import locale
import numpy as np
from pandas._libs import tslib
from pandas._libs.tslibs.nattype import NaTType
from pandas.core.indexes.datetimes import DatetimeIndex
from openpyxl import Workbook
from openpyxl.styles import Border, Side, PatternFill, numbers
from openpyxl.utils import get_column_letter
from apps.doc import consts
class BSWorkbook(Workbook):
def __init__(self, interest_keyword, salary_keyword, loan_keyword, *args, **kwargs):
super().__init__(*args, **kwargs)
locale.setlocale(locale.LC_NUMERIC, 'en_US.UTF-8')
self.meta_sheet_title = '关键信息提取和展示'
self.blank_row = (None,)
self.code_header = ('页数', '电子回单验证码')
self.date_header = ('打印时间', '起始日期', '终止日期', '流水区间结果')
self.keyword_header = ('关键词', '记账日期', '金额')
self.interest_keyword = interest_keyword
self.salary_keyword = salary_keyword
self.loan_keyword = loan_keyword
self.proof_res = ('对', '错')
self.loan_fill = PatternFill("solid", fgColor="00FFCC00")
self.amount_fill = PatternFill("solid", fgColor="00FFFF00")
# self.bd = Side(style='thin', color="000000")
# self.border = Border(left=self.bd, top=self.bd, right=self.bd, bottom=self.bd)
self.MAX_MEAN = 31
@staticmethod
def header_collect(ws, sheet_header_info, header_info, max_column_list, classify):
# sheet_header_info = {
# 'sheet_name': {
# 'summary_col': 1,
# 'date_col': 1,
# 'amount_col': 1,
# 'over_col': 1,
# 'income_col': 1,
# 'outlay_col': 1,
# 'borrow_col': 1,
# 'min_row': 2,
# 'find_count': 3,
# 'find_col': {1},
# 'header': ('日期', '金额')
# }
# }
# header_info = {
# 'summary_col': {
# 5: 2,
# 3: 1,
# },
# 'date_col': {},
# 'amount_col': {},
# 'over_col': {},
# 'income_col': {},
# 'outlay_col': {},
# 'borrow_col': {},
# }
# 第一行关键词
find_count = 0
for first_row in ws.iter_rows(max_row=1, min_row=1, values_only=True):
sheet_header_info.setdefault(ws.title, {}).setdefault(consts.HEADER_KEY, first_row)
for idx, header_value in enumerate(first_row):
if classify == consts.WECHART_CLASSIFY:
header_col = consts.WECHART_HEADERS_MAPPING.get(header_value)
else:
header_col = consts.HEADERS_MAPPING.get(header_value)
if header_col is not None:
find_count += 1
sheet_header_info.setdefault(ws.title, {}).setdefault(header_col, idx)
find_col_set = sheet_header_info.setdefault(ws.title, {}).setdefault(consts.FIND_COL_KEY, set())
find_col_set.add(idx)
col_count = header_info.setdefault(header_col, {}).get(idx)
header_info.setdefault(header_col, {})[idx] = 1 if col_count is None else col_count+1
sheet_header_info.setdefault(ws.title, {}).setdefault(consts.FIND_COUNT_KEY, find_count)
min_row = 1 if find_count == 0 else 2
sheet_header_info.setdefault(ws.title, {}).setdefault(consts.MIN_ROW_KEY, min_row)
max_column_list.append(ws.max_column)
@staticmethod
def header_statistics(sheet_header_info, header_info, classify):
# statistics_header_info = {
# SUMMARY_KEY: 2,
# DATE_KEY: 3,
# AMOUNT_KEY: 4,
# OVER_KEY: 5,
# IMCOME_KEY: 6,
# OUTLAY_KEY: 7,
# BORROW_KEY: 8,
# 'header': ('日期', '金额')
# }
statistics_header_info = {}
sheet_order_list = sorted(sheet_header_info, reverse=True,
key=lambda x: sheet_header_info[x][consts.FIND_COUNT_KEY])
best_sheet_info = sheet_header_info.get(sheet_order_list[0])
if best_sheet_info.get(consts.FIND_COUNT_KEY, 0) == 0:
for key, value in consts.CLASSIFY_MAP.items():
col = consts.CLASSIFY_LIST[classify][1][value]
statistics_header_info[key] = col - 1 if isinstance(col, int) else None
statistics_header_info[consts.HEADER_KEY] = consts.CLASSIFY_HEADER_LIST[classify]
else:
find_col_set = best_sheet_info.get(consts.FIND_COL_KEY, set())
# SUMMARY_KEY DATE_KEY OVER_KEY BORROW_KEY
for key in consts.KEY_LIST:
col = best_sheet_info.get(key)
if col is None:
col_dict = header_info.get(key, {})
for idx in sorted(col_dict, key=lambda x: col_dict[x], reverse=True):
if idx in find_col_set:
continue
col = idx
find_col_set.add(col)
break
else:
fixed_col = consts.CLASSIFY_LIST[classify][1][consts.CLASSIFY_MAP[key]]
if fixed_col not in find_col_set and isinstance(fixed_col, int):
col = fixed_col - 1
find_col_set.add(col)
statistics_header_info[key] = col
statistics_header_info[consts.HEADER_KEY] = best_sheet_info.get(consts.HEADER_KEY)
return statistics_header_info
@staticmethod
def get_data_col_min_row(sheet, sheet_header_info, header_info, classify):
date_col = sheet_header_info.get(sheet, {}).get(consts.DATE_KEY)
if date_col is None:
date_col_dict = header_info.get(consts.DATE_KEY, {})
find_col_set = sheet_header_info.get(sheet, {}).get(consts.FIND_COL_KEY, set())
for idx in sorted(date_col_dict, key=lambda x: date_col_dict[x], reverse=True):
if idx in find_col_set:
continue
date_col = idx
break
else:
fixed_col = consts.CLASSIFY_LIST[classify][1][consts.CLASSIFY_MAP[consts.DATE_KEY]]
if fixed_col not in find_col_set and isinstance(fixed_col, int):
date_col = fixed_col - 1
min_row = sheet_header_info.get(sheet, {}).get(consts.MIN_ROW_KEY, 2)
return date_col, min_row
@staticmethod
def month_split(dti, date_list, date_statistics):
month_list = []
idx_list = []
month_pre = None
for idx, month_str in enumerate(dti.strftime('%Y-%m')):
if isinstance(month_str, float):
continue
if month_str != month_pre:
month_list.append(month_str)
if month_pre is None:
if date_statistics:
date_list.append(dti[idx].date())
idx = 0
idx_list.append(idx)
month_pre = month_str
if date_statistics:
for idx in range(len(dti) - 1, -1, -1):
if isinstance(dti[idx], NaTType):
continue
date_list.append(dti[idx].date())
break
return month_list, idx_list
@staticmethod
def get_reverse_trend(day_idx, idx_list):
reverse_trend = 0
pre_day = None
for idx, day in enumerate(day_idx):
if np.isnan(day):
continue
if idx in idx_list or pre_day is None:
pre_day = day
continue
if day < pre_day:
reverse_trend += 1
pre_day = day
elif day > pre_day:
reverse_trend -= 1
pre_day = day
if reverse_trend > 0:
reverse_trend = 1
elif reverse_trend < 0:
reverse_trend = -1
return reverse_trend
def sheet_split(self, ws, date_col, min_row, month_mapping, reverse_trend_list, date_list, date_statistics):
if date_col is None:
# month_info process
month_info = month_mapping.setdefault('xxxx-xx', [])
month_info.append((ws.title, min_row, ws.max_row, 0))
return
date_col = date_col + 1
for date_tuple_src in ws.iter_cols(min_col=date_col, max_col=date_col, min_row=min_row, values_only=True):
date_tuple = [date[:10] if isinstance(date, str) else date for date in date_tuple_src]
dt_array, tz_parsed = tslib.array_to_datetime(
np.array(date_tuple, copy=False, dtype=np.object_),
errors="coerce",
utc=False,
dayfirst=False,
yearfirst=False,
require_iso8601=True,
)
dti = DatetimeIndex(dt_array, tz=None, name=None)
month_list, idx_list = self.month_split(dti, date_list, date_statistics)
if len(month_list) == 0:
# month_info process
month_info = month_mapping.setdefault('xxxx-xx', [])
month_info.append((ws.title, min_row, ws.max_row, 0))
else:
# reverse_trend_list process
reverse_trend = self.get_reverse_trend(dti.day, idx_list)
reverse_trend_list.append(reverse_trend)
# month_info process
day_idx = dti.day
idx_list_max_idx = len(idx_list) - 1
for i, item in enumerate(month_list):
if i == idx_list_max_idx:
day_mean = np.mean(day_idx[idx_list[i]:].dropna())
month_mapping.setdefault(item, []).append(
(ws.title, idx_list[i] + min_row, ws.max_row, day_mean))
else:
day_mean = np.mean(day_idx[idx_list[i]: idx_list[i + 1]].dropna())
month_mapping.setdefault(item, []).append(
(ws.title, idx_list[i] + min_row, idx_list[i + 1] + min_row - 1, day_mean))
def build_metadata_rows(self, confidence, code, print_time, start_date, end_date):
if start_date is None or end_date is None:
timedelta = None
else:
timedelta = (end_date - start_date).days
metadata_rows = [
('流水识别置信度', confidence),
self.blank_row,
self.code_header,
]
metadata_rows.extend(code)
metadata_rows.extend(
[self.blank_row,
self.date_header,
(print_time, start_date, end_date, timedelta),
self.blank_row,
self.keyword_header]
)
return metadata_rows
def create_meta_sheet(self, card):
if self.worksheets[0].title == 'Sheet':
ms = self.worksheets[0]
ms.title = '{0}({1})'.format(self.meta_sheet_title, card[-6:])
else:
ms = self.create_sheet('{0}({1})'.format(self.meta_sheet_title, card[-6:]))
return ms
def build_meta_sheet(self, card, confidence, code, print_time, start_date, end_date):
metadata_rows = self.build_metadata_rows(confidence, code, print_time, start_date, end_date)
ms = self.create_meta_sheet(card)
for row in metadata_rows:
ms.append(row)
return ms
@staticmethod
def amount_format(amount_str):
if not isinstance(amount_str, str) or amount_str == '':
return amount_str
# 1.替换
res_str = amount_str.translate(consts.TRANS)
# 2.首字符处理
first_char = res_str[0]
if first_char in consts.ERROR_CHARS:
first_char = '-'
# 3.删除多余的-
res_str = first_char + res_str[1:].replace('-', '')
# 4.逗号与句号处理
if len(res_str) >= 4:
period_idx = len(res_str) - 3
if res_str[period_idx] == '.' and res_str[period_idx - 1] == ',':
res_str = '{0}{1}'.format(res_str[:period_idx - 1], res_str[period_idx:])
elif res_str[period_idx] == ',':
res_str = '{0}.{1}'.format(res_str[:period_idx], res_str[period_idx + 1:])
return res_str
def build_month_sheet(self, ms, card, month_mapping, is_reverse, statistics_header_info, max_column):
summary_cell_idx = statistics_header_info.get(consts.SUMMARY_KEY)
date_cell_idx = statistics_header_info.get(consts.DATE_KEY)
amount_cell_idx = statistics_header_info.get(consts.AMOUNT_KEY) # None or src or append
over_cell_idx = statistics_header_info.get(consts.OVER_KEY)
income_cell_idx = statistics_header_info.get(consts.IMCOME_KEY)
outlay_cell_idx = statistics_header_info.get(consts.OUTLAY_KEY)
borrow_cell_idx = statistics_header_info.get(consts.BORROW_KEY)
header = list(statistics_header_info.get(consts.HEADER_KEY))
src_header_len = len(header)
if max_column > src_header_len:
for i in range(max_column - src_header_len):
header.append(None)
add_col = ['核对结果']
if amount_cell_idx is None:
if income_cell_idx is not None or outlay_cell_idx is not None:
add_col = ['金额', '核对结果']
amount_cell_idx = len(header)
header.extend(add_col)
result_idx = len(header) - 1
tmp_ws = self.create_sheet('tmp_ws')
for month in sorted(month_mapping.keys()):
# 3.1.拷贝数据
parts = month_mapping.get(month)
new_ws = self.create_sheet('{0}({1})'.format(month, card[-6:]))
new_ws.append(header)
for part in parts:
ws = self.get_sheet_by_name(part[0])
for row_value in ws.iter_rows(min_row=part[1], max_row=part[2], values_only=True):
if any(row_value):
new_ws.append(row_value)
# 3.2.提取信息、高亮
amount_mapping = {}
amount_fill_row = set()
for rows in new_ws.iter_rows(min_row=2):
# TODO 删除空行
summary_cell = None if summary_cell_idx is None else rows[summary_cell_idx]
date_cell = None if date_cell_idx is None else rows[date_cell_idx]
amount_cell = None if amount_cell_idx is None else rows[amount_cell_idx]
over_cell = None if over_cell_idx is None else rows[over_cell_idx]
income_cell = None if income_cell_idx is None else rows[income_cell_idx]
outlay_cell = None if outlay_cell_idx is None else rows[outlay_cell_idx]
borrow_cell = None if borrow_cell_idx is None else rows[borrow_cell_idx]
summary_cell_value = None if summary_cell is None else summary_cell.value
date_cell_value = None if date_cell is None else date_cell.value
amount_cell_value = None if amount_cell is None else amount_cell.value
over_cell_value = None if over_cell is None else over_cell.value
income_cell_value = None if income_cell is None else income_cell.value
outlay_cell_value = None if outlay_cell is None else outlay_cell.value
borrow_cell_value = None if borrow_cell is None else borrow_cell.value
# row = summary_cell.row
if summary_cell is not None:
# 关键词1提取
if summary_cell_value in self.interest_keyword:
ms.append((summary_cell_value, date_cell_value, amount_cell_value))
# 关键词2提取至临时表
elif summary_cell_value in self.salary_keyword:
tmp_ws.append((summary_cell_value, date_cell_value, amount_cell_value))
# 贷款关键词高亮
elif summary_cell_value in self.loan_keyword:
summary_cell.fill = self.amount_fill
if amount_cell is not None:
amount_cell.fill = self.amount_fill
# 3.3.余额转数值
over_success = False
if over_cell is not None:
try:
over_cell.value = locale.atof(self.amount_format(over_cell_value))
except Exception as e:
pass
else:
over_success = True
over_cell.number_format = numbers.FORMAT_NUMBER_00
# 3.4.金额转数值
amount_success = False
if amount_cell is not None:
try:
try:
amount_cell.value = locale.atof(self.amount_format(amount_cell_value))
except Exception as e:
try:
amount_cell.value = locale.atof(self.amount_format(income_cell_value))
if amount_cell.value == 0:
raise
elif amount_cell.value < 0:
amount_cell.value = -amount_cell.value
except Exception as e:
amount_cell.value = locale.atof(self.amount_format(outlay_cell_value))
if amount_cell.value > 0:
amount_cell.value = -amount_cell.value
except Exception as e:
pass
else:
amount_success = True
if borrow_cell_value in consts.BORROW_OUTLAY_SET:
amount_cell.value = -amount_cell.value
amount_cell.number_format = numbers.FORMAT_NUMBER_00
if date_cell is not None:
same_amount_mapping = amount_mapping.get(date_cell.value, {})
fill_rows = same_amount_mapping.get(-amount_cell.value)
if fill_rows:
amount_fill_row.add(amount_cell.row)
amount_fill_row.update(fill_rows)
amount_mapping.setdefault(date_cell.value, {}).setdefault(
amount_cell.value, []).append(amount_cell.row)
# 3.5.核对结果
if amount_success and over_success and amount_cell.row > 2:
amount_col_letter = get_column_letter(amount_cell_idx + 1)
over_col_letter = get_column_letter(over_cell_idx + 1)
if is_reverse:
rows[result_idx].value = '=IF({2}{0}=ROUND(SUM({2}{1},{3}{0}),4), "{4}", "{5}")'.format(
amount_cell.row - 1, amount_cell.row, over_col_letter, amount_col_letter, *self.proof_res)
else:
rows[result_idx].value = '=IF({2}{0}=ROUND(SUM({2}{1},{3}{0}),4), "{4}", "{5}")'.format(
amount_cell.row, amount_cell.row - 1, over_col_letter, amount_col_letter, *self.proof_res)
# 3.6.同一天相同进出账高亮
del amount_mapping
for row in amount_fill_row:
new_ws[row][amount_cell_idx].fill = self.amount_fill
if summary_cell_idx is not None:
new_ws[row][summary_cell_idx].fill = self.amount_fill
# 关键词2信息提取
ms.append(self.blank_row)
ms.append(self.keyword_header)
for row in tmp_ws.iter_rows(values_only=True):
ms.append(row)
self.remove(tmp_ws)
def bs_rebuild(self, bs_summary):
# bs_summary = {
# '卡号': {
# 'classify': 0,
# 'confidence': 0.9,
# 'role': '柳雪',
# 'code': [('page', 'code')],
# 'print_time': 'datetime',
# 'start_date': 'datetime',
# 'end_date': 'datetime',
# 'sheet': ['sheet_name']
# }
# }
for card, summary in bs_summary.items():
# 1.原表表头收集、按照月份分割
# 1.1 总结首行信息
classify = summary.get('classify', 0)
sheet_header_info = {}
header_info = {}
max_column_list = []
for sheet in summary.get('sheet', []):
ws = self.get_sheet_by_name(sheet)
self.header_collect(ws, sheet_header_info, header_info, max_column_list, classify)
statistics_header_info = self.header_statistics(sheet_header_info, header_info, classify)
max_column = max(max_column_list)
# 1.2.按月份分割 min_row 正文第一行 date_col 日期行
start_date = summary.get('start_date')
end_date = summary.get('end_date')
date_statistics = True if start_date is None or end_date is None else False # 用于判断是否需要收集各表中日期
date_list = [] # 用于收集各表中日期
month_mapping = {} # 用于创建月份表
reverse_trend_list = [] # 用于判断倒序与正序
for sheet in summary.get('sheet', []):
ws = self.get_sheet_by_name(sheet)
date_col, min_row = self.get_data_col_min_row(sheet, sheet_header_info, header_info, classify)
self.sheet_split(ws, date_col, min_row, month_mapping, reverse_trend_list, date_list, date_statistics)
if date_statistics is True and len(date_list) > 1:
start_date = min(date_list) if start_date is None else start_date
end_date = max(date_list) if end_date is None else end_date
# 2.元信息提取表
ms = self.build_meta_sheet(card,
summary.get('confidence', 1),
summary.get('code'),
summary.get('print_time'),
start_date,
end_date)
# 3.创建月份表、提取/高亮关键行
# 倒序处理
is_reverse = True if sum(reverse_trend_list) > 0 else False
for month_list in month_mapping.values():
month_list.sort(key=lambda x: x[-1], reverse=is_reverse)
self.build_month_sheet(ms, card, month_mapping, is_reverse, statistics_header_info, max_column)
# 4.删除原表
for sheet in summary.get('sheet'):
self.remove(self.get_sheet_by_name(sheet))
def license_rebuild(self, license_summary, document_scheme):
for classify, (_, name, field_order, side_diff, scheme_diff) in consts.LICENSE_ORDER:
license_list = license_summary.get(classify)
if not license_list:
continue
ws = self.create_sheet(name)
if scheme_diff and document_scheme == consts.DOC_SCHEME_LIST[1]:
classify = consts.MVC_CLASSIFY_SE
for license_dict in license_list:
if classify == consts.IC_CLASSIFY and license_dict.get('类别') == '1':
license_summary.setdefault(consts.RP_CLASSIFY, []).append(license_dict)
continue
if side_diff:
key, field_order_yes, field_order_no = consts.FIELD_ORDER_MAP.get(classify)
field_order = field_order_yes if key in license_dict else field_order_no
for search_field, write_field in field_order:
ws.append((write_field, license_dict.get(search_field, '')))
ws.append((None, ))
def skip_img_sheet(self, skip_img):
if skip_img:
ws = self.create_sheet(consts.SKIP_IMG_SHEET_NAME)
ws.append(consts.SKIP_IMG_SHEET_HEADER)
for img_tuple in skip_img:
ws.append(img_tuple)
def rebuild(self, bs_summary, license_summary, skip_img, document_scheme):
self.bs_rebuild(bs_summary)
self.license_rebuild(license_summary, document_scheme)
self.skip_img_sheet(skip_img)