def parse_with_groupings(self, grouping_col, headers, header_row):
# grouping_col is an integer
# header_row is an integer
# Puts group at end
header_map = self.construct_header_map(headers, header_row)
row_range = self.get_row_range()
cur_group = ""
result = []
for i in row_range:
line = self.lines[i]
fields = line.split("\t")
# Update group if necessary
if fields[grouping_col] != "":
cur_group = input_conditioner.condition_user_input(fields[grouping_col])
item = []
for h in headers:
item.append(self.get_field_value(fields, header_map[h]))
# Add grouping to the end of the result
item.append(cur_group)
result.append(item)
return result
def parse(self):
"""
Parses staff data.
Tries to figure out where the start and end rows are. This will
always be a little delicate.
"""
# Figure out spreadsheet area
start_row = self.get_start_row()
end_row = self.get_end_row()
header_row = start_row - 1
track_headers = filter(is_not_empty,
self.staff_lines[header_row].split("\t"))
area = SpreadsheetArea(self.staff_lines,
start_row=start_row, end_row=end_row)
grouping_column = 0
staff = area.parse_with_groupings(grouping_column,
track_headers, header_row)
# Construct assignments by track
result = []
for row in staff:
skill = row[-1]
# Iterate across track columns
for i in range(len(track_headers)):
track = input_conditioner.condition_user_input(track_headers[i])
if not self.skip_cell(row[i]):
result.append({
'name': input_conditioner.condition_user_input(row[i]),
'skill': skill,
'track': track
})
return result
def parse(self):
"""
Parses data from work stream.
"""
area = SpreadsheetArea(self.work_lines, start_row=self.start_row)
parsed_work = area.parse(self.headers + self.skill_headers,
self.header_row)
# Gathers tags from columns that have headings like "Tag",
# "Tag:Product", etc.
tags = area.parse_tags(self.header_row)
# Rearrange data in work records
num_headers = len(self.headers)
num_skill_headers = len(self.skill_headers)
result = []
cur_rank = 1
for rec_index in range(len(parsed_work)):
record = parsed_work[rec_index]
rank = str(cur_rank)
cur_rank += 1
item = []
estimates = []
if self.skip_record(record):
print("Skipping %d" % cur_rank)
continue
# The first part of the record will be our normal headers
for i in range(0, num_headers):
value = input_conditioner.condition_user_input(record[i])
item.append(value)
# After the normal headers we'll augment with the rank
item.append(rank)
# The last part of the record is the estimate. We'll gather them
# here and then pack them here.
for i in range(num_headers, num_headers + num_skill_headers):
estimates.append(self.condition_manweeks(record[i]))
print(record, estimates)
item.append(sectionize.pack_string(self.skill_headers, estimates))
# Condition work record fields
conditioned_record = self.condition_work_record(item, tags[rec_index])
if conditioned_record:
result.append(conditioned_record)
return result