PyGSLIB

QQ and PP plots



In [1]:

    
#general imports
import matplotlib.pyplot as plt   
import pygslib
import numpy as np

#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
cluster= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')  
true= pygslib.gslib.read_gslib_file('../datasets/true.dat')  

true['Declustering Weight'] = 1

Testing qpplt

This is not plotting results but is handy to get declustered bins for plots



In [3]:

    
print pygslib.gslib.__plot.qpplt.__doc__









    



vr1,vr2,error = qpplt(qqorpp,npts,va1,va2,wt1,wt2,[n1,n2])

Wrapper for ``qpplt``.

Parameters
----------
qqorpp : input int
npts : input int
va1 : input rank-1 array('d') with bounds (n1)
va2 : input rank-1 array('d') with bounds (n2)
wt1 : input rank-1 array('d') with bounds (n1)
wt2 : input rank-1 array('d') with bounds (n2)

Other Parameters
----------------
n1 : input int, optional
    Default: len(va1)
n2 : input int, optional
    Default: len(va2)

Returns
-------
vr1 : rank-1 array('d') with bounds (npts)
vr2 : rank-1 array('d') with bounds (npts)
error : int

QQ-Plot



In [4]:

    
npoints = len(cluster['Primary'])
true['Declustering Weight'] = 1


#using declustering wight
parameters_qpplt = {
     'qqorpp'  : 0,                             # Q-Q plot (qqorpp=0); P-P plot (qqorpp=1)
      'npts'   : npoints,                       # number of points to use on the Q-Q or P-P plot (should not exceed the smallest number of data in data1 / data2
       'va1'   : cluster['Primary'],            # array('d') with bounds (nd)
       'va2'   : true['Primary'],               # array('d') with bounds (nd)
       'wt1'   : cluster['Declustering Weight'],# array('d') with bounds (nd)
       'wt2'   : true['Declustering Weight']}   # array('d') with bounds (nd)
    
vr1a,vr2a,error = pygslib.gslib.__plot.qpplt(**parameters_qpplt)

print 'error ? ',  error != 0

#ignoring declustering wight
cluster['No Weight'] =1
parameters_qpplt['wt1'] =  cluster['No Weight']
       
vr1b,vr2b,error = pygslib.gslib.__plot.qpplt(**parameters_qpplt)

print 'error ? ',  error != 0









    



error ?  False
error ?  False



In [5]:

    
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.plot (vr1a, vr2a, 'o', label = 'declustered')
plt.plot (vr1b, vr2b, 'o', label = ' non declustered')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel ('cluster dataset')
ax.set_ylabel ('true dataset')
ax.set_title ('QQ PLot')
plt.legend(loc=4)
plt.grid(True)
fig.show









    Out[5]:





<bound method Figure.show of <matplotlib.figure.Figure object at 0x000000000B3BDCC0>>

PP-PLot



In [6]:

    
npoints = len(cluster['Primary'])
true['Declustering Weight'] = 1


#using declustering wight
parameters_qpplt = {
     'qqorpp'  : 1,                             # Q-Q plot (qqorpp=0); P-P plot (qqorpp=1)
      'npts'   : npoints,                       # number of points to use on the Q-Q or P-P plot (should not exceed the smallest number of data in data1 / data2
       'va1'   : cluster['Primary'],            # array('d') with bounds (nd)
       'va2'   : true['Primary'],               # array('d') with bounds (nd)
       'wt1'   : cluster['Declustering Weight'],# array('d') with bounds (nd)
       'wt2'   : true['Declustering Weight']}   # array('d') with bounds (nd)
    
vr1a,vr2a,error = pygslib.gslib.__plot.qpplt(**parameters_qpplt)

print 'error ? ',  error != 0

#ignoring declustering wight
cluster['No Weight'] =1
parameters_qpplt['wt1'] =  cluster['No Weight']
       
vr1b,vr2b,error = pygslib.gslib.__plot.qpplt(**parameters_qpplt)

print 'error ? ',  error != 0









    



error ?  False
error ?  False



In [7]:

    
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
plt.plot (vr1a, vr2a, 'o', label = 'declustered')
plt.plot (vr1b, vr2b, 'o', label = ' non declustered')
#ax.set_xscale('log')
#ax.set_yscale('log')
ax.set_xlabel ('cluster dataset')
ax.set_ylabel ('true dataset')
ax.set_title ('PP PLot')
plt.legend(loc=4)
plt.grid(True)
fig.show









    Out[7]:





<bound method Figure.show of <matplotlib.figure.Figure object at 0x000000000B3406D8>>



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