def parse_narc_content(content, n=0, to_queryset=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=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',
'get_dept_nm'
],
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.update([('잔량', lambda row: round(row['잔량'], 2)),
('폐기량', lambda row: round(row['폐기량'], 2))])
recs.select('*', where=lambda row: row['잔량'] > 0).order_by(
['name', 'narct_owarh_ymd', 'ward'])
if len(recs.records) == 0:
return [], []
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', '약품코드', '처방량(규격단위)', '잔량',
'규격단위', '폐기량', '폐기단위', 'get_dept_nm'
])
table.add_column([('ord_amt', lambda row: row['처방량(규격단위)'])])
if to_queryset == False:
table = table.to2darry()
grp = recs.group_by(
columns=['폐기약품명'],
aggset=[('폐기량', sum, '폐기량__sum'), ('폐기약품명', len, '폐기약품명__len')],
selects=['폐기약품명', '폐기약품명__len', '규격단위', '폐기량__sum', '폐기단위', '약품코드'],
inplace=False)
# grp = map(lambda row: round(row['폐기량__sum'], 2), grp)
for row in grp:
row['폐기량__sum'] = round(row['폐기량__sum'], 2)
return table[n:], grp
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_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_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_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 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_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_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