las_reader is a Python package to read in Log ASCII Standard (LAS) files, used in geophysical logging.
The file format is specified by the Canadian Well Logging Society. The package only works for versions 1.2 and 2.0 so far.
The las_reader.read() function accepts a filename or file-like object and returns a Las object:
In [1]:
import las_reader
l = las_reader.read("GT14574.LAS")
print(type(l))
Opened file at GT14574.LAS
<class 'las_reader.las.Las'>
In [2]:
print(l._text)
~Version Information
VERS. 1.20: CWLS log ASCII Standard - version 1.20
WRAP. NO: one line per depth step
~Well Information
#MNEM.UNIT Data Type Information
#_________ _________ ________________________
STRT.M 20.000:
STOP.M 54.350:
STEP.M 0.050:
NULL. -0.0:
COMP. COMPANY:
WELL. WELL: GT14574
FLD. FIELD:
LOC. LOCATION:
CNTY. COUNTY:
STAT. STATE:
CTRY. COUNTRY:
SRVC. SERVICE COMPANY:
DATE. LOG DATE: 07/05/2014
UWI. UNIQUE WELL ID: unknown
~Curve Information
#MNEM.UNIT Curve Information
#_________ _____ ___________________
DEPT.M : 1 DEPTH
GCPS.cps : 2 GCPS
NCPS.cps : 3 NCPS
CCLC.cps : 4 CCLCPS
CCL .cps : 5 CCL
GAPI.Gapi : 6 GAPI
NEUT.cps : 7 NEUTRON
~Parameter Information
~Other Information
~A Depth GCPS NCPS CCLC CCL GAPI NEUT
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30.25 -0.0 -0.0 -0.0 -0.0 58.428 -0.0
30.30 -0.0 -0.0 -0.0 -0.0 60.592 -0.0
30.35 -0.0 -0.0 -0.0 -0.0 60.592 -0.0
30.40 -0.0 -0.0 -0.0 -0.0 71.412 -0.0
30.45 -0.0 -0.0 -0.0 -0.0 71.412 -0.0
30.50 -0.0 -0.0 -0.0 -0.0 77.904 -0.0
30.55 -0.0 -0.0 -0.0 -0.0 64.920 -0.0
30.60 -0.0 -0.0 -0.0 -0.0 54.100 -0.0
30.65 -0.0 -0.0 -0.0 -0.0 62.756 -0.0
30.70 -0.0 -0.0 -0.0 -0.0 45.444 -0.0
30.75 -0.0 -0.0 -0.0 -0.0 73.576 -0.0
30.80 -0.0 -0.0 -0.0 -0.0 51.936 -0.0
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31.00 -0.0 -0.0 -0.0 -0.0 69.248 -0.0
31.05 -0.0 -0.0 -0.0 -0.0 84.396 -0.0
31.10 -0.0 -0.0 -0.0 -0.0 80.068 -0.0
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31.25 20.000 82.000 4870.000 4870.000 84.396 -0.0
31.30 20.000 93.000 4869.000 4869.000 60.592 -0.0
31.35 12.000 82.000 4869.000 4869.000 64.920 -0.0
31.40 14.000 74.000 4869.000 4869.000 64.920 -0.0
31.45 17.000 77.000 4869.000 4869.000 73.576 -0.0
31.50 15.000 83.000 4869.000 4869.000 60.592 77.000
31.55 17.000 85.000 4869.000 4869.000 51.936 77.000
31.60 27.000 92.000 4868.000 4868.000 51.936 82.000
31.65 24.000 91.000 4868.000 4868.000 58.428 93.000
31.70 27.000 77.000 4868.000 4868.000 58.428 82.000
31.75 28.000 76.000 4868.000 4868.000 41.116 74.000
31.80 28.000 74.000 4868.000 4868.000 58.428 77.000
31.85 33.000 58.000 4868.000 4868.000 58.428 83.000
31.90 33.000 61.000 4868.000 4868.000 67.084 85.000
31.95 36.000 73.000 4868.000 4868.000 60.592 92.000
32.00 30.000 59.000 4868.000 4868.000 58.428 91.000
32.05 25.000 61.000 4868.000 4868.000 73.576 77.000
32.10 29.000 61.000 4868.000 4868.000 47.608 76.000
32.15 21.000 63.000 4868.000 4868.000 43.280 74.000
32.20 34.000 78.000 4868.000 4868.000 45.444 58.000
32.25 24.000 70.000 4868.000 4868.000 45.444 61.000
32.30 25.000 71.000 4868.000 4868.000 47.608 73.000
32.35 31.000 75.000 4867.000 4867.000 43.280 59.000
32.40 31.000 75.000 4867.000 4867.000 43.280 61.000
32.45 32.000 69.000 4868.000 4868.000 69.248 61.000
32.50 39.000 77.000 4867.000 4867.000 67.084 63.000
32.55 37.000 69.000 4867.000 4867.000 58.428 78.000
32.60 33.000 63.000 4868.000 4868.000 62.756 70.000
32.65 36.000 74.000 4867.000 4867.000 58.428 71.000
32.70 39.000 58.000 4867.000 4867.000 67.084 75.000
32.75 28.000 68.000 4867.000 4867.000 45.444 75.000
32.80 30.000 78.000 4868.000 4868.000 51.936 69.000
32.85 30.000 67.000 4867.000 4867.000 56.264 77.000
32.90 34.000 69.000 4867.000 4867.000 56.264 69.000
32.95 28.000 69.000 4868.000 4868.000 54.100 63.000
33.00 24.000 72.000 4867.000 4867.000 47.608 74.000
33.05 24.000 72.000 4867.000 4867.000 41.116 58.000
33.10 27.000 76.000 4868.000 4868.000 43.280 68.000
33.15 27.000 75.000 4868.000 4868.000 38.952 78.000
33.20 19.000 75.000 4867.000 4867.000 71.412 67.000
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49.30 15.000 71.000 5220.000 5220.000 315.944 140.000
49.35 17.000 63.000 5042.000 5042.000 320.272 136.000
49.40 17.000 63.000 5042.000 5042.000 309.452 151.000
49.45 26.000 68.000 4916.000 4916.000 311.616 151.000
49.50 11.000 43.000 4301.000 4301.000 259.680 146.000
49.55 13.000 49.000 4687.000 4687.000 246.696 146.000
49.60 14.000 52.000 5082.000 5082.000 311.616 127.000
49.65 14.000 55.000 5121.000 5121.000 276.992 71.000
49.70 21.000 43.000 4974.000 4974.000 251.024 63.000
49.75 18.000 44.000 4242.000 4242.000 251.024 63.000
49.80 24.000 50.000 3671.000 3671.000 298.632 68.000
49.85 21.000 46.000 6190.000 6190.000 287.812 43.000
49.90 31.000 43.000 4794.000 4794.000 289.976 49.000
49.95 44.000 39.000 4587.000 4587.000 253.188 52.000
50.00 75.000 51.000 5335.000 5335.000 261.844 55.000
50.05 75.000 51.000 5335.000 5335.000 294.304 43.000
50.10 256.000 51.000 4494.000 4494.000 281.320 44.000
50.15 878.000 45.000 3869.000 3869.000 268.336 50.000
50.20 1244.000 45.000 4408.000 4408.000 235.876 46.000
50.25 1674.000 43.000 5192.000 5192.000 276.992 43.000
50.30 1705.000 42.000 3577.000 3577.000 335.420 39.000
50.35 1600.000 49.000 6139.000 6139.000 255.352 51.000
50.40 1543.000 43.000 6980.000 6980.000 272.664 51.000
50.45 1521.000 45.000 3255.000 3255.000 264.008 51.000
50.50 1521.000 45.000 3255.000 3255.000 300.796 45.000
50.55 1338.000 49.000 5869.000 5869.000 259.680 45.000
50.60 605.000 52.000 4947.000 4947.000 300.796 43.000
50.65 347.000 48.000 5320.000 5320.000 292.140 42.000
50.70 177.000 45.000 4226.000 4226.000 370.044 49.000
50.75 146.000 53.000 4608.000 4608.000 437.128 43.000
50.80 148.000 53.000 5783.000 5783.000 647.036 45.000
50.85 143.000 59.000 5959.000 5959.000 2001.700 45.000
50.90 144.000 48.000 5190.000 5190.000 3018.780 49.000
50.95 120.000 73.000 3356.000 3356.000 4014.220 52.000
51.00 114.000 64.000 5708.000 5708.000 4055.336 48.000
51.05 144.000 73.000 4540.000 4540.000 4044.516 45.000
51.10 128.000 70.000 4749.000 4749.000 4198.160 53.000
51.15 116.000 95.000 4860.000 4860.000 4250.096 53.000
51.20 116.000 95.000 4860.000 4860.000 4291.212 59.000
51.25 138.000 106.000 4872.000 4872.000 4057.500 48.000
51.30 133.000 125.000 4871.000 4871.000 4198.160 73.000
51.35 134.000 102.000 4870.000 4870.000 4198.160 64.000
51.40 117.000 120.000 4869.000 4869.000 4193.832 73.000
51.45 121.000 125.000 4859.000 4859.000 4085.632 70.000
51.50 136.000 124.000 4843.000 4843.000 3618.208 95.000
51.55 130.000 88.000 4910.000 4910.000 1925.960 95.000
51.60 124.000 96.000 4861.000 4861.000 1172.888 106.000
51.65 109.000 65.000 4896.000 4896.000 621.068 125.000
51.70 128.000 55.000 4928.000 4928.000 110.364 102.000
51.75 155.000 62.000 4857.000 4857.000 58.428 120.000
51.80 118.000 55.000 4884.000 4884.000 19.476 125.000
51.85 126.000 46.000 4869.000 4869.000 30.296 124.000
51.90 122.000 51.000 4879.000 4879.000 8.656 88.000
51.95 139.000 45.000 4877.000 4877.000 8.656 96.000
52.00 120.000 43.000 4850.000 4850.000 15.148 65.000
52.05 139.000 61.000 4857.000 4857.000 17.312 55.000
52.10 135.000 44.000 4794.000 4794.000 8.656 62.000
52.15 171.000 40.000 4958.000 4958.000 10.820 55.000
52.20 202.000 44.000 4831.000 4831.000 10.820 46.000
52.25 299.000 49.000 4786.000 4786.000 21.640 51.000
52.30 925.000 45.000 4922.000 4922.000 21.640 45.000
52.35 1395.000 40.000 4848.000 4848.000 10.820 43.000
52.40 1855.000 42.000 4934.000 4934.000 12.984 61.000
52.45 1874.000 46.000 4854.000 4854.000 28.132 44.000
52.50 1869.000 52.000 4803.000 4803.000 19.476 40.000
52.55 1940.000 42.000 4917.000 4917.000 -2163.978 44.000
52.60 1964.000 45.000 4874.000 4874.000 -2163.978 49.000
52.65 1983.000 41.000 4868.000 4868.000 -2163.978 45.000
52.70 1875.000 40.000 4914.000 4914.000 -2163.978 40.000
52.75 1940.000 56.000 4825.000 4825.000 -2163.978 42.000
52.80 1940.000 56.000 4825.000 4825.000 -2163.978 46.000
52.85 1938.000 46.000 4889.000 4889.000 -2163.978 52.000
52.90 1888.000 38.000 4862.000 4862.000 -2163.978 42.000
52.95 1672.000 46.000 4991.000 4991.000 -2163.978 45.000
53.00 890.000 45.000 4724.000 4724.000 -2163.978 41.000
53.05 542.000 54.000 4816.000 4816.000 -2163.978 40.000
53.10 287.000 51.000 4952.000 4952.000 -2163.978 56.000
53.15 51.000 57.000 4840.000 4840.000 -2163.978 56.000
53.20 27.000 46.000 4856.000 4856.000 -2163.978 46.000
53.25 9.000 51.000 4865.000 4865.000 -2163.978 38.000
53.30 14.000 42.000 4868.000 4868.000 -2163.978 46.000
53.35 4.000 39.000 4868.000 4868.000 -2163.978 45.000
53.40 4.000 39.000 4868.000 4868.000 -2163.978 54.000
53.45 7.000 40.000 4868.000 4868.000 -2163.978 51.000
53.50 8.000 53.000 4868.000 4868.000 -2163.978 57.000
53.55 4.000 47.000 4869.000 4869.000 -2163.978 46.000
53.60 5.000 51.000 4869.000 4869.000 -2163.978 51.000
53.65 5.000 54.000 4868.000 4868.000 -2163.978 42.000
53.70 10.000 35.000 4867.000 4867.000 -2163.978 39.000
53.75 10.000 42.000 4867.000 4867.000 -2163.978 39.000
53.80 5.000 43.000 4869.000 4869.000 -2163.978 40.000
53.85 6.000 46.000 4870.000 4870.000 -2163.978 53.000
53.90 13.000 51.000 4867.000 4867.000 -2163.978 47.000
53.95 9.000 44.000 4868.000 4868.000 -2163.978 51.000
54.00 -0.0 -0.0 -0.0 -0.0 -2163.978 54.000
54.05 -0.0 -0.0 -0.0 -0.0 -2163.978 35.000
54.10 -0.0 -0.0 -0.0 -0.0 -2163.978 42.000
54.15 -0.0 -0.0 -0.0 -0.0 -2163.978 43.000
54.20 -0.0 -0.0 -0.0 -0.0 -2163.978 46.000
54.25 -0.0 -0.0 -0.0 -0.0 -2163.978 51.000
54.30 -0.0 -0.0 -0.0 -0.0 -2163.978 44.000
54.35 -0.0 -0.0 -0.0 -0.0 -2163.978 0.000
Curves are available as numpy.ndarray objects as items in the Las object itself:
In [3]:
l.keys()
Out[3]:
['DEPT', 'GCPS', 'NCPS', 'CCLC', 'CCL', 'GAPI', 'NEUT']
In [4]:
l['NEUT']
Out[4]:
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 77., 77., 82., 93., 82.,
74., 77., 83., 85., 92., 91., 77., 76., 74.,
58., 61., 73., 59., 61., 61., 63., 78., 70.,
71., 75., 75., 69., 77., 69., 63., 74., 58.,
68., 78., 67., 69., 69., 72., 72., 76., 75.,
75., 58., 59., 77., 75., 72., 73., 76., 73.,
75., 75., 62., 73., 64., 71., 74., 68., 71.,
58., 65., 74., 66., 83., 83., 72., 77., 79.,
64., 79., 73., 74., 75., 70., 86., 76., 72.,
65., 82., 90., 93., 75., 77., 71., 66., 55.,
63., 74., 75., 85., 85., 70., 73., 84., 83.,
79., 87., 75., 68., 72., 88., 88., 82., 82.,
79., 85., 74., 86., 95., 68., 89., 95., 85.,
101., 78., 79., 79., 76., 86., 85., 70., 77.,
96., 95., 82., 77., 79., 75., 99., 99., 77.,
89., 97., 97., 96., 90., 97., 97., 98., 100.,
86., 75., 62., 90., 82., 82., 98., 90., 86.,
94., 85., 91., 92., 82., 75., 74., 98., 74.,
80., 86., 86., 76., 76., 83., 83., 72., 82.,
79., 77., 75., 68., 84., 90., 85., 97., 79.,
73., 85., 88., 85., 79., 85., 95., 72., 81.,
91., 98., 101., 97., 80., 83., 69., 69., 89.,
89., 90., 90., 78., 69., 69., 68., 71., 79.,
80., 75., 75., 109., 100., 94., 83., 79., 92.,
96., 98., 77., 73., 90., 92., 92., 88., 76.,
66., 49., 46., 55., 52., 61., 58., 51., 62.,
52., 52., 58., 58., 54., 48., 57., 57., 46.,
45., 47., 62., 60., 53., 41., 62., 56., 65.,
58., 61., 67., 73., 79., 84., 95., 73., 72.,
61., 73., 64., 61., 53., 53., 56., 56., 59.,
59., 50., 50., 64., 55., 64., 56., 56., 54.,
47., 62., 73., 51., 42., 55., 60., 56., 42.,
42., 45., 54., 58., 57., 56., 61., 69., 59.,
59., 52., 52., 65., 65., 75., 59., 54., 49.,
44., 61., 64., 67., 59., 45., 52., 44., 40.,
61., 56., 42., 41., 64., 50., 37., 48., 42.,
50., 59., 59., 61., 61., 45., 45., 38., 38.,
46., 77., 71., 84., 71., 63., 58., 44., 37.,
51., 43., 39., 31., 35., 46., 39., 48., 60.,
61., 64., 64., 73., 75., 88., 116., 139., 136.,
140., 136., 151., 151., 146., 146., 127., 71., 63.,
63., 68., 43., 49., 52., 55., 43., 44., 50.,
46., 43., 39., 51., 51., 51., 45., 45., 43.,
42., 49., 43., 45., 45., 49., 52., 48., 45.,
53., 53., 59., 48., 73., 64., 73., 70., 95.,
95., 106., 125., 102., 120., 125., 124., 88., 96.,
65., 55., 62., 55., 46., 51., 45., 43., 61.,
44., 40., 44., 49., 45., 40., 42., 46., 52.,
42., 45., 41., 40., 56., 56., 46., 38., 46.,
45., 54., 51., 57., 46., 51., 42., 39., 39.,
40., 53., 47., 51., 54., 35., 42., 43., 46.,
51., 44., nan])
You can also access curves by index (remembering that the first index is zero but the first curve is the depth). For example, the last twelve values:
In [5]:
print(l['GCPS'][-12:])
print(l[1][-12:])
[ 5. 6. 13. 9. nan nan nan nan nan nan nan nan]
[ 5. 6. 13. 9. nan nan nan nan nan nan nan nan]
In [6]:
print(l['NEUT'][-12:])
print(l[-1][-12:])
[ 40. 53. 47. 51. 54. 35. 42. 43. 46. 51. 44. nan]
[ 40. 53. 47. 51. 54. 35. 42. 43. 46. 51. 44. nan]
All the curves are stored as a two-dimensional ndarray under the data attribute:
In [7]:
print(l.data.shape)
print(l.data)
(687, 7)
[[ 20.05 nan nan ..., nan -2163.978 nan]
[ 20.1 nan nan ..., nan -2163.978 nan]
[ 20.15 nan nan ..., nan -2163.978 nan]
...,
[ 54.25 nan nan ..., nan -2163.978 51. ]
[ 54.3 nan nan ..., nan -2163.978 44. ]
[ 54.35 nan nan ..., nan -2163.978 nan]]
Metadata from the LAS file's header is stored in an OrderedDictionary attribute, one for each section (version, well, curves, and params). OrderedDictionary objects are just what they sound like: very similar to a normal Python dictionary.
Each entry in the header sections is a custom Python object which depends on the section of the LAS header in which it occurs: either a Metadata, Curve, or Parameter. All these are very similar though, they are produced by the namedlist package, which is a mutable named tuple. It sounds more complex than it is:
In [8]:
m = las_reader.las.Metadata(mnemonic="SPEED", unit='something?', value=1.5, descr='The speed of light')
m.value = 3 * 10 ** 8
m.unit = 'm/s'
print(m)
Metadata(mnemonic='SPEED', unit='m/s', value=300000000, descr='The speed of light')
In order from top to bottom as they usually are in LAS files, the section attributes are:
In [9]:
l.version
Out[9]:
{'VERS': Metadata(mnemonic='VERS', unit='', value=1.2, descr='CWLS log ASCII Standard - version 1.20'),
'WRAP': Metadata(mnemonic='WRAP', unit='', value='NO', descr='one line per depth step')}
In [10]:
l.version['VERS'].value
Out[10]:
1.2
In [11]:
l.well
Out[11]:
{'STRT': Metadata(mnemonic='STRT', unit='M', value=20.0, descr=''),
'STOP': Metadata(mnemonic='STOP', unit='M', value=54.35, descr=''),
'STEP': Metadata(mnemonic='STEP', unit='M', value=0.05, descr=''),
'NULL': Metadata(mnemonic='NULL', unit='', value=-0.0, descr=''),
'COMP': Metadata(mnemonic='COMP', unit='', value='', descr='COMPANY'),
'WELL': Metadata(mnemonic='WELL', unit='', value='GT14574', descr='WELL'),
'FLD': Metadata(mnemonic='FLD', unit='', value='', descr='FIELD'),
'LOC': Metadata(mnemonic='LOC', unit='', value='', descr='LOCATION'),
'CNTY': Metadata(mnemonic='CNTY', unit='', value='', descr='COUNTY'),
'STAT': Metadata(mnemonic='STAT', unit='', value='', descr='STATE'),
'CTRY': Metadata(mnemonic='CTRY', unit='', value='', descr='COUNTRY'),
'SRVC': Metadata(mnemonic='SRVC', unit='', value='', descr='SERVICE COMPANY'),
'DATE': Metadata(mnemonic='DATE', unit='', value='07/05/2014', descr='LOG DATE'),
'UWI': Metadata(mnemonic='UWI', unit='', value='unknown', descr='UNIQUE WELL ID')}
A simpler way of viewing this is to use the special _d attribute of each section's dictionary, but note that any changes to this dictionary do not have any effect:
In [12]:
l.well._d
Out[12]:
{'CNTY': '',
'COMP': '',
'CTRY': '',
'DATE': '07/05/2014',
'FLD': '',
'LOC': '',
'NULL': -0.0,
'SRVC': '',
'STAT': '',
'STEP': 0.05,
'STOP': 54.35,
'STRT': 20.0,
'UWI': 'unknown',
'WELL': 'GT14574'}
To change metadata information you have to edit the Metadata object's particular field explicitly:
In [13]:
l.well['SRVC'].value = "My logging company -- not anyone's"
l.well._d
Out[13]:
{'CNTY': '',
'COMP': '',
'CTRY': '',
'DATE': '07/05/2014',
'FLD': '',
'LOC': '',
'NULL': -0.0,
'SRVC': "My logging company -- not anyone's",
'STAT': '',
'STEP': 0.05,
'STOP': 54.35,
'STRT': 20.0,
'UWI': 'unknown',
'WELL': 'GT14574'}
The other sections have slightly different classes for records:
In [14]:
l.params
Out[14]:
{}
For convenience there's a special attribute which is an ordinary dictionary that brings together all the metadata in the file. Again, changes to this dictionary have no effect.
In [15]:
l.metadata
Out[15]:
{'CNTY': '',
'COMP': '',
'CTRY': '',
'DATE': '07/05/2014',
'FLD': '',
'LOC': '',
'NULL': -0.0,
'SRVC': "My logging company -- not anyone's",
'STAT': '',
'STEP': 0.05,
'STOP': 54.35,
'STRT': 20.0,
'UWI': 'unknown',
'VERS': 1.2,
'WELL': 'GT14574',
'WRAP': 'NO'}
As per the standard, the ~Other section is free text. I know many companies put LAS-formatted metadata in here, but this isn't parsed yet.
In [16]:
print l.other
Finally there is some useful information in the provenance attribute that depends on how you opened the file in the first place:
In [17]:
l.provenance
Out[17]:
{'name': 'GT14574.LAS',
'path': 'GT14574.LAS',
'time_opened': datetime.datetime(2015, 7, 8, 11, 36, 37, 994000),
'url': None}
The curve definitions in the ~Curve section are stored as Curve objects (just like the Metadata and Parameter objects above). However Curve objects have the data itself as an additional .data attribute:
In [18]:
l.curves
Out[18]:
[Curve(mnemonic='DEPT', unit='M', API_code='', descr='1 DEPTH', data=array([ 20.05, 20.1 , 20.15, 20.2 , 20.25, 20.3 , 20.35, 20.4 ,
20.45, 20.5 , 20.55, 20.6 , 20.65, 20.7 , 20.75, 20.8 ,
20.85, 20.9 , 20.95, 21. , 21.05, 21.1 , 21.15, 21.2 ,
21.25, 21.3 , 21.35, 21.4 , 21.45, 21.5 , 21.55, 21.6 ,
21.65, 21.7 , 21.75, 21.8 , 21.85, 21.9 , 21.95, 22. ,
22.05, 22.1 , 22.15, 22.2 , 22.25, 22.3 , 22.35, 22.4 ,
22.45, 22.5 , 22.55, 22.6 , 22.65, 22.7 , 22.75, 22.8 ,
22.85, 22.9 , 22.95, 23. , 23.05, 23.1 , 23.15, 23.2 ,
23.25, 23.3 , 23.35, 23.4 , 23.45, 23.5 , 23.55, 23.6 ,
23.65, 23.7 , 23.75, 23.8 , 23.85, 23.9 , 23.95, 24. ,
24.05, 24.1 , 24.15, 24.2 , 24.25, 24.3 , 24.35, 24.4 ,
24.45, 24.5 , 24.55, 24.6 , 24.65, 24.7 , 24.75, 24.8 ,
24.85, 24.9 , 24.95, 25. , 25.05, 25.1 , 25.15, 25.2 ,
25.25, 25.3 , 25.35, 25.4 , 25.45, 25.5 , 25.55, 25.6 ,
25.65, 25.7 , 25.75, 25.8 , 25.85, 25.9 , 25.95, 26. ,
26.05, 26.1 , 26.15, 26.2 , 26.25, 26.3 , 26.35, 26.4 ,
26.45, 26.5 , 26.55, 26.6 , 26.65, 26.7 , 26.75, 26.8 ,
26.85, 26.9 , 26.95, 27. , 27.05, 27.1 , 27.15, 27.2 ,
27.25, 27.3 , 27.35, 27.4 , 27.45, 27.5 , 27.55, 27.6 ,
27.65, 27.7 , 27.75, 27.8 , 27.85, 27.9 , 27.95, 28. ,
28.05, 28.1 , 28.15, 28.2 , 28.25, 28.3 , 28.35, 28.4 ,
28.45, 28.5 , 28.55, 28.6 , 28.65, 28.7 , 28.75, 28.8 ,
28.85, 28.9 , 28.95, 29. , 29.05, 29.1 , 29.15, 29.2 ,
29.25, 29.3 , 29.35, 29.4 , 29.45, 29.5 , 29.55, 29.6 ,
29.65, 29.7 , 29.75, 29.8 , 29.85, 29.9 , 29.95, 30. ,
30.05, 30.1 , 30.15, 30.2 , 30.25, 30.3 , 30.35, 30.4 ,
30.45, 30.5 , 30.55, 30.6 , 30.65, 30.7 , 30.75, 30.8 ,
30.85, 30.9 , 30.95, 31. , 31.05, 31.1 , 31.15, 31.2 ,
31.25, 31.3 , 31.35, 31.4 , 31.45, 31.5 , 31.55, 31.6 ,
31.65, 31.7 , 31.75, 31.8 , 31.85, 31.9 , 31.95, 32. ,
32.05, 32.1 , 32.15, 32.2 , 32.25, 32.3 , 32.35, 32.4 ,
32.45, 32.5 , 32.55, 32.6 , 32.65, 32.7 , 32.75, 32.8 ,
32.85, 32.9 , 32.95, 33. , 33.05, 33.1 , 33.15, 33.2 ,
33.25, 33.3 , 33.35, 33.4 , 33.45, 33.5 , 33.55, 33.6 ,
33.65, 33.7 , 33.75, 33.8 , 33.85, 33.9 , 33.95, 34. ,
34.05, 34.1 , 34.15, 34.2 , 34.25, 34.3 , 34.35, 34.4 ,
34.45, 34.5 , 34.55, 34.6 , 34.65, 34.7 , 34.75, 34.8 ,
34.85, 34.9 , 34.95, 35. , 35.05, 35.1 , 35.15, 35.2 ,
35.25, 35.3 , 35.35, 35.4 , 35.45, 35.5 , 35.55, 35.6 ,
35.65, 35.7 , 35.75, 35.8 , 35.85, 35.9 , 35.95, 36. ,
36.05, 36.1 , 36.15, 36.2 , 36.25, 36.3 , 36.35, 36.4 ,
36.45, 36.5 , 36.55, 36.6 , 36.65, 36.7 , 36.75, 36.8 ,
36.85, 36.9 , 36.95, 37. , 37.05, 37.1 , 37.15, 37.2 ,
37.25, 37.3 , 37.35, 37.4 , 37.45, 37.5 , 37.55, 37.6 ,
37.65, 37.7 , 37.75, 37.8 , 37.85, 37.9 , 37.95, 38. ,
38.05, 38.1 , 38.15, 38.2 , 38.25, 38.3 , 38.35, 38.4 ,
38.45, 38.5 , 38.55, 38.6 , 38.65, 38.7 , 38.75, 38.8 ,
38.85, 38.9 , 38.95, 39. , 39.05, 39.1 , 39.15, 39.2 ,
39.25, 39.3 , 39.35, 39.4 , 39.45, 39.5 , 39.55, 39.6 ,
39.65, 39.7 , 39.75, 39.8 , 39.85, 39.9 , 39.95, 40. ,
40.05, 40.1 , 40.15, 40.2 , 40.25, 40.3 , 40.35, 40.4 ,
40.45, 40.5 , 40.55, 40.6 , 40.65, 40.7 , 40.75, 40.8 ,
40.85, 40.9 , 40.95, 41. , 41.05, 41.1 , 41.15, 41.2 ,
41.25, 41.3 , 41.35, 41.4 , 41.45, 41.5 , 41.55, 41.6 ,
41.65, 41.7 , 41.75, 41.8 , 41.85, 41.9 , 41.95, 42. ,
42.05, 42.1 , 42.15, 42.2 , 42.25, 42.3 , 42.35, 42.4 ,
42.45, 42.5 , 42.55, 42.6 , 42.65, 42.7 , 42.75, 42.8 ,
42.85, 42.9 , 42.95, 43. , 43.05, 43.1 , 43.15, 43.2 ,
43.25, 43.3 , 43.35, 43.4 , 43.45, 43.5 , 43.55, 43.6 ,
43.65, 43.7 , 43.75, 43.8 , 43.85, 43.9 , 43.95, 44. ,
44.05, 44.1 , 44.15, 44.2 , 44.25, 44.3 , 44.35, 44.4 ,
44.45, 44.5 , 44.55, 44.6 , 44.65, 44.7 , 44.75, 44.8 ,
44.85, 44.9 , 44.95, 45. , 45.05, 45.1 , 45.15, 45.2 ,
45.25, 45.3 , 45.35, 45.4 , 45.45, 45.5 , 45.55, 45.6 ,
45.65, 45.7 , 45.75, 45.8 , 45.85, 45.9 , 45.95, 46. ,
46.05, 46.1 , 46.15, 46.2 , 46.25, 46.3 , 46.35, 46.4 ,
46.45, 46.5 , 46.55, 46.6 , 46.65, 46.7 , 46.75, 46.8 ,
46.85, 46.9 , 46.95, 47. , 47.05, 47.1 , 47.15, 47.2 ,
47.25, 47.3 , 47.35, 47.4 , 47.45, 47.5 , 47.55, 47.6 ,
47.65, 47.7 , 47.75, 47.8 , 47.85, 47.9 , 47.95, 48. ,
48.05, 48.1 , 48.15, 48.2 , 48.25, 48.3 , 48.35, 48.4 ,
48.45, 48.5 , 48.55, 48.6 , 48.65, 48.7 , 48.75, 48.8 ,
48.85, 48.9 , 48.95, 49. , 49.05, 49.1 , 49.15, 49.2 ,
49.25, 49.3 , 49.35, 49.4 , 49.45, 49.5 , 49.55, 49.6 ,
49.65, 49.7 , 49.75, 49.8 , 49.85, 49.9 , 49.95, 50. ,
50.05, 50.1 , 50.15, 50.2 , 50.25, 50.3 , 50.35, 50.4 ,
50.45, 50.5 , 50.55, 50.6 , 50.65, 50.7 , 50.75, 50.8 ,
50.85, 50.9 , 50.95, 51. , 51.05, 51.1 , 51.15, 51.2 ,
51.25, 51.3 , 51.35, 51.4 , 51.45, 51.5 , 51.55, 51.6 ,
51.65, 51.7 , 51.75, 51.8 , 51.85, 51.9 , 51.95, 52. ,
52.05, 52.1 , 52.15, 52.2 , 52.25, 52.3 , 52.35, 52.4 ,
52.45, 52.5 , 52.55, 52.6 , 52.65, 52.7 , 52.75, 52.8 ,
52.85, 52.9 , 52.95, 53. , 53.05, 53.1 , 53.15, 53.2 ,
53.25, 53.3 , 53.35, 53.4 , 53.45, 53.5 , 53.55, 53.6 ,
53.65, 53.7 , 53.75, 53.8 , 53.85, 53.9 , 53.95, 54. ,
54.05, 54.1 , 54.15, 54.2 , 54.25, 54.3 , 54.35]), name='DEPT'),
Curve(mnemonic='GCPS', unit='cps', API_code='', descr='2 GCPS', data=array([ nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
2.00000000e+01, 2.00000000e+01, 2.00000000e+01,
2.00000000e+01, 1.20000000e+01, 1.40000000e+01,
1.70000000e+01, 1.50000000e+01, 1.70000000e+01,
2.70000000e+01, 2.40000000e+01, 2.70000000e+01,
2.80000000e+01, 2.80000000e+01, 3.30000000e+01,
3.30000000e+01, 3.60000000e+01, 3.00000000e+01,
2.50000000e+01, 2.90000000e+01, 2.10000000e+01,
3.40000000e+01, 2.40000000e+01, 2.50000000e+01,
3.10000000e+01, 3.10000000e+01, 3.20000000e+01,
3.90000000e+01, 3.70000000e+01, 3.30000000e+01,
3.60000000e+01, 3.90000000e+01, 2.80000000e+01,
3.00000000e+01, 3.00000000e+01, 3.40000000e+01,
2.80000000e+01, 2.40000000e+01, 2.40000000e+01,
2.70000000e+01, 2.70000000e+01, 1.90000000e+01,
2.70000000e+01, 2.70000000e+01, 3.10000000e+01,
2.80000000e+01, 2.70000000e+01, 3.40000000e+01,
2.20000000e+01, 2.00000000e+01, 2.10000000e+01,
2.10000000e+01, 2.20000000e+01, 2.00000000e+01,
2.00000000e+01, 3.20000000e+01, 3.10000000e+01,
2.70000000e+01, 2.90000000e+01, 2.70000000e+01,
3.10000000e+01, 2.10000000e+01, 2.40000000e+01,
2.60000000e+01, 2.60000000e+01, 2.50000000e+01,
2.20000000e+01, 1.90000000e+01, 2.00000000e+01,
1.80000000e+01, 3.30000000e+01, 3.00000000e+01,
2.80000000e+01, 2.90000000e+01, 3.50000000e+01,
2.50000000e+01, 2.40000000e+01, 2.20000000e+01,
1.70000000e+01, 2.00000000e+01, 1.80000000e+01,
2.10000000e+01, 2.00000000e+01, 1.60000000e+01,
2.10000000e+01, 3.40000000e+01, 2.90000000e+01,
2.20000000e+01, 2.20000000e+01, 2.90000000e+01,
2.90000000e+01, 2.20000000e+01, 2.90000000e+01,
2.80000000e+01, 2.80000000e+01, 2.60000000e+01,
2.50000000e+01, 2.30000000e+01, 2.90000000e+01,
2.00000000e+01, 1.60000000e+01, 1.60000000e+01,
2.40000000e+01, 2.40000000e+01, 2.70000000e+01,
1.70000000e+01, 1.80000000e+01, 2.10000000e+01,
1.40000000e+01, 1.90000000e+01, 1.80000000e+01,
1.60000000e+01, 2.20000000e+01, 2.90000000e+01,
3.10000000e+01, 2.80000000e+01, 2.80000000e+01,
2.40000000e+01, 3.00000000e+01, 2.90000000e+01,
1.60000000e+01, 1.70000000e+01, 1.90000000e+01,
2.00000000e+01, 2.40000000e+01, 3.00000000e+01,
2.40000000e+01, 2.10000000e+01, 2.80000000e+01,
2.80000000e+01, 2.60000000e+01, 2.10000000e+01,
2.40000000e+01, 2.40000000e+01, 2.90000000e+01,
2.20000000e+01, 2.80000000e+01, 2.80000000e+01,
2.90000000e+01, 3.80000000e+01, 2.50000000e+01,
2.50000000e+01, 2.20000000e+01, 2.10000000e+01,
2.90000000e+01, 3.20000000e+01, 3.90000000e+01,
2.70000000e+01, 2.30000000e+01, 3.20000000e+01,
3.00000000e+01, 3.00000000e+01, 2.90000000e+01,
1.90000000e+01, 2.60000000e+01, 2.60000000e+01,
2.70000000e+01, 1.80000000e+01, 2.20000000e+01,
3.10000000e+01, 3.10000000e+01, 2.80000000e+01,
2.80000000e+01, 2.80000000e+01, 2.80000000e+01,
2.30000000e+01, 2.00000000e+01, 2.10000000e+01,
2.20000000e+01, 1.70000000e+01, 1.90000000e+01,
3.10000000e+01, 3.00000000e+01, 2.40000000e+01,
2.00000000e+01, 2.40000000e+01, 2.30000000e+01,
1.80000000e+01, 1.70000000e+01, 2.20000000e+01,
1.80000000e+01, 2.20000000e+01, 2.30000000e+01,
3.60000000e+01, 2.70000000e+01, 2.90000000e+01,
3.10000000e+01, 2.20000000e+01, 2.40000000e+01,
3.30000000e+01, 2.80000000e+01, 2.60000000e+01,
2.60000000e+01, 2.60000000e+01, 2.60000000e+01,
2.60000000e+01, 2.60000000e+01, 2.00000000e+01,
2.50000000e+01, 2.20000000e+01, 2.40000000e+01,
3.20000000e+01, 2.60000000e+01, 3.60000000e+01,
3.20000000e+01, 3.50000000e+01, 2.40000000e+01,
2.40000000e+01, 2.70000000e+01, 2.60000000e+01,
2.30000000e+01, 2.50000000e+01, 3.00000000e+01,
2.40000000e+01, 2.30000000e+01, 2.60000000e+01,
3.00000000e+01, 2.00000000e+01, 2.40000000e+01,
2.10000000e+01, 2.30000000e+01, 1.70000000e+01,
2.90000000e+01, 3.30000000e+01, 1.70000000e+01,
2.40000000e+01, 2.50000000e+01, 2.50000000e+01,
1.90000000e+01, 1.80000000e+01, 3.60000000e+01,
3.60000000e+01, 1.80000000e+01, 1.80000000e+01,
3.00000000e+01, 2.50000000e+01, 3.10000000e+01,
3.10000000e+01, 2.30000000e+01, 3.70000000e+01,
3.30000000e+01, 2.20000000e+01, 2.30000000e+01,
2.20000000e+01, 2.90000000e+01, 2.60000000e+01,
2.80000000e+01, 2.90000000e+01, 3.10000000e+01,
3.10000000e+01, 2.50000000e+01, 3.10000000e+01,
3.00000000e+01, 2.50000000e+01, 3.30000000e+01,
3.30000000e+01, 3.80000000e+01, 8.30000000e+01,
9.10000000e+01, 1.04000000e+02, 1.06000000e+02,
1.58000000e+02, 1.58000000e+02, 5.53000000e+02,
5.53000000e+02, 1.29400000e+03, 1.29400000e+03,
2.55500000e+03, 2.55500000e+03, 3.94200000e+03,
5.47900000e+03, 6.21100000e+03, 7.09700000e+03,
7.42200000e+03, 7.57600000e+03, 7.74800000e+03,
8.01200000e+03, 8.23200000e+03, 8.68100000e+03,
8.50200000e+03, 8.66900000e+03, 8.69500000e+03,
8.47500000e+03, 8.44100000e+03, 8.01100000e+03,
7.67700000e+03, 5.81600000e+03, 4.21100000e+03,
1.50700000e+03, 9.94000000e+02, 4.83000000e+02,
3.79000000e+02, 2.51000000e+02, 2.51000000e+02,
1.39000000e+02, 1.39000000e+02, 1.90000000e+01,
1.90000000e+01, 2.10000000e+01, 1.60000000e+01,
1.30000000e+01, 1.60000000e+01, 1.70000000e+01,
1.40000000e+01, 8.00000000e+00, 9.00000000e+00,
9.00000000e+00, 8.00000000e+00, 1.60000000e+01,
9.00000000e+00, 1.00000000e+01, 1.30000000e+01,
1.10000000e+01, 8.00000000e+00, 6.00000000e+00,
8.00000000e+00, 6.00000000e+00, 1.70000000e+01,
1.90000000e+01, 9.00000000e+00, 6.00000000e+00,
2.40000000e+01, 2.40000000e+01, 1.70000000e+01,
1.70000000e+01, 2.00000000e+01, 2.00000000e+01,
1.60000000e+01, 1.60000000e+01, 1.10000000e+01,
1.60000000e+01, 2.00000000e+01, 1.10000000e+01,
1.20000000e+01, 1.10000000e+01, 9.00000000e+00,
1.20000000e+01, 9.00000000e+00, 4.00000000e+00,
8.00000000e+00, 1.10000000e+01, 1.00000000e+01,
7.00000000e+00, 1.00000000e+01, 1.20000000e+01,
1.20000000e+01, 1.50000000e+01, 1.50000000e+01,
2.40000000e+01, 2.10000000e+01, 1.40000000e+01,
2.00000000e+01, 2.90000000e+01, 2.50000000e+01,
2.30000000e+01, 2.20000000e+01, 2.80000000e+01,
1.50000000e+01, 2.20000000e+01, 2.20000000e+01,
1.70000000e+01, 1.70000000e+01, 2.40000000e+01,
1.50000000e+01, 1.70000000e+01, 1.70000000e+01,
2.60000000e+01, 1.10000000e+01, 1.30000000e+01,
1.40000000e+01, 1.40000000e+01, 2.10000000e+01,
1.80000000e+01, 2.40000000e+01, 2.10000000e+01,
3.10000000e+01, 4.40000000e+01, 7.50000000e+01,
7.50000000e+01, 2.56000000e+02, 8.78000000e+02,
1.24400000e+03, 1.67400000e+03, 1.70500000e+03,
1.60000000e+03, 1.54300000e+03, 1.52100000e+03,
1.52100000e+03, 1.33800000e+03, 6.05000000e+02,
3.47000000e+02, 1.77000000e+02, 1.46000000e+02,
1.48000000e+02, 1.43000000e+02, 1.44000000e+02,
1.20000000e+02, 1.14000000e+02, 1.44000000e+02,
1.28000000e+02, 1.16000000e+02, 1.16000000e+02,
1.38000000e+02, 1.33000000e+02, 1.34000000e+02,
1.17000000e+02, 1.21000000e+02, 1.36000000e+02,
1.30000000e+02, 1.24000000e+02, 1.09000000e+02,
1.28000000e+02, 1.55000000e+02, 1.18000000e+02,
1.26000000e+02, 1.22000000e+02, 1.39000000e+02,
1.20000000e+02, 1.39000000e+02, 1.35000000e+02,
1.71000000e+02, 2.02000000e+02, 2.99000000e+02,
9.25000000e+02, 1.39500000e+03, 1.85500000e+03,
1.87400000e+03, 1.86900000e+03, 1.94000000e+03,
1.96400000e+03, 1.98300000e+03, 1.87500000e+03,
1.94000000e+03, 1.94000000e+03, 1.93800000e+03,
1.88800000e+03, 1.67200000e+03, 8.90000000e+02,
5.42000000e+02, 2.87000000e+02, 5.10000000e+01,
2.70000000e+01, 9.00000000e+00, 1.40000000e+01,
4.00000000e+00, 4.00000000e+00, 7.00000000e+00,
8.00000000e+00, 4.00000000e+00, 5.00000000e+00,
5.00000000e+00, 1.00000000e+01, 1.00000000e+01,
5.00000000e+00, 6.00000000e+00, 1.30000000e+01,
9.00000000e+00, nan, nan,
nan, nan, nan,
nan, nan, nan]), name='GCPS'),
Curve(mnemonic='NCPS', unit='cps', API_code='', descr='3 NCPS', data=array([ nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, 77., 77., 82.,
93., 82., 74., 77., 83., 85., 92., 91., 77.,
76., 74., 58., 61., 73., 59., 61., 61., 63.,
78., 70., 71., 75., 75., 69., 77., 69., 63.,
74., 58., 68., 78., 67., 69., 69., 72., 72.,
76., 75., 75., 58., 59., 77., 75., 72., 73.,
76., 73., 75., 75., 62., 73., 64., 71., 74.,
68., 71., 58., 65., 74., 66., 83., 83., 72.,
77., 79., 64., 79., 73., 74., 75., 70., 86.,
76., 72., 65., 82., 90., 93., 75., 77., 71.,
66., 55., 63., 74., 75., 85., 85., 70., 73.,
84., 83., 79., 87., 75., 68., 72., 88., 88.,
82., 82., 79., 85., 74., 86., 95., 68., 89.,
95., 85., 101., 78., 79., 79., 76., 86., 85.,
70., 77., 96., 95., 82., 77., 79., 75., 99.,
99., 77., 89., 97., 97., 96., 90., 97., 97.,
98., 100., 86., 75., 62., 90., 82., 82., 98.,
90., 86., 94., 85., 91., 92., 82., 75., 74.,
98., 74., 80., 86., 86., 76., 76., 83., 83.,
72., 82., 79., 77., 75., 68., 84., 90., 85.,
97., 79., 73., 85., 88., 85., 79., 85., 95.,
72., 81., 91., 98., 101., 97., 80., 83., 69.,
69., 89., 89., 90., 90., 78., 69., 69., 68.,
71., 79., 80., 75., 75., 109., 100., 94., 83.,
79., 92., 96., 98., 77., 73., 90., 92., 92.,
88., 76., 66., 49., 46., 55., 52., 61., 58.,
51., 62., 52., 52., 58., 58., 54., 48., 57.,
57., 46., 45., 47., 62., 60., 53., 41., 62.,
56., 65., 58., 61., 67., 73., 79., 84., 95.,
73., 72., 61., 73., 64., 61., 53., 53., 56.,
56., 59., 59., 50., 50., 64., 55., 64., 56.,
56., 54., 47., 62., 73., 51., 42., 55., 60.,
56., 42., 42., 45., 54., 58., 57., 56., 61.,
69., 59., 59., 52., 52., 65., 65., 75., 59.,
54., 49., 44., 61., 64., 67., 59., 45., 52.,
44., 40., 61., 56., 42., 41., 64., 50., 37.,
48., 42., 50., 59., 59., 61., 61., 45., 45.,
38., 38., 46., 77., 71., 84., 71., 63., 58.,
44., 37., 51., 43., 39., 31., 35., 46., 39.,
48., 60., 61., 64., 64., 73., 75., 88., 116.,
139., 136., 140., 136., 151., 151., 146., 146., 127.,
71., 63., 63., 68., 43., 49., 52., 55., 43.,
44., 50., 46., 43., 39., 51., 51., 51., 45.,
45., 43., 42., 49., 43., 45., 45., 49., 52.,
48., 45., 53., 53., 59., 48., 73., 64., 73.,
70., 95., 95., 106., 125., 102., 120., 125., 124.,
88., 96., 65., 55., 62., 55., 46., 51., 45.,
43., 61., 44., 40., 44., 49., 45., 40., 42.,
46., 52., 42., 45., 41., 40., 56., 56., 46.,
38., 46., 45., 54., 51., 57., 46., 51., 42.,
39., 39., 40., 53., 47., 51., 54., 35., 42.,
43., 46., 51., 44., nan, nan, nan, nan, nan,
nan, nan, nan]), name='NCPS'),
Curve(mnemonic='CCLC', unit='cps', API_code='', descr='4 CCLCPS', data=array([ nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, 4871., 4871.,
4870., 4869., 4869., 4869., 4869., 4869., 4869., 4868.,
4868., 4868., 4868., 4868., 4868., 4868., 4868., 4868.,
4868., 4868., 4868., 4868., 4868., 4868., 4867., 4867.,
4868., 4867., 4867., 4868., 4867., 4867., 4867., 4868.,
4867., 4867., 4868., 4867., 4867., 4868., 4868., 4867.,
4867., 4868., 4868., 4867., 4867., 4867., 4868., 4867.,
4867., 4867., 4867., 4867., 4868., 4866., 4867., 4867.,
4868., 4866., 4867., 4868., 4868., 4867., 4867., 4867.,
4867., 4867., 4867., 4867., 4868., 4866., 4866., 4866.,
4867., 4866., 4868., 4868., 4868., 4867., 4870., 4867.,
4866., 4864., 4865., 4870., 4870., 4867., 4868., 4868.,
4868., 4865., 4868., 4868., 4867., 4868., 4867., 4866.,
4868., 4868., 4868., 4868., 4870., 4870., 4863., 4869.,
4866., 4867., 4868., 4869., 4869., 4868., 4867., 4867.,
4867., 4867., 4867., 4867., 4867., 4867., 4867., 4868.,
4868., 4867., 4867., 4868., 4867., 4867., 4867., 4867.,
4867., 4867., 4867., 4867., 4867., 4867., 4867., 4867.,
4867., 4868., 4867., 4867., 4867., 4868., 4867., 4867.,
4867., 4867., 4868., 4868., 4867., 4867., 4867., 4867.,
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9.11260400e+03, 3.26114800e+03, 2.15101600e+03,
1.04521200e+03, 8.20156000e+02, 5.43164000e+02,
5.43164000e+02, 3.00796000e+02, 3.00796000e+02,
4.11160000e+01, 4.11160000e+01, 4.54440000e+01,
3.46240000e+01, 2.81320000e+01, 3.46240000e+01,
3.67880000e+01, 3.02960000e+01, 1.73120000e+01,
1.94760000e+01, 1.94760000e+01, 1.73120000e+01,
3.46240000e+01, 1.94760000e+01, 2.16400000e+01,
2.81320000e+01, 2.38040000e+01, 1.73120000e+01,
1.29840000e+01, 1.73120000e+01, 1.29840000e+01,
3.67880000e+01, 4.11160000e+01, 1.94760000e+01,
1.29840000e+01, 5.19360000e+01, 5.19360000e+01,
3.67880000e+01, 3.67880000e+01, 4.32800000e+01,
4.32800000e+01, 3.46240000e+01, 3.46240000e+01,
2.38040000e+01, 3.46240000e+01, 4.32800000e+01,
2.38040000e+01, 2.59680000e+01, 2.38040000e+01,
1.94760000e+01, 2.59680000e+01, 1.94760000e+01,
8.65600000e+00, 1.73120000e+01, 2.38040000e+01,
2.16400000e+01, 1.51480000e+01, 2.16400000e+01,
2.59680000e+01, 2.59680000e+01, 3.24600000e+01,
3.24600000e+01, 5.19360000e+01, 4.54440000e+01,
3.02960000e+01, 4.32800000e+01, 6.27560000e+01,
5.41000000e+01, 4.97720000e+01, 4.76080000e+01,
6.05920000e+01, 3.24600000e+01, 4.76080000e+01,
4.76080000e+01, 3.67880000e+01, 3.67880000e+01,
5.19360000e+01, 3.24600000e+01, 3.67880000e+01,
3.67880000e+01, 5.62640000e+01, 2.38040000e+01,
2.81320000e+01, 3.02960000e+01, 3.02960000e+01,
4.54440000e+01, 3.89520000e+01, 5.19360000e+01,
4.54440000e+01, 6.70840000e+01, 9.52160000e+01,
1.62300000e+02, 1.62300000e+02, 5.53984000e+02,
1.89999200e+03, 2.69201600e+03, 3.62253600e+03,
3.68962000e+03, 3.46240000e+03, 3.33905200e+03,
3.29144400e+03, 3.29144400e+03, 2.89543200e+03,
1.30922000e+03, 7.50908000e+02, 3.83028000e+02,
3.15944000e+02, 3.20272000e+02, 3.09452000e+02,
3.11616000e+02, 2.59680000e+02, 2.46696000e+02,
3.11616000e+02, 2.76992000e+02, 2.51024000e+02,
2.51024000e+02, 2.98632000e+02, 2.87812000e+02,
2.89976000e+02, 2.53188000e+02, 2.61844000e+02,
2.94304000e+02, 2.81320000e+02, 2.68336000e+02,
2.35876000e+02, 2.76992000e+02, 3.35420000e+02,
2.55352000e+02, 2.72664000e+02, 2.64008000e+02,
3.00796000e+02, 2.59680000e+02, 3.00796000e+02,
2.92140000e+02, 3.70044000e+02, 4.37128000e+02,
6.47036000e+02, 2.00170000e+03, 3.01878000e+03,
4.01422000e+03, 4.05533600e+03, 4.04451600e+03,
4.19816000e+03, 4.25009600e+03, 4.29121200e+03,
4.05750000e+03, 4.19816000e+03, 4.19816000e+03,
4.19383200e+03, 4.08563200e+03, 3.61820800e+03,
1.92596000e+03, 1.17288800e+03, 6.21068000e+02,
1.10364000e+02, 5.84280000e+01, 1.94760000e+01,
3.02960000e+01, 8.65600000e+00, 8.65600000e+00,
1.51480000e+01, 1.73120000e+01, 8.65600000e+00,
1.08200000e+01, 1.08200000e+01, 2.16400000e+01,
2.16400000e+01, 1.08200000e+01, 1.29840000e+01,
2.81320000e+01, 1.94760000e+01, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03,
-2.16397800e+03, -2.16397800e+03, -2.16397800e+03]), name='GAPI'),
Curve(mnemonic='NEUT', unit='cps', API_code='', descr='7 NEUTRON', data=array([ nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 77., 77., 82., 93., 82.,
74., 77., 83., 85., 92., 91., 77., 76., 74.,
58., 61., 73., 59., 61., 61., 63., 78., 70.,
71., 75., 75., 69., 77., 69., 63., 74., 58.,
68., 78., 67., 69., 69., 72., 72., 76., 75.,
75., 58., 59., 77., 75., 72., 73., 76., 73.,
75., 75., 62., 73., 64., 71., 74., 68., 71.,
58., 65., 74., 66., 83., 83., 72., 77., 79.,
64., 79., 73., 74., 75., 70., 86., 76., 72.,
65., 82., 90., 93., 75., 77., 71., 66., 55.,
63., 74., 75., 85., 85., 70., 73., 84., 83.,
79., 87., 75., 68., 72., 88., 88., 82., 82.,
79., 85., 74., 86., 95., 68., 89., 95., 85.,
101., 78., 79., 79., 76., 86., 85., 70., 77.,
96., 95., 82., 77., 79., 75., 99., 99., 77.,
89., 97., 97., 96., 90., 97., 97., 98., 100.,
86., 75., 62., 90., 82., 82., 98., 90., 86.,
94., 85., 91., 92., 82., 75., 74., 98., 74.,
80., 86., 86., 76., 76., 83., 83., 72., 82.,
79., 77., 75., 68., 84., 90., 85., 97., 79.,
73., 85., 88., 85., 79., 85., 95., 72., 81.,
91., 98., 101., 97., 80., 83., 69., 69., 89.,
89., 90., 90., 78., 69., 69., 68., 71., 79.,
80., 75., 75., 109., 100., 94., 83., 79., 92.,
96., 98., 77., 73., 90., 92., 92., 88., 76.,
66., 49., 46., 55., 52., 61., 58., 51., 62.,
52., 52., 58., 58., 54., 48., 57., 57., 46.,
45., 47., 62., 60., 53., 41., 62., 56., 65.,
58., 61., 67., 73., 79., 84., 95., 73., 72.,
61., 73., 64., 61., 53., 53., 56., 56., 59.,
59., 50., 50., 64., 55., 64., 56., 56., 54.,
47., 62., 73., 51., 42., 55., 60., 56., 42.,
42., 45., 54., 58., 57., 56., 61., 69., 59.,
59., 52., 52., 65., 65., 75., 59., 54., 49.,
44., 61., 64., 67., 59., 45., 52., 44., 40.,
61., 56., 42., 41., 64., 50., 37., 48., 42.,
50., 59., 59., 61., 61., 45., 45., 38., 38.,
46., 77., 71., 84., 71., 63., 58., 44., 37.,
51., 43., 39., 31., 35., 46., 39., 48., 60.,
61., 64., 64., 73., 75., 88., 116., 139., 136.,
140., 136., 151., 151., 146., 146., 127., 71., 63.,
63., 68., 43., 49., 52., 55., 43., 44., 50.,
46., 43., 39., 51., 51., 51., 45., 45., 43.,
42., 49., 43., 45., 45., 49., 52., 48., 45.,
53., 53., 59., 48., 73., 64., 73., 70., 95.,
95., 106., 125., 102., 120., 125., 124., 88., 96.,
65., 55., 62., 55., 46., 51., 45., 43., 61.,
44., 40., 44., 49., 45., 40., 42., 46., 52.,
42., 45., 41., 40., 56., 56., 46., 38., 46.,
45., 54., 51., 57., 46., 51., 42., 39., 39.,
40., 53., 47., 51., 54., 35., 42., 43., 46.,
51., 44., nan]), name='NEUT')]
In [19]:
neutron_log = l.get_curve_name("NEUT")
print(neutron_log)
Curve(mnemonic='NEUT', unit='cps', API_code='', descr='7 NEUTRON', data=array([ nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 77., 77., 82., 93., 82.,
74., 77., 83., 85., 92., 91., 77., 76., 74.,
58., 61., 73., 59., 61., 61., 63., 78., 70.,
71., 75., 75., 69., 77., 69., 63., 74., 58.,
68., 78., 67., 69., 69., 72., 72., 76., 75.,
75., 58., 59., 77., 75., 72., 73., 76., 73.,
75., 75., 62., 73., 64., 71., 74., 68., 71.,
58., 65., 74., 66., 83., 83., 72., 77., 79.,
64., 79., 73., 74., 75., 70., 86., 76., 72.,
65., 82., 90., 93., 75., 77., 71., 66., 55.,
63., 74., 75., 85., 85., 70., 73., 84., 83.,
79., 87., 75., 68., 72., 88., 88., 82., 82.,
79., 85., 74., 86., 95., 68., 89., 95., 85.,
101., 78., 79., 79., 76., 86., 85., 70., 77.,
96., 95., 82., 77., 79., 75., 99., 99., 77.,
89., 97., 97., 96., 90., 97., 97., 98., 100.,
86., 75., 62., 90., 82., 82., 98., 90., 86.,
94., 85., 91., 92., 82., 75., 74., 98., 74.,
80., 86., 86., 76., 76., 83., 83., 72., 82.,
79., 77., 75., 68., 84., 90., 85., 97., 79.,
73., 85., 88., 85., 79., 85., 95., 72., 81.,
91., 98., 101., 97., 80., 83., 69., 69., 89.,
89., 90., 90., 78., 69., 69., 68., 71., 79.,
80., 75., 75., 109., 100., 94., 83., 79., 92.,
96., 98., 77., 73., 90., 92., 92., 88., 76.,
66., 49., 46., 55., 52., 61., 58., 51., 62.,
52., 52., 58., 58., 54., 48., 57., 57., 46.,
45., 47., 62., 60., 53., 41., 62., 56., 65.,
58., 61., 67., 73., 79., 84., 95., 73., 72.,
61., 73., 64., 61., 53., 53., 56., 56., 59.,
59., 50., 50., 64., 55., 64., 56., 56., 54.,
47., 62., 73., 51., 42., 55., 60., 56., 42.,
42., 45., 54., 58., 57., 56., 61., 69., 59.,
59., 52., 52., 65., 65., 75., 59., 54., 49.,
44., 61., 64., 67., 59., 45., 52., 44., 40.,
61., 56., 42., 41., 64., 50., 37., 48., 42.,
50., 59., 59., 61., 61., 45., 45., 38., 38.,
46., 77., 71., 84., 71., 63., 58., 44., 37.,
51., 43., 39., 31., 35., 46., 39., 48., 60.,
61., 64., 64., 73., 75., 88., 116., 139., 136.,
140., 136., 151., 151., 146., 146., 127., 71., 63.,
63., 68., 43., 49., 52., 55., 43., 44., 50.,
46., 43., 39., 51., 51., 51., 45., 45., 43.,
42., 49., 43., 45., 45., 49., 52., 48., 45.,
53., 53., 59., 48., 73., 64., 73., 70., 95.,
95., 106., 125., 102., 120., 125., 124., 88., 96.,
65., 55., 62., 55., 46., 51., 45., 43., 61.,
44., 40., 44., 49., 45., 40., 42., 46., 52.,
42., 45., 41., 40., 56., 56., 46., 38., 46.,
45., 54., 51., 57., 46., 51., 42., 39., 39.,
40., 53., 47., 51., 54., 35., 42., 43., 46.,
51., 44., nan]), name='NEUT')
In [20]:
neutron_log.data
Out[20]:
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 77., 77., 82., 93., 82.,
74., 77., 83., 85., 92., 91., 77., 76., 74.,
58., 61., 73., 59., 61., 61., 63., 78., 70.,
71., 75., 75., 69., 77., 69., 63., 74., 58.,
68., 78., 67., 69., 69., 72., 72., 76., 75.,
75., 58., 59., 77., 75., 72., 73., 76., 73.,
75., 75., 62., 73., 64., 71., 74., 68., 71.,
58., 65., 74., 66., 83., 83., 72., 77., 79.,
64., 79., 73., 74., 75., 70., 86., 76., 72.,
65., 82., 90., 93., 75., 77., 71., 66., 55.,
63., 74., 75., 85., 85., 70., 73., 84., 83.,
79., 87., 75., 68., 72., 88., 88., 82., 82.,
79., 85., 74., 86., 95., 68., 89., 95., 85.,
101., 78., 79., 79., 76., 86., 85., 70., 77.,
96., 95., 82., 77., 79., 75., 99., 99., 77.,
89., 97., 97., 96., 90., 97., 97., 98., 100.,
86., 75., 62., 90., 82., 82., 98., 90., 86.,
94., 85., 91., 92., 82., 75., 74., 98., 74.,
80., 86., 86., 76., 76., 83., 83., 72., 82.,
79., 77., 75., 68., 84., 90., 85., 97., 79.,
73., 85., 88., 85., 79., 85., 95., 72., 81.,
91., 98., 101., 97., 80., 83., 69., 69., 89.,
89., 90., 90., 78., 69., 69., 68., 71., 79.,
80., 75., 75., 109., 100., 94., 83., 79., 92.,
96., 98., 77., 73., 90., 92., 92., 88., 76.,
66., 49., 46., 55., 52., 61., 58., 51., 62.,
52., 52., 58., 58., 54., 48., 57., 57., 46.,
45., 47., 62., 60., 53., 41., 62., 56., 65.,
58., 61., 67., 73., 79., 84., 95., 73., 72.,
61., 73., 64., 61., 53., 53., 56., 56., 59.,
59., 50., 50., 64., 55., 64., 56., 56., 54.,
47., 62., 73., 51., 42., 55., 60., 56., 42.,
42., 45., 54., 58., 57., 56., 61., 69., 59.,
59., 52., 52., 65., 65., 75., 59., 54., 49.,
44., 61., 64., 67., 59., 45., 52., 44., 40.,
61., 56., 42., 41., 64., 50., 37., 48., 42.,
50., 59., 59., 61., 61., 45., 45., 38., 38.,
46., 77., 71., 84., 71., 63., 58., 44., 37.,
51., 43., 39., 31., 35., 46., 39., 48., 60.,
61., 64., 64., 73., 75., 88., 116., 139., 136.,
140., 136., 151., 151., 146., 146., 127., 71., 63.,
63., 68., 43., 49., 52., 55., 43., 44., 50.,
46., 43., 39., 51., 51., 51., 45., 45., 43.,
42., 49., 43., 45., 45., 49., 52., 48., 45.,
53., 53., 59., 48., 73., 64., 73., 70., 95.,
95., 106., 125., 102., 120., 125., 124., 88., 96.,
65., 55., 62., 55., 46., 51., 45., 43., 61.,
44., 40., 44., 49., 45., 40., 42., 46., 52.,
42., 45., 41., 40., 56., 56., 46., 38., 46.,
45., 54., 51., 57., 46., 51., 42., 39., 39.,
40., 53., 47., 51., 54., 35., 42., 43., 46.,
51., 44., nan])
Also, the index of the LAS file can be accessed directly by the .index attribute:
In [21]:
print(l["DEPT"][:10])
print(l.index[:10])
[ 20.05 20.1 20.15 20.2 20.25 20.3 20.35 20.4 20.45 20.5 ]
[ 20.05 20.1 20.15 20.2 20.25 20.3 20.35 20.4 20.45 20.5 ]
In [22]:
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
%matplotlib inline
l2 = las_reader.read("GT1457B.LAS")
fig = plt.figure(figsize=(18, 5))
gs = gridspec.GridSpec(1, len(l2.curves)-1, wspace=0.2)
for i, curve in enumerate(l2.curves[1:]):
if i > 0:
shareax = ax
else:
shareax = False
ax = fig.add_subplot(gs[i], sharey=None)
ax.plot(curve.data, l2.index)
ax.set_title(curve.mnemonic)
if i > 0:
ax.set_yticklabels([])
ax.set_ylim(*ax.get_ylim()[::-1])
ax.set_xticklabels(["%.0f" % xi if xi in ax.get_xlim() else "" for xi in ax.get_xticks()], rotation=30)
Opened file at GT1457B.LAS
In [23]:
print(l2._text)
~Version Information
VERS. 1.20: CWLS log ASCII Standard - version 1.20
WRAP. NO: one line per depth step
~Well Information
#MNEM.UNIT Data Type Information
#_________ _________ ________________________
STRT.M 30.000:
STOP.M 53.700:
STEP.M 0.050:
NULL. -0.0:
COMP. COMPANY:
WELL. WELL: GT14571
FLD. FIELD:
LOC. LOCATION:
CNTY. COUNTY:
STAT. STATE:
CTRY. COUNTRY:
SRVC. SERVICE COMPANY:
DATE. LOG DATE: 07/05/2014
UWI. UNIQUE WELL ID: unknown
~Curve Information
#MNEM.UNIT Curve Information
#_________ _____ ___________________
DEPT.M : 1 DEPTH
DNCP.cps : 2 DNCPS
DFCP.cps : 3 DFCPS
GCPS.cps : 4 GCPS
DENF.cps : 5 DENS_F
DENN.cps : 6 DENS_N
GAMM.cps : 7 GAMMA
~Parameter Information
~Other Information
~A Depth DNCP DFCP GCPS DENF DENN GAMM
30.05 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.10 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.15 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.20 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.25 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.30 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.35 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.40 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.45 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.50 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.55 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.60 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.65 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.70 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.75 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.80 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.85 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.90 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
30.95 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
31.00 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
31.05 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
31.10 -0.0 -0.0 -0.0 -0.0 -0.0 -2163.978
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41.80 13329.000 2311.000 22.000 2652.000 12022.000 47.608
41.85 13574.000 2431.000 22.000 2766.000 12082.000 47.608
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42.00 14142.000 2652.000 29.000 2842.000 13329.000 62.756
42.05 14537.000 2766.000 28.000 2880.000 13574.000 60.592
42.10 14116.000 2771.000 22.000 2764.000 13945.000 47.608
42.15 13230.000 2815.000 20.000 2699.000 14206.000 43.280
42.20 11236.000 2842.000 27.000 2334.000 14142.000 58.428
42.25 9474.000 2880.000 31.000 2039.000 14537.000 67.084
42.30 7929.000 2764.000 17.000 1687.000 14116.000 36.788
42.35 6755.000 2699.000 21.000 1537.000 13230.000 45.444
42.40 6619.000 2334.000 34.000 1447.000 11236.000 73.576
42.45 5844.000 2039.000 35.000 1425.000 9474.000 75.740
42.50 6252.000 1687.000 44.000 1270.000 7929.000 95.216
42.55 6879.000 1537.000 74.000 1188.000 6755.000 160.136
42.60 7913.000 1447.000 83.000 1207.000 6619.000 179.612
42.65 9498.000 1425.000 119.000 1245.000 5844.000 257.516
42.70 11297.000 1270.000 111.000 1402.000 6252.000 240.204
42.75 12202.000 1188.000 134.000 1555.000 6879.000 289.976
42.80 12523.000 1207.000 209.000 1844.000 7913.000 452.276
42.85 12143.000 1245.000 397.000 1876.000 9498.000 859.108
42.90 11429.000 1402.000 738.000 2069.000 11297.000 1597.032
42.95 10942.000 1555.000 1061.000 2109.000 12202.000 2296.004
43.00 10374.000 1844.000 1673.000 2060.000 12523.000 3620.372
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43.10 9655.000 2069.000 3216.000 1882.000 11429.000 6959.424
43.15 9448.000 2109.000 3621.000 1765.000 10942.000 7835.844
43.20 9157.000 2060.000 4827.000 1797.000 10374.000 10445.628
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43.35 9413.000 1765.000 7269.000 2636.000 9448.000 15730.116
43.40 9497.000 1797.000 7493.000 3174.000 9157.000 16214.852
43.45 9930.000 1979.000 7558.000 3826.000 9139.000 16355.512
43.50 10177.000 2117.000 7667.000 4754.000 9215.000 16591.388
43.55 9955.000 2636.000 8404.000 5681.000 9413.000 18186.256
43.60 10284.000 3174.000 8679.000 6830.000 9497.000 18781.356
43.65 10229.000 3826.000 8770.000 7674.000 9930.000 18978.280
43.70 10115.000 4754.000 8754.000 8257.000 10177.000 18943.656
43.75 10342.000 5681.000 8681.000 8477.000 9955.000 18785.684
43.80 10342.000 6830.000 8646.000 8477.000 10284.000 18709.944
43.85 10578.000 7674.000 8476.000 8601.000 10229.000 18342.064
43.90 10866.000 8257.000 8535.000 8838.000 10115.000 18469.740
43.95 10995.000 8477.000 8127.000 9479.000 10342.000 17586.828
44.00 11101.000 8477.000 7134.000 9661.000 10342.000 15437.976
44.05 11599.000 8601.000 4792.000 9763.000 10578.000 10369.888
44.10 11870.000 8838.000 2383.000 9797.000 10866.000 5156.812
44.15 12373.000 9479.000 1128.000 9719.000 10995.000 2440.992
44.20 12431.000 9661.000 580.000 9779.000 11101.000 1255.120
44.25 11472.000 9763.000 378.000 9677.000 11599.000 817.992
44.30 10406.000 9797.000 260.000 9235.000 11870.000 562.640
44.35 9986.000 9719.000 201.000 8711.000 12373.000 434.964
44.40 9897.000 9779.000 110.000 7930.000 12431.000 238.040
44.45 9437.000 9677.000 83.000 5436.000 11472.000 179.612
44.50 9054.000 9235.000 45.000 3250.000 10406.000 97.380
44.55 9010.000 8711.000 34.000 2474.000 9986.000 73.576
44.60 8913.000 7930.000 23.000 1938.000 9897.000 49.772
44.65 8965.000 5436.000 15.000 1779.000 9437.000 32.460
44.70 8809.000 3250.000 17.000 1705.000 9054.000 36.788
44.75 8875.000 2474.000 10.000 1528.000 9010.000 21.640
44.80 9108.000 1938.000 13.000 1467.000 8913.000 28.132
44.85 8889.000 1779.000 14.000 1394.000 8965.000 30.296
44.90 9080.000 1705.000 9.000 1340.000 8809.000 19.476
44.95 8951.000 1528.000 11.000 1333.000 8875.000 23.804
45.00 8907.000 1467.000 13.000 1392.000 9108.000 28.132
45.05 9031.000 1394.000 10.000 1461.000 8889.000 21.640
45.10 8940.000 1340.000 11.000 1412.000 9080.000 23.804
45.15 9222.000 1333.000 9.000 1340.000 8951.000 19.476
45.20 9400.000 1392.000 11.000 1409.000 8907.000 23.804
45.25 9180.000 1461.000 9.000 1424.000 9031.000 19.476
45.30 9640.000 1412.000 14.000 1476.000 8940.000 30.296
45.35 9540.000 1340.000 11.000 1502.000 9222.000 23.804
45.40 9788.000 1409.000 10.000 1546.000 9400.000 21.640
45.45 9817.000 1424.000 13.000 1567.000 9180.000 28.132
45.50 9794.000 1476.000 6.000 1594.000 9640.000 12.984
45.55 9811.000 1502.000 10.000 1517.000 9540.000 21.640
45.60 9898.000 1546.000 14.000 1622.000 9788.000 30.296
45.65 9862.000 1567.000 10.000 1509.000 9817.000 21.640
45.70 9772.000 1594.000 10.000 1552.000 9794.000 21.640
45.75 9753.000 1517.000 16.000 1644.000 9811.000 34.624
45.80 9841.000 1622.000 11.000 1681.000 9898.000 23.804
45.85 9939.000 1509.000 24.000 1706.000 9862.000 51.936
45.90 10113.000 1552.000 18.000 1595.000 9772.000 38.952
45.95 10230.000 1644.000 16.000 1721.000 9753.000 34.624
46.00 10607.000 1681.000 19.000 1679.000 9841.000 41.116
46.05 10977.000 1706.000 11.000 1763.000 9939.000 23.804
46.10 11221.000 1595.000 17.000 1913.000 10113.000 36.788
46.15 11940.000 1721.000 16.000 1937.000 10230.000 34.624
46.20 12296.000 1679.000 9.000 2061.000 10607.000 19.476
46.25 11972.000 1763.000 16.000 2043.000 10977.000 34.624
46.30 11064.000 1913.000 8.000 1913.000 11221.000 17.312
46.35 9884.000 1937.000 10.000 1736.000 11940.000 21.640
46.40 9558.000 2061.000 8.000 1665.000 12296.000 17.312
46.45 8796.000 2043.000 17.000 1429.000 11972.000 36.788
46.50 8021.000 1913.000 10.000 1447.000 11064.000 21.640
46.55 7103.000 1736.000 10.000 1366.000 9884.000 21.640
46.60 6060.000 1665.000 8.000 1237.000 9558.000 17.312
46.65 5354.000 1429.000 8.000 1167.000 8796.000 17.312
46.70 5323.000 1447.000 12.000 1071.000 8021.000 25.968
46.75 5685.000 1366.000 9.000 1069.000 7103.000 19.476
46.80 6413.000 1237.000 8.000 1019.000 6060.000 17.312
46.85 6954.000 1167.000 12.000 1047.000 5354.000 25.968
46.90 7551.000 1071.000 15.000 1082.000 5323.000 32.460
46.95 8104.000 1069.000 15.000 1188.000 5685.000 32.460
47.00 8563.000 1019.000 12.000 1242.000 6413.000 25.968
47.05 9812.000 1047.000 8.000 1245.000 6954.000 17.312
47.10 11420.000 1082.000 13.000 1455.000 7551.000 28.132
47.15 12546.000 1188.000 11.000 1575.000 8104.000 23.804
47.20 12511.000 1242.000 21.000 1758.000 8563.000 45.444
47.25 12557.000 1245.000 15.000 2017.000 9812.000 32.460
47.30 12542.000 1455.000 21.000 2269.000 11420.000 45.444
47.35 12487.000 1575.000 17.000 2399.000 12546.000 36.788
47.40 12668.000 1758.000 12.000 2482.000 12511.000 25.968
47.45 12877.000 2017.000 20.000 2482.000 12557.000 43.280
47.50 12928.000 2269.000 18.000 2484.000 12542.000 38.952
47.55 12895.000 2399.000 20.000 2511.000 12487.000 43.280
47.60 12966.000 2482.000 22.000 2476.000 12668.000 47.608
47.65 13144.000 2482.000 13.000 2492.000 12877.000 28.132
47.70 13393.000 2484.000 20.000 2547.000 12928.000 43.280
47.75 13440.000 2511.000 17.000 2623.000 12895.000 36.788
47.80 13247.000 2476.000 15.000 2583.000 12966.000 32.460
47.85 13469.000 2492.000 9.000 2663.000 13144.000 19.476
47.90 13345.000 2547.000 12.000 2795.000 13393.000 25.968
47.95 13737.000 2623.000 13.000 2842.000 13440.000 28.132
48.00 15383.000 2583.000 22.000 3144.000 13247.000 47.608
48.05 16529.000 2663.000 15.000 3438.000 13469.000 32.460
48.10 17129.000 2795.000 12.000 3533.000 13345.000 25.968
48.15 17649.000 2842.000 10.000 3702.000 13737.000 21.640
48.20 17994.000 3144.000 14.000 3719.000 15383.000 30.296
48.25 18309.000 3438.000 15.000 3772.000 16529.000 32.460
48.30 18099.000 3533.000 13.000 3898.000 17129.000 28.132
48.35 16830.000 3702.000 16.000 4009.000 17649.000 34.624
48.40 15575.000 3719.000 14.000 4067.000 17994.000 30.296
48.45 15885.000 3772.000 21.000 4138.000 18309.000 45.444
48.50 15439.000 3898.000 40.000 3881.000 18099.000 86.560
48.55 14347.000 4009.000 67.000 3339.000 16830.000 144.988
48.60 13060.000 4067.000 144.000 2980.000 15575.000 311.616
48.65 11939.000 4138.000 334.000 2666.000 15885.000 722.776
48.70 11628.000 3881.000 908.000 2489.000 15439.000 1964.912
48.75 11102.000 3339.000 1210.000 2419.000 14347.000 2618.440
48.80 9981.000 2980.000 1585.000 2295.000 13060.000 3429.940
48.85 8112.000 2666.000 1661.000 2105.000 11939.000 3594.404
48.90 7137.000 2489.000 1682.000 2139.000 11628.000 3639.848
48.95 7177.000 2419.000 1563.000 2057.000 11102.000 3382.332
49.00 7634.000 2295.000 1535.000 2068.000 9981.000 3321.740
49.05 8383.000 2105.000 1562.000 2095.000 8112.000 3380.168
49.10 8967.000 2139.000 1379.000 2508.000 7137.000 2984.156
49.15 9904.000 2057.000 824.000 3015.000 7177.000 1783.136
49.20 10815.000 2068.000 314.000 3194.000 7634.000 679.496
49.25 12512.000 2095.000 208.000 3515.000 8383.000 450.112
49.30 14487.000 2508.000 167.000 3651.000 8967.000 361.388
49.35 15558.000 3015.000 159.000 3956.000 9904.000 344.076
49.40 16338.000 3194.000 114.000 4303.000 10815.000 246.696
49.45 16441.000 3515.000 102.000 4631.000 12512.000 220.728
49.50 16717.000 3651.000 119.000 5013.000 14487.000 257.516
49.55 17081.000 3956.000 132.000 5069.000 15558.000 285.648
49.60 17280.000 4303.000 124.000 4705.000 16338.000 268.336
49.65 15984.000 4631.000 126.000 4469.000 16441.000 272.664
49.70 14861.000 5013.000 129.000 4031.000 16717.000 279.156
49.75 13845.000 5069.000 140.000 3665.000 17081.000 302.960
49.80 12597.000 4705.000 130.000 3243.000 17280.000 281.320
49.85 11930.000 4469.000 146.000 3168.000 15984.000 315.944
49.90 11384.000 4031.000 161.000 2889.000 14861.000 348.404
49.95 11105.000 3665.000 137.000 2694.000 13845.000 296.468
50.00 10790.000 3243.000 120.000 2552.000 12597.000 259.680
50.05 11168.000 3168.000 131.000 2369.000 11930.000 283.484
50.10 11337.000 2889.000 128.000 2324.000 11384.000 276.992
50.15 11984.000 2694.000 141.000 2274.000 11105.000 305.124
50.20 12286.000 2552.000 136.000 2207.000 10790.000 294.304
50.25 12613.000 2369.000 128.000 2201.000 11168.000 276.992
50.30 12334.000 2324.000 118.000 2324.000 11337.000 255.352
50.35 12176.000 2274.000 147.000 2288.000 11984.000 318.108
50.40 11564.000 2207.000 123.000 2341.000 12286.000 266.172
50.45 11239.000 2201.000 131.000 2365.000 12613.000 283.484
50.50 10903.000 2324.000 111.000 2304.000 12334.000 240.204
50.55 10385.000 2288.000 117.000 2237.000 12176.000 253.188
50.60 10222.000 2341.000 121.000 2014.000 11564.000 261.844
50.65 9675.000 2365.000 135.000 1978.000 11239.000 292.140
50.70 9109.000 2304.000 126.000 1944.000 10903.000 272.664
50.75 8837.000 2237.000 179.000 1846.000 10385.000 387.356
50.80 8558.000 2014.000 286.000 1658.000 10222.000 618.904
50.85 8068.000 1978.000 668.000 1567.000 9675.000 1445.552
50.90 7696.000 1944.000 1426.000 1429.000 9109.000 3085.864
50.95 7164.000 1846.000 1824.000 1514.000 8837.000 3947.136
51.00 6549.000 1658.000 1909.000 1396.000 8558.000 4131.076
51.05 5536.000 1567.000 2016.000 1340.000 8068.000 4362.624
51.10 5472.000 1429.000 1995.000 1305.000 7696.000 4317.180
51.15 5628.000 1514.000 1955.000 1206.000 7164.000 4230.620
51.20 5640.000 1396.000 2010.000 1342.000 6549.000 4349.640
51.25 5574.000 1340.000 1996.000 1901.000 5536.000 4319.344
51.30 5586.000 1305.000 1966.000 2285.000 5472.000 4254.424
51.35 5663.000 1206.000 1887.000 2548.000 5628.000 4083.468
51.40 5867.000 1342.000 2013.000 2737.000 5640.000 4356.132
51.45 5857.000 1901.000 1991.000 2695.000 5574.000 4308.524
51.50 6033.000 2285.000 1860.000 2759.000 5586.000 4025.040
51.55 5978.000 2548.000 1497.000 2733.000 5663.000 3239.508
51.60 6276.000 2737.000 0.000 2735.000 5867.000 0.000
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