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PyGSLIB

Introduction

This is a simple example on adding coordinates to a GSLIB grid. Note that the function addcord is a Fortran modified copy of the standalone GSLIB program addcoord.





In [1]:



    


#general imports
import matplotlib.pyplot as plt  
import pygslib      

#make the plots inline
%matplotlib inline

Getting the data ready for work

You can use Pandas to import your data from csv, execel, sql database, json, html, among others. 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 [3]:



    


# To put the GSLIB file into a Pandas DataFrame
mydata= pygslib.gslib.read_gslib_file(u'../datasets/true.dat')



    











In [5]:



    


# This is a 2D grid file with two variables
# To add a dummy variable BHID = 1 use this code
mydata['bhid']=1

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



    


















    






 
 **** 5 first rows in my datafile **** 

         Primary  Secondary  bhid
2495     7.11       8.82     1
2496     9.26       7.97     1
2497     1.86       8.62     1
2498     1.12       6.59     1
2499     1.65       5.83     1

















In [6]:



    


# GSLIB grids have not coordinates
# To add coordinates use addcoord function

#first prepare a parameter file
par= {  'nx'  : 50,
        'ny'  : 50,
        'nz'  : 1,
        'xmn' : 0.5,
        'ymn' : 0.5,
        'zmn' : 1,
        'xsiz': 1,
        'ysiz': 1,
        'zsiz': 10,
        'grid': mydata }

# then run the function
mydataxyz=pygslib.gslib.addcoord(**par)

# this is to see the output
mydataxyz



    


















    
Out[6]:











  
    

      
      x
      y
      z
      Primary
      Secondary
      bhid
    

  
  
    

      0
      0.5
      0.5
      1.0
      2.26
      3.26
      1
    

    

      1
      1.5
      0.5
      1.0
      3.28
      2.64
      1
    

    

      2
      2.5
      0.5
      1.0
      2.80
      2.15
      1
    

    

      3
      3.5
      0.5
      1.0
      0.95
      1.69
      1
    

    

      4
      4.5
      0.5
      1.0
      0.12
      0.51
      1
    

    

      5
      5.5
      0.5
      1.0
      0.13
      0.27
      1
    

    

      6
      6.5
      0.5
      1.0
      0.39
      0.16
      1
    

    

      7
      7.5
      0.5
      1.0
      0.24
      0.31
      1
    

    

      8
      8.5
      0.5
      1.0
      0.30
      0.39
      1
    

    

      9
      9.5
      0.5
      1.0
      0.22
      0.40
      1
    

    

      10
      10.5
      0.5
      1.0
      0.18
      0.42
      1
    

    

      11
      11.5
      0.5
      1.0
      0.26
      0.42
      1
    

    

      12
      12.5
      0.5
      1.0
      0.55
      0.30
      1
    

    

      13
      13.5
      0.5
      1.0
      0.12
      0.25
      1
    

    

      14
      14.5
      0.5
      1.0
      0.44
      0.32
      1
    

    

      15
      15.5
      0.5
      1.0
      0.14
      0.57
      1
    

    

      16
      16.5
      0.5
      1.0
      0.50
      0.61
      1
    

    

      17
      17.5
      0.5
      1.0
      0.82
      0.64
      1
    

    

      18
      18.5
      0.5
      1.0
      0.51
      0.69
      1
    

    

      19
      19.5
      0.5
      1.0
      0.73
      0.65
      1
    

    

      20
      20.5
      0.5
      1.0
      0.36
      0.46
      1
    

    

      21
      21.5
      0.5
      1.0
      0.16
      0.42
      1
    

    

      22
      22.5
      0.5
      1.0
      0.10
      0.47
      1
    

    

      23
      23.5
      0.5
      1.0
      0.47
      0.49
      1
    

    

      24
      24.5
      0.5
      1.0
      0.52
      0.60
      1
    

    

      25
      25.5
      0.5
      1.0
      0.29
      0.66
      1
    

    

      26
      26.5
      0.5
      1.0
      0.98
      0.94
      1
    

    

      27
      27.5
      0.5
      1.0
      2.14
      1.44
      1
    

    

      28
      28.5
      0.5
      1.0
      1.31
      1.85
      1
    

    

      29
      29.5
      0.5
      1.0
      1.97
      2.22
      1
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      2470
      20.5
      49.5
      1.0
      0.03
      0.54
      1
    

    

      2471
      21.5
      49.5
      1.0
      0.05
      0.42
      1
    

    

      2472
      22.5
      49.5
      1.0
      0.36
      0.31
      1
    

    

      2473
      23.5
      49.5
      1.0
      0.13
      0.35
      1
    

    

      2474
      24.5
      49.5
      1.0
      0.03
      0.41
      1
    

    

      2475
      25.5
      49.5
      1.0
      0.36
      0.61
      1
    

    

      2476
      26.5
      49.5
      1.0
      1.52
      1.52
      1
    

    

      2477
      27.5
      49.5
      1.0
      2.32
      3.60
      1
    

    

      2478
      28.5
      49.5
      1.0
      2.91
      6.03
      1
    

    

      2479
      29.5
      49.5
      1.0
      8.13
      8.36
      1
    

    

      2480
      30.5
      49.5
      1.0
      12.08
      13.96
      1
    

    

      2481
      31.5
      49.5
      1.0
      5.49
      16.22
      1
    

    

      2482
      32.5
      49.5
      1.0
      27.14
      17.93
      1
    

    

      2483
      33.5
      49.5
      1.0
      29.36
      17.08
      1
    

    

      2484
      34.5
      49.5
      1.0
      35.38
      15.32
      1
    

    

      2485
      35.5
      49.5
      1.0
      14.50
      10.40
      1
    

    

      2486
      36.5
      49.5
      1.0
      1.96
      8.04
      1
    

    

      2487
      37.5
      49.5
      1.0
      7.88
      7.10
      1
    

    

      2488
      38.5
      49.5
      1.0
      9.68
      6.10
      1
    

    

      2489
      39.5
      49.5
      1.0
      13.57
      6.68
      1
    

    

      2490
      40.5
      49.5
      1.0
      34.58
      7.22
      1
    

    

      2491
      41.5
      49.5
      1.0
      13.59
      8.03
      1
    

    

      2492
      42.5
      49.5
      1.0
      4.26
      5.70
      1
    

    

      2493
      43.5
      49.5
      1.0
      3.73
      8.36
      1
    

    

      2494
      44.5
      49.5
      1.0
      1.43
      8.84
      1
    

    

      2495
      45.5
      49.5
      1.0
      7.11
      8.82
      1
    

    

      2496
      46.5
      49.5
      1.0
      9.26
      7.97
      1
    

    

      2497
      47.5
      49.5
      1.0
      1.86
      8.62
      1
    

    

      2498
      48.5
      49.5
      1.0
      1.12
      6.59
      1
    

    

      2499
      49.5
      49.5
      1.0
      1.65
      5.83
      1
    

  



2500 rows × 6 columns



















In [7]:



    


# Now we can plot the centroids of the cells
plt.plot(mydataxyz['x'],mydataxyz['y'], '+')



    


















    
Out[7]:








[<matplotlib.lines.Line2D at 0x2ee7eb5e390>]










    
























In [ ]:

Content source: opengeostat/pygslib

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