def guess_association_dict(self, csv_datas_headers, user_defined=None):
"""
Try to build an association dict between the header of the current csv
file we want to import and the model we want to generate
:param dict user_defined: An already defined association_dict we want to
use as reference
:returns: a dict with {headername: associated_column}
:rtype: dict that's pickable (can be stored in the session)
"""
result = OrderedDict()
for header in csv_datas_headers:
header = ascii.force_unicode(header)
if not header:
continue
result[header] = None
toguess = header.replace('*', '').lower()
for column in self.columns.values():
name = column['name'].lower()
label = column['label'].lower()
if toguess in [name, label]:
result[header] = column['name']
if user_defined is not None:
for key, value in user_defined.items():
if key in result:
result[key] = value
return result
def guess_association_dict(self, csv_datas_headers, user_defined=None):
"""
Try to build an association dict between the header of the current csv
file we want to import and the model we want to generate
:param dict user_defined: An already defined association_dict we want to
use as reference
:returns: a dict with {headername: associated_column}
:rtype: dict that's pickable (can be stored in the session)
"""
result = OrderedDict()
for header in csv_datas_headers:
header = ascii.force_unicode(header)
if not header:
continue
result[header] = None
toguess = header.replace('*', '').lower()
for column in self.columns.values():
name = column['name'].lower()
label = column['label'].lower()
if toguess in [name, label]:
result[header] = column['name']
if user_defined is not None:
for key, value in user_defined.items():
if key in result:
result[key] = value
return result
def get_value(arguments, key, default=None):
"""
Return the value for key in arguments or default
"""
val = arguments.get('--%s' % key)
if not val:
val = default
return ascii.force_unicode(val)
def collect_args(self, csv_line, force_rel_creation=False):
"""
Collect the arguments to be used to build the new model
* get the value from the csv_line
* format it thanks to the informations provided in the column info attr
* place it in a new dict with the model attribute names as keys
:param dict line: a csv line as a dict
:param bool force_rel_creation: Should we try to build related
configuration option on the fly ?
:returns: a tuple with the args to be used for instanciation and the
resting values
"""
kwargs = {}
unhandled = {}
for csv_key, value in csv_line.items():
value = ascii.force_unicode(value)
if csv_key == 'id':
column_name = 'id'
else:
key = ascii.force_unicode(csv_key)
column_name = self.association_dict.get(key)
if column_name is None:
column_name = self.association_dict.get(csv_key)
if column_name is None:
unhandled[csv_key] = value
else:
column = self.columns.get(column_name)
if column is not None:
new_value = format_input_value(
value,
column,
force_rel_creation,
)
if new_value is not None:
kwargs[column_name] = new_value
else:
unhandled[csv_key] = value
else:
kwargs[column_name] = value
return kwargs, unhandled
def collect_args(self, csv_line, force_rel_creation=False):
"""
Collect the arguments to be used to build the new model
* get the value from the csv_line
* format it thanks to the informations provided in the column info attr
* place it in a new dict with the model attribute names as keys
:param dict line: a csv line as a dict
:param bool force_rel_creation: Should we try to build related
configuration option on the fly ?
:returns: a tuple with the args to be used for instanciation and the
resting values
"""
kwargs = {}
unhandled = {}
for csv_key, value in csv_line.items():
value = ascii.force_unicode(value)
if csv_key == 'id':
column_name = 'id'
else:
key = ascii.force_unicode(csv_key)
column_name = self.association_dict.get(key)
if column_name is None:
column_name = self.association_dict.get(csv_key)
if column_name is None:
unhandled[csv_key] = value
else:
column = self.columns.get(column_name)
if column is not None:
new_value = format_input_value(
value,
column,
force_rel_creation,
)
if new_value is not None:
kwargs[column_name] = new_value
else:
unhandled[csv_key] = value
else:
kwargs[column_name] = value
return kwargs, unhandled
