def bloodsugar():
db_class = mod_dbconn.Database()
email = request.args.get("email")
sql = "SELECT bloodsugar FROM db.bloodsugar order by idx desc limit 10"
row = db_class.executeAll(sql)
return jsonify(row)
def supply_desc(p_id):
# 상세페이지 조회하는 함수
db_class = mod_dbconn.Database()
sql = """SELECT *
FROM taxocr.t_provider
WHERE p_id = %s"""
desc_dict = db_class.executeAll(sql, args=p_id)
return desc_dict
def readCarb():
db_class = mod_dbconn.Database()
email = request.args.get("email")
sql = "SELECT * FROM db.carb WHERE email=" + email
row = db_class.executeAll(sql)
return jsonify(row)
def deleteInsulin():
idx = request.args.get("idx")
db_class = mod_dbconn.Database()
sql = "DELETE FROM db.insulin WHERE idx=" + idx
row = db_class.executeAll(sql)
db_class.commit()
return jsonify(row)
def readInsulin():
email = request.args.get("email")
db_class = mod_dbconn.Database()
sql = "SELECT * FROM db.insulin WHERE email=" + email
row = db_class.executeAll(sql)
return jsonify(row)
def addInsulin():
email = request.form['email']
insulin = request.form['value']
fDate = request.form['fDate']
db_class = mod_dbconn.Database()
'''
sql = "INSERT INTO db.insulin (insulin, email, fDate) VALUES(" + insulin + ", \'" + email + "\', \'" + fDate + "\')"
row = db_class.executeAll(sql)
print(sql)
db_class.commit()
'''
sql = "SELECT * FROM db.features WHERE email = \'" + email + "\' and fDate >= \'" + fDate + "\' ORDER BY fDate DESC"
print(sql)
row = db_class.executeAll(sql)
#x = 투여량, insulin
#t = 마지막 투여로부터 지난 시간, fDate - curDate
for i in row:
curDate = i.get('fDate')
x = float(insulin)
date_time_obj = datetime.strptime(fDate, '%Y-%m-%d %H:%M:%S')
diff = curDate - date_time_obj
t = diff.seconds / 60
print(t)
print("인슐린량")
print(x)
input = x * ((0.0008 * x + 0.028) * (0.0008 * x + 0.028)) * 2 * t * (
2.7**0.09 * t) / (t * t + (0.0008 * x + 0.028) *
(0.0008 * x + 0.028)) * (t * t +
(0.0008 * x + 0.028) *
(0.0008 * x + 0.028))
print(insulin.__class__)
print(curDate.__class__)
sql = "UPDATE db.features SET insulin = " + str(
input) + " WHERE fDate = \'" + str(
curDate) + "\' and email = \'" + email + "\'"
print(sql)
row = db_class.executeAll(sql)
db_class.commit()
return jsonify(row)
def addCarb():
email = request.form['email']
carb = request.form['value']
fDate = request.form['fDate']
db_class = mod_dbconn.Database()
'''
sql = "INSERT INTO db.carb (carb, email, fDate) VALUES(" + carb + ", \'" + email + "\', \'" + fDate + "\')"
row = db_class.executeAll(sql)
print(sql)
db_class.commit()
'''
sql = "SELECT * FROM db.features WHERE email = \'" + email + "\' and fDate >= \'" + fDate + "\' ORDER BY fDate DESC"
print(sql)
row = db_class.executeAll(sql)
#x = 투여량, insulin
#t = 마지막 투여로부터 지난 시간, fDate - curDate
for i in row:
curDate = i.get('fDate')
v = float(carb)
date_time_obj = datetime.strptime(fDate, '%Y-%m-%d %H:%M:%S')
diff = curDate - date_time_obj
t = diff.seconds / 60
print(t)
if t >= 0 and t < 30:
input = (t - 1) / 15 + math.exp(-t)
elif t >= 30 and t < 30 + (5.5 * v - 2) / 240:
input = 2 + math.exp(-t)
elif 30 + (5.5 * v - 2) / 240 <= t and t < 60 + (5.5 * v - 2) / 240:
input = -0.0015 * v + math.exp(-t) - 0.06 * t + 4.06
else:
input = math.exp(-t)
sql = "UPDATE db.features SET carb = " + str(
input) + " WHERE fDate = \'" + str(
curDate) + "\' and email = \'" + email + "\'"
print(sql)
row = db_class.executeAll(sql)
db_class.commit()
return jsonify(row)
from app import mod_dbconn
year_='2010' # 이거 웹에서 받아올거라 변수 여기서 선언 안되어있어도 됨(?)
db_class = mod_dbconn.Database()
# 세금 데이터----------------------------------------
def taxdata(year_):
taxsql = """SELECT SUM(t_bill.b_cost_tax)
FROM t_bill
WHERE YEAR(t_bill.b_date) = %s"""
taxrow = db_class.executeOne(taxsql, year_)
taxdata = int(taxrow["SUM(t_bill.b_cost_tax)"])
return taxdata
# Doughnut graph------------------------------------
def doughnutGraph(year_):
labels = [] # 그래프 x축: 회사명
data = [] #그래프 y축 : 거래 금액
temp = [] # 디비에서 빼온 것 잠깐 담을 리스트
temp2 = [] # 위와 동일
temptuple = [] # 위와 동일
sql = """SELECT t_provider.p_corp_name as p_corp_name,
SUM(t_bill.b_cost_total) as b_cost_total_sum
FROM t_provider, t_bill
WHERE t_provider.p_id = t_bill.FK_p_id AND YEAR(t_bill.b_date) = %s
GROUP BY FK_p_id
ORDER BY SUM(t_bill.b_cost_total) DESC"""
row = db_class.executeAll(sql, year_)
def user():
email = request.form['email']
#email = request.args.get("email")
print(email)
db_class = mod_dbconn.Database()
sql = "SELECT id, cycle, bloodsugar, carb, insulin FROM db.features WHERE email=\"" + email + "\"ORDER BY fDate DESC LIMIT 1301"
row = db_class.executeAll(sql)
train_df = DataFrame(row)
print(train_df)
print(train_df.shape)
train_df = train_df.sort_values(['id', 'cycle'])
# Data Labeling - generate column RUL (Remaining Useful Life or Time to Failure)
rul = pd.DataFrame(train_df.groupby('id')['cycle'].max()).reset_index()
rul.columns = ['id', 'max']
train_df = train_df.merge(rul, on=['id'], how='left')
train_df['RUL'] = train_df['max'] - train_df['cycle']
train_df.drop('max', axis=1, inplace=True)
print("러벨링")
print(train_df.shape)
# MinMax normalization (from 0 to 1)
train_df['cycle_norm'] = train_df['cycle']
cols_normalize = train_df.columns.difference(
['id', 'cycle', 'RUL', 'label1', 'label2', 'label3'])
min_max_scaler = preprocessing.MinMaxScaler()
norm_train_df = pd.DataFrame(min_max_scaler.fit_transform(
train_df[cols_normalize]),
columns=cols_normalize,
index=train_df.index)
join_df = train_df[train_df.columns.difference(cols_normalize)].join(
norm_train_df)
train_df = join_df.reindex(columns=train_df.columns)
print("노말라이제이션")
print(train_df.shape)
train_df[train_df["id"] == 1].tail()
max_batch_len = train_df['id'].value_counts().max()
train_cols = ['bloodsugar', 'carb', 'insulin'] + ['cycle_norm']
test_cols = ["RUL"]
xhat = pad_engines(train_df, train_cols, 1301)
print(xhat)
print(xhat.shape)
#(1301, 4)
print(xhat.shape)
#xhat = np.zeros((1, 1301, 4))
y_pred = model.predict(xhat)
print(y_pred.shape)
print(y_pred[-1].shape)
#tte = {
#'tte': json.dumps(str(y_pred[-1].flatten()[-1]))
#'tte':str(y_pred[-1].flatten()[-1])
#}
return str(y_pred[-1].flatten()[-1])