def main(path):
"""
Read in the Antibody
"""
sheet_name = 'Sheet1'
sheet = iu.readtable([path, sheet_name, 0])
properties = ('model_field', 'required', 'default', 'converter')
column_definitions = {
'AR_Name': ('name', True),
'AR_LINCS_ID':
'lincs_id',
'AR_Alternative_Name':
'alternative_names',
'AR_Center_Specific_ID':
('facility_id', True, None, lambda x: x[x.index('HMSL') + 4:]),
'AR_Clone_Name':
'clone_name',
'AR_RRID':
'rrid',
'AR_Antibody_Type':
'type',
'target_protein_lincs_id': ('target_protein_lincs_id', False, None,
lambda x: x[x.index('HMSL') + 4:]
if x else None),
'AR_Non-Protein_Target':
'non_protein_target_name',
'AR_Target_Organism':
'target_organism',
'AR_Immunogen':
'immunogen',
'AR_Immunogen_Sequence':
'immunogen_sequence',
'AR_Antibody_Species':
'species',
'AR_Antibody_Clonality':
'clonality',
'AR_Antibody_Isotype':
'isotype',
'AR_Antibody_Production_Source_Organism':
'source_organism',
'AR_Antibody_Production_Details':
'production_details',
'AR_Antibody_Labeling':
'labeling',
'AR_Antibody_Labeling_Details':
'labeling_details',
'AR_Relevant_Citations':
'relevant_citations',
'Date Data Received':
('date_data_received', False, None, util.date_converter),
'Date Loaded': ('date_loaded', False, None, util.date_converter),
'Date Publicly Available':
('date_publicly_available', False, None, util.date_converter),
'Most Recent Update':
('date_updated', False, None, util.date_converter),
'Is Restricted': ('is_restricted', False, False, util.bool_converter)
}
# convert the labels to fleshed out dict's, with strategies for optional, default and converter
column_definitions = util.fill_in_column_definitions(
properties, column_definitions)
# create a dict mapping the column ordinal to the proper column definition dict
cols = util.find_columns(column_definitions, sheet.labels)
rows = 0
logger.debug('cols: %s' % cols)
for row in sheet:
logger.debug('row %s - %s' % (rows, row))
r = util.make_row(row)
dict = {}
initializer = {}
for i, value in enumerate(r):
if i not in cols: continue
properties = cols[i]
logger.debug('read col: %d: %s' % (i, properties))
required = properties['required']
default = properties['default']
converter = properties['converter']
model_field = properties['model_field']
logger.debug('raw value %r' % value)
if (value == None or value == 'None'):
value = None
if (default != None):
value = default
if (value == None and required == True):
raise Exception('Field is required: %s, record: %d' %
(properties['column_label'], rows))
if (value and converter != None):
value = converter(value)
logger.debug('model_field: %s, converted value %r' %
(model_field, value))
initializer[model_field] = value
try:
logger.debug('row: %s, initializer: %s' % (rows, initializer))
target_protein_lincs_id = initializer.pop(
'target_protein_lincs_id', None)
if target_protein_lincs_id:
try:
target_protein = Protein.objects.get(
lincs_id=target_protein_lincs_id)
initializer['target_protein'] = target_protein
except ObjectDoesNotExist, e:
logger.error(
'target_protein_lincs_id "%s" does not exist, row: %d'
% (target_protein_lincs_id, i))
antibody = Antibody(**initializer)
antibody.save()
logger.info('antibody created: %s' % antibody)
rows += 1
# create a default batch - 0
AntibodyBatch.objects.create(reagent=antibody, batch_id=0)
except Exception, e:
logger.error("Invalid antibody initializer: %s" % initializer)
raise
def main(path):
"""
Read in the Antibody
"""
sheet_name = 'Sheet1'
sheet = iu.readtable([path, sheet_name, 1]) # Note, skipping the header row by default
properties = ('model_field','required','default','converter')
column_definitions = {
'AR_Name': ('name',True),
'AR_LINCS_ID': 'lincs_id',
'AR_Alternative_Name': 'alternative_names',
'AR_Center_ID': ('facility_id', True),
'AR_Target_Protein': 'target_protein_name',
'AR_Target_Protein_ID': 'target_protein_uniprot_id',
'AR_Target_Gene': 'target_gene_name',
'AR_Target_Gene_ID': 'target_gene_id',
'AR_Target_Organism': 'target_organism',
'AR_Immunogen': 'immunogen',
'AR_Immunogen_Sequence': 'immunogen_sequence',
'AR_AntibodyClonality': 'antibody_clonality',
'AR_Source_Organism': 'source_organism',
'AR_Antibody_Isotype': 'antibody_isotype',
'AR_Engineering': 'engineering',
'AR_Antibody_Purity': 'antibody_purity',
'AR_Antibody_Labeling': 'antibody_labeling',
'AR_Recommended_Experiment_Type': 'recommended_experiment_type',
'AR_Relevant_Reference': 'relevant_reference',
'AR_Specificity': 'specificity',
'Date Data Received':('date_data_received',False,None,util.date_converter),
'Date Loaded': ('date_loaded',False,None,util.date_converter),
'Date Publicly Available': ('date_publicly_available',False,None,util.date_converter),
'Most Recent Update': ('date_updated',False,None,util.date_converter),
'Is Restricted':('is_restricted',False,False)}
# convert the labels to fleshed out dict's, with strategies for optional, default and converter
column_definitions = util.fill_in_column_definitions(properties,column_definitions)
# create a dict mapping the column ordinal to the proper column definition dict
cols = util.find_columns(column_definitions, sheet.labels)
rows = 0
logger.debug(str(('cols: ' , cols)))
for row in sheet:
r = util.make_row(row)
dict = {}
initializer = {}
for i,value in enumerate(r):
if i not in cols: continue
properties = cols[i]
logger.debug(str(('read col: ', i, ', ', properties)))
required = properties['required']
default = properties['default']
converter = properties['converter']
model_field = properties['model_field']
# Todo, refactor to a method
logger.debug(str(('raw value', value)))
if(converter != None):
value = converter(value)
if(value == None ):
if( default != None ):
value = default
if(value == None and required == True):
raise Exception('Field is required: %s, record: %d' % (properties['column_label'],rows))
logger.debug(str(('model_field: ' , model_field, ', value: ', value)))
initializer[model_field] = value
try:
logger.debug(str(('initializer: ', initializer)))
antibody = Antibody(**initializer)
antibody.save()
logger.info(str(('antibody created: ', antibody)))
rows += 1
except Exception, e:
logger.error(str(( "Invalid antibody initializer: ", initializer)))
raise
def build_schema(self):
schema = super(AntibodyResource,self).build_schema()
schema['fields'] = get_detail_schema(Antibody(),['antibody'])
return schema
def main(path):
"""
Read in the Antibody
"""
sheet_name = 'Sheet1'
sheet = iu.readtable([path, sheet_name, 0])
properties = ('model_field','required','default','converter')
column_definitions = {
'AR_Name': ('name',True),
'AR_LINCS_ID': 'lincs_id',
'AR_Alternative_Name': 'alternative_names',
'AR_Center_Specific_ID': ('facility_id',True,None, lambda x: x[x.index('HMSL')+4:]),
'AR_Clone_Name': 'clone_name',
'AR_RRID': 'rrid',
'AR_Antibody_Type': 'type',
'target_protein_lincs_id': (
'target_protein_lincs_id',False,None,
lambda x: x[x.index('HMSL')+4:] if x else None ),
'AR_Non-Protein_Target': 'non_protein_target_name',
'AR_Target_Organism': 'target_organism',
'AR_Immunogen': 'immunogen',
'AR_Immunogen_Sequence': 'immunogen_sequence',
'AR_Antibody_Species': 'species',
'AR_Antibody_Clonality': 'clonality',
'AR_Antibody_Isotype': 'isotype',
'AR_Antibody_Production_Source_Organism': 'source_organism',
'AR_Antibody_Production_Details': 'production_details',
'AR_Antibody_Labeling': 'labeling',
'AR_Antibody_Labeling_Details': 'labeling_details',
'AR_Relevant_Citations': 'relevant_citations',
'Date Data Received':('date_data_received',False,None,util.date_converter),
'Date Loaded': ('date_loaded',False,None,util.date_converter),
'Date Publicly Available': ('date_publicly_available',False,None,util.date_converter),
'Most Recent Update': ('date_updated',False,None,util.date_converter),
'Is Restricted':('is_restricted',False,False,util.bool_converter)}
# convert the labels to fleshed out dict's, with strategies for optional, default and converter
column_definitions = util.fill_in_column_definitions(properties,column_definitions)
# create a dict mapping the column ordinal to the proper column definition dict
cols = util.find_columns(column_definitions, sheet.labels)
rows = 0
logger.debug('cols: %s' % cols)
for row in sheet:
logger.debug('row %s - %s' %(rows,row))
r = util.make_row(row)
dict = {}
initializer = {}
for i,value in enumerate(r):
if i not in cols: continue
properties = cols[i]
logger.debug('read col: %d: %s' % (i,properties))
required = properties['required']
default = properties['default']
converter = properties['converter']
model_field = properties['model_field']
logger.debug('raw value %r' % value)
if(value == None or value == 'None'):
value = None
if( default != None ):
value = default
if(value == None and required == True):
raise Exception('Field is required: %s, record: %d'
% (properties['column_label'],rows))
if(value and converter != None):
value = converter(value)
logger.debug('model_field: %s, converted value %r'
% (model_field, value) )
initializer[model_field] = value
try:
logger.debug('row: %s, initializer: %s' % (rows,initializer))
target_protein_lincs_id = initializer.pop('target_protein_lincs_id',None)
if target_protein_lincs_id:
try:
target_protein = Protein.objects.get(lincs_id=target_protein_lincs_id)
initializer['target_protein'] = target_protein
except ObjectDoesNotExist, e:
logger.error('target_protein_lincs_id "%s" does not exist, row: %d'
% (target_protein_lincs_id,i))
antibody = Antibody(**initializer)
antibody.save()
logger.info('antibody created: %s' % antibody)
rows += 1
# create a default batch - 0
AntibodyBatch.objects.create(reagent=antibody,batch_id=0)
except Exception, e:
logger.error("Invalid antibody initializer: %s" % initializer)
raise
