
dataColumnDefinitions.append({labels['Worksheet Column']:v})
# now, for each row, create the appropriate dictionary entry in the dataColumnDefinitions
for row in dataColumnSheet:
rowAsUnicode = util.make_row(row)
keyRead = rowAsUnicode[0]
for i,cellText in enumerate(rowAsUnicode[1:]):
for key,fieldName in labels.items():
if re.match(key,keyRead,re.M|re.I): # if the row is one of the DataColumn fields, then add it to the dict
dataColumnDefinitions[i][fieldName] = util.convertdata(cellText,_typelookup.get(fieldName, None)) # Note: convert the data to the model field type
else:
logger.debug(str(( '"Data Column definition not used: ', cellText)) )
pass
logger.debug(str(("definitions: ", dataColumnDefinitions)) )
return dataColumnDefinitions
def readDataColumns(path):
# Read in the DataColumn Sheet
sheetname = 'Data Columns'
dataColumnSheet = iu.readtable([path, sheetname])
_fields = util.get_fields(DataColumn)
_typelookup = dict((f.name, iu.totype(f)) for f in _fields)
labels = {'Worksheet Column':'worksheet_column',
'Name':'name',
'Data Type':'data_type',
'Decimal Places':'precision',
'Description':'description',
'Replicate Number':'replicate',
'Time point':'time_point',
'Assay readout type':'readout_type',
'Comments':'comments'}
# create an array of dict's, each dict defines a DataColumn
dataColumnDefinitions = []
# first put the label row in (it contains the worksheet column, and its unique)
for v in dataColumnSheet.labels[1:]:
dataColumnDefinitions.append({labels['Worksheet Column']:v})
# now, for each row, create the appropriate dictionary entry in the dataColumnDefinitions
for row in dataColumnSheet:
rowAsUnicode = util.make_row(row)
keyRead = rowAsUnicode[0]
for i,cellText in enumerate(rowAsUnicode[1:]):
for key,fieldName in labels.items():
if re.match(key,keyRead,re.M|re.I): # if the row is one of the DataColumn fields, then add it to the dict
dataColumnDefinitions[i][fieldName] = util.convertdata(cellText,_typelookup.get(fieldName, None)) # Note: convert the data to the model field type
else:
pass
# print '"Data Column definition not used: ', cellText
print "definitions: ", dataColumnDefinitions
return dataColumnDefinitions
def readDataColumns(path):
# Read in the DataColumn Sheet
sheetname = 'Data Columns'
dataColumnSheet = iu.readtable([path, sheetname])
# Lookup all of the field types of the Datacolumn table.
# These will be used to validate input type by converting on read
_fields = util.get_fields(DataColumn)
_typelookup = dict((f.name, iu.totype(f)) for f in _fields)
# TODO: Use the import_utils methods here
# TODO: compare and combine this with the fieldinformation entity
labels = {'Worksheet Column':'worksheet_column',
'Display Order':'display_order',
'Name':'name',
'Display Name':'display_name',
'Data Type':'data_type',
'Decimal Places':'precision',
'Description':'description',
'Replicate Number':'replicate',
'Unit':'unit',
'Assay readout type':'readout_type',
'Comments':'comments',
'Protein HMS LINCS ID': 'protein',
'Cell HMS LINCS ID': 'cell'}
# create an array of dict's, each dict defines a DataColumn
dataColumnDefinitions = []
#Note we also allow a list of pro
# first the label row (it contains the worksheet column, it is unique)
for v in dataColumnSheet.labels[1:]:
dataColumnDefinitions.append({labels['Worksheet Column']:v})
logger.debug(str(('========== datacolumns:',dataColumnDefinitions)))
# for each row, create the dictionary entry in the dataColumnDefinitions
for row in dataColumnSheet:
rowAsUnicode = util.make_row(row)
keyRead = rowAsUnicode[0]
for i,cellText in enumerate(rowAsUnicode[1:]):
try:
for key,fieldName in labels.items():
# if one of the DataColumn fields, add it to the dict
if re.match(key,keyRead,re.M|re.I):
if re.match('Protein HMS LINCS ID', keyRead, re.M|re.I):
facility_id = util.convertdata(cellText, int);
if facility_id:
dataColumnDefinitions[i][fieldName] = \
Protein.objects.get(lincs_id=facility_id)
elif re.match('Cell HMS LINCS ID', keyRead, re.M|re.I):
facility_id = util.convertdata(cellText, int);
if facility_id:
dataColumnDefinitions[i][fieldName] = \
Cell.objects.get(facility_id=facility_id)
else:
# Use the type from the fieldinformation table
# to read in the data for each DC field
dataColumnDefinitions[i][fieldName] = \
util.convertdata(cellText,
_typelookup.get(fieldName, None))
else:
logger.debug(str((
'"Data Column definition not used: ', cellText)) )
pass
except Exception, e:
logger.error(str(('Exception reading data for cell', i, cellText, e)))
raise e
logger.debug(str(("definitions: ", dataColumnDefinitions)) )
def read_datacolumns(book):
'''
@return an array of data column definition dicts
'''
data_column_sheet = book.sheet_by_name('Data Columns')
labels = {
'Worksheet Column': 'worksheet_column',
'"Data" Worksheet Column': 'worksheet_column',
'Display Order': 'display_order',
'Display Name': 'display_name',
'Name': 'name',
'Data Type': 'data_type',
'Decimal Places': 'precision',
'Description': 'description',
'Replicate Number': 'replicate',
'Unit': 'unit',
'Assay readout type': 'readout_type',
'Comments': 'comments',
}
dc_definitions = []
datacolumn_fields = util.get_fields(DataColumn)
type_lookup = dict((f.name, iu.totype(f)) for f in datacolumn_fields)
logger.debug('datacolumn type lookups: %s' % type_lookup)
required_labels = ['name', 'data_type']
logger.info('read the data column definitions...')
for i in xrange(data_column_sheet.nrows):
row_values = data_column_sheet.row_values(i)
if i == 0:
for val in row_values[1:]:
dc_definitions.append({})
label_read = row_values[0]
recognized_label = next(
(field_name for label, field_name in labels.items()
if label_read and label.lower() == label_read.lower()), None)
if recognized_label:
logger.debug('label: %r, recognized_label: %r' %
(label_read, recognized_label))
for j, val in enumerate(row_values[1:]):
dc_dict = dc_definitions[j]
logger.debug('data column %s:%d:%d:%r' %
(recognized_label, i, j, val))
final_val = util.convertdata(
val, type_lookup.get(recognized_label, None))
if final_val != None:
dc_dict[recognized_label] = final_val
if recognized_label == 'display_order':
# add 10 to the order, so default reagent cols can go first
dc_dict['display_order'] = (dc_dict['display_order'] +
10)
if recognized_label == 'name':
# split on non-alphanumeric chars
temp = re.split(r'[^a-zA-Z0-9]+', dc_dict['name'])
# convert, if needed
if len(temp) > 1:
dc_dict['name'] = camel_case_dwg(dc_dict['name'])
else:
if recognized_label in required_labels:
raise Exception(
'Error, data column field is required: %s, col: %r'
% (recognized_label, colname(j + 1)))
else:
logger.debug('unrecognized label in "Data Columns" sheet %r' %
label_read)
for dc_dict in dc_definitions:
for label in required_labels:
if label not in dc_dict:
raise Exception('required "Data Column" label not defined %r' %
label)
logger.info('find the data columns on the "Data" sheet...')
data_sheet = book.sheet_by_name('Data')
data_sheet_labels = data_sheet.row_values(0)
dc_definitions_found = []
data_labels_found = []
for i, data_label in enumerate(data_sheet_labels):
if not data_label or not data_label.strip():
logger.info('break on data sheet col %d, blank' % i)
break
data_label = data_label.upper()
col_letter = colname(i)
for dc_dict in dc_definitions:
_dict = None
if 'worksheet_column' in dc_dict:
v = dc_dict['worksheet_column']
if v.upper() == col_letter:
data_labels_found.append(i)
dc_definitions_found.append(dc_dict)
_dict = dc_dict
elif 'name' in dc_dict or 'display_name' in dc_dict:
if (dc_dict.get('name', '').upper() == data_label or
dc_dict.get('display_name', '').upper() == data_label):
dc_dict['worksheet_column'] = col_letter
data_labels_found.append(i)
dc_definitions_found.append(dc_dict)
_dict = dc_dict
if _dict and 'display_order' not in _dict:
_dict['display_order'] = i + 10
logger.warn('auto assigning "display_order" for col %r as %d' %
(_dict['name'], i + 10))
if i not in data_labels_found:
logger.debug(('Data sheet label not found %r,'
' looking in default reagent definitions %s') %
(data_label, default_reagent_columns.keys()))
for key, dc_dict in default_reagent_columns.items():
if (key.upper() == data_label
or dc_dict.get('name', '').upper() == data_label or
dc_dict.get('display_name', '').upper() == data_label):
dc_dict['worksheet_column'] = col_letter
data_labels_found.append(i)
dc_definitions_found.append(dc_dict)
data_labels_not_found = [
data_label for i, data_label in enumerate(data_sheet_labels)
if data_label and data_label.strip() and i not in data_labels_found
and data_label not in meta_columns
]
if data_labels_not_found:
logger.warn('data sheet labels not recognized %s' %
data_labels_not_found)
# for legacy datasets: make sure the small molecule column 1 is always created
small_mol_col = None
for dc_dict in dc_definitions_found:
if dc_dict['data_type'] == 'small_molecule':
small_mol_col = dc_dict
break
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])
return dc_definitions_found
'"Data" Worksheet Column':'worksheet_column',
'Display Order':'display_order',
'Display Name':'display_name',
'Name':'name',
'Data Type':'data_type',
'Decimal Places':'precision',
'Description':'description',
'Replicate Number':'replicate',
'Unit':'unit',
'Assay readout type':'readout_type',
'Comments':'comments',
}
dc_definitions = []
datacolumn_fields = util.get_fields(DataColumn)
type_lookup = dict((f.name, iu.totype(f)) for f in datacolumn_fields)
logger.debug('datacolumn type lookups: %s' % type_lookup)
required_labels = ['name', 'data_type']
logger.info('read the data column definitions...')
for i in xrange(data_column_sheet.nrows):
row_values = data_column_sheet.row_values(i)
if i == 0:
for val in row_values[1:]:
dc_definitions.append({})
label_read = row_values[0]
recognized_label = next(
(field_name for label, field_name in labels.items()
def read_datacolumns(book):
'''
@return an array of data column definition dicts
'''
data_column_sheet = book.sheet_by_name('Data Columns')
labels = {
'Worksheet Column':'worksheet_column',
'"Data" Worksheet Column':'worksheet_column',
'Display Order':'display_order',
'Display Name':'display_name',
'Name':'name',
'Data Type':'data_type',
'Decimal Places':'precision',
'Description':'description',
'Replicate Number':'replicate',
'Unit':'unit',
'Assay readout type':'readout_type',
'Comments':'comments',
}
dc_definitions = []
datacolumn_fields = util.get_fields(DataColumn)
type_lookup = dict((f.name, iu.totype(f)) for f in datacolumn_fields)
logger.debug('datacolumn type lookups: %s' % type_lookup)
required_labels = ['name', 'data_type']
logger.info('read the data column definitions...')
for i in xrange(data_column_sheet.nrows):
row_values = data_column_sheet.row_values(i)
if i == 0:
for val in row_values[1:]:
dc_definitions.append({})
label_read = row_values[0]
recognized_label = next(
(field_name for label, field_name in labels.items()
if label_read and label.lower() == label_read.lower() ), None)
if recognized_label:
logger.debug(
'label: %r, recognized_label: %r' % (label_read, recognized_label))
for j,val in enumerate(row_values[1:]):
dc_dict = dc_definitions[j]
logger.debug('data column %s:%d:%d:%r'
% ( recognized_label, i, j, val))
final_val = util.convertdata(
val,type_lookup.get(recognized_label, None))
if final_val != None:
dc_dict[recognized_label] = final_val
if recognized_label == 'display_order':
# add 10 to the order, so default reagent cols can go first
dc_dict['display_order'] = (
dc_dict['display_order'] + 10)
if recognized_label == 'name':
# split on non-alphanumeric chars
temp = re.split(r'[^a-zA-Z0-9]+',dc_dict['name'])
# convert, if needed
if len(temp) > 1:
dc_dict['name'] = camel_case_dwg(dc_dict['name'])
else:
if recognized_label in required_labels:
raise Exception(
'Error, data column field is required: %s, col: %r'
% ( recognized_label, colname(j+1) ) )
else:
logger.debug(
'unrecognized label in "Data Columns" sheet %r' % label_read)
for dc_dict in dc_definitions:
for label in required_labels:
if label not in dc_dict:
raise Exception(
'required "Data Column" label not defined %r' % label)
logger.info('find the data columns on the "Data" sheet...')
data_sheet = book.sheet_by_name('Data')
data_sheet_labels = data_sheet.row_values(0)
dc_definitions_found = []
data_labels_found = []
for i,data_label in enumerate(data_sheet_labels):
if not data_label or not data_label.strip():
logger.info('break on data sheet col %d, blank' % i)
break
data_label = data_label.upper()
col_letter = colname(i)
for dc_dict in dc_definitions:
_dict = None
if 'worksheet_column' in dc_dict:
v = dc_dict['worksheet_column']
if v.upper() == col_letter:
data_labels_found.append(i)
dc_definitions_found.append(dc_dict)
_dict = dc_dict
elif 'name' in dc_dict or 'display_name' in dc_dict:
if ( dc_dict.get('name', '').upper() == data_label
or dc_dict.get('display_name', '').upper() == data_label):
dc_dict['worksheet_column'] = col_letter
data_labels_found.append(i)
dc_definitions_found.append(dc_dict)
_dict = dc_dict
if _dict and 'display_order' not in _dict:
_dict['display_order'] = i+10
logger.warn(
'auto assigning "display_order" for col %r as %d'
% (_dict['name'], i+10))
if i not in data_labels_found:
logger.debug( (
'Data sheet label not found %r,'
' looking in default reagent definitions %s' )
% ( data_label, default_reagent_columns.keys() ) )
for key,dc_dict in default_reagent_columns.items():
if (key.upper() == data_label
or dc_dict.get('name', '').upper() == data_label
or dc_dict.get('display_name', '').upper() == data_label):
dc_dict['worksheet_column'] = col_letter
data_labels_found.append(i)
dc_definitions_found.append(dc_dict)
data_labels_not_found = [
data_label for i,data_label in enumerate(data_sheet_labels)
if data_label and data_label.strip()
and i not in data_labels_found and data_label not in meta_columns ]
if data_labels_not_found:
logger.warn(
'data sheet labels not recognized %s' % data_labels_not_found )
# for legacy datasets: make sure the small molecule column 1 is always created
small_mol_col = None
for dc_dict in dc_definitions_found:
if dc_dict['data_type'] == 'small_molecule':
small_mol_col = dc_dict
break
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])
return dc_definitions_found
