def create_btable(self, tablename, csv, crosscat_column_types):
"""Upload a csv table to the predictive db.
Crosscat_column_types must be a dictionary mapping column names
to either 'ignore', 'continuous', or 'multinomial'. Not every
column name must be present in the dictionary: default is continuous."""
# First, test if table with this name already exists, and fail if it does
if self.persistence_layer.check_if_table_exists(tablename):
raise Exception('Error: btable with that name already exists.')
csv_abs_path = self.persistence_layer.write_csv(tablename, csv)
# Parse column names to create table
csv = csv.replace('\r', '')
colnames = csv.split('\n')[0].split(',')
# Guess schema and create table
header, values = du.read_csv(csv_abs_path, has_header=True)
postgres_coltypes, cctypes = self._guess_schema(
header, values, crosscat_column_types, colnames)
self.persistence_layer.create_btable_from_csv(tablename, csv_abs_path,
cctypes,
postgres_coltypes,
colnames)
return dict(columns=colnames,
data=[cctypes],
message='Created btable %s. Inferred schema:' % tablename)
def parse_timing_file(filename):
header, rows = du.read_csv(filename)
_timing_row = namedtuple('timing_row', ' '.join(header))
timing_rows = []
for row in rows:
row = map(do_strip, row)
timing_row = _timing_row(*row)
timing_rows.append(timing_row)
return timing_rows
def parse_timing_file(filename):
header, rows = du.read_csv(filename)
_timing_row = namedtuple('timing_row', ' '.join(header))
timing_rows = []
for row in rows:
row = map(do_strip, row)
timing_row = _timing_row(*row)
timing_rows.append(timing_row)
return timing_rows
