Testing the nscore transformation table



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 pygslib.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]:

    
# This is a 2D file, in this GSLIB version we require 3D data and drillhole name or domain code
# so, we are adding constant elevation = 0 and a dummy BHID = 1 
mydata['Zlocation']=0
mydata['bhid']=1

# printing to verify results
print (' \n **** 5 first rows in my datafile \n\n  ', mydata.head(n=5))









    



 
 **** 5 first rows in my datafile 

      Xlocation  Ylocation  Primary  Secondary  Declustering Weight  Zlocation  \
0       39.5       18.5     0.06       0.22                1.619          0   
1        5.5        1.5     0.06       0.27                1.619          0   
2       38.5        5.5     0.08       0.40                1.416          0   
3       20.5        1.5     0.09       0.39                1.821          0   
4       27.5       14.5     0.09       0.24                1.349          0   

   bhid  
0     1  
1     1  
2     1  
3     1  
4     1



In [4]:

    
#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 [6]:

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









    



nsc = nscore(va,transin,transout,getrank,[overwrite_va,overwrite_transin,overwrite_transout])

Wrapper for ``nscore``.

Parameters
----------
va : 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)
getrank : input int

Other Parameters
----------------
overwrite_va : input int, optional
    Default: 0
overwrite_transin : input int, optional
    Default: 0
overwrite_transout : input int, optional
    Default: 0

Returns
-------
nsc : rank-1 array('d') with bounds (nd)

Important.

You may run ns_ttable in order to optain transin, transout

Get the transformation table



In [7]:

    
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 [8]:

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



In [9]:

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









    Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x256ae71db38>

Normal score transformation using rank



In [10]:

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



In [11]:

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









    Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x256ae81ab38>



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