add pymssql in kpi.py

kpi.py

 import math
 import sys
 import numpy as np
+from numpy.lib.function_base import append
 import pandas as pd
 from pandas import DataFrame as df
 from sklearn.cluster import DBSCAN
+import pymssql
 #import matplotlib.pyplot as plt
 
 
 file_path = '/hdd/' + ssd_dir[5:]
 count = 1
 
+def put_kpi_to_db(kpi):
+    conn = pymssql.connect(server='52.231.39.219',port=1433, user='mc365', password='tkadbrdhmc1!', database='master')
+    
+    cursor = conn.cursor()
+    cursor.execute("INSERT INTO tsfmc_mailsystem.dbo.KPI(surgeryID, totalStrokeCount, totalStrokeVelocity, totalStrokeDepth, upDownTotalStrokeCount, upDownTotalStrokeVelocity, upDownTotalStrokeDepth, leftRightTotalStrokeCount, leftRightTotalStrokeVelocity, leftRightTotalStrokeDepth, quadrant1TotalStrokeCount, quadrant1TotalStrokeVelocity, quadrant1TotalStrokeDepth, quadrant2TotalStrokeCount, quadrant2TotalStrokeVelocity, quadrant2TotalStrokeDepth, quadrant3TotalStrokeCount, quadrant3TotalStrokeVelocity, quadrant3TotalStrokeDepth, quadrant4TotalStrokeCount, quadrant4TotalStrokeVelocity, quadrant4TotalStrokeDepth) VALUES ("+str(kpi[0])+", "+kpi[1]+", "+kpi[2]+", "+kpi[3]+", "+kpi[4]+", "+kpi[5]+", "+kpi[6]+", "+kpi[7]+", "+kpi[8]+", "+kpi[9]+", "+kpi[10]+", "+kpi[11]+", "+kpi[12]+", "+kpi[13]+", "+kpi[14]+", "+kpi[15]+", "+kpi[16]+", "+kpi[17]+", "+kpi[18]+", "+kpi[19]+", "+kpi[20]+", "+kpi[21]+")")  
+    conn.commit()
+    conn.close()
+
+
 def get_quad(df):
     if df['x'] >= 0 and df['y'] >= 0:
         return 1
     kpi['qaud2_c'] = len(quad2_c)
     kpi['qaud3_c'] = len(quad3_c)
     kpi['qaud4_c'] = len(quad4_c)
+    #FIXME: this values are huristic value. please change this values after research
     kpi['qaud1_s'] = quad1_c['sqrt'].mean() / 30
     kpi['qaud2_s'] = quad2_c['sqrt'].mean() / 30
     kpi['qaud3_s'] = quad3_c['sqrt'].mean() / 30
     #lr_d_rate =
     val_list.append("{:.4f}".format((kpi_1['depth'][2] + kpi_1['depth'][3] - kpi_1['depth'][0] - kpi_1['depth'][1])/2))
 
+    #q1 s, v, d
+    val_list.append(len(quad1_c))
+    val_list.append(quad1_c['sqrt'].mean() / 30)
+    val_list.append(quad1_c['sqrt'].mean() / 45)
+    val_list.append(len(quad2_c))
+    val_list.append(quad2_c['sqrt'].mean() / 30)
+    val_list.append(quad2_c['sqrt'].mean() / 45)
+    val_list.append(len(quad3_c))
+    val_list.append(quad3_c['sqrt'].mean() / 30)
+    val_list.append(quad3_c['sqrt'].mean() / 45)
+    val_list.append(len(quad4_c))
+    val_list.append(quad4_c['sqrt'].mean() / 30)
+    val_list.append(quad4_c['sqrt'].mean() / 45)
+
 
-    #print(val_list)
 
 
+    return val_list
 
+    #print(val_list)
 
 
 
-dist()
+if __name__ == "__main__":
+    kpi = dist()
+    put_kpi_to_db(kpi)