text: 
  import pandas as pd
  import pymysql
  import sys
  from sqlalchemy import create_engine
  # import pandas as pd
  import numpy as np
  import seaborn as sns
  from scipy import stats
  import matplotlib.pyplot as plt
  
  id = sys.argv[1]
  # print(id)
  engine = create_engine("mysql+pymysql://pmauser:password_here@localhost:3306/tacovid")
  df = pd.read_sql_query("SELECT * FROM output_jan_maret where id="+id, engine)
  
  m1=df['m1'][0].replace("[","").replace("]","").split(',')
  m1 = np.array(m1).astype(int)
  
  m2=df['m2'][0].replace("[","").replace("]","").split(',')
  m2 = np.array(m2).astype(int)
  
  res = stats.linregress(m1,m2)
  print(res.slope)
  print('%%')
  print(res.intercept)
  
  diff = m1-m2
  mean = (m1+m2)/2
  persen = diff/mean
  persen=persen*100
  average = np.mean(persen)
  
  deviasi = np.std(persen)
  batas_atas = average+(deviasi*1.96)
  batas_bawah = average-(deviasi*1.96)
  
  average_asli = np.mean(diff)
  deviasi_asli = np.std(diff)
  max_asli = average_asli+(deviasi_asli*1.96)
  min_asli = average_asli-(deviasi_asli*1.96)
  
  titik_diluar_max = diff[diff>max_asli]
  titik_diluar_min = diff[diff<min_asli]
  
  titik = len(titik_diluar_max) + len(titik_diluar_min)
  
  agreement=1.96
  confidence=.95
  n = m1.size
  dof = n - 1
  diff = m1 - m2
  mean_diff = np.mean(diff)
  std_diff = np.std(diff, ddof=1)
  mean_diff_se = np.sqrt(std_diff**2 / n)
      # Limits of agreements
  high = mean_diff + agreement * std_diff
  low = mean_diff - agreement * std_diff
  high_low_se = np.sqrt(3 * std_diff**2 / n)
  # ci = dict()
  cimean = stats.t.interval(
              confidence, dof, loc=mean_diff, scale=mean_diff_se)
  cihigh = stats.t.interval(
              confidence, dof, loc=high, scale=high_low_se)
  cilow = stats.t.interval(
              confidence, dof, loc=low, scale=high_low_se)
  
  
  
  print('%%')
  print(round(batas_atas,2))
  print('%%')
  print(round(batas_bawah,2))
  print('%%')
  print(titik)
  print('%%')
  print(round(max_asli,2))
  print('%%')
  print(round(min_asli,2))
  print('%%')
  print(round(cimean[0],2))
  print('%%')
  print(round(cimean[1],2))
  print('%%')
  print(round(cihigh[0],2))
  print('%%')
  print(round(cihigh[1],2))
  print('%%')
  print(round(cilow[0],2))
  print('%%')
  print(round(cilow[1],2))
  