add column sid and date

script/createkpi.py

         sys.exit(1)
     root = input[1]
     listdir = os.listdir(root)
-    df_all = pd.DataFrame(columns=['q1','q2','q3','q4','q1_uptime','q2_uptime','q3_uptime','q4_uptime','q1_speed','q2_speed','q3_speed','q4_speed','q1_variance','q2_variance','q3_variance','q4_variance','f01_dis','f02_dis','f03_dis','f04_dis','f05_dis','f06_dis','f07_dis','f08_dis','f09_dis','f10_dis','f11_dis','f12_dis','f13_dis','f14_dis','f15_dis','f16_dis','f17_dis','f18_dis','f19_dis','f20_dis','f01_rate','f02_rate','f03_rate','f04_rate','f05_rate','f06_rate','f07_rate','f08_rate','f09_rate','f10_rate','f11_rate','f12_rate','f13_rate','f14_rate','f15_rate','f16_rate','f17_rate','f18_rate','f19_rate','f20_rate','f01_stroke','f02_stroke','f03_stroke','f04_stroke','f05_stroke','f06_stroke','f07_stroke','f08_stroke','f09_stroke','f10_stroke','f11_stroke','f12_stroke','f13_stroke','f14_stroke','f15_stroke','f16_stroke','f17_stroke','f18_stroke','f19_stroke','f20_stroke'])
+    df_all = pd.DataFrame(columns=['sid','date','q1','q2','q3','q4','q1_uptime','q2_uptime','q3_uptime','q4_uptime','q1_speed','q2_speed','q3_speed','q4_speed','q1_variance','q2_variance','q3_variance','q4_variance','f01_dis','f02_dis','f03_dis','f04_dis','f05_dis','f06_dis','f07_dis','f08_dis','f09_dis','f10_dis','f11_dis','f12_dis','f13_dis','f14_dis','f15_dis','f16_dis','f17_dis','f18_dis','f19_dis','f20_dis','f01_rate','f02_rate','f03_rate','f04_rate','f05_rate','f06_rate','f07_rate','f08_rate','f09_rate','f10_rate','f11_rate','f12_rate','f13_rate','f14_rate','f15_rate','f16_rate','f17_rate','f18_rate','f19_rate','f20_rate','f01_stroke','f02_stroke','f03_stroke','f04_stroke','f05_stroke','f06_stroke','f07_stroke','f08_stroke','f09_stroke','f10_stroke','f11_stroke','f12_stroke','f13_stroke','f14_stroke','f15_stroke','f16_stroke','f17_stroke','f18_stroke','f19_stroke','f20_stroke'])
 
     for index in listdir:
         try:
             filepath = os.path.join(root, index)
+            sid = index[:9]
+            date = index[9:-4]
             df = pd.read_csv(filepath)
             q1 = df[(df['status']=='in') & (df['x'] >= 0) & (df['y'] >= 0)]
             # in q2 is the frame which status is 'in' and value of x is over 0, y is under 0.
             stroke_20_sum = np.array(dis_20) / len(df_20)
 
             #make a list of all variables
-            all_variables = [len(q1), len(q2), len(q3), len(q4), q1_uptime, q2_uptime, q3_uptime, q4_uptime, q1_velocity_mean, q1_velocity_var, q2_velocity_mean, q2_velocity_var, q3_velocity_mean, q3_velocity_var, q4_velocity_mean, q4_velocity_var]
+            all_variables = [sid, date, len(q1), len(q2), len(q3), len(q4), q1_uptime, q2_uptime, q3_uptime, q4_uptime, q1_velocity_mean, q1_velocity_var, q2_velocity_mean, q2_velocity_var, q3_velocity_mean, q3_velocity_var, q4_velocity_mean, q4_velocity_var]
             all_variables.extend(dis_20)
             all_variables.extend(active_rate_20)
             all_variables.extend(stroke_20_sum)