def populate(self, indata):
for required in ('row_count', 'col_count', 'headers', 'titles'):
if not hasattr(self, required):
raise Exception("missing required attribute to Grid: %s" % required)
# create a grid
self.grid_data = [[None] * self.col_count for i in range(self.row_count)]
using_lists = False # support for lists #2
if type(indata[0]) != dict:
using_lists = True
# now populate
# this is pass one
# want to do as much processing here as we can
# we populate left to right, top to bottom
for row_id in range(self.row_count):
for col_id in range(self.col_count):
field_type_class = self.headers[col_id]
# headers from seelf.data_model.field_headers, sorted
if using_lists:
data = indata[row_id][col_id] # direct data access lists
else:
data = indata[row_id][self.titles[col_id]] # direct data access dicts
if is_custom_mm_type(data):
# explicit type
self.grid_data[row_id][col_id] = data
else:
# wrap in type from headers
self.grid_data[row_id][col_id] = field_type_class(data)
log.info("populated grid %sX%s" % (self.row_count, self.col_count))
def populate(self, indata):
for required in ('row_count', 'col_count', 'headers'):
if not hasattr(self, required):
raise Exception("missing required attribute to Grid: %s" % required)
# create a grid
self.grid_data = [ [None]*self.col_count for i in range(self.row_count) ]
# now populate
# this is pass one
# want to do as much processing here as we can
# we populate left to right, top to bottom
for row_id in range(self.row_count):
for col_id in range(self.col_count):
field_type_class = self.headers[col_id]
data = indata[row_id][field_type_class.header_title] # direct data access
if is_custom_mm_type(data):
# explicit type
self.grid_data[row_id][col_id] = data
else:
# wrap in type from headers
self.grid_data[row_id][col_id] = field_type_class(data)
log.info("populated grid %sX%s" % (self.row_count,self.col_count))
def populate(self, indata, config):
for required in ('row_count', 'col_count', 'headers', 'titles'):
if not hasattr(self, required):
raise Exception("missing required attribute to Grid: %s" %
required)
# create a grid
self.grid_data = [[None] * self.col_count
for i in range(self.row_count)]
using_lists = False # support for lists #2
if type(indata[0]) != dict:
using_lists = True
# now populate
# this is pass one
# want to do as much processing here as we can
# we populate left to right, top to bottom
n_missing = 0
for row_id in range(self.row_count):
for col_id in range(self.col_count):
field_type_class = self.headers[col_id]
# headers from seelf.data_model.field_headers, sorted
if using_lists:
try:
# direct data access lists
data = indata[row_id][col_id]
except IndexError:
log.warning('No index found in row %d column %d' %
(row_id, col_id))
if config.INGORE_DATA_MISMATCH:
data = ''
n_missing += 1
else:
if len(indata[row_id]) > self.col_count:
raise Exception(
"Data mismatch: Row %d has %d more columns than row 1"
% ((row_id + 1),
len(indata[row_id]) - self.col_count))
try:
#direct data access dicts
data = indata[row_id][self.titles[col_id]]
except IndexError:
log.warning('No index found in row %d column %d' %
(row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ''
except KeyError:
log.warning('No key found in row %d column %d' %
(row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ''
if is_custom_mm_type(data):
# explicit type
try:
self.grid_data[row_id][col_id] = data
except IndexError:
log.warning('No index found in row %d column %d' %
(row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ''
else:
# wrap in type from headers
try:
self.grid_data[row_id][col_id] = field_type_class(data)
except IndexError:
log.warning('No index found in row %d column %d' %
(row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ''
log.info("populated grid %sX%s" % (self.row_count, self.col_count))
if n_missing > 0:
log.info('%d missing items' % n_missing)
def populate(self, indata, config):
for required in ("row_count", "col_count", "headers", "titles"):
if not hasattr(self, required):
raise Exception("missing required attribute to Grid: %s" % required)
# create a grid
self.grid_data = [[None] * self.col_count for i in range(self.row_count)]
using_lists = False # support for lists #2
if type(indata[0]) != dict:
using_lists = True
# now populate
# this is pass one
# want to do as much processing here as we can
# we populate left to right, top to bottom
n_missing = 0
for row_id in range(self.row_count):
for col_id in range(self.col_count):
field_type_class = self.headers[col_id]
# headers from seelf.data_model.field_headers, sorted
if using_lists:
try:
# direct data access lists
data = indata[row_id][col_id]
except IndexError:
log.warning("No index found in row %d column %d" % (row_id, col_id))
if config.INGORE_DATA_MISMATCH:
data = ""
n_missing += 1
else:
if len(indata[row_id]) > self.col_count:
raise Exception(
"Data mismatch: Row %d has %d more columns than row 1"
% ((row_id + 1), len(indata[row_id]) - self.col_count)
)
try:
# direct data access dicts
data = indata[row_id][self.titles[col_id]]
except IndexError:
log.warning("No index found in row %d column %d" % (row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ""
except KeyError:
log.warning("No key found in row %d column %d" % (row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ""
if is_custom_mm_type(data):
# explicit type
try:
self.grid_data[row_id][col_id] = data
except IndexError:
log.warning("No index found in row %d column %d" % (row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ""
else:
# wrap in type from headers
try:
self.grid_data[row_id][col_id] = field_type_class(data)
except IndexError:
log.warning("No index found in row %d column %d" % (row_id, col_id))
n_missing += 1
if config.INGORE_DATA_MISMATCH:
data = ""
log.info("populated grid %sX%s" % (self.row_count, self.col_count))
if n_missing > 0:
log.info("%d missing items" % n_missing)
