mailsys_server/mailkpi.py

import os
import time
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


#ssd_dir = str(sys.argv[1])
#file_path = '/hdd/' + ssd_dir[5:]


def put_kpi_to_db(kpi):
    try:
        conn = pymssql.connect(server='52.231.39.219',port=1433, user='mc365', password='tkadbrdhmc1!', database='master')
        #print("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]) + ", "+str(kpi[1])+", "+str(kpi[2])+", "+str(kpi[3])+", "+str(kpi[4])+", "+str(kpi[5])+", "+str(kpi[6])+", "+str(kpi[7])+", "+str(kpi[8])+", "+str(kpi[9])+", "+str(kpi[10])+", "+str(kpi[11])+", "+str(kpi[12])+", "+str(kpi[13])+", "+str(kpi[14])+", "+str(kpi[15])+", "+str(kpi[16])+", "+str(kpi[17])+", "+str(kpi[18])+", "+str(kpi[19])+", "+str(kpi[20])+", "+str(kpi[21])+")")
        #print("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]) + "', '"+str(kpi[1])+"', '"+str(kpi[2])+"', '"+str(kpi[3])+"', '"+str(kpi[4])+"', '"+str(kpi[5])+"', '"+str(kpi[6])+"', '"+str(kpi[7])+"', '"+str(kpi[8])+"', '"+str(kpi[9])+"', '"+str(kpi[10])+"', '"+str(kpi[11])+"', '"+str(kpi[12])+"', '"+str(kpi[13])+"', '"+str(kpi[14])+"', '"+str(kpi[15])+"', '"+str(kpi[16])+"', '"+str(kpi[17])+"', '"+str(kpi[18])+"', '"+str(kpi[19])+"', '"+str(kpi[20])+"', '"+str(kpi[21])+"')")
        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]) + "', '"+str(kpi[1])+"', '"+str(kpi[2])+"', '"+str(kpi[3])+"', '"+str(kpi[4])+"', '"+str(kpi[5])+"', '"+str(kpi[6])+"', '"+str(kpi[7])+"', '"+str(kpi[8])+"', '"+str(kpi[9])+"', '"+str(kpi[10])+"', '"+str(kpi[11])+"', '"+str(kpi[12])+"', '"+str(kpi[13])+"', '"+str(kpi[14])+"', '"+str(kpi[15])+"', '"+str(kpi[16])+"', '"+str(kpi[17])+"', '"+str(kpi[18])+"', '"+str(kpi[19])+"', '"+str(kpi[20])+"', '"+str(kpi[21])+"')")
    
        conn.commit()
        conn.close()
    except:
        pass


def get_quad(df):
    if df['x'] >= 0 and df['y'] >= 0:
        return 1
    elif df['x'] >= 0 and df['y'] < 0:
        return 2
    elif df['x'] < 0 and df['y'] < 0:
        return 3
    elif df['x'] < 0 and df['y'] >= 0:
        return 4
def get_inc_num(df):
    count = 1
    prev_count = 0
    if abs(df['diff_diff']) > 0.0008:                
        prev_count = df['inc_num']
        df['inc_num'] = count
        #print(prev_count)
        return count        
    else:
        if prev_count != 0:            
            count += 1
            df['inc_num'] = 0
            prev_count = df['inc_num']
            return 0
        else:
            df['inc_num'] = 0
            prev_count = df['inc_num']
            return 0


def count_inc(df):
    e= 0
    count  = 1
    prev_count = 0
    for x in df['diff_diff']:
        if abs(x) > 0.05:
            df['inc_num'][e] = count
            prev_count = count
            e +=1
        else:
            if prev_count != 0:
                count += 1
                df['inc_num'][e] = 0
                prev_count = 0
                e +=1
            else:
                df['inc_num'][e] = 0
                prev_count = 0
                e +=1

def stroke_count(df):
    e = 0
    count = 1    
    for x in df['diff_diff']:
        if x > 0.03:
            df['stroke_count'][e] = count
            e +=1
        else:
            df['stroke_count'][e] = 0
            e+=1
            
def get_sqrt(df):
    prev_x = 0.0
    prev_y = 0.0
    prev_z = 0.0

    
    for index, row in df.iterrows():        
            df['sqrt'][index] = np.sqrt((df['x'][index] - prev_x) **2 + (df['y'][index] - prev_y) ** 2 +(df['z'][index] - prev_z) ** 2)
            prev_x = df['x'][index]
            prev_y = df['y'][index]
            prev_z = df['z'][index]
    df['sqrt'][0] = 0.0
            

        


def dist(filepath):
    file = filepath
    surgeon_dir = file.split('/')
    #print(surgeon_dir)

    #filepath_slice = file.slice('/')
    norm = pd.read_csv(file)
    #print(norm)
    #norm = norm.drop(norm.columns[0], axis=1)
    #norm = norm.fillna(0)
    data = norm[['x', 'y', 'z']]
    norm['sqrt'] = 0.0

    get_sqrt(norm)

    #norm = norm.apply(lambda x:np.sqrt(x) if x.name in ['sqrt'] else x, axis =1)
    norm['diff'] = norm.diff()['sqrt']
    norm['diff_diff'] = norm.diff()['diff']


    norm['quad'] = norm.apply(get_quad, axis =1)
    
    
    norm = norm.fillna(0)

    norm['inc_num'] = 0
    #norm['inc_num'] = norm.apply(get_inc_num, axis = 1)
    count_inc(norm)
    norm['stroke_count'] = 0
    stroke_count(norm)
    
    #print('num of inc')
    #print(norm['inc_num'].unique())

    #print('total stroke')
    #print(norm['stroke_count'].unique())
    #print(norm)
    diff_csv = norm[['frame','diff_diff']]    
    norm.to_csv(os.path.join(file[:-14]+ 'norm.csv'), index=False)
    diff_csv.to_csv(os.path.join(file[:-14]+ 'diff.csv'))
    #plt.plot(norm['frame'],norm['diff_diff'])
    #plt.savefig('./diff_diff.png')    



    kpi = pd.DataFrame(index=range(0,1))

    quad_count = norm[norm['stroke_count'] != 0]
    
    quad1_c = quad_count[quad_count['quad'] == 1]
    quad2_c = quad_count[quad_count['quad'] == 2]
    quad3_c = quad_count[quad_count['quad'] == 3]
    quad4_c = quad_count[quad_count['quad'] == 4]
    #print(len(quad1_c))
    #print(len(quad2_c))
    #print(len(quad3_c))
    #print(len(quad4_c))
    #print(quad1_c)
    kpi['PK'] = surgeon_dir[2]
    kpi['qaud1_c'] = len(quad1_c)
    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
    kpi['qaud4_s'] = quad4_c['sqrt'].mean() / 30
    kpi['qaud1_d'] = quad1_c['sqrt'].mean() / 45
    kpi['qaud2_d'] = quad2_c['sqrt'].mean() / 45
    kpi['qaud3_d'] = quad3_c['sqrt'].mean() / 45
    kpi['qaud4_d'] = quad4_c['sqrt'].mean() / 45
    kpi = kpi.fillna(0)


    kpi_1 = {
        "stroke": [len(quad1_c),len(quad2_c),len(quad3_c),len(quad4_c)],
        "velocity": [quad1_c['sqrt'].mean() / 30, quad2_c['sqrt'].mean() / 30, quad3_c['sqrt'].mean() / 30, quad4_c['sqrt'].mean() / 30],
        "depth": [quad1_c['sqrt'].mean() / 45, quad2_c['sqrt'].mean() / 45, quad3_c['sqrt'].mean() / 45, quad4_c['sqrt'].mean() / 45]
    }


    #print(kpi)
    val_list = []

    #PK =
    val_list.append(str(surgeon_dir[2]))

    #t_stroke = 
    val_list.append(sum(kpi_1['stroke']))
    #t_vel
    val_list.append("{:.4f}".format(sum(kpi_1['velocity'])/4)) 
    #t_dep
    val_list.append("{:.4f}".format(sum(kpi_1['depth'])/4)) 
    #ud_s_rate =
    val_list.append("{:.4f}".format((kpi_1['stroke'][0] + kpi_1['stroke'][3] - kpi_1['stroke'][1] - kpi_1['stroke'][2]) / sum(kpi_1['stroke'])*100))
    #ud_v_rate =
    val_list.append("{:.4f}".format((kpi_1['velocity'][0] + kpi_1['velocity'][3] - kpi_1['velocity'][1] - kpi_1['velocity'][2])/2))
    #ud_d_rate =
    val_list.append("{:.4f}".format((kpi_1['depth'][0] + kpi_1['depth'][3] - kpi_1['depth'][1] - kpi_1['depth'][2])/2))

    #lr_s_rate =
    val_list.append("{:.4f}".format((kpi_1['stroke'][2] + kpi_1['stroke'][3] - kpi_1['stroke'][0] - kpi_1['stroke'][1]) / sum(kpi_1['stroke'])*100))
    #lr_v_rate =
    val_list.append("{:.4f}".format((kpi_1['velocity'][2] + kpi_1['velocity'][3] - kpi_1['velocity'][0] - kpi_1['velocity'][1])/2))
    #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(str(len(quad1_c)))
    val_list.append(str(quad1_c['sqrt'].mean() / 30))
    val_list.append(str(quad1_c['sqrt'].mean() / 45))
    val_list.append(str(len(quad2_c)))
    val_list.append(str(quad2_c['sqrt'].mean() / 30))
    val_list.append(str(quad2_c['sqrt'].mean() / 45))
    val_list.append(str(len(quad3_c)))
    val_list.append(str(quad3_c['sqrt'].mean() / 30))
    val_list.append(str(quad3_c['sqrt'].mean() / 45))
    val_list.append(str(len(quad4_c)))
    val_list.append(str(quad4_c['sqrt'].mean() / 30))
    val_list.append(str(quad4_c['sqrt'].mean() / 45))
    
    #print(val_list)
    for x in range(0,len(val_list)):        
        if val_list[x] == 'nan':
            val_list[x] = 0        
    #print(val_list)
    return val_list    

    #print(val_list)

def main():
    kpi = dist()
    #kpi_csv = pd.DataFrame([kpi])
    #kpi_csv = kpi_csv.fillna(0)
    #kpi_csv.to_csv(os.path.join(file[:-14],'kpi.csv'))
    #put_kpi_to_db(kpi_csv)



if __name__ == "__main__":
    main()