Testing the back normalscore transformation



In [1]:

    
#general imports
import matplotlib.pyplot as plt   
import pygslib    
from matplotlib.patches import Ellipse
import numpy as np
import pandas as pd

#make the plots inline
%matplotlib inline

Getting the data ready for work

If the data is in GSLIB format you can use the function gslib.read_gslib_file(filename) to import the data into a Pandas DataFrame.



In [2]:

    
#get the data in gslib format into a pandas Dataframe
mydata= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')



In [3]:

    
#view data in a 2D projection
plt.scatter(mydata['Xlocation'],mydata['Ylocation'], c=mydata['Primary'])
plt.colorbar()
plt.grid(True)
plt.show()

The nscore transformation table function



In [5]:

    
print (pygslib.gslib.__dist_transf.backtr.__doc__)









    



va,error = backtr(vnsc,transin,transout,ltail,utail,ltpar,utpar,zmin,zmax,getrank,[nd,nt])

Wrapper for ``backtr``.

Parameters
----------
vnsc : input rank-1 array('d') with bounds (nd)
transin : input rank-1 array('d') with bounds (nt)
transout : input rank-1 array('d') with bounds (nt)
ltail : input int
utail : input int
ltpar : input float
utpar : input float
zmin : input float
zmax : input float
getrank : input int

Other Parameters
----------------
nd : input int, optional
    Default: len(vnsc)
nt : input int, optional
    Default: len(transin)

Returns
-------
va : rank-1 array('d') with bounds (nd)
error : int

Get the transformation table



In [6]:

    
transin,transout, error = pygslib.gslib.__dist_transf.ns_ttable(mydata['Primary'],mydata['Declustering Weight'])
print ('there was any error?: ', error!=0)









    



there was any error?:  False

Get the normal score transformation

Note that the declustering is applied on the transformation tables



In [7]:

    
mydata['NS_Primary'] = pygslib.gslib.__dist_transf.nscore(mydata['Primary'],transin,transout,getrank=False)



In [8]:

    
mydata['NS_Primary'].hist(bins=30)









    Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x1d17a4646a0>

Doing the back transformation



In [9]:

    
mydata['NS_Primary_BT'],error = pygslib.gslib.__dist_transf.backtr(mydata['NS_Primary'],
                                     transin,transout,
                                     ltail=1,utail=1,ltpar=0,utpar=60,
                                     zmin=0,zmax=60,getrank=False)
print ('there was any error?: ', error!=0, error)









    



there was any error?:  False 0



In [10]:

    
mydata[['Primary','NS_Primary_BT']].hist(bins=30)









    Out[10]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001D17A4B16A0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x000001D17A563128>]],
      dtype=object)



In [11]:

    
mydata[['Primary','NS_Primary_BT', 'NS_Primary']].head()









    Out[11]:







  
    
      
      Primary
      NS_Primary_BT
      NS_Primary
    
  
  
    
      0
      0.06
      0.06
      -2.318555
    
    
      1
      0.06
      0.06
      -2.318555
    
    
      2
      0.08
      0.08
      -1.908162
    
    
      3
      0.09
      0.09
      -1.773658
    
    
      4
      0.09
      0.09
      -1.773658



In [ ]:

	Primary	NS_Primary_BT	NS_Primary
0	0.06	0.06	-2.318555
1	0.06	0.06	-2.318555
2	0.08	0.08	-1.908162
3	0.09	0.09	-1.773658
4	0.09	0.09	-1.773658