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extract_data_from_xls.py
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extract_data_from_xls.py
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# -*- coding: utf-8 -*-
from collections import Counter, defaultdict
import os
import xlrd
import json
import sh
import re
import itertools
import pprint
import multiprocessing
from log import logger
import config
from constituency_areas import ALL_FILES
from translation_fix import ERRATA
from table_meta_data import TABLE_META_DATA
base_path = os.path.abspath(config.DIR_DATA_DOWNLOAD)
def check_sheet(wb, tab_name, col, check_col):
"""
Checks a sheet.
Arguments:
----------
col: integer
Either 0 or 7, 0 for column a, 7 for column h
check_col: list
The list for the column to compare against
tab_name: string
The tab name to extract the column from
wb: workbook object
The workbook to use
"""
matches = True
tmp_sheet = wb.sheet_by_name(tab_name)
tmp = [x.value for x in tmp_sheet.col(col)]
error_rows = []
if tmp != check_col:
matches = False
logger.warning(u"File tab {} column A differs:".format(tab_name))
max_row = max(len(tmp), len(check_col))
for i in range(max_row):
if i < len(tmp) and i < len(check_col):
if tmp[i] != check_col[i]:
error_rows.append(i)
logger.warning(u"Row {}, base is {}, file is {}".format(i + 1, check_col[i], tmp[i]))
elif i >= len(tmp) and i < len(check_col):
logger.warning(u"Row {}, base is {}, but doesn't exist in file".format(i + 1, check_col[i]))
elif i< len(tmp) and i >= len(check_col):
logger.warning(u"Row {}, doesn't exist in base, but in file is {}".format(i + 1, tmp[i]))
if matches:
return True
else:
return error_rows
def check_for_differences():
"""
Checks that columns A and H in all of the workbooks match up.
Just a simple check to see if we need to do more complicated parsing
Really slow, I think because of opening each file.
Here's the results of this:
Counter({0: 1233, 11: 1224, 10: 1200, 9: 1191, 7: 1173, 12: 1023, 13: 1023, 14: 1023, 8: 708})
Basically the problem is the ethnicity section, which seems to change sorts for each district. Not a big problem.
"""
# Make the base versions to compare against
a01 = xlrd.open_workbook(os.path.join(base_path, 'A01.xlsx'))
tmp_sheet = a01.sheet_by_name('A01e')
base_english_a = [x.value for x in tmp_sheet.col(0)]
base_english_h = [x.value for x in tmp_sheet.col(7)]
tmp_sheet = a01.sheet_by_name('A01t')
base_traditional_a = [x.value for x in tmp_sheet.col(0)]
base_traditional_h = [x.value for x in tmp_sheet.col(7)]
tmp_sheet = a01.sheet_by_name('A01s')
base_simplified_a = [x.value for x in tmp_sheet.col(0)]
base_simplified_h = [x.value for x in tmp_sheet.col(7)]
counter = 0
errors = 0
frequency = Counter()
for f in ALL_FILES:
base_sheet_name = f[:3]
filepath = os.path.join(base_path, f)
logger.info("Checking file {}".format(f))
wb = xlrd.open_workbook(filepath)
# Check the sheets
results = [
check_sheet(wb, base_sheet_name + 'e', 0, base_english_a),
check_sheet(wb, base_sheet_name + 'e', 7, base_english_h),
check_sheet(wb, base_sheet_name + 's', 0, base_simplified_a),
check_sheet(wb, base_sheet_name + 's', 7, base_simplified_h),
check_sheet(wb, base_sheet_name + 't', 0, base_traditional_a),
check_sheet(wb, base_sheet_name + 't', 7, base_traditional_h),
]
# Results is a list of either lists or Trues. If true, it means that check passed.
# If it's a list, then it's a list of the row numbers where a mismatch occured. We store these
# so that we can quickly see which rows need extra attention
[frequency.update(a) for a in results if a is not True]
if all([x is True for x in results]):
counter += 1
logger.info(u"No errors in file {}".format(f))
else:
errors += 1
counter += 1
logger.info(u"{} files checked, {} mismatches".format(counter, errors))
logger.info(u"Rows that had errors, and their frequency:")
logger.info(frequency)
# Sample:
#TABLE_META_DATA = [
# {
# 'name': 'household',
# 'header': ['A114', 'E114'],
# 'body': ['A115', 'E126']
# }
#]
OUTPUT_PREFIX = config.DIR_DATA_CLEAN_JSON
INPUT_PREFIX = config.DIR_DATA_DOWNLOAD
def cell_name_to_pos(cellPosition):
#NOTE:
# only works for single letter column
col = ord(cellPosition[0]) - ord('A') #convert letter to ASCII, then suyb
row = int(cellPosition[1:]) - 1 # minus 1
return row, col
def pos_to_cell_name(row, col):
#NOTE:
# only works for single letter column
return '%s%d' % (chr(col + ord('A')), row + 1)
def extract_table(sheet, table, name, names, header, body, **kwargs):
# header: (A6, E6)
# body: (A7, E13)
row1, col1 = cell_name_to_pos(header[0])
row2, col2 = cell_name_to_pos(header[1])
column_names = [get_identifier(sheet, table, row1, j) for j in range(col1,col2+1)]
row1, col1 = cell_name_to_pos(body[0])
row2, col2 = cell_name_to_pos(body[1])
row_names = [get_identifier(sheet, table, i, col1) for i in range(row1,row2+1)]
#logger.debug(str((sheet, table, name, names, header, body)))
data = []
for i in range(row1, row2 + 1):
data.append([sheet.cell(i, j).value for j in range(col1 + 1, col2 + 1)])
return {
'meta': {
'table_id': table,
'table_name': name,
'table_names': names,
# other meta data
},
'row_names': row_names,
'column_names': column_names,
'data': data}
def extract_sheet(book, index):
st = book.sheet_by_index(index)
tables = {}
for (i, md) in enumerate(TABLE_META_DATA):
data = extract_table(st, i, **md)
tables['table' + str(i)] = data
return tables
def extract_book(filename):
# 0: CH T
# 1: CH S
# 2: EN
#NOTE:
# Only sheet English sheet is concerned.
# Other sheets can be easily reconstructed with identifier and translation.
wb = xlrd.open_workbook(filename)
return extract_sheet(wb, 2)
#sheets = {}
##for i in [0, 1, 2]:
#for i in [2]:
# tables = extract_sheet(wb, i)
# sheets['sheet' + str(i)] = tables
#return sheets
#def add_meta_info(table_data, area, sheet_name):
# mapping = {'sheet0': MAPPING_AREA_CODE_TO_TRADITIONAL,
# 'sheet1': MAPPING_AREA_CODE_TO_SIMPLIFIED,
# 'sheet2': MAPPING_AREA_CODE_TO_ENGLISH}[sheet_name]
# table_data['meta'].update({'area': mapping[area.lower()]})
# lang = {'sheet0': 'traditional',
# 'sheet1': 'simplified',
# 'sheet2': 'english'}[sheet_name]
# table_data['meta'].update({'language': lang})
# return table_data
def process_one_file(fn):
area = fn[:3]
fullpath = os.path.join(config.DIR_DATA_DOWNLOAD, fn)
tables = extract_book(fullpath)
for (tn, td) in tables.iteritems():
output_dir = os.path.join(OUTPUT_PREFIX, 'areas', area)
sh.mkdir('-p', output_dir)
output_path = os.path.join(output_dir, tn) + '.json'
#add_meta_info(td, area)
td['meta']['area'] = area
json.dump(td, open(output_path, 'w'))
logger.info('process one xls done:' + fn)
_IDENTIFIER_CLEANER = re.compile(ur'[\(\)\$#&,/]')
#_IDENTIFIER_BLANKS = re.compile(r'\s')
def get_identifier(sheet, table, row, col):
value = unicode(sheet.cell(row, col).value)
# clean and shorten human readable strings
value = _IDENTIFIER_CLEANER.sub('', value)
# ≧ -> >=
value = value.replace(u'\u2267', u'>=')
terms = value.strip().split()
if terms:
leading_term = terms[0]
else:
leading_term = None
if table in [0]:
# NOTE:
# Use table as prefix.
# This is to solve problems in table0,
# where the order can be different across areas.
return ('tab%s_%s' % (table, leading_term)).lower()
else:
cell_name = pos_to_cell_name(row, col)
return ('%s_%s' % (cell_name, leading_term)).lower()
def translate_sheet(book, names_from='all'):
sheetNum = [0, 1, 2] #0 - Traditional, 1 - Simplifed, 2 - English
tables = {}
translateDict = {}
count = 0
for (i, md) in enumerate(TABLE_META_DATA):
#heck the header
#extract the field on different sheets
header = md['header'] #['H41', 'N41']
body = md['body'] #['A7', 'E13']
name = md['name'] # 'Place of Study'
row1, col1 = cell_name_to_pos(header[0])
row2, col2 = cell_name_to_pos(header[1])
body_row1, body_col1 = cell_name_to_pos(body[0])
body_row2, body_col2 = cell_name_to_pos(body[1])
column_positions = [(row1, j) for j in range(col1, col2 + 1)]
row_positions = [(j, body_col1) for j in range(body_row1, body_row2 + 1)]
#TODO:
# rows and columns may be handled differently.
# e.g. rows like "1 - 1000" do carry some special information
if names_from == 'all':
all_positions = column_positions + row_positions
elif names_from == 'column':
all_positions = column_positions
elif names_from == 'row':
all_positions = row_positions
else:
raise 'unknow names_from'
# Get identifier from sheet2 (English)
sheet_english = book.sheet_by_index(2)
ids = [get_identifier(sheet_english, i, *pos) for pos in all_positions]
names = {}
# Get names in different language sheet
for j in sheetNum:
sheet = book.sheet_by_index(j)
names[j] = [unicode(sheet.cell(*pos).value).strip() for pos in all_positions]
#print len(ids), ids
#print len(names[0]), names
for c in range(len(all_positions)):
traditional_col = names[0][c]
simplified_col = names[1][c]
english_col = names[2][c]
identifier = ids[c]
translateDict[identifier] = {'T':traditional_col, 'S':simplified_col,'E':english_col}
#print translateDict
return translateDict
def _merge_translation_dict(dest, src, new_keys=True):
'''
Merge translation dict. In case of discrepancies, use values from src.
In-place operation.
:new_keys:
If True, keys exist in src but non-exist in dest will also be added.
Use to False to perform a translation 'fixing' operation.
The format of translation dict:
{
'identifier': {
'E': 'English name',
'S': 'Simplified Chinese name',
'T': 'Traditional Chinese name',
}
}
'''
for identifier, trans in src.iteritems():
if new_keys or identifier in dest:
dest.setdefault(identifier, {}).update(trans)
return dest
def gen_translation_for_one_group(wb, names_from):
translate_dict = translate_sheet(wb, names_from)
#print translate_dict
#print _merge_translation_dict(translate_dict, ERRATA)
return _merge_translation_dict(translate_dict, ERRATA, False)
def gen_translation_for_table():
translate_dict = {}
for (i, table) in enumerate(TABLE_META_DATA):
translate_dict[i] = table['names']
return translate_dict
def gen_translation_for_one_area(args):
fn, names_from = args
fullpath = os.path.join(config.DIR_DATA_DOWNLOAD, fn)
wb = xlrd.open_workbook(fullpath)
logger.info('translation %s, %s', fn, names_from)
return gen_translation_for_one_group(wb, names_from)
def gen_translation(files):
translate_dict_all = defaultdict(dict)
translate_dict_row = defaultdict(dict)
translate_dict_column = defaultdict(dict)
pool = multiprocessing.Pool()
translate_dict_all = reduce(_merge_translation_dict,
pool.map(gen_translation_for_one_area, zip(files, ['all'] * len(files))),
defaultdict(dict))
translate_dict_row = reduce(_merge_translation_dict,
pool.map(gen_translation_for_one_area, zip(files, ['row'] * len(files))),
defaultdict(dict))
translate_dict_column = reduce(_merge_translation_dict,
pool.map(gen_translation_for_one_area, zip(files, ['column'] * len(files))),
defaultdict(dict))
with open(os.path.join(config.DIR_DATA_CLEAN_JSON, 'translation.json'), 'w') as outfile:
json.dump(translate_dict_all, outfile)
with open(os.path.join(config.DIR_DATA_CLEAN_JSON, 'translation-row.json'), 'w') as outfile:
json.dump(translate_dict_row, outfile)
with open(os.path.join(config.DIR_DATA_CLEAN_JSON, 'translation-column.json'), 'w') as outfile:
json.dump(translate_dict_column, outfile)
# table translations from table_meta_data.py
with open(os.path.join(config.DIR_DATA_CLEAN_JSON, 'translation-table.json'), 'w') as outfile:
json.dump(gen_translation_for_table(), outfile)
def main():
logger.info('Start to parse individual xls files')
sh.rm('-rf', OUTPUT_PREFIX)
sh.mkdir('-p', OUTPUT_PREFIX)
files = [fn for fn in sh.ls(INPUT_PREFIX).split()]
files = [fn for fn in files if fn.endswith('.xlsx')]
# Extract xls to JSON
pool = multiprocessing.Pool()
pool.map(process_one_file, files)
# Translation
logger.info('Start to generate translation dicts')
gen_translation(files)
if __name__ == '__main__':
main()
# NOTE:
# Following is to show that merged cells (C76-E76) only have data in the first one.
# This excludes the possibility of automatic column extension for name fix.
# We need to use errata.
#fullpath = os.path.join(config.DIR_DATA_DOWNLOAD, 'A01.xlsx')
#wb = xlrd.open_workbook(fullpath)
#sheet = wb.sheet_by_index(2)
#print sheet.cell(*cell_name_to_pos('C76'))
#print sheet.cell(*cell_name_to_pos('D76'))
#print sheet.cell(*cell_name_to_pos('E76'))