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_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
def get_records(types,
wards,
ord_start_date,
ord_end_date,
start_dt,
end_dt,
test,
shape='po'):
# print(ord_start_date, ord_end_date, wards)
ord_request = OrderSelectApiRequest(ord_start_date, ord_end_date, wards)
if test:
ord_request.set_test_response('response_samples/orderselect')
drug_lst = listorm.read_excel(DRUG_DB_PATH)
ord_lst = listorm.Listorm(ord_request.get_records())
else:
ord_request.api_calls()
ord_lst = listorm.Listorm(ord_request.get_records())
try:
if shape == 'inj':
drug_lst = get_inj_list()
else:
drug_lst = get_label_list()
except:
drug_lst = read_excel(DRUG_DB_PATH)
else:
ord_lst = ord_lst.filter(where=lambda row: row.rcpt_dt and start_dt
<= row['rcpt_dt'] < end_dt)
drug_lst = drug_lst.select('약품코드', '단일포장구분', '효능코드(보건복지부)')
pk_set = drug_lst.unique('약품코드')
ord_lst = ord_lst.filter(
where=lambda row: row.get('ord_cd') in pk_set and row.get(
'rcpt_dt') and row.get('rcpt_ord_tp_nm') in types)
ord_lst = ord_lst.join(drug_lst, left_on='ord_cd', right_on='약품코드')
ord_lst = ord_lst.set_number_type(ord_qty=0.0, ord_frq=0, ord_day=0)
rcpt_dt_list = ord_lst.column_values('rcpt_dt')
if rcpt_dt_list:
f, l = min(rcpt_dt_list), max(rcpt_dt_list)
ord_lst = ord_lst.add_columns(rcpt_dt_min=lambda x: f,
rcpt_dt_max=lambda x: l)
return ord_lst
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)
from recordlib import RecordParser, read_excel
path1 = r'C:\Users\HS\Desktop\향정잔량.xls'
path2 = "C:\\Users\\HS\\Desktop\\향정잔량2.xls"
path3 = "C:\\Users\\HS\\Desktop\\약품정보.xls"
path4 = "C:\\Users\\HS\\Desktop\\약품정보2.xls"
# recs = read_excel(path1, drop_if=lambda row: row['불출일자'] == '')
# recs2 = read_excel(path2, drop_if=lambda row: row['불출일자'] == '')
recs3 = read_excel(path3)
recs4 = read_excel(path4)
# recs.round_float_fields([('총량', 0), ('처방량(규격단위)', 2), ('불출량', 2)])
# for row in recs[:1]:
# print(row['처방량(규격단위)'])
# # print(recs.nsmallest_rows(3, ['병동', '총량']))
info = recs3.get_changes(recs4, '약품코드')
print(info.updated[0].where)
# info = recs.get_changes(recs2, ['불출일자', '병동', '약품코드'])
# recs3._put_changes(info)
# recs3.set_pk(['투여경로', '제약회사명'])
# recs.set_pk(['접수일련번호', '접수처방일련번호'])
# for row in recs:
# print(row['pk'])