# ------------------------------------------------
#
#     Copyright (C) 2021 Alessandro Languasco
#
# ------------------------------------------------
import pandas as pd
import numpy as np 
#import mpmath as mp 
#mp.dps = 38

pd.options.display.float_format = '{:.10f}'.format
        
# functions needed to create the normalised values
def f(x): 
        #mp.dps = 38
        #x1 = mp.mpmathify(x)
        #y = mp.log(x1)
        y= np.log(x)
        return y
            
for r in range(1, 7): # iteration for r from 1 to 6
  if r == 1:
    LvRfile = "outputs/LvR-r=1"
    Sqfile = "inputs/10^10-mergedS1qfile" 
    Iqfile = "inputs/I1qfile"
  elif r == 2:
    LvRfile = "outputs/LvR-r=2"
    Sqfile = "inputs/10^10-mergedS2qfile" 
    Iqfile = "inputs/I2qfile"
  elif r == 3:
    LvRfile = "outputs/LvR-r=3"
    Sqfile = "inputs/10^10-mergedS3qfile" 
    Iqfile = "inputs/I3qfile"
  elif r == 4:
    LvRfile = "outputs/LvR-r=4"
    Sqfile = "inputs/10^10-mergedS4qfile" 
    Iqfile = "inputs/I4qfile"
  elif r == 5:
    LvRfile = "outputs/LvR-r=5"
    Sqfile = "inputs/10^10-mergedS5qfile" 
    Iqfile = "inputs/I5qfile"
  elif r == 6:
    LvRfile = "outputs/LvR-r=6"
    Sqfile = "inputs/10^10-mergedS6qfile" 
    Iqfile = "inputs/I6qfile"

  print('---------------')         
  print(' Case r = ', r)         

  # input data from csv files
  data=pd.read_csv(f'{Sqfile}.csv', sep=';',dtype='str')
  data.head(4)
  dfSr = pd.DataFrame(data, columns= ['q','r','Sr','errSr'])
  dfSr = dfSr.astype('float128')
  #print(dfSr)

  data=pd.read_csv(f'{Iqfile}.csv', sep=';',dtype='str')
  data.head(6)
  dfgamma = pd.DataFrame(data, columns= ['q','gammaKr','gammaK2r','gammaKcomb','Ir','Irprime'])
  dfgamma = dfgamma.astype('float128')
  #print(dfgamma)
 

  # remove the redundant columns q, gammaKcomb from dfgamma
  del dfgamma['q'] 
  #del dfgamma['gammaKr'] 
  #del dfgamma['gammaK2r']
  del dfgamma['gammaKcomb']    
  #print(dfgamma)


  dftotal=dfSr.join(dfgamma) #append dfgamma to dfSr

  dftotal = dftotal.astype('float128') # just to be sure ...
  dftotal['q'] = dftotal['q'].astype('int') # convert q to int
  dftotal['r'] = dftotal['r'].astype('int') # convert r to int

  #create gammakq for this r
  dftotal['gammakqinf'] = (dftotal['Ir'] + dftotal['Sr'] - dftotal['errSr']) # lower bound
  dftotal['gammakqsup'] = (dftotal['Ir'] + dftotal['Sr'] + dftotal['errSr']) # upper bound

  # and the winner is ...
  dftotal['Winner'] = np.where(dftotal['gammakqsup'] < 0.5, 'Ramanujan', 'Landau')

  #create gammaprimekq for this r
  dftotal['gammaprimekqinf'] = (dftotal['Irprime'] + dftotal['Sr'] - dftotal['errSr']) # lower bound
  dftotal['gammaprimekqsup'] = (dftotal['Irprime'] + dftotal['Sr'] + dftotal['errSr']) # upper bound

  # and the winnerprime is ...
  dftotal['Winnerprime'] = np.where(dftotal['gammaprimekqsup'] < 0.5, 'Ramanujan', 'Landau')

  # repeat the q coulmn to improve readability
  dftotal['q-again'] = dftotal['q']  
  #print(dftotal)

  # save the result on a csv file
  dftotal.to_csv (f'{LvRfile}.csv', sep=';', index = False, header=True)
  
  # deleting results for this particular case	
  del dftotal 
  del dfSr
  del dfgamma
# endfor (end iteration over r)
print('---------------')         
print(' *****     End analysis script     *****')