def parse_opstock_content(content, drop_if=lambda row: False):
soup = BeautifulSoup(content, 'html.parser')
recs = RecordParser(
records = [OrderedDict((child.name, child.text) for child in table.children) for table in soup.find_all('table1')],
drop_if = drop_if
)
recs.format([('stock_qty', 0.0)])
recs.update([('drug_nm', get_std_name), ('drug_cd', code_with_count)])
recs.group_by(
columns = ['drug_nm'],
aggset = [('stock_qty', sum, 'stock')],
selects = ['drug_cd', 'drug_nm', 'stock', 'stock_unit']
)
recs.update([('stock', lambda row: row['stock'] if int(row['stock'])!=row['stock'] else int(row['stock']) )])
return recs.records
def report_excel_response(invset_slugs):
records = []
invests = Invest.objects.filter(slug__in=invset_slugs)
for invest in invests:
for item in invest.investitem_set.all():
records.append({
'EDI코드': item.drug.edi, '판매사': item.drug.firm, '약품명': item.drug.name_as, '실사량': item.total, '규격단위': item.drug.standard_unit,
'재고단가': item.price, '재고구분': item.drug.invest_class, '유효기한': item.expire
})
recs = RecordParser(records)
recs = recs.select(['EDI코드', '판매사', '약품명', '실사량', '규격단위', '재고단가', '재고구분', '유효기한'])
output = recs.to_excel()
filename = '{}.xlsx'.format(' '.join(invset_slugs))
response = HttpResponse(output, content_type='application/vnd.ms-excel')
response['Content-Disposition'] = 'attachment; filename='+ filename
return response
def save_queryset(self, agg, detail, ord_tp, form_data):
today = date.today().strftime('%Y-%m-%d')
seq_list = [row['seq'] for row in self.label_history_list if row['date'] == today and row['ord_tp'] == ord_types[ord_tp]]
seq = max(seq_list) + 1 if seq_list else 0
queryset = []
recs = RecordParser(detail)
today_queryset = [history for history in self.label_history_list if history['date'] == today]
today_subqueryset = []
for rec in today_queryset:
collect_seq = rec['seq'] + 1
for row in rec['records']:
today_subqueryset.append((collect_seq, row['sub_object_list']))
for item in agg:
item['sub_object_list'] = recs.select('*', where = lambda row: row['ord_cd'] == item['ord_cd'], inplace=False).order_by(['ord_ymd', 'rcpt_dt']).records
item['duplicated'] = False
for sub in item['sub_object_list']:
sub['duplicated'] = 0
for cseq, today_sub_list in today_subqueryset:
for today_sub in today_sub_list:
sub_dup_save = sub.pop('duplicated')
today_sub_save = today_sub.pop('duplicated')
if sub == today_sub:
sub['duplicated'] = cseq
item['duplicated'] = True
else:
sub['duplicated'] = sub_dup_save
today_sub['duplicated'] = today_sub_save
queryset.append(item)
log = {
'date' : today,
'seq': seq,
'ord_tp': ord_types[ord_tp],
'form_data': form_data,
'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
'records': queryset
}
self.label_history_list.append(log)
with open(self.json_path, 'w') as fp:
fp.write(json.dumps(self.label_history_list[-MAX_COLLECT_LENGTH:]))
return log['records']
def save_drug_list(self,
page_start,
page_end,
categorys,
market_status,
excel=None,
csv=None,
append=False,
set_detail=False):
''' present_code = {'Y':'급여', 'N': '비급여'}
categorys = {'E': '전문', 'O': '일반', 'Q': '의약외품'}
market_status = {'AS': '유통/생산 중', 'AE': '유통/생산 미확인', 'AD': '유통/생산 중단'}
'''
if append:
recs = read_excel(excel) if excel else read_csv(csv,
encoding='cp949')
else:
recs = RecordParser()
for ctg in categorys:
for mks in market_status:
for npage in range(page_start, page_end):
try:
record = RecordParser(
self.get_all(category=ctg,
market_status=mks,
page=npage,
set_detail=set_detail))
except Exception as e:
print("Except on", ctg, mks, npage, e)
break
if not record:
break
recs += record
print('{}-{}-{}'.format(ctg, mks, npage))
recs.distinct(['drug_id'])
if excel:
recs.to_excel(excel)
elif csv:
recs.to_csv(csv)
def get_object_list_test(start_dtime, end_dtime, kind):
req = LabelCollectingApiRequest(API_REQ['collecting'][kind], start_dtime,
end_dtime)
req.set_test_response('response_samples/LabelCollecting.sample.rsp')
records = req.get_records('table1')
recs = RecordParser(records=records)
recs.format([('tot_qty', 0)])
recs.group_by(['drug_nm'], [('tot_qty', sum, 'agg_qty')],
['drug_nm', 'drug_cd', 'agg_qty'])
return recs.records
def get_object_list(start_dtime, end_dtime, kind):
req = LabelCollectingApiRequest(API_REQ['collecting'][kind], start_dtime,
end_dtime)
req.api_call()
records = req.get_records('table1')
recs = RecordParser(records=records)
for row in recs:
print(row)
recs.format([('tot_qty', 0)])
recs.group_by(['drug_nm'], [('tot_qty', sum, 'agg_qty'),
('drug_nm', len, 'ord_cnt')],
['drug_nm', 'drug_cd', 'agg_qty', 'ord_cnt'])
return recs
def parse_narc_content(content, n=0):
soup = BeautifulSoup(content, 'html.parser')
recs = RecordParser(records=[
OrderedDict((child.name, child.text) for child in table.children)
for table in soup.find_all('table1')
],
drop_if=lambda row: not row.get('narct_owarh_ymd') or
row['drug_cd'] not in drugDB or not row.get('ptnt_no'))
recs.select([
'narct_owarh_ymd', 'ward', 'ori_ord_ymd', 'ord_no', 'ptnt_no',
'ptnt_nm', 'drug_cd', 'drug_nm', 'ord_qty_std', 'tot_qty'
],
where=lambda row: row['ret_gb'] not in ['D/C', '반납', '수납취소'])
recs.vlookup(drugDB.values(), 'drug_cd', 'code', [('amount', 0),
('amount_unit', ''),
('name', ""),
('std_unit', "")])
recs.format([('tot_qty', 0.0), ('ord_qty_std', 0.0)])
recs.add_column([('잔량', lambda row: row['tot_qty'] - row['ord_qty_std']),
('폐기량', lambda row: row['잔량'] * row['amount'])])
recs.select('*', where=lambda row: row['잔량'] > 0).order_by(
['name', 'narct_owarh_ymd', 'ward'])
recs.rename([('narct_owarh_ymd', '불출일자'), ('ori_ord_ymd', '원처방일자'),
('ord_no', '처방번호[묶음]'), ('tot_qty', '집계량'), ('name', '폐기약품명'),
('drug_cd', '약품코드'), ('amount', '집계량'),
('ord_qty_std', '처방량(규격단위)'), ('drug_nm', '약품명'),
('amount_unit', '폐기단위'), ('ptnt_nm', '환자명'),
('ptnt_no', '환자번호'), ('std_unit', '규격단위'), ('ward', '병동')])
table = recs.select([
'불출일자', '병동', '환자번호', '환자명', '폐기약품명', '처방량(규격단위)', 'GET_DEPT_NM', '잔량',
'규격단위', '폐기량', '폐기단위'
]).to2darry()
grp = recs.group_by(
columns=['폐기약품명'],
aggset=[('폐기량', sum, '폐기량__sum'), ('폐기약품명', len, '폐기약품명__len')],
selects=['폐기약품명', '폐기약품명__len', '규격단위', '폐기량__sum', '폐기단위'],
inplace=False)
for row in grp:
row['폐기량'] = round(row['폐기량1'], 2)
return table[n:], grp
def get_all(self,
category="E",
market_status='AS',
page=1,
set_detail=False):
''' present_code = {'Y':'급여', 'N': '비급여'}
prodtype_code = {'E': '전문', 'O': '일반', 'Q': '의약외품'}
market_status_code = {'AS': '유통/생산 중', 'AE': '유통/생산 미확인', 'AD': '유통/생산 중단'}
'''
params = {
"Page": page,
"prodtype": category,
"MktStatus": market_status
}
r = requests.get(self.get_all_url,
params=params,
headers=self.api_headers)
soup = BeautifulSoup(r.text, 'html.parser')
table = soup.find('div', {'class': 'search_result_list'})
tab_as = soup.find(id='tabAS')
tab_ae = soup.find(id='tabAE')
tab_ad = soup.find(id='tabAD')
as_count = tab_as.text
ret = []
for row in table('li'):
img = row.find(
'img', {'src': re.compile(r'^/Data/DrugPictures_Small/.+$')})
img_src = img['src'] if img else ''
subject = row.find(class_='subject_kr')
if not subject:
return ret
href = subject.a['href'] if subject.a else ''
name = subject.a.text if subject.a else ''
pow_yesno = '불가' if row.find(
'span', {'class': re.compile('.*icon_crs')}) else '가능'
cmpnt = row.find(class_='subject_eng')
component = cmpnt.text if cmpnt else ''
info_area = row.find(class_='info_area')
price = info_area.find(class_='price')
edi = info_area.find(class_='type')
company = info_area.find(class_='company')
present_val = info_area.find(
'span', {'class': re.compile('.+[YNX]')})['class'][1][-1]
present_code = {'Y': '급여', 'N': '비급여'}
prodtype_code = {'E': '전문', 'O': '일반', 'Q': '의약외품'}
market_status_code = {
'AS': '유통/생산 중',
'AE': '유통/생산 미확인',
'AD': '유통/생산 중단'
}
ret.append({
'market_status':
market_status,
'prod_type':
prodtype_code.get(category),
'img_src':
urljoin(self.host, img_src) if img_src else '',
'href':
urljoin(self.host, href),
'name':
name,
'component_summary':
component,
'company':
company.text if company else '',
'edi':
edi.text if edi else '',
'price':
price.text.replace(',', '').replace('원', '') if price else '',
'drug_id':
os.path.basename(href),
'powder_yesno':
pow_yesno,
'present_yesno':
present_code.get(present_val, '산정불가')
})
if set_detail:
recs_list = RecordParser(ret)
id_list = recs_list.select(['drug_id'],
inplace=False).to2darry(headers=False)
id_list = [e[0] for e in id_list]
with ThreadPoolExecutor(min(MAX_WORKER, len(ret))) as executor:
details = executor.map(self.detail, id_list)
recs_detail = RecordParser(list(details))
recs_list.vlookup(recs_detail, 'drug_id', 'drug_id',
[('manufacture', ''), ('celler', ''),
('pkg_info', ''), ('official_sort', ''),
('components', ''), ('component_code', '')])
print('Detail: ', len(recs_detail))
return recs_list.records
return ret
def get_chemo_label_object(wards, ord_start_date, ord_end_date, start_dt,
end_dt):
from recordlib import RecordParser, read_excel
drugs_recs = read_excel(DRUG_DB_PATH)
odr = OrderSelectApiRequest(ord_start_date, ord_end_date, wards)
odr.api_calls()
records = odr.get_records()
ord_recs = RecordParser(records=records,
drop_if=lambda row: row.get('rcpt_dt', "") == "")
# if real
ord_recs.select('*', where=lambda row: start_dt <= row['rcpt_dt'] < end_dt)
ord_recs.vlookup(drugs_recs, 'ord_cd',
'약품코드', [('항암제구분', '0'), ('함량1', 0.0), ('함량단위1', ''),
('함량2', 0.0), ('함량단위2', '')])
ord_recs.format([('ord_qty', 0.0), ('함량1', 0.0), ('함량2', 0.0)])
ord_recs.add_column([('amt_vol', lambda row: row['ord_qty'] * row['함량1']),
('amt_wgt', lambda row: row['ord_qty'] * row['함량2'])])
chemo_index = ord_recs.select(['ord_ymd', 'rcpt_seq', 'medi_no', 'ord_no'],
where=lambda row: row['항암제구분'] == '1',
inplace=False).to2darry(headers=False)
ord_recs.select('*',
where=lambda r: [
r['ord_ymd'], r['rcpt_seq'], r['medi_no'], r['ord_no']
] in chemo_index)
detail = ord_recs.records.copy()
f, l = ord_recs.min('rcpt_dt'), ord_recs.max('rcpt_dt')
ord_recs.group_by(columns=['ord_ymd', 'rcpt_seq', 'medi_no', 'ord_cd'],
aggset=[('amt_vol', sum, 'amt_vol_sum'),
('amt_wgt', sum, 'amt_wgt_sum'),
('ord_qty', sum, 'ord_qty_sum'),
('drug_nm', len, 'drug_nm_count')],
selects=[
'ord_cd', 'drug_nm', 'ord_unit_nm', 'amt_vol_sum',
'amt_wgt_sum', 'ord_qty_sum', '함량단위1', '함량단위2',
'drug_nm_count'
],
inplace=True)
ord_recs.add_column([('rcpt_dt_min', lambda x: f),
('rcpt_dt_max', lambda x: l)])
return ord_recs.records, detail
# path = 'C:\\Users\\user\\Desktop\\집계표.xlsx'
# ret = get_label_object(['P', 'S'], ['51', '52', '61', '71', '81', '92', 'IC'], '2017-04-09','2017-04-10', '2017-04-08 00:00:00', '2017-04-08 23:23:00')
# ret.to_excel(path)
# os.startfile(path)
# get_label_object_test(['P', 'S'], ['51', '52', '61', '71', '81', '92', 'IC'], '2017-04-09','2017-04-10', '2017-04-08 00:00:00', '2017-04-08 23:23:00')
def get_chemo_label_object_test(wards, ord_start_date, ord_end_date, start_dt,
end_dt):
from recordlib import RecordParser, read_excel
drugs_recs = read_excel(DRUG_DB_PATH)
odr = OrderSelectApiRequest(ord_start_date, ord_end_date, wards)
odr.set_test_response('response_samples/ordSelect51.sample.rsp')
records = odr.get_records()
ord_recs = RecordParser(records=records,
drop_if=lambda row: row.get('rcpt_dt', "") == "")
# if real
# ord_recs.select('*', where= lambda row: start_dt <= row['rcpt_dt'] < end_dt)
ord_recs.vlookup(drugs_recs, 'ord_cd',
'약품코드', [('항암제구분', '0'), ('함량1', 0.0), ('함량단위1', ''),
('함량2', 0.0), ('함량단위2', '')])
ord_recs.format([('ord_qty', 0.0), ('함량1', 0.0), ('함량2', 0.0)])
ord_recs.add_column([('amt_vol', lambda row: row['ord_qty'] * row['함량1']),
('amt_wgt', lambda row: row['ord_qty'] * row['함량2'])])
chemo_index = ord_recs.select(['ord_ymd', 'rcpt_seq', 'medi_no', 'ord_no'],
where=lambda row: row['항암제구분'] == '1',
inplace=False).to2darry(headers=False)
ord_recs.select('*',
where=lambda r: [
r['ord_ymd'], r['rcpt_seq'], r['medi_no'], r['ord_no']
] in chemo_index)
detail = ord_recs.records.copy()
f, l = ord_recs.min('rcpt_dt'), ord_recs.max('rcpt_dt')
ord_recs.group_by(columns=['ord_ymd', 'rcpt_seq', 'medi_no', 'ord_cd'],
aggset=[('amt_vol', sum, 'amt_vol_sum'),
('amt_wgt', sum, 'amt_wgt_sum'),
('ord_qty', sum, 'ord_qty_sum'),
('drug_nm', len, 'drug_nm_count')],
selects=[
'ord_cd', 'drug_nm', 'ord_unit_nm', 'amt_vol_sum',
'amt_wgt_sum', 'ord_qty_sum', '함량단위1', '함량단위2',
'drug_nm_count'
],
inplace=True)
ord_recs.add_column([('rcpt_dt_min', lambda x: f),
('rcpt_dt_max', lambda x: l)])
return ord_recs.records, detail
def get_inj_object(types,
wards,
ord_start_date,
ord_end_date,
start_dt,
end_dt,
test=False):
from recordlib import RecordParser, read_excel
drug_db_recs = read_excel(
DRUG_DB_PATH,
drop_if=lambda row: row['투여경로'] != '3' or row['효능코드명'] in
['혈액대용제', '당류제'] or row['항암제구분'] == '1' or row['약품법적구분'] in ['1', '2'])
pk_set = drug_db_recs.unique('약품코드')
odr = OrderSelectApiRequest(ord_start_date, ord_end_date, wards)
if test:
odr.set_test_response('response_samples/ordSelect51.sample.rsp')
else:
odr.api_calls()
ord_recs = RecordParser(
records=odr.get_records(),
drop_if=lambda row: row.get('ord_cd') not in pk_set or row.get(
'rcpt_dt', "") == "" or row.get('rcpt_ord_tp_nm') not in types)
ord_recs.format([('ord_qty', 0.0), ('ord_frq', 0), ('ord_day', 0)])
if not test:
ord_recs.select('*',
where=lambda row: start_dt <= row['rcpt_dt'] < end_dt)
ord_recs.add_column([
('once_amt', lambda row: round(row['ord_qty'] / row['ord_frq'], 2)),
('total_amt', lambda row: row['ord_qty'] * row['ord_day'])
])
detail = ord_recs.records.copy()
f, l = ord_recs.min('rcpt_dt'), ord_recs.max('rcpt_dt')
ord_recs.group_by(columns=['ord_cd'],
aggset=[('ord_qty', sum, 'ord_qty_sum'),
('drug_nm', len, 'drug_nm_count'),
('total_amt', sum, 'total_amt_sum')],
selects=[
'ord_cd', 'drug_nm', 'ord_qty_sum', 'ord_unit_nm',
'drug_nm_count', 'total_amt_sum'
])
ord_recs.add_column([('rcpt_dt_min', lambda x: f),
('rcpt_dt_max', lambda x: l)])
return ord_recs.records, detail
def get_label_object(kinds, types, wards, ord_start_date, ord_end_date,
start_dt, end_dt):
from recordlib import RecordParser, read_excel
try:
drug_db_recs = get_label_list()
drug_db_recs = RecordParser(
drug_db_recs, drop_if=lambda row: row['단일포장구분'] not in ['S', 'P'])
except:
drug_db_recs = read_excel(
DRUG_DB_PATH, drop_if=lambda row: row['단일포장구분'] not in ['S', 'P'])
pk_set = drug_db_recs.unique('약품코드')
odr = OrderSelectApiRequest(ord_start_date, ord_end_date, wards)
odr.api_calls()
records = odr.get_records()
ord_recs = RecordParser(
records=records,
drop_if=lambda row: row.get('ord_cd') not in pk_set or row.get(
'rcpt_dt', "") == "" or row.get('rcpt_ord_tp_nm') not in types)
ord_recs.vlookup(drug_db_recs, 'ord_cd', '약품코드', [('단일포장구분', 'S')])
ord_recs.format([('ord_qty', 0.0), ('ord_frq', 0), ('ord_day', 0)])
ord_recs.select('*', where=lambda row: start_dt <= row['rcpt_dt'] < end_dt)
ord_recs.add_column([
('once_amt', lambda row: round(row['ord_qty'] / row['ord_frq'], 2)),
('total_amt', lambda row: row['ord_qty'] * row['ord_day'])
])
detail = ord_recs.records.copy()
f, l = ord_recs.min('rcpt_dt'), ord_recs.max('rcpt_dt')
ord_recs.group_by(columns=['단일포장구분', 'drug_nm'],
aggset=[('ord_qty', sum, 'ord_qty_sum'),
('drug_nm', len, 'drug_nm_count'),
('total_amt', sum, 'total_amt_sum')],
selects=[
'단일포장구분', 'ord_cd', 'drug_nm', 'ord_qty_sum',
'ord_unit_nm', 'drug_nm_count', 'total_amt_sum'
])
ord_recs.add_column([('rcpt_dt_min', lambda x: f),
('rcpt_dt_max', lambda x: l)])
ord_recs.value_map([('단일포장구분', {'S': '작은라벨', 'P': '큰라벨'}, '')])
return ord_recs.records, detail