lasio - Log ASCII Standard (LAS) files in Python¶
lasio is a Python 3 package to read and write Log ASCII Standard (LAS) files, used for borehole data such as geophysical, geological, or petrophysical logs. It’s compatible with versions 1.2 and 2.0 of the LAS file specification, published by the Canadian Well Logging Society. In principle it is designed to read as many types of LAS files as possible, including ones containing common errors or non-compliant formatting.
Depending on your particular application you may also want to check out striplog for stratigraphic/lithological data, or (still in alpha dev) welly for dealing with data at the well level. lasio is primarily for reading & writing LAS files.
Note this is not a package for reading LiDAR data, which is also stored in “LAS” files.
The final version of lasio with Python 2.7 support is v0.26.
Installation¶
lasio is written to be compatible with Python 3.2+. The best way to install is using pip.
$ pip install lasio
This will make sure that the dependency numpy is installed as well.
The final version of lasio with Python 2.7 support is v0.26.
There are some other packages which lasio will use to provide extra functionality if they are installed (pandas, cChardet and/or chardet, and openpyxl). I recommend installing these with:
lasio is now installed.
To upgrade to the latest PyPI version, use:
Basic example¶
>>> import lasio
You can use lasio.read() to open any file or URL. For this tutorial I
will use the lasio.examples module to load a LAS file which is bundled
with lasio:
>>> import lasio.examples
>>> las = lasio.examples.open("1001178549.las")
The lasio.read() function returns a lasio.LASFile object. Each
of the standard LAS sections can be accessed as an attribute:
>>> las.version
[HeaderItem(mnemonic="VERS", unit=, value="2.0", descr="CWLS Log ASCII Standard -V...),
HeaderItem(mnemonic="WRAP", unit=, value="YES", descr="Multiple lines per depth ...)]
Each LAS section is represented as a lasio.SectionItems object. The
others, for LAS 2.0 files, are present as las.well, las.curves, and
las.params; the ~O section is a string accessible at las.other.
You can also see the sections printed as an easier-to-read table:
>>> print(las.curves)
Mnemonic Unit Value Description
-------- ---- ----- -----------
DEPT FT 0 1 0 0 1 DEPTH
GSGR API 31 310 0 0 2 GAMMA RAY
GSTK API 31 797 0 0 3 ????????
GST API 99 999 99 0 4 ????????
GSK PERCNT 31 721 1 0 5 ????????
GSTH PPM 31 790 0 0 6 THORIUM
GSUR PPM 31 792 0 0 7 URANIUM
NCNPL PERCNT 42 890 1 0 8 NEUTRON POROSITY (LIMESTONE)
DLDPL PERCNT 43 890 10 0 9 DENSITY POROSITY (LIMESTONE)
DLDC GM/CC 43 356 0 0 10 DENSITY CORRECTION
DLPE B/E 43 358 0 0 11 PHOTO-ELECTRIC EFFECT
DLDN GM/CC 43 350 0 0 12 BULK DENSITY
DLCL INCHES 43 280 0 0 13 CALIPER
DLTN LBS 43 635 0 0 14 ????????
IDGR API 7 310 0 0 15 GAMMA RAY
ACCL1 INCHES 60 280 1 0 16 DENSITY CALIPER
ACCL2 INCHES 60 280 2 0 17 NEUTRON CALIPER
ACTC US/FT 60 520 0 0 18 SONIC INTERVAL TRANSIT TIME (COMPENSATED)
ACAPL PERCNT 60 890 20 0 19 POROSITY
IDIM OHMM 7 120 44 0 20 MEDIUM INDUCTION
IDID OHMM 7 120 46 0 21 DEEP INDUCTION
IDIDC MMHOS 7 110 46 0 22 INDUCTION (CONDUCTIVITY UNITS)
IDL3 OHMM 7 220 3 0 23 FOCUSSED RESISTIVITY
IDTN LBS 7 635 0 0 24 ????????
IDSP MVOLT 7 10 0 0 25 SPONTANEOUS POTENTIAL
MEL1 OHMM 15 250 2 0 26 MICRO INVERSE 1"
ME OHMM 15 252 2 0 27 MICRO NORMAL 2"
The data is present as a numpy.ndarray at las.data:
>>> las.data.shape
(5, 27)
>>> las.data
array([[1.7835000e+03, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, 5.0646500e+01, 8.3871000e+00,
8.4396000e+00, 5.5100000e+01, 5.6900000e-02, 5.6000000e+02,
1.7500000e+02, 5.0000000e-02, 4.5330000e-01, 1.8930420e+03,
9.2605000e+01, nan, nan],
[1.7837500e+03, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, 4.9676700e+01, 8.3951000e+00,
8.4460000e+00, 5.4355500e+01, 5.9000000e-02, 5.6000000e+02,
1.7500000e+02, 5.0000000e-02, 4.5340000e-01, 1.8523320e+03,
9.2778000e+01, nan, nan],
[1.7840000e+03, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, 4.8631300e+01, 8.4052000e+00,
8.4460000e+00, 5.4444400e+01, 5.8100000e-02, 5.6000000e+02,
1.7500000e+02, 5.0000000e-02, 4.5370000e-01, 1.8319766e+03,
9.2948200e+01, nan, nan],
[1.7842500e+03, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, 4.7771700e+01, 8.4173000e+00,
8.4438000e+00, 5.5311100e+01, 5.7700000e-02, 5.6000000e+02,
1.7500000e+02, 5.0000000e-02, 4.5380000e-01, 1.8319766e+03,
9.3110300e+01, nan, nan],
[1.7845000e+03, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, nan, nan,
nan, nan, 4.8114900e+01, 8.4253000e+00,
8.4460000e+00, 5.6322200e+01, 5.8500000e-02, 5.6000000e+02,
1.7500000e+02, 5.0000000e-02, 4.5390000e-01, 1.8116211e+03,
9.3267100e+01, nan, nan]])
Although it might be easier for you to iterate over the curves:
>>> for curve in las.curves:
... print(curve.mnemonic + ": " + str(curve.data))
DEPT: [1783.5 1783.75 1784. 1784.25 1784.5 ]
GSGR: [nan nan nan nan nan]
GSTK: [nan nan nan nan nan]
GST: [nan nan nan nan nan]
GSK: [nan nan nan nan nan]
GSTH: [nan nan nan nan nan]
GSUR: [nan nan nan nan nan]
NCNPL: [nan nan nan nan nan]
DLDPL: [nan nan nan nan nan]
DLDC: [nan nan nan nan nan]
DLPE: [nan nan nan nan nan]
DLDN: [nan nan nan nan nan]
DLCL: [nan nan nan nan nan]
DLTN: [nan nan nan nan nan]
IDGR: [50.6465 49.6767 48.6313 47.7717 48.1149]
ACCL1: [8.3871 8.3951 8.4052 8.4173 8.4253]
ACCL2: [8.4396 8.446 8.446 8.4438 8.446 ]
ACTC: [55.1 54.3555 54.4444 55.3111 56.3222]
ACAPL: [0.0569 0.059 0.0581 0.0577 0.0585]
IDIM: [560. 560. 560. 560. 560.]
IDID: [175. 175. 175. 175. 175.]
IDIDC: [0.05 0.05 0.05 0.05 0.05]
IDL3: [0.4533 0.4534 0.4537 0.4538 0.4539]
IDTN: [1893.042 1852.332 1831.9766 1831.9766 1811.6211]
IDSP: [92.605 92.778 92.9482 93.1103 93.2671]
MEL1: [nan nan nan nan nan]
ME: [nan nan nan nan nan]
The first curve in the LAS file – usually the depth – is present as
las.index, and curves are also accessible from the LASFile object as
items. For example:
>>> las.index
array([1783.5 , 1783.75, 1784. , 1784.25, 1784.5 ])
>>> las["IDTN"]
array([1893.042 , 1852.332 , 1831.9766, 1831.9766, 1811.6211])
Integration with pandas.DataFrame¶
The lasio.LASFile.df() method converts the LAS data to a
pandas.DataFrame. Any changes that you make to the DataFrame can be
brought back into the LASFile object with lasio.LASFile.set_data().
>>> import lasio.examples
>>> las = lasio.examples.open('6038187_v1.2.las')
>>> df = las.df()
There are some summary methods handy for data exploration:
>>> df.head(10)
CALI DFAR DNEAR GAMN NEUT PR SP COND
DEPT
0.05 49.765 4.587 3.382 NaN NaN NaN NaN NaN
0.10 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.15 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.20 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.25 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.30 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.35 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.40 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.45 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
0.50 49.765 4.587 3.382 -2324.28 NaN 115.508 -3.049 -116.998
>>> df.tail(40)
CALI DFAR DNEAR GAMN NEUT PR SP COND
DEPT
134.65 100.983 1.563 1.357 -2324.28 158.0 115.508 -3.049 578.643
134.70 100.833 1.570 1.357 NaN NaN NaN NaN 571.233
134.75 93.760 1.582 1.378 NaN NaN NaN NaN 565.552
134.80 88.086 1.561 1.361 NaN NaN NaN NaN 570.490
134.85 86.443 1.516 1.338 NaN NaN NaN NaN 574.937
134.90 79.617 5.989 1.356 NaN NaN NaN NaN 579.137
134.95 65.236 4.587 1.397 NaN NaN NaN NaN NaN
135.00 55.833 4.587 1.351 NaN NaN NaN NaN NaN
135.05 49.061 4.587 1.329 NaN NaN NaN NaN NaN
135.10 49.036 NaN NaN NaN NaN NaN NaN NaN
135.15 49.024 NaN NaN NaN NaN NaN NaN NaN
135.20 49.005 NaN NaN NaN NaN NaN NaN NaN
135.25 48.999 NaN NaN NaN NaN NaN NaN NaN
135.30 48.987 NaN NaN NaN NaN NaN NaN NaN
135.35 48.980 NaN NaN NaN NaN NaN NaN NaN
135.40 48.962 NaN NaN NaN NaN NaN NaN NaN
135.45 48.962 NaN NaN NaN NaN NaN NaN NaN
135.50 48.925 NaN NaN NaN NaN NaN NaN NaN
135.55 48.931 NaN NaN NaN NaN NaN NaN NaN
135.60 48.919 NaN NaN NaN NaN NaN NaN NaN
135.65 48.900 NaN NaN NaN NaN NaN NaN NaN
135.70 48.882 NaN NaN NaN NaN NaN NaN NaN
135.75 48.863 NaN NaN NaN NaN NaN NaN NaN
135.80 48.857 NaN NaN NaN NaN NaN NaN NaN
135.85 48.839 NaN NaN NaN NaN NaN NaN NaN
135.90 48.808 NaN NaN NaN NaN NaN NaN NaN
135.95 48.802 NaN NaN NaN NaN NaN NaN NaN
136.00 48.789 NaN NaN NaN NaN NaN NaN NaN
136.05 48.771 NaN NaN NaN NaN NaN NaN NaN
136.10 48.765 NaN NaN NaN NaN NaN NaN NaN
136.15 48.752 NaN NaN NaN NaN NaN NaN NaN
136.20 48.734 NaN NaN NaN NaN NaN NaN NaN
136.25 48.684 NaN NaN NaN NaN NaN NaN NaN
136.30 48.666 NaN NaN NaN NaN NaN NaN NaN
136.35 48.647 NaN NaN NaN NaN NaN NaN NaN
136.40 48.604 NaN NaN NaN NaN NaN NaN NaN
136.45 48.555 NaN NaN NaN NaN NaN NaN NaN
136.50 48.555 NaN NaN NaN NaN NaN NaN NaN
136.55 48.438 NaN NaN NaN NaN NaN NaN NaN
136.60 -56.275 NaN NaN NaN NaN NaN NaN NaN
>>> df.describe()
CALI DFAR DNEAR GAMN NEUT \
count 2732.000000 2701.000000 2701.000000 2691.000000 2492.000000
mean 97.432002 1.767922 1.729209 -102.330033 441.600013
std 13.939547 0.480333 0.372412 630.106420 370.138208
min -56.275000 0.725000 0.657001 -2324.280000 81.001800
25% 101.077500 1.526000 1.535000 55.783000 158.002000
50% 101.426000 1.758000 1.785000 74.376900 256.501500
75% 101.582000 1.993000 1.948000 88.326900 680.500250
max 103.380000 5.989000 3.382000 169.672000 1665.990000
PR SP COND
count 2692.000000 2692.000000 2697.000000
mean 17940.522307 90.393464 478.670791
std 22089.297212 26.725547 753.869866
min 115.508000 -3.049000 -116.998000
25% 2652.470000 93.495500 200.981000
50% 2709.345000 99.994000 266.435000
75% 50499.900000 100.623000 505.530000
max 50499.900000 102.902000 4978.160000
There’s obviously a problem with the GAMN log: -2324.28 is not a valid value. Let’s fix that.
>>> import numpy as np
>>> df['GAMN'][df['GAMN'] == -2324.28] = np.nan
>>> df.describe()['GAMN']
count 2491.000000
mean 76.068198
std 23.120160
min 13.946000
25% 60.434100
50% 76.700700
75% 90.647500
max 169.672000
Name: GAMN, dtype: float64
Let’s create a new log with the moving average of the GAMN log, over
1 m. This is easy enough to do with the pandas pandas.Series.rolling()
method and the LAS file’s STEP value:
>>> df['GAMN_avg'] = df['GAMN'].rolling(int(1 / las.well.STEP.value), center=True).mean()
Now we want to apply this DataFrame df back to the las LASFile object,
and check that it’s all there:
>>> las.set_data(df)
>>> las.curves
[CurveItem(mnemonic="DEPT", unit="M", value="", descr="DEPTH", original_mnemonic="DEPT", data.shape=(2732,)),
CurveItem(mnemonic="CALI", unit="MM", value="", descr="CALI", original_mnemonic="CALI", data.shape=(2732,)),
CurveItem(mnemonic="DFAR", unit="G/CM3", value="", descr="DFAR", original_mnemonic="DFAR", data.shape=(2732,)),
CurveItem(mnemonic="DNEAR", unit="G/CM3", value="", descr="DNEAR", original_mnemonic="DNEAR", data.shape=(2732,)),
CurveItem(mnemonic="GAMN", unit="GAPI", value="", descr="GAMN", original_mnemonic="GAMN", data.shape=(2732,)),
CurveItem(mnemonic="NEUT", unit="CPS", value="", descr="NEUT", original_mnemonic="NEUT", data.shape=(2732,)),
CurveItem(mnemonic="PR", unit="OHM/M", value="", descr="PR", original_mnemonic="PR", data.shape=(2732,)),
CurveItem(mnemonic="SP", unit="MV", value="", descr="SP", original_mnemonic="SP", data.shape=(2732,)),
CurveItem(mnemonic="COND", unit="MS/M", value="", descr="COND", original_mnemonic="COND", data.shape=(2732,)),
CurveItem(mnemonic="GAMN_avg", unit="", value="", descr="", original_mnemonic="GAMN_avg", data.shape=(2732,))]
>>> las.df().describe()
CALI DFAR DNEAR GAMN NEUT \
count 2732.000000 2701.000000 2701.000000 2491.000000 2492.000000
mean 97.432002 1.767922 1.729209 76.068198 441.600013
std 13.939547 0.480333 0.372412 23.120160 370.138208
min -56.275000 0.725000 0.657001 13.946000 81.001800
25% 101.077500 1.526000 1.535000 60.434100 158.002000
50% 101.426000 1.758000 1.785000 76.700700 256.501500
75% 101.582000 1.993000 1.948000 90.647500 680.500250
max 103.380000 5.989000 3.382000 169.672000 1665.990000
PR SP COND GAMN_avg
count 2692.000000 2692.000000 2697.000000 2472.000000
mean 17940.522307 90.393464 478.670791 76.326075
std 22089.297212 26.725547 753.869866 18.208038
min 115.508000 -3.049000 -116.998000 24.753655
25% 2652.470000 93.495500 200.981000 64.848379
50% 2709.345000 99.994000 266.435000 77.747517
75% 50499.900000 100.623000 505.530000 88.323376
max 50499.900000 102.902000 4978.160000 120.049300
All good, the new curve is in there.
See the pandas documentation for more information!
Header section metadata¶
Tutorial¶
One of the primary motivations in writing lasio was to be able to reliably parse LAS header sections. This is working fairly well for LAS 1.2 and 2.0 files, and lasio does not require LAS files to be strictly compliant with either standard.
>>> import lasio.examples
>>> las = lasio.examples.open('6038187_v1.2_short.las')
The header sections are stored in the dictionary las.sections:
>>> type(las.sections)
dict
>>> las.sections.keys()
dict_keys(['Version', 'Well', 'Curves', 'Parameter', 'Other'])
These are special names reserved for LAS 1.2 and 2.0 files, as defined by the standard. Non-standard header sections are also allowed but not fully parsed.
| LAS file | Read in as | References in LASFile |
|---|---|---|
~v or ~V |
lasio.SectionItems |
LASFile.version and LASFile.sections['Version'] |
~w or ~W |
lasio.SectionItems |
LASFile.well and LASFile.sections['Well'] |
~c or ~C |
lasio.SectionItems |
LASFile.curves and LASFile.sections['Curves'] |
~p or ~P |
lasio.SectionItems |
LASFile.params and LASFile.sections['Parameter'] |
~o or ~O |
str |
LASFile.other and LASFile.sections['Other'] |
~extra section |
str |
LASFile.sections['extra section'] |
~a or ~A |
numpy.ndarray |
LASFile.data or each column is in LASFile.curves[...].data |
For example:
>>> las.sections['Version']
[HeaderItem(mnemonic="VERS", unit="", value="2.0", descr="CWLS LOG ASCII STANDA"),
HeaderItem(mnemonic="WRAP", unit="", value="NO", descr="ONE LINE PER DEPTH STE")]
>>> las.version
[HeaderItem(mnemonic="VERS", unit="", value="2.0", descr="CWLS LOG ASCII STANDA"),
HeaderItem(mnemonic="WRAP", unit="", value="NO", descr="ONE LINE PER DEPTH STE")]
Sections themselves are represented by lasio.SectionItems
objects. This is a list which has been extended to allow you to access the
items within by their mnemonic:
>>> las.version.VERS
HeaderItem(mnemonic="VERS", unit="", value="2.0", descr="CWLS LOG ASCII STANDA")
>>> las.version['VERS']
HeaderItem(mnemonic="VERS", unit="", value="2.0", descr="CWLS LOG ASCII STANDA")
>>> las.version[0]
HeaderItem(mnemonic="VERS", unit="", value="2.0", descr="CWLS LOG ASCII STANDA")
As you can see, either attribute-style or item-style access is fine.
Let’s take a look at the next special section, ~W:
>>> las.well
[HeaderItem(mnemonic="STRT", unit="M", value="0.05", descr="FIRST INDEX VALUE"),
HeaderItem(mnemonic="STOP", unit="M", value="136.6", descr="LAST INDEX VALUE"),
HeaderItem(mnemonic="STEP", unit="M", value="0.05", descr="STEP"),
HeaderItem(mnemonic="NULL", unit="", value="-99999", descr="NULL VALUE"),
HeaderItem(mnemonic="COMP", unit="", value="", descr="COMP"),
HeaderItem(mnemonic="WELL", unit="", value="Scorpio E1", descr="WELL"),
HeaderItem(mnemonic="FLD", unit="", value="", descr=""),
HeaderItem(mnemonic="LOC", unit="", value="Mt Eba", descr="LOC"),
HeaderItem(mnemonic="SRVC", unit="", value="", descr=""),
HeaderItem(mnemonic="CTRY", unit="", value="", descr=""),
HeaderItem(mnemonic="STAT", unit="", value="SA", descr="STAT"),
HeaderItem(mnemonic="CNTY", unit="", value="", descr=""),
HeaderItem(mnemonic="DATE", unit="", value="15/03/2015", descr="DATE"),
HeaderItem(mnemonic="UWI", unit="", value="6038-187", descr="WUNT")]
The CTRY item is blank. We will set it:
>>> las.well.CTRY = 'Australia'
>>> las.well.CTRY
HeaderItem(mnemonic="CTRY", unit="", value="Australia", descr="")
Notice that lasio.SectionItems plays a little trick here. It actually
sets the header_item.value attribute, instead of replacing the entire
lasio.HeaderItem object.
You can set any of the attributes directly. Let’s take an example from the ~C section:
>>> las.curves
[CurveItem(mnemonic="DEPT", unit="M", value="", descr="DEPTH", original_mnemonic="DEPT", data.shape=(121,)),
CurveItem(mnemonic="CALI", unit="MM", value="", descr="CALI", original_mnemonic="CALI", data.shape=(121,)),
CurveItem(mnemonic="DFAR", unit="G/CM3", value="", descr="DFAR", original_mnemonic="DFAR", data.shape=(121,)),
CurveItem(mnemonic="DNEAR", unit="G/CM3", value="", descr="DNEAR", original_mnemonic="DNEAR", data.shape=(121,)),
CurveItem(mnemonic="GAMN", unit="GAPI", value="", descr="GAMN", original_mnemonic="GAMN", data.shape=(121,)),
CurveItem(mnemonic="NEUT", unit="CPS", value="", descr="NEUT", original_mnemonic="NEUT", data.shape=(121,)),
CurveItem(mnemonic="PR", unit="OHM/M", value="", descr="PR", original_mnemonic="PR", data.shape=(121,)),
CurveItem(mnemonic="SP", unit="MV", value="", descr="SP", original_mnemonic="SP", data.shape=(121,)),
CurveItem(mnemonic="COND", unit="MS/M", value="", descr="COND", original_mnemonic="COND", data.shape=(121,))]
>>> las.curves.PR.unit = 'ohmm'
>>> las.curves.PR
CurveItem(mnemonic="PR", unit="ohmm", value="", descr="PR", original_mnemonic="PR", data.shape=(121,))
Now let’s look more closely at how to manipulate and add or remove items from a section.
In [195]: las.params
Out[195]:
[HeaderItem(mnemonic="BS", unit="", value="216 mm", descr="BS"),
HeaderItem(mnemonic="JOBN", unit="", value="", descr="JOBN"),
HeaderItem(mnemonic="WPMT", unit="", value="", descr="WPMT"),
HeaderItem(mnemonic="AGL", unit="", value="", descr="AGL"),
HeaderItem(mnemonic="PURP", unit="", value="Cased hole stratigraphy", descr="P"),
HeaderItem(mnemonic="X", unit="", value="560160", descr="X"),
HeaderItem(mnemonic="CSGL", unit="", value="0 m - 135 m", descr="CSGL"),
HeaderItem(mnemonic="UNIT", unit="", value="", descr="UNIT"),
HeaderItem(mnemonic="Y", unit="", value="6686430", descr="Y"),
HeaderItem(mnemonic="TDL", unit="", value="135.2 m", descr="TDL"),
HeaderItem(mnemonic="PROD", unit="", value="", descr="PROD"),
HeaderItem(mnemonic="MUD", unit="", value="Water", descr="MUD"),
HeaderItem(mnemonic="CSGS", unit="", value="100 mm", descr="CSGS"),
HeaderItem(mnemonic="ENG", unit="", value="", descr="ENG"),
HeaderItem(mnemonic="STEP", unit="", value="5 cm", descr="STEP"),
HeaderItem(mnemonic="FLUIDLEVEL", unit="", value="54 m", descr="FluidLevel"),
HeaderItem(mnemonic="CSGT", unit="", value="PVC", descr="CSGT"),
HeaderItem(mnemonic="WIT", unit="", value="", descr="WIT"),
HeaderItem(mnemonic="EREF", unit="", value="", descr="EREF"),
HeaderItem(mnemonic="PROJ", unit="", value="", descr="PROJ"),
HeaderItem(mnemonic="ZONE", unit="", value="53J", descr="ZONE"),
HeaderItem(mnemonic="DREF", unit="", value="GL", descr="DREF"),
HeaderItem(mnemonic="TDD", unit="", value="136 m", descr="TDD")]
We want to rename the DREF mnemonic as LMF. We can do so by changing the
header_item.mnemonic attribute.
>>> las.params.DREF.mnemonic = 'LMF'
>>> las.params
[HeaderItem(mnemonic="BS", unit="", value="216 mm", descr="BS"),
HeaderItem(mnemonic="JOBN", unit="", value="", descr="JOBN"),
HeaderItem(mnemonic="WPMT", unit="", value="", descr="WPMT"),
HeaderItem(mnemonic="AGL", unit="", value="", descr="AGL"),
HeaderItem(mnemonic="PURP", unit="", value="Cased hole stratigraphy", descr="P"),
HeaderItem(mnemonic="X", unit="", value="560160", descr="X"),
HeaderItem(mnemonic="CSGL", unit="", value="0 m - 135 m", descr="CSGL"),
HeaderItem(mnemonic="UNIT", unit="", value="", descr="UNIT"),
HeaderItem(mnemonic="Y", unit="", value="6686430", descr="Y"),
HeaderItem(mnemonic="TDL", unit="", value="135.2 m", descr="TDL"),
HeaderItem(mnemonic="PROD", unit="", value="", descr="PROD"),
HeaderItem(mnemonic="MUD", unit="", value="Water", descr="MUD"),
HeaderItem(mnemonic="CSGS", unit="", value="100 mm", descr="CSGS"),
HeaderItem(mnemonic="ENG", unit="", value="", descr="ENG"),
HeaderItem(mnemonic="STEP", unit="", value="5 cm", descr="STEP"),
HeaderItem(mnemonic="FLUIDLEVEL", unit="", value="54 m", descr="FluidLevel"),
HeaderItem(mnemonic="CSGT", unit="", value="PVC", descr="CSGT"),
HeaderItem(mnemonic="WIT", unit="", value="", descr="WIT"),
HeaderItem(mnemonic="EREF", unit="", value="", descr="EREF"),
HeaderItem(mnemonic="PROJ", unit="", value="", descr="PROJ"),
HeaderItem(mnemonic="ZONE", unit="", value="53J", descr="ZONE"),
HeaderItem(mnemonic="LMF", unit="", value="GL", descr="DREF"),
HeaderItem(mnemonic="TDD", unit="", value="136 m", descr="TDD")]
And now we need to add a new mnemonic.
>>> las.params.DRILL = lasio.HeaderItem(mnemonic='DRILL', value='John Smith', descr='Driller on site')
>>> las.params
[HeaderItem(mnemonic="BS", unit="", value="216 mm", descr="BS"),
HeaderItem(mnemonic="JOBN", unit="", value="", descr="JOBN"),
HeaderItem(mnemonic="WPMT", unit="", value="", descr="WPMT"),
HeaderItem(mnemonic="AGL", unit="", value="", descr="AGL"),
HeaderItem(mnemonic="PURP", unit="", value="Cased hole stratigraphy", descr="P"),
HeaderItem(mnemonic="X", unit="", value="560160", descr="X"),
HeaderItem(mnemonic="CSGL", unit="", value="0 m - 135 m", descr="CSGL"),
HeaderItem(mnemonic="UNIT", unit="", value="", descr="UNIT"),
HeaderItem(mnemonic="Y", unit="", value="6686430", descr="Y"),
HeaderItem(mnemonic="TDL", unit="", value="135.2 m", descr="TDL"),
HeaderItem(mnemonic="PROD", unit="", value="", descr="PROD"),
HeaderItem(mnemonic="MUD", unit="", value="Water", descr="MUD"),
HeaderItem(mnemonic="CSGS", unit="", value="100 mm", descr="CSGS"),
HeaderItem(mnemonic="ENG", unit="", value="", descr="ENG"),
HeaderItem(mnemonic="STEP", unit="", value="5 cm", descr="STEP"),
HeaderItem(mnemonic="FLUIDLEVEL", unit="", value="54 m", descr="FluidLevel"),
HeaderItem(mnemonic="CSGT", unit="", value="PVC", descr="CSGT"),
HeaderItem(mnemonic="WIT", unit="", value="", descr="WIT"),
HeaderItem(mnemonic="EREF", unit="", value="", descr="EREF"),
HeaderItem(mnemonic="PROJ", unit="", value="", descr="PROJ"),
HeaderItem(mnemonic="ZONE", unit="", value="53J", descr="ZONE"),
HeaderItem(mnemonic="LMF", unit="", value="GL", descr="DREF"),
HeaderItem(mnemonic="TDD", unit="", value="136 m", descr="TDD"),
HeaderItem(mnemonic="DRILL", unit="", value="John Smith", descr="Driller on si")]
Bingo.
What if we want to delete or remove an item? You can delete items the same way you would remove an item from a dictionary. Let’s remove the item we just added (DRILL):
>>> del las.well["DRILL"]
There are methods intended for removing curves. Say you want to remove the PR curve:
>>> las.delete_curve("PR")
[CurveItem(mnemonic="DEPT", unit="M", value="", descr="DEPTH", original_mnemonic="DEPT", data.shape=(121,)),
CurveItem(mnemonic="CALI", unit="MM", value="", descr="CALI", original_mnemonic="CALI", data.shape=(121,)),
CurveItem(mnemonic="DFAR", unit="G/CM3", value="", descr="DFAR", original_mnemonic="DFAR", data.shape=(121,)),
CurveItem(mnemonic="DNEAR", unit="G/CM3", value="", descr="DNEAR", original_mnemonic="DNEAR", data.shape=(121,)),
CurveItem(mnemonic="GAMN", unit="GAPI", value="", descr="GAMN", original_mnemonic="GAMN", data.shape=(121,)),
CurveItem(mnemonic="NEUT", unit="CPS", value="", descr="NEUT", original_mnemonic="NEUT", data.shape=(121,)),
CurveItem(mnemonic="SP", unit="MV", value="", descr="SP", original_mnemonic="SP", data.shape=(121,)),
CurveItem(mnemonic="COND", unit="MS/M", value="", descr="COND", original_mnemonic="COND", data.shape=(121,))]
Warning
Common mistake!
A common job is to iterate through the curves and remove all but a few that you are interested in. When doing this, be careful to iterate over a copy of the curves section:
>>> keep_curves = ['DEPT', 'DFAR', 'DNEAR']
>>> for curve in las.curves[:]:
... if curve.mnemonic not in keep_curves:
... las.delete_curve(curve.mnemonic)
...
>>> las.curves
[CurveItem(mnemonic="DEPT", unit="M", value="", descr="DEPTH", original_mnemonic="DEPT", data.shape=(121,)),
CurveItem(mnemonic="DFAR", unit="G/CM3", value="", descr="DFAR", original_mnemonic="DFAR", data.shape=(121,)),
CurveItem(mnemonic="DNEAR", unit="G/CM3", value="", descr="DNEAR", original_mnemonic="DNEAR", data.shape=(121,))]
Handling errors¶
lasio will do its best to read every line from the header section. If it can make sense of it, it will parse it into a mnemonic, unit, value, and description. However often there are problems in LAS files. For example, a header section might contain something like:
COUNTY: RUSSELL
This line is missing a period. It should be COUNTY. : RUSSELL. Or
another example:
API . : API Number (required if CTRY = US)
"# Surface Coords: 1,000' FNL & 2,000' FWL"
LATI .DEG : Latitude - see Surface Coords comment above
LONG .DEG : Longitude - see Surface Coords comment above
Obviously the line with ” causes an error.
All these (and any other kind of error in the header section) can be turned
from LASHeaderError exceptions into logger.warning() calls instead by
using lasio.read(..., ignore_header_errors=True). Here is an example.
First we try reading a file without this argument:
>>> las = lasio.examples.open('dodgy_param_sect.las', ignore_header_errors=False)
Unable to parse line as LAS header: DEPTH DT RHOB NPHI SFLU SFLA ILM ILD
Traceback (most recent call last):
File "C:\Users\kinve\code\lasio\lasio\reader.py", line 525, in parse_header_section
values = read_line(line, section_name=parser.section_name2)
File "C:\Users\kinve\code\lasio\lasio\reader.py", line 711, in read_line
return read_header_line(*args, **kwargs)
File "C:\Users\kinve\code\lasio\lasio\reader.py", line 780, in read_header_line
mdict = m.groupdict()
AttributeError: 'NoneType' object has no attribute 'groupdict'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\kinve\code\lasio\lasio\examples.py", line 46, in open
return open_local_example(filename, **kwargs)
File "C:\Users\kinve\code\lasio\lasio\examples.py", line 106, in open_local_example
return LASFile(os.path.join(examples_path, *filename.split("/")), **kwargs)
File "C:\Users\kinve\code\lasio\lasio\las.py", line 84, in __init__
self.read(file_ref, **read_kwargs)
File "C:\Users\kinve\code\lasio\lasio\las.py", line 222, in read
mnemonic_case=mnemonic_case,
File "C:\Users\kinve\code\lasio\lasio\las.py", line 142, in add_section
raw_section, **sect_kws
File "C:\Users\kinve\code\lasio\lasio\reader.py", line 536, in parse_header_section
raise exceptions.LASHeaderError(message)
lasio.exceptions.LASHeaderError: line 31 (section ~PARAMETER INFORMATION): "DEPTH DT RHOB NPHI SFLU SFLA ILM ILD"
Now if we use ignore_header_errors=True:
>>> las = lasio.examples.open('dodgy_param_sect.las', ignore_header_errors=True)
Unable to parse line as LAS header: DEPTH DT RHOB NPHI SFLU SFLA ILM ILD
line 31 (section ~PARAMETER INFORMATION): "DEPTH DT RHOB NPHI SFLU SFLA ILM ILD"
Only a warning is issued, and the rest of the LAS file loads OK:
>>> las.params
[]
>>> las.curves
[CurveItem(mnemonic="DEPT", unit="M", value="", descr="1 DEPTH", original_mnemonic="DEPT", data.shape=(3,)),
CurveItem(mnemonic="DT", unit="US/M", value="", descr="2 SONIC TRANSIT TIME", original_mnemonic="DT", data.shape=(3,)),
CurveItem(mnemonic="RHOB", unit="K/M3", value="", descr="3 BULK DENSITY", original_mnemonic="RHOB", data.shape=(3,)),
CurveItem(mnemonic="NPHI", unit="V/V", value="", descr="4 NEUTRON POROSITY", original_mnemonic="NPHI", data.shape=(3,)),
CurveItem(mnemonic="SFLU", unit="OHMM", value="", descr="5 RXO RESISTIVITY", original_mnemonic="SFLU", data.shape=(3,)),
CurveItem(mnemonic="SFLA", unit="OHMM", value="", descr="6 SHALLOW RESISTIVITY", original_mnemonic="SFLA", data.shape=(3,)),
CurveItem(mnemonic="ILM", unit="OHMM", value="", descr="7 MEDIUM RESISTIVITY", original_mnemonic="ILM", data.shape=(3,)),
CurveItem(mnemonic="ILD", unit="OHMM", value="", descr="8 DEEP RESISTIVITY", original_mnemonic="ILD", data.shape=(3,))
]
Handling duplicate mnemonics¶
Take this LAS file as an example, containing this ~C section:
~CURVE INFORMATION
DEPT.M : 1 DEPTH
DT .US/M : 2 SONIC TRANSIT TIME
RHOB.K/M3 : 3 BULK DENSITY
NPHI.V/V : 4 NEUTRON POROSITY
RXO.OHMM : 5 RXO RESISTIVITY
RES.OHMM : 6 SHALLOW RESISTIVITY
RES.OHMM : 7 MEDIUM RESISTIVITY
RES.OHMM : 8 DEEP RESISTIVITY
Notice there are three curves with the mnemonic RES. When we load the file in, lasio distinguishes between these duplicates:
>>> las = lasio.read('tests/examples/mnemonic_duplicate2.las')
>>> las.curves
[CurveItem(mnemonic="DEPT", unit="M", value="", descr="1 DEPTH", original_mnemonic="DEPT", data.shape=(3,)),
CurveItem(mnemonic="DT", unit="US/M", value="", descr="2 SONIC TRANSIT TIME", original_mnemonic="DT", data.shape=(3,)),
CurveItem(mnemonic="RHOB", unit="K/M3", value="", descr="3 BULK DENSITY", original_mnemonic="RHOB", data.shape=(3,)),
CurveItem(mnemonic="NPHI", unit="V/V", value="", descr="4 NEUTRON POROSITY", original_mnemonic="NPHI", data.shape=(3,)),
CurveItem(mnemonic="RXO", unit="OHMM", value="", descr="5 RXO RESISTIVITY", original_mnemonic="RXO", data.shape=(3,)),
CurveItem(mnemonic="RES:1", unit="OHMM", value="", descr="6 SHALLOW RESISTIVITY", original_mnemonic="RES", data.shape=(3,)),
CurveItem(mnemonic="RES:2", unit="OHMM", value="", descr="7 MEDIUM RESISTIVITY", original_mnemonic="RES", data.shape=(3,)),
CurveItem(mnemonic="RES:3", unit="OHMM", value="", descr="8 DEEP RESISTIVITY", original_mnemonic="RES", data.shape=(3,))
]
>>> las.curves['RES:2']
CurveItem(mnemonic="RES:2", unit="OHMM", value="", descr="7 MEDIUM RESISTIVITY", original_mnemonic="RES", data.shape=(3,))
It remembers the original mnemonic, so when you write the file back out, they come back:
>>> from sys import stdout
>>> las.write(stdout)
~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : ONE LINE PER DEPTH STEP
~Well ------------------------------------------------------
STRT.M 1670.0 :
STOP.M 1669.75 :
STEP.M -0.125 :
NULL. -999.25 :
COMP. COMPANY : # ANY OIL COMPANY LTD.
WELL. WELL : ANY ET AL OIL WELL #12
FLD . FIELD : EDAM
LOC . LOCATION : A9-16-49-20W3M
PROV. PROVINCE : SASKATCHEWAN
SRVC. SERVICE COMPANY : ANY LOGGING COMPANY LTD.
DATE. LOG DATE : 25-DEC-1988
UWI . UNIQUE WELL ID : 100091604920W300
~Curves ----------------------------------------------------
DEPT.M : 1 DEPTH
DT .US/M : 2 SONIC TRANSIT TIME
RHOB.K/M3 : 3 BULK DENSITY
NPHI.V/V : 4 NEUTRON POROSITY
RXO .OHMM : 5 RXO RESISTIVITY
RES .OHMM : 6 SHALLOW RESISTIVITY
RES .OHMM : 7 MEDIUM RESISTIVITY
RES .OHMM : 8 DEEP RESISTIVITY
~Params ----------------------------------------------------
BHT .DEGC 35.5 : BOTTOM HOLE TEMPERATURE
BS .MM 200.0 : BIT SIZE
FD .K/M3 1000.0 : FLUID DENSITY
MATR. 0.0 : NEUTRON MATRIX(0=LIME,1=SAND,2=DOLO)
MDEN. 2710.0 : LOGGING MATRIX DENSITY
RMF .OHMM 0.216 : MUD FILTRATE RESISTIVITY
DFD .K/M3 1525.0 : DRILL FLUID DENSITY
~Other -----------------------------------------------------
Note: The logging tools became stuck at 625 meters causing the data
between 625 meters and 615 meters to be invalid.
~ASCII -----------------------------------------------------
1670 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.9 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.8 123.45 2550 0.45 123.45 123.45 110.2 105.6
Normalising mnemonic case¶
If there is a mix of upper and lower case characters in the mnemonics, by default lasio will convert all mnemonics to uppercase to avoid problems with producing the :1, :2, :3, and so on. There is a keyword argument which will preserve the original formatting if that is what you prefer.
>>> las = lasio.read('tests/examples/mnemonic_case.las')
>>> las.curves
[CurveItem(mnemonic="DEPT", unit="M", value="", descr="1 DEPTH", original_mnemonic="DEPT", data.shape=(3,)),
CurveItem(mnemonic="SFLU:1", unit="K/M3", value="", descr="3 BULK DENSITY", original_mnemonic="SFLU", data.shape=(3,)),
CurveItem(mnemonic="NPHI", unit="V/V", value="", descr="4 NEUTRON POROSITY", original_mnemonic="NPHI", data.shape=(3,)),
CurveItem(mnemonic="SFLU:2", unit="OHMM", value="", descr="5 RXO RESISTIVITY", original_mnemonic="SFLU", data.shape=(3,)),
CurveItem(mnemonic="SFLU:3", unit="OHMM", value="", descr="6 SHALLOW RESISTIVITY", original_mnemonic="SFLU", data.shape=(3,)),
CurveItem(mnemonic="SFLU:4", unit="OHMM", value="", descr="7 MEDIUM RESISTIVITY", original_mnemonic="SFLU", data.shape=(3,)),
CurveItem(mnemonic="SFLU:5", unit="OHMM", value="", descr="8 DEEP RESISTIVITY", original_mnemonic="SFLU", data.shape=(3,))
]
>>> las = lasio.read('tests/examples/mnemonic_case.las', mnemonic_case='preserve')
>>> las.curves
[CurveItem(mnemonic="Dept", unit="M", value="", descr="1 DEPTH", original_mnemonic="Dept", data.shape=(3,)),
CurveItem(mnemonic="Sflu", unit="K/M3", value="", descr="3 BULK DENSITY", original_mnemonic="Sflu", data.shape=(3,)),
CurveItem(mnemonic="NPHI", unit="V/V", value="", descr="4 NEUTRON POROSITY", original_mnemonic="NPHI", data.shape=(3,)),
CurveItem(mnemonic="SFLU:1", unit="OHMM", value="", descr="5 RXO RESISTIVITY", original_mnemonic="SFLU", data.shape=(3,)),
CurveItem(mnemonic="SFLU:2", unit="OHMM", value="", descr="6 SHALLOW RESISTIVITY", original_mnemonic="SFLU", data.shape=(3,)),
CurveItem(mnemonic="sflu", unit="OHMM", value="", descr="7 MEDIUM RESISTIVITY", original_mnemonic="sflu", data.shape=(3,)),
CurveItem(mnemonic="SfLu", unit="OHMM", value="", descr="8 DEEP RESISTIVITY", original_mnemonic="SfLu", data.shape=(3,))
]
Data section¶
Ignoring commented-out lines¶
Sometimes data sections have comment line inside them. By default lasio will ignore
any lines starting with the “#” character within the data section. You can
control this using the remove_data_line_filter='#' argument to
lasio.LASFile.read().
Handling errors¶
lasio has a flexible way of handling “errors” in the ~ASCII data section to accommodate how strict or flexible you want to be.
Example errors¶
Here are some examples of errors.
- Files could contain a variety of indicators for an invalid data point other than that defined by the NULL line in the LAS header (usually -999.25).
- Fixed-width columns could run into each other:
7686.500 64.932 0.123 0.395 12.403 156.271 10.649 -0.005 193.223 327.902 -0.023 4.491 2.074 29.652
7686.000 67.354 0.140 0.415 9.207 4648.011 10.609 -0.004 3778.709 1893.751 -0.048 4.513 2.041 291.910
7685.500 69.004 0.151 0.412 7.020101130.188 10.560 -0.004 60000.000 2901.317 -0.047 4.492 2.046 310.119
7685.000 68.809 0.150 0.411 7.330109508.961 10.424 -0.005 60000.000 2846.619 -0.042 4.538 2.049 376.968
7684.500 68.633 0.149 0.402 7.345116238.453 10.515 -0.005 60000.000 2290.275 -0.051 4.543 2.063 404.972
7684.000 68.008 0.144 0.386 7.682 4182.679 10.515 -0.004 3085.681 1545.842 -0.046 4.484 2.089 438.195
- Odd text such as
(null):
8090.00 -999.25 -999.25 -999.25 0 0 0 0 0 0 0 0
8091.000 0.70 337.70 (null) 0 0 0 0 0 0 0 0
8092.000 -999.25 -999.25 -999.25 0 0 0 0 0 0 0 0
Handling run-on errors¶
lasio detects and handles these problems by default using lasio.read(f,
read_policy='default'). For example a file with this data section:
~A
7686.000 67.354 0.140 0.415 9.207 4648.011 10.609
7685.500 69.004 0.151 0.412 7.020101130.188 10.560
7685.000 68.809 0.150 0.411 7.330-19508.961 10.424
7684.500 68.633 0.149 0.402 7.345116238.453 10.515
7684.000 68.008 0.144 0.386 7.682 4182.679 10.515
is loaded by default as the following:
>>> import lasio.examples
>>> las = lasio.examples.open('null_policy_runon.las')
>>> las.data
array([[7686.0, 67.354, 0.14, 0.415, 9.207, 4648.011, 10.609],
[7685.5, 69.004, 0.151, 0.412, nan, nan, 10.56],
[7685.0, 68.809, 0.15, 0.411, 7.33, -19508.961, 10.424],
[7684.5, 68.633, 0.149, 0.402, nan, nan, 10.515],
[7684.0, 68.008, 0.144, 0.386, 7.682, 4182.679, 10.515]])
Handling invalid data indicators automatically¶
These are detected by lasio to a degree which you can control with the null_policy keyword argument.
You can specify a policy of ‘none’, ‘strict’, ‘common’, ‘aggressive’, or
‘all’. These policies all include a subset of pre-defined substitutions. Or
you can give your own list of substitutions. Here is the list of predefined
policies and substitutions from lasio.defaults.
Policies that you can pick with e.g. null_policy='common':
NULL_POLICIES = {
'none': [],
'strict': ['NULL', ],
'common': ['NULL', '(null)', '-',
'9999.25', '999.25', 'NA', 'INF', 'IO', 'IND'],
'aggressive': ['NULL', '(null)', '--',
'9999.25', '999.25', 'NA', 'INF', 'IO', 'IND',
'999', '999.99', '9999', '9999.99' '2147483647', '32767',
'-0.0', ],
'all': ['NULL', '(null)', '-',
'9999.25', '999.25', 'NA', 'INF', 'IO', 'IND',
'999', '999.99', '9999', '9999.99' '2147483647', '32767', '-0.0',
'numbers-only', ],
'numbers-only': ['numbers-only', ]
}
Or substitutions you could specify with e.g. null_policy=['NULL', '999.25',
'INF']:
NULL_SUBS = {
'NULL': [None, ], # special case to be handled
'999.25': [-999.25, 999.25],
'9999.25': [-9999.25, 9999.25],
'999.99': [-999.99, 999.99],
'9999.99': [-9999.99, 9999.99],
'999': [-999, 999],
'9999': [-9999, 9999],
'2147483647': [-2147483647, 2147483647],
'32767': [-32767, 32767],
'NA': [(re.compile(r'(#N/A)[ ]'), ' NaN '),
(re.compile(r'[ ](#N/A)'), ' NaN '), ],
'INF': [(re.compile(r'(-?1\.#INF)[ ]'), ' NaN '),
(re.compile(r'[ ](-?1\.#INF)'), ' NaN '), ],
'IO': [(re.compile(r'(-?1\.#IO)[ ]'), ' NaN '),
(re.compile(r'[ ](-?1\.#IO)'), ' NaN '), ],
'IND': [(re.compile(r'(-?1\.#IND)[ ]'), ' NaN '),
(re.compile(r'[ ](-?1\.#IND)'), ' NaN '), ],
'-0.0': [(re.compile(r'(-?0\.0+)[ ]'), ' NaN '),
(re.compile(r'[ ](-?0\.0+)'), ' NaN '), ],
'numbers-only': [(re.compile(r'([^ 0-9.\-+]+)[ ]'), ' NaN '),
(re.compile(r'[ ]([^ 0-9.\-+]+)'), ' NaN '), ],
}
You can also specify substitutions directly. E.g. for a file with this data section:
~A DEPTH DT RHOB NPHI SFLU SFLA ILM ILD
1670.000 9998 2550.000 0.450 123.450 123.450 110.200 105.600
1669.875 9999 2550.000 0.450 123.450 123.450 110.200 105.600
1669.750 10000 ERR 0.450 123.450 -999.25 110.200 105.600
By default, it will read all data as a string due to the presence of “ERR”:
>>> las = lasio.examples.open('null_policy_ERR.las')
>>> las.data
array([['1670.0', '9998.0', '2550.0', '0.45', '123.45', '123.45',
'110.2', '105.6'],
['1669.875', '9999.0', '2550.0', '0.45', '123.45', '123.45',
'110.2', '105.6'],
['1669.75', '10000.0', 'ERR', '0.45', '123.45', '-999.25',
'110.2', '105.6']], dtype='<U32')
We can fix it by using an explicit NULL policy.
>>> las = lasio.examples.open('null_policy_ERR.las', null_policy=[('ERR', ' NaN ')])
>>> las.data
array([[ 1.670000e+03, 9.998000e+03, 2.550000e+03, 4.500000e-01,
1.234500e+02, 1.234500e+02, 1.102000e+02, 1.056000e+02],
[ 1.669875e+03, 9.999000e+03, 2.550000e+03, 4.500000e-01,
1.234500e+02, 1.234500e+02, 1.102000e+02, 1.056000e+02],
[ 1.669750e+03, 1.000000e+04, nan, 4.500000e-01,
1.234500e+02, -9.992500e+02, 1.102000e+02, 1.056000e+02]])
See tests/test_null_policy.py (link)
for some examples.
Writing LAS files¶
Any LASFile object can be written to a new LAS file using the
lasio.LASFile.write() method.
Converting between v1.2 and v2.0¶
Take this sample LAS 2.0 file:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ~VERSION INFORMATION
VERS. 2.0 : CWLS LOG ASCII STANDARD -VERSION 2.0
WRAP. NO : ONE LINE PER DEPTH STEP
~WELL INFORMATION
#MNEM.UNIT DATA DESCRIPTION
#----- ----- ---------- -------------------------
STRT .M 1670.0000 :START DEPTH
STOP .M 1660.0000 :STOP DEPTH
STEP .M -0.1250 :STEP
NULL . -999.25 :NULL VALUE
COMP . ANY OIL COMPANY INC. :COMPANY
WELL . AAAAA_2 :WELL
FLD . WILDCAT :FIELD
LOC . 12-34-12-34W5M :LOCATION
PROV . ALBERTA :PROVINCE
SRVC . ANY LOGGING COMPANY INC. :SERVICE COMPANY
DATE . 13-DEC-86 :LOG DATE
UWI . 100123401234W500 :UNIQUE WELL ID
~CURVE INFORMATION
#MNEM.UNIT API CODES CURVE DESCRIPTION
#------------------ ------------ -------------------------
DEPT .M : 1 DEPTH
DT .US/M 60 520 32 00 : 2 SONIC TRANSIT TIME
RHOB .K/M3 45 350 01 00 : 3 BULK DENSITY
NPHI .V/V 42 890 00 00 : 4 NEUTRON POROSITY
SFLU .OHMM 07 220 04 00 : 5 SHALLOW RESISTIVITY
SFLA .OHMM 07 222 01 00 : 6 SHALLOW RESISTIVITY
ILM .OHMM 07 120 44 00 : 7 MEDIUM RESISTIVITY
ILD .OHMM 07 120 46 00 : 8 DEEP RESISTIVITY
~PARAMETER INFORMATION
#MNEM.UNIT VALUE DESCRIPTION
#-------------- ---------------- -----------------------------------------------
MUD . GEL CHEM : MUD TYPE
BHT .DEGC 35.5000 : BOTTOM HOLE TEMPERATURE
BS .MM 200.0000 : BIT SIZE
FD .K/M3 1000.0000 : FLUID DENSITY
MATR . SAND : NEUTRON MATRIX
MDEN . 2710.0000 : LOGGING MATRIX DENSITY
RMF .OHMM 0.2160 : MUD FILTRATE RESISTIVITY
DFD .K/M3 1525.0000 : DRILL FLUID DENSITY
~OTHER
Note: The logging tools became stuck at 625 metres causing the data
between 625 metres and 615 metres to be invalid.
~A DEPTH DT RHOB NPHI SFLU SFLA ILM ILD
1670.000 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
1669.875 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
1669.750 123.450 2550.000 0.450 123.450 123.450 110.200 105.600
|
And we can use lasio to convert it to LAS 1.2:
>>> las = lasio.examples.open("2.0/sample_2.0.las")
>>> las.write('example-as-v1.2.las', version=1.2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : ONE LINE PER DEPTH STEP
~Well ------------------------------------------------------
STRT.M 1670.0 : START DEPTH
STOP.M 1669.75 : STOP DEPTH
STEP.M -0.125 : STEP
NULL. -999.25 : NULL VALUE
COMP. COMPANY : ANY OIL COMPANY INC.
WELL. WELL : AAAAA_2
FLD . FIELD : WILDCAT
LOC . LOCATION : 12-34-12-34W5M
PROV. PROVINCE : ALBERTA
SRVC. SERVICE COMPANY : ANY LOGGING COMPANY INC.
DATE. LOG DATE : 13-DEC-86
UWI . UNIQUE WELL ID : 100123401234W500
~Curves ----------------------------------------------------
DEPT.M : 1 DEPTH
DT .US/M 60 520 32 00 : 2 SONIC TRANSIT TIME
RHOB.K/M3 45 350 01 00 : 3 BULK DENSITY
NPHI.V/V 42 890 00 00 : 4 NEUTRON POROSITY
SFLU.OHMM 07 220 04 00 : 5 SHALLOW RESISTIVITY
SFLA.OHMM 07 222 01 00 : 6 SHALLOW RESISTIVITY
ILM .OHMM 07 120 44 00 : 7 MEDIUM RESISTIVITY
ILD .OHMM 07 120 46 00 : 8 DEEP RESISTIVITY
~Params ----------------------------------------------------
MUD . GEL CHEM : MUD TYPE
BHT .DEGC 35.5 : BOTTOM HOLE TEMPERATURE
BS .MM 200.0 : BIT SIZE
FD .K/M3 1000.0 : FLUID DENSITY
MATR. SAND : NEUTRON MATRIX
MDEN. 2710.0 : LOGGING MATRIX DENSITY
RMF .OHMM 0.216 : MUD FILTRATE RESISTIVITY
DFD .K/M3 1525.0 : DRILL FLUID DENSITY
~Other -----------------------------------------------------
Note: The logging tools became stuck at 625 metres causing the data
between 625 metres and 615 metres to be invalid.
~ASCII -----------------------------------------------------
1670 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.9 123.45 2550 0.45 123.45 123.45 110.2 105.6
1669.8 123.45 2550 0.45 123.45 123.45 110.2 105.6
|
Converting between wrapped/unwrapped¶
Here is an example using this file to convert a wrapped data section to unwrapped.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | ~Version Information
VERS. 1.20: CWLS log ASCII Standard -VERSION 1.20
WRAP. YES: Multiple lines per depth step
~Well Information
#MNEM.UNIT Data Type Information
#--------- ------------- ------------------------------
STRT.M 910.000:
STOP.M 901.000:
STEP.M -0.1250:
NULL. -999.2500: Null value
COMP. COMPANY: ANY OIL COMPANY INC.
WELL. WELL: ANY ET AL XX-XX-XX-XX
FLD . FIELD: WILDCAT
LOC . LOCATION: XX-XX-XX-XXW3M
PROV. PROVINCE: SASKATCHEWAN
SRVC. SERVICE COMPANY: ANY LOGGING COMPANY INC.
SON . SERVICE ORDER : 142085
DATE. LOG DATE: 13-DEC-86
UWI . UNIQUE WELL ID:
~Curve Information
#MNEM.UNIT API CODE Curve Description
#--------- ------------- ------------------------------
DEPT.M : Depth
DT .US/M : 1 Sonic Travel Time
RHOB.K/M : 2 Density-Bulk Density
NPHI.V/V : 3 Porosity -Neutron
RX0 .OHMM : 4 Resistivity -Rxo
RESS.OHMM : 5 Resistivity -Shallow
RESM.OHMM : 6 Resistivity -Medium
RESD.OHMM : 7 Resistivity -Deep
SP .MV : 8 Spon. Potential
GR .GAPI : 9 Gamma Ray
CALI.MM : 10 Caliper
DRHO.K/M3 : 11 Delta-Rho
EATT.DBM : 12 EPT Attenuation
TPL .NS/M : 13 TP -EPT
PEF . : 14 PhotoElectric Factor
FFI .V/V : 15 Porosity -NML FFI
DCAL.MM : 16 Caliper-Differential
RHGF.K/M3 : 17 Density-Formation
RHGA.K/M3 : 18 Density-Apparent
SPBL.MV : 19 Baselined SP
GRC .GAPI : 20 Gamma Ray BHC
PHIA.V/V : 21 Porosity -Apparent
PHID.V/V : 22 Porosity -Density
PHIE.V/V : 23 Porosity -Effective
PHIN.V/V : 24 Porosity -Neut BHC
PHIC.V/V : 25 Porosity -Total HCC
R0 .OHMM : 26 Ro
RWA .OHMM : 27 Rfa
SW . : 28 Sw -Effective
MSI . : 29 Sh Idx -Min
BVW . : 30 BVW
FGAS. : 31 Flag -Gas Index
PIDX. : 32 Prod Idx
FBH . : 33 Flag -Bad Hole
FHCC. : 34 Flag -HC Correction
LSWB. : 35 Flag -Limit SWB
~A Log data section
910.000000
-999.2500 2692.7075 0.3140 19.4086 19.4086 13.1709 12.2681
-1.5010 96.5306 204.7177 30.5822 -999.2500 -999.2500 3.2515
-999.2500 4.7177 3025.0264 3025.0264 -1.5010 93.1378 0.1641
0.0101 0.1641 0.3140 0.1641 11.1397 0.3304 0.9529
0.0000 0.1564 0.0000 11.1397 0.0000 0.0000 0.0000
909.875000
-999.2500 2712.6460 0.2886 23.3987 23.3987 13.6129 12.4744
-1.4720 90.2803 203.1093 18.7566 -999.2500 -999.2500 3.7058
-999.2500 3.1093 3004.6050 3004.6050 -1.4720 86.9078 0.1456
-0.0015 0.1456 0.2886 0.1456 14.1428 0.2646 1.0000
0.0000 0.1456 0.0000 14.1428 0.0000 0.0000 0.0000
909.750000
-999.2500 2692.8137 0.2730 22.5909 22.5909 13.6821 12.6146
-1.4804 89.8492 201.9287 3.1551 -999.2500 -999.2500 4.3124
-999.2500 1.9287 2976.4451 2976.4451 -1.4804 86.3465 0.1435
0.0101 0.1435 0.2730 0.1435 14.5674 0.2598 1.0000
0.0000 0.1435 0.0000 14.5674 0.0000 0.0000 0.0000
909.625000
-999.2500 2644.3650 0.2765 18.4831 18.4831 13.4159 12.6900
-1.5010 93.3999 201.5826 -6.5861 -999.2500 -999.2500 4.3822
-999.2500 1.5826 2955.3528 2955.3528 -1.5010 89.7142 0.1590
0.0384 0.1590 0.2765 0.1590 11.8600 0.3210 0.9667
0.0000 0.1538 0.0000 11.8600 0.0000 0.0000 0.0000
909.500000
-999.2500 2586.2822 0.2996 13.9187 13.9187 12.9195 12.7016
-1.4916 98.1214 201.7126 -4.5574 -999.2500 -999.2500 3.5967
-999.2500 1.7126 2953.5940 2953.5940 -1.4916 94.2670 0.1880
0.0723 0.1880 0.2996 0.1880 8.4863 0.4490 0.8174
0.0000 0.1537 0.0000 8.4863 0.0000 0.0000 0.0000
|
We will change the wrap by adjusting the relevant header section in the LASFile header:
>>> las.version
[HeaderItem(mnemonic="VERS", unit="", value="1.2", descr="CWLS log ASCII Standa"),
HeaderItem(mnemonic="WRAP", unit="", value="YES", descr="Multiple lines per de")]
>>> las.version.WRAP = 'NO'
>>> las.version.WRAP
HeaderItem(mnemonic="WRAP", unit="", value="NO", descr="Multiple lines per dep")
>>> las.write('example-unwrapped.las')
WARNING:lasio.writer:[v1.2] line #58 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #59 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #60 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #61 has 396 chars (>256)
WARNING:lasio.writer:[v1.2] line #62 has 396 chars (>256)
We get warnings because the LAS 1.2 standard doesn’t allow writing lines longer than 256 characters. lasio provides the warning but still produces the long lines:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | ~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : Multiple lines per depth step
~Well ------------------------------------------------------
STRT.M 910.0 :
STOP.M 909.5 :
STEP.M -0.125 :
NULL. -999.25 : Null value
COMP. COMPANY : ANY OIL COMPANY INC.
WELL. WELL : ANY ET AL XX-XX-XX-XX
FLD . FIELD : WILDCAT
LOC . LOCATION : XX-XX-XX-XXW3M
PROV. PROVINCE : SASKATCHEWAN
SRVC. SERVICE COMPANY : ANY LOGGING COMPANY INC.
SON . SERVICE ORDER : 142085
DATE. LOG DATE : 13-DEC-86
UWI . UNIQUE WELL ID :
~Curves ----------------------------------------------------
DEPT.M : Depth
DT .US/M : 1 Sonic Travel Time
RHOB.K/M : 2 Density-Bulk Density
NPHI.V/V : 3 Porosity -Neutron
RX0 .OHMM : 4 Resistivity -Rxo
RESS.OHMM : 5 Resistivity -Shallow
RESM.OHMM : 6 Resistivity -Medium
RESD.OHMM : 7 Resistivity -Deep
SP .MV : 8 Spon. Potential
GR .GAPI : 9 Gamma Ray
CALI.MM : 10 Caliper
DRHO.K/M3 : 11 Delta-Rho
EATT.DBM : 12 EPT Attenuation
TPL .NS/M : 13 TP -EPT
PEF . : 14 PhotoElectric Factor
FFI .V/V : 15 Porosity -NML FFI
DCAL.MM : 16 Caliper-Differential
RHGF.K/M3 : 17 Density-Formation
RHGA.K/M3 : 18 Density-Apparent
SPBL.MV : 19 Baselined SP
GRC .GAPI : 20 Gamma Ray BHC
PHIA.V/V : 21 Porosity -Apparent
PHID.V/V : 22 Porosity -Density
PHIE.V/V : 23 Porosity -Effective
PHIN.V/V : 24 Porosity -Neut BHC
PHIC.V/V : 25 Porosity -Total HCC
R0 .OHMM : 26 Ro
RWA .OHMM : 27 Rfa
SW . : 28 Sw -Effective
MSI . : 29 Sh Idx -Min
BVW . : 30 BVW
FGAS. : 31 Flag -Gas Index
PIDX. : 32 Prod Idx
FBH . : 33 Flag -Bad Hole
FHCC. : 34 Flag -HC Correction
LSWB. : 35 Flag -Limit SWB
~Params ----------------------------------------------------
~Other -----------------------------------------------------
~ASCII -----------------------------------------------------
910 -999.25 2692.7 0.314 19.409 19.409 13.171 12.268 -1.501 96.531 204.72 30.582 -999.25 -999.25 3.2515 -999.25 4.7177 3025 3025 -1.501 93.138 0.1641 0.0101 0.1641 0.314 0.1641 11.14 0.3304 0.9529 0 0.1564 0 11.14 0 0 0
909.88 -999.25 2712.6 0.2886 23.399 23.399 13.613 12.474 -1.472 90.28 203.11 18.757 -999.25 -999.25 3.7058 -999.25 3.1093 3004.6 3004.6 -1.472 86.908 0.1456 -0.0015 0.1456 0.2886 0.1456 14.143 0.2646 1 0 0.1456 0 14.143 0 0 0
909.75 -999.25 2692.8 0.273 22.591 22.591 13.682 12.615 -1.4804 89.849 201.93 3.1551 -999.25 -999.25 4.3124 -999.25 1.9287 2976.4 2976.4 -1.4804 86.347 0.1435 0.0101 0.1435 0.273 0.1435 14.567 0.2598 1 0 0.1435 0 14.567 0 0 0
909.62 -999.25 2644.4 0.2765 18.483 18.483 13.416 12.69 -1.501 93.4 201.58 -6.5861 -999.25 -999.25 4.3822 -999.25 1.5826 2955.4 2955.4 -1.501 89.714 0.159 0.0384 0.159 0.2765 0.159 11.86 0.321 0.9667 0 0.1538 0 11.86 0 0 0
909.5 -999.25 2586.3 0.2996 13.919 13.919 12.919 12.702 -1.4916 98.121 201.71 -4.5574 -999.25 -999.25 3.5967 -999.25 1.7126 2953.6 2953.6 -1.4916 94.267 0.188 0.0723 0.188 0.2996 0.188 8.4863 0.449 0.8174 0 0.1537 0 8.4863 0 0 0
|
If we decide to write the file in LAS 2.0 format, the warnings will go away:
>>> las.write('example-version-2.0.las', version=2.0)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | ~Version ---------------------------------------------------
VERS. 2.0 : CWLS log ASCII Standard -VERSION 2.0
WRAP. NO : Multiple lines per depth step
~Well ------------------------------------------------------
STRT.M 910.0 :
STOP.M 909.5 :
STEP.M -0.125 :
NULL. -999.25 : Null value
COMP. ANY OIL COMPANY INC. : COMPANY
WELL. ANY ET AL XX-XX-XX-XX : WELL
FLD . WILDCAT : FIELD
LOC . XX-XX-XX-XXW3M : LOCATION
PROV. SASKATCHEWAN : PROVINCE
SRVC. ANY LOGGING COMPANY INC. : SERVICE COMPANY
SON . 142085 : SERVICE ORDER
DATE. 13-DEC-86 : LOG DATE
UWI . : UNIQUE WELL ID
~Curves ----------------------------------------------------
DEPT.M : Depth
DT .US/M : 1 Sonic Travel Time
RHOB.K/M : 2 Density-Bulk Density
NPHI.V/V : 3 Porosity -Neutron
RX0 .OHMM : 4 Resistivity -Rxo
RESS.OHMM : 5 Resistivity -Shallow
RESM.OHMM : 6 Resistivity -Medium
RESD.OHMM : 7 Resistivity -Deep
SP .MV : 8 Spon. Potential
GR .GAPI : 9 Gamma Ray
CALI.MM : 10 Caliper
DRHO.K/M3 : 11 Delta-Rho
EATT.DBM : 12 EPT Attenuation
TPL .NS/M : 13 TP -EPT
PEF . : 14 PhotoElectric Factor
FFI .V/V : 15 Porosity -NML FFI
DCAL.MM : 16 Caliper-Differential
RHGF.K/M3 : 17 Density-Formation
RHGA.K/M3 : 18 Density-Apparent
SPBL.MV : 19 Baselined SP
GRC .GAPI : 20 Gamma Ray BHC
PHIA.V/V : 21 Porosity -Apparent
PHID.V/V : 22 Porosity -Density
PHIE.V/V : 23 Porosity -Effective
PHIN.V/V : 24 Porosity -Neut BHC
PHIC.V/V : 25 Porosity -Total HCC
R0 .OHMM : 26 Ro
RWA .OHMM : 27 Rfa
SW . : 28 Sw -Effective
MSI . : 29 Sh Idx -Min
BVW . : 30 BVW
FGAS. : 31 Flag -Gas Index
PIDX. : 32 Prod Idx
FBH . : 33 Flag -Bad Hole
FHCC. : 34 Flag -HC Correction
LSWB. : 35 Flag -Limit SWB
~Params ----------------------------------------------------
~Other -----------------------------------------------------
~ASCII -----------------------------------------------------
910 -999.25 2692.7 0.314 19.409 19.409 13.171 12.268 -1.501 96.531 204.72 30.582 -999.25 -999.25 3.2515 -999.25 4.7177 3025 3025 -1.501 93.138 0.1641 0.0101 0.1641 0.314 0.1641 11.14 0.3304 0.9529 0 0.1564 0 11.14 0 0 0
909.88 -999.25 2712.6 0.2886 23.399 23.399 13.613 12.474 -1.472 90.28 203.11 18.757 -999.25 -999.25 3.7058 -999.25 3.1093 3004.6 3004.6 -1.472 86.908 0.1456 -0.0015 0.1456 0.2886 0.1456 14.143 0.2646 1 0 0.1456 0 14.143 0 0 0
909.75 -999.25 2692.8 0.273 22.591 22.591 13.682 12.615 -1.4804 89.849 201.93 3.1551 -999.25 -999.25 4.3124 -999.25 1.9287 2976.4 2976.4 -1.4804 86.347 0.1435 0.0101 0.1435 0.273 0.1435 14.567 0.2598 1 0 0.1435 0 14.567 0 0 0
909.62 -999.25 2644.4 0.2765 18.483 18.483 13.416 12.69 -1.501 93.4 201.58 -6.5861 -999.25 -999.25 4.3822 -999.25 1.5826 2955.4 2955.4 -1.501 89.714 0.159 0.0384 0.159 0.2765 0.159 11.86 0.321 0.9667 0 0.1538 0 11.86 0 0 0
909.5 -999.25 2586.3 0.2996 13.919 13.919 12.919 12.702 -1.4916 98.121 201.71 -4.5574 -999.25 -999.25 3.5967 -999.25 1.7126 2953.6 2953.6 -1.4916 94.267 0.188 0.0723 0.188 0.2996 0.188 8.4863 0.449 0.8174 0 0.1537 0 8.4863 0 0 0
|
Exporting to other formats¶
Comma-separated values (CSV)¶
lasio.LASFile objects can be converted to CSV files with a few
options for how mnemonics and units are included (or not). It uses the
lasio.LASFile.to_csv() method.
>>> import lasio.examples
>>> from sys import stdout
>>> las = lasio.examples.open('sample.las')
>>> las.to_csv(stdout)
DEPT,DT,RHOB,NPHI,SFLU,SFLA,ILM,ILD
M,US/M,K/M3,V/V,OHMM,OHMM,OHMM,OHMM
1670.0,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.875,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.75,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
There are options for putting the units together with mnemonics:
>>> las.to_csv(stdout, units_loc='[]')
DEPT [M],DT [US/M],RHOB [K/M3],NPHI [V/V],SFLU [OHMM],SFLA [OHMM],ILM [OHMM],ILD [OHMM]
1670.0,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.875,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.75,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
Or leaving things out altogether:
>>> las.to_csv(stdout, mnemonics=False, units=False)
1670.0,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.875,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
1669.75,123.45,2550.0,0.45,123.45,123.45,110.2,105.6
Excel spreadsheet (XLSX)¶
You can easily convert LAS files into Excel, retaining the header information. If we are working in Python, you export like this:
>>> las.to_excel('sample.xlsx')
You will need to have openpyxl
installed ($ pip install openpyxl).
Format of exported Excel file¶
The exported spreadsheet has two sheets named “Header” and “Curves”. The “Header” sheet has five columns named “Section”, “Mnemonic”, “Unit”, “Value”, and “Description”, containing the information from all the sections in the header.
The “Curves” sheet contains the data as a table, with the curve mnemonics as a header row.
Script interfaces¶
Single file¶
$ las2excel --help
usage: Convert LAS file to XLSX [-h] LAS_filename XLSX_filename
positional arguments:
LAS_filename
XLSX_filename
optional arguments:
-h, --help show this help message and exit
$ las2excel sample.las sample.xlsx
Multiple files (las2excelbulk)¶
The better script to use is las2excelbulk:
$ las2excelbulk --help
usage: Convert LAS files to XLSX [-h] [-g GLOB] [-r] [-i] path
positional arguments:
path
optional arguments:
-h, --help show this help message and exit
-g GLOB, --glob GLOB Match LAS files with this pattern (default: *.las)
-r, --recursive Recurse through subfolders. (default: False)
-i, --ignore-header-errors
Ignore header section errors. (default: False)
Here is the command to create Excel versions of all the LAS files contained
within the folder test_folder, and any sub-folders:
Notice that some LAS files raised exceptions (in this case, ValueError)
and were not converted. In some cases these will relate to errors in the
header sections:
$ las2excelbulk.exe -r .
Converting .\4424\PN31769.LAS -> .\4424\pn31769.xlsx
Converting .\4424\PN31769L.LAS -> .\4424\pn31769l.xlsx
Converting .\4424\PN31769R.LAS -> .\4424\pn31769r.xlsx
Converting .\4428\pn31769.las -> .\4428\pn31769.xlsx
Failed to convert file. Error message:
Traceback (most recent call last):
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 366, in parse_header_section
values = read_line(line)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 522, in read_line
return read_header_line(*args, **kwargs)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 548, in read_header_line
mdict = m.groupdict()
AttributeError: 'NoneType' object has no attribute 'groupdict'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\program files (x86)\misc\kentcode\lasio\lasio\excel.py", line 133, in main_bulk
l = las.LASFile(lasfn, ignore_header_errors=args.ignore_header_errors)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 77, in __init__
self.read(file_ref, **read_kwargs)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 156, in read
ignore_header_errors=ignore_header_errors)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\las.py", line 110, in add_section
**sect_kws)
File "c:\program files (x86)\misc\kentcode\lasio\lasio\reader.py", line 375, in parse_header_section
raise exceptions.LASHeaderError(message)
lasio.exceptions.LASHeaderError: Line #21 - failed in ~Well Information section on line:
PN PERMIT NUMBER: 31769AttributeError: 'NoneType' object has no attribute 'groupdict'
Converting .\4526\PENRICE.LAS -> .\4526\penrice.xlsx
But in this case I’m happy to lose that single corrupted line in the header in
the conversion. In order to force lasio to ignore the error and continue to
convert the file, use the --ignore-header-errors flag (-i for short):
$ las2excelbulk.exe -r -i .
Converting .\4424\PN31769.LAS -> .\4424\pn31769.xlsx
Converting .\4424\PN31769L.LAS -> .\4424\pn31769l.xlsx
Converting .\4424\PN31769R.LAS -> .\4424\pn31769r.xlsx
Converting .\4428\pn31769.las -> .\4428\pn31769.xlsx
Line #21 - failed in ~Well Information section on line:
PN PERMIT NUMBER: 31769AttributeError: 'NoneType' object has no attribute 'groupdict'
Converting .\4526\PENRICE.LAS -> .\4526\penrice.xlsx
lasio still reports the problem, but ignores it and continues the conversion of the file.
Building a LAS file from scratch¶
When you create a lasio.LASFile from scratch, it comes with some
default metadata:
>>> import lasio
>>> las = lasio.LASFile()
>>> las.header
{'Version': [HeaderItem(mnemonic="VERS", unit="", value="2.0", descr="CWLS log ASCII Standa"),
HeaderItem(mnemonic="WRAP", unit="", value="NO", descr="One line per depth ste"),
HeaderItem(mnemonic="DLM", unit="", value="SPACE", descr="Column Data Section ")],
'Well': [HeaderItem(mnemonic="STRT", unit="m", value="nan", descr="START DEPTH"),
HeaderItem(mnemonic="STOP", unit="m", value="nan", descr="STOP DEPTH"),
HeaderItem(mnemonic="STEP", unit="m", value="nan", descr="STEP"),
HeaderItem(mnemonic="NULL", unit="", value="-9999.25", descr="NULL VALUE"),
HeaderItem(mnemonic="COMP", unit="", value="", descr="COMPANY"),
HeaderItem(mnemonic="WELL", unit="", value="", descr="WELL"),
HeaderItem(mnemonic="FLD", unit="", value="", descr="FIELD"),
HeaderItem(mnemonic="LOC", unit="", value="", descr="LOCATION"),
HeaderItem(mnemonic="PROV", unit="", value="", descr="PROVINCE"),
HeaderItem(mnemonic="CNTY", unit="", value="", descr="COUNTY"),
HeaderItem(mnemonic="STAT", unit="", value="", descr="STATE"),
HeaderItem(mnemonic="CTRY", unit="", value="", descr="COUNTRY"),
HeaderItem(mnemonic="SRVC", unit="", value="", descr="SERVICE COMPANY"),
HeaderItem(mnemonic="DATE", unit="", value="", descr="DATE"),
HeaderItem(mnemonic="UWI", unit="", value="", descr="UNIQUE WELL ID"),
HeaderItem(mnemonic="API", unit="", value="", descr="API NUMBER")],
'Curves': [],
'Parameter': [],
'Other': ''}
In our case, let’s set the correct date:
>>> from datetime import datetime
>>> las.well.DATE = datetime.today().strftime('%Y-%m-%d %H:%M:%S')
And add some new header fields:
>>> las.params['ENG'] = lasio.HeaderItem('ENG', value='Kent Inverarity')
>>> las.params['LMF'] = lasio.HeaderItem('LMF', value='GL')
>>> las.other = 'Example of how to create a LAS file from scratch using lasio'
We will invent some data for a curve:
>>> import numpy as np
>>> depths = np.arange(10, 50, 0.5)
>>> synth = np.log10(depths)*5+np.random.random(len(depths))
>>> synth[:8] = np.nan
…add these to the LASFile object:
>>> las.add_curve('DEPT', depths, unit='m')
>>> las.add_curve('SYNTH', synth, descr='fake data')
And write the result to files:
>>> las.write('scratch_v1.2.las', version=1.2)
>>> las.write('scratch_v2.las', version=2)
Here is the resulting scratch_v1.2.las:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | ~Version ---------------------------------------------------
VERS. 1.2 : CWLS LOG ASCII STANDARD - VERSION 1.2
WRAP. NO : One line per depth step
DLM . SPACE : Column Data Section Delimiter
~Well ------------------------------------------------------
STRT.m 10.0 : START DEPTH
STOP.m 49.5 : STOP DEPTH
STEP.m 0.5 : STEP
NULL. -9999.25 : NULL VALUE
COMP. COMPANY :
WELL. WELL :
FLD . FIELD :
LOC . LOCATION :
PROV. PROVINCE :
CNTY. COUNTY :
STAT. STATE :
CTRY. COUNTRY :
SRVC. SERVICE COMPANY :
DATE. DATE : 2017-11-04 15:33:20.963287
UWI . UNIQUE WELL ID :
API . API NUMBER :
~Curves ----------------------------------------------------
DEPT .m :
SYNTH. : fake data
~Params ----------------------------------------------------
ENG. Kent Inverarity :
LMF. GL :
~Other -----------------------------------------------------
Example of how to create a LAS file from scratch using lasio
~ASCII -----------------------------------------------------
10 -9999.25
10.5 -9999.25
11 -9999.25
11.5 -9999.25
12 -9999.25
12.5 -9999.25
13 -9999.25
13.5 -9999.25
14 5.799
14.5 6.3938
15 6.4122
15.5 6.4605
16 6.9518
16.5 6.567
17 6.3816
17.5 6.2872
18 6.4336
18.5 7.0252
19 6.7988
19.5 6.7172
20 6.6929
20.5 7.0971
21 7.145
21.5 6.7192
22 7.6034
22.5 7.3078
23 7.2213
23.5 7.668
24 7.853
24.5 7.4073
25 7.4238
25.5 7.9173
26 7.1282
26.5 7.4131
27 7.8014
27.5 7.348
28 7.9
28.5 7.6294
29 8.1244
29.5 7.9835
30 7.4759
30.5 8.3766
31 7.4717
31.5 7.6432
32 8.2327
32.5 7.6541
33 8.4481
33.5 7.8811
34 8.2332
34.5 8.4302
35 7.7218
35.5 8.71
36 8.3965
36.5 8.4355
37 8.6836
37.5 8.2236
38 8.4997
38.5 8.6656
39 8.8295
39.5 8.1707
40 8.9034
40.5 8.681
41 8.1698
41.5 8.3001
42 9.0266
42.5 8.4398
43 8.7562
43.5 8.2673
44 8.4682
44.5 8.5801
45 8.9065
45.5 8.8392
46 8.661
46.5 9.2355
47 9.0468
47.5 8.8249
48 9.0298
48.5 8.6864
49 8.5745
49.5 8.6143
|
and scratch_v2.las:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | ~Version ---------------------------------------------------
VERS. 2.0 : CWLS log ASCII Standard -VERSION 2.0
WRAP. NO : One line per depth step
DLM . SPACE : Column Data Section Delimiter
~Well ------------------------------------------------------
STRT.m 10.0 : START DEPTH
STOP.m 49.5 : STOP DEPTH
STEP.m 0.5 : STEP
NULL. -9999.25 : NULL VALUE
COMP. : COMPANY
WELL. : WELL
FLD . : FIELD
LOC . : LOCATION
PROV. : PROVINCE
CNTY. : COUNTY
STAT. : STATE
CTRY. : COUNTRY
SRVC. : SERVICE COMPANY
DATE. 2017-11-04 15:33:20.963287 : DATE
UWI . : UNIQUE WELL ID
API . : API NUMBER
~Curves ----------------------------------------------------
DEPT .m :
SYNTH. : fake data
~Params ----------------------------------------------------
ENG. Kent Inverarity :
LMF. GL :
~Other -----------------------------------------------------
Example of how to create a LAS file from scratch using lasio
~ASCII -----------------------------------------------------
10 -9999.25
10.5 -9999.25
11 -9999.25
11.5 -9999.25
12 -9999.25
12.5 -9999.25
13 -9999.25
13.5 -9999.25
14 5.799
14.5 6.3938
15 6.4122
15.5 6.4605
16 6.9518
16.5 6.567
17 6.3816
17.5 6.2872
18 6.4336
18.5 7.0252
19 6.7988
19.5 6.7172
20 6.6929
20.5 7.0971
21 7.145
21.5 6.7192
22 7.6034
22.5 7.3078
23 7.2213
23.5 7.668
24 7.853
24.5 7.4073
25 7.4238
25.5 7.9173
26 7.1282
26.5 7.4131
27 7.8014
27.5 7.348
28 7.9
28.5 7.6294
29 8.1244
29.5 7.9835
30 7.4759
30.5 8.3766
31 7.4717
31.5 7.6432
32 8.2327
32.5 7.6541
33 8.4481
33.5 7.8811
34 8.2332
34.5 8.4302
35 7.7218
35.5 8.71
36 8.3965
36.5 8.4355
37 8.6836
37.5 8.2236
38 8.4997
38.5 8.6656
39 8.8295
39.5 8.1707
40 8.9034
40.5 8.681
41 8.1698
41.5 8.3001
42 9.0266
42.5 8.4398
43 8.7562
43.5 8.2673
44 8.4682
44.5 8.5801
45 8.9065
45.5 8.8392
46 8.661
46.5 9.2355
47 9.0468
47.5 8.8249
48 9.0298
48.5 8.6864
49 8.5745
49.5 8.6143
|
Character encodings¶
There are four options:
1. Specify the encoding (internally lasio uses the open function from codecs which is part of the standard library):
>>> las = lasio.read('example.las', encoding='windows-1252')
2. Do nothing. By default lasio.read() uses the keyword argument
autodetect_encoding=True. This will try to open the file with a few
different encodings, like ‘ascii’, ‘windows-1252’, and ‘latin-1’. The
first one to raise no UnicodeDecodeError exceptions will be used.
This may still result in an error, or incorrectly decoded characters.
3. Install a package like cChardet (faster) or chardet (slower) to automatically detect the character encoding. If these packages are installed then lasio will use them by default:
>>> import logging
>>> logging.basicConfig()
>>> logging.getLogger().setLevel(logging.DEBUG)
>>> las = lasio.read('encodings_utf8.las')
DEBUG:lasio.reader:get_encoding Using cchardet
DEBUG:lasio.reader:cchardet method detected encoding of UTF-8 at confidence 0.9900000095367432
INFO:lasio.reader:Opening encodings_utf8.las as UTF-8 and treating errors with "replace"
DEBUG:lasio.las:n_curves=8 ncols=8
DEBUG:lasio.las:set_data data.shape = (3, 8)
DEBUG:lasio.las:set_data self.data.shape = (3, 8)
This may still result in an error, or incorrectly decoded characters.
If you are certain that you have no “extended characters” (or that you don’t care), you can easily speed up lasio’s performance by using:
>>> try:
... las = lasio.read('example.las', autodetect_encoding=False)
... except UnicodeDecodeError:
... continue
Docstrings for the lasio package¶
Reading LAS files¶
-
lasio.read(file_ref, **kwargs)[source]¶ Read a LAS file.
Note that only versions 1.2 and 2.0 of the LAS file specification are currently supported.
Parameters: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file. Returns: a lasio.LASFileobject representing the file – see aboveThere are a number of optional keyword arguments that can be passed to this function that control how the LAS file is opened and parsed. Any of the keyword arguments from the below functions can be used here:
lasio.reader.open_with_codecs()- manage issues relate to character encodingslasio.las.LASFile.read()- control how NULL values and errors are handled during parsing
-
class
lasio.LASFile(file_ref=None, **read_kwargs)[source]¶ LAS file object.
Keyword Arguments: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file. See these routines for additional keyword arguments you can use when reading in a LAS file:
lasio.reader.open_with_codecs()- manage issues relate to character encodingslasio.LASFile.read()- control how NULL values and errors are handled during parsing
-
LASFile.read(file_ref, ignore_data=False, read_policy='default', null_policy='strict', ignore_header_errors=False, ignore_comments=('#', ), mnemonic_case='upper', index_unit=None, remove_data_line_filter='#', **kwargs)[source]¶ Read a LAS file.
Parameters: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file.
Keyword Arguments: - null_policy (str or list) – see http://lasio.readthedocs.io/en/latest/data-section.html#handling-invalid-data-indicators-automatically
- ignore_data (bool) – if True, do not read in any of the actual data, just the header metadata. False by default.
- ignore_header_errors (bool) – ignore LASHeaderErrors (False by default)
- ignore_comments (tuple/str) – ignore comments beginning with characters
e.g.
("#", '"')in header sections - mnemonic_case (str) – ‘preserve’: keep the case of HeaderItem mnemonics ‘upper’: convert all HeaderItem mnemonics to uppercase ‘lower’: convert all HeaderItem mnemonics to lowercase
- index_unit (str) – Optionally force-set the index curve’s unit to “m” or “ft”
- remove_data_line_filter (str, func) – string or function for removing/ignoring lines
in the data section e.g. a function which accepts a string (a line from the
data section) and returns either True (do not parse the line) or False
(parse the line). If this argument is a string it will instead be converted
to a function which rejects all lines starting with that value e.g.
"#"will be converted tolambda line: line.strip().startswith("#")
See
lasio.reader.open_with_codecs()for additional keyword arguments which help to manage issues relate to character encodings.
-
lasio.open_file(file_ref, **encoding_kwargs)[source]¶ Open a file if necessary.
If
autodetect_encoding=Truethen eithercchardetorchardetneeds to be installed, or else anImportErrorwill be raised.Parameters: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file. See
lasio.reader.open_with_codecs()for keyword arguments that can be used here.Returns: tuple of an open file-like object, and the encoding that was used to decode it (if it were read from disk).
-
lasio.reader.open_with_codecs(filename, encoding=None, encoding_errors='replace', autodetect_encoding=True, autodetect_encoding_chars=4000)[source]¶ Read Unicode data from file.
Parameters: filename (str) – path to file
Keyword Arguments: - encoding (str) – character encoding to open file_ref with, using
io.open(). - encoding_errors (str) – ‘strict’, ‘replace’ (default), ‘ignore’ - how to
handle errors with encodings (see
this section
of the standard library’s
codecsmodule for more information) - autodetect_encoding (str or bool) – default True to use chardet/cchardet to detect encoding. Note if set to False several common encodings will be tried but chardet won’t be used.
- autodetect_encoding_chars (int/None) – number of chars to read from LAS file for auto-detection of encoding.
Returns: a unicode or string object
This function is called by
lasio.reader.open_file().- encoding (str) – character encoding to open file_ref with, using
-
lasio.reader.get_encoding(auto, raw)[source]¶ Automatically detect character encoding.
Parameters: Returns: A string specifying the character encoding.
-
lasio.reader.read_file_contents(file_obj, regexp_subs, value_null_subs, ignore_data=False, remove_line_filter='#')[source]¶ Read file contents into memory.
Parameters: file_obj (open file-like object) –
Keyword Arguments: - null_subs (bool) – True will substitute
numpy.nanfor invalid values - ignore_data (bool) – if True, do not read in the numerical data in the ~ASCII section
- remove_line_filter (str, func) – string or function for removing/ignoring lines
in the data section e.g. a function which accepts a string (a line from the
data section) and returns either True (do not parse the line) or False
(parse the line). If this argument is a string it will instead be converted
to a function which rejects all lines starting with that value e.g.
"#"will be converted tolambda line: line.strip().startswith("#")
Returns: OrderedDict
I think of the returned dictionary as a “raw section”. The keys are the first line of the LAS section, including the tilde. Each value is a dict with either:
{"section_type": "header", "title": str, # title of section (including the ~) "lines": [str, ], # a list of the lines from the lAS file "line_nos": [int, ] # line nos from the original file }
or:
{"section_type": "data", "title": str, # title of section (including the ~) "start_line": int, # location of data section (the title line) "ncols": int, # no. of columns on first line of data, "array": ndarray # 1-D numpy.ndarray, }
- null_subs (bool) – True will substitute
-
LASFile.match_raw_section(pattern, re_func='match', flags=<RegexFlag.IGNORECASE: 2>)[source]¶ Find raw section with a regular expression.
Parameters: pattern (str) – regular expression (you need to include the tilde)
Keyword Arguments: - re_func (str) – either “match” or “search”, see python
remodule. - flags (int) – flags for
re.compile()
Returns: dict
Intended for internal use only.
- re_func (str) – either “match” or “search”, see python
-
lasio.reader.read_data_section_iterative(file_obj, line_nos, regexp_subs, value_null_subs, remove_line_filter)[source]¶ Read data section into memory.
Parameters: - file_obj – file-like object open for reading at the beginning of the section
- line_nos (tuple) – the first and last line no of the section to read
- regexp_subs (list) – each item should be a tuple of the pattern and substitution string for a call to re.sub() on each line of the data section. See defaults.py READ_SUBS and NULL_SUBS for examples.
- value_null_subs (list) – list of numerical values to be replaced by numpy.nan values.
- remove_line_filter (str or func) – string or function for removing/ignoring lines
in the data section e.g. a function which accepts a string (a line from the
data section) and returns either True (do not parse the line) or False
(parse the line). If this argument is a string it will instead be converted
to a function which rejects all lines starting with that value e.g.
"#"will be converted tolambda line: line.strip().startswith("#")
Returns: A 1-D numpy ndarray.
-
lasio.reader.get_substitutions(read_policy, null_policy)[source]¶ Parse read and null policy definitions into a list of regexp and value substitutions.
Parameters: - read_policy (str, list, or substitution) – either (1) a string defined in defaults.READ_POLICIES; (2) a list of substitutions as defined by the keys of defaults.READ_SUBS; or (3) a list of actual substitutions similar to the values of defaults.READ_SUBS. You can mix (2) and (3) together if you want.
- null_policy (str, list, or sub) – as for read_policy but for defaults.NULL_POLICIES and defaults.NULL_SUBS
Returns: regexp_subs, value_null_subs, version_NULL - two lists and a bool. The first list is pairs of regexp patterns and substrs, and the second list is just a list of floats or integers. The bool is whether or not ‘NULL’ was located as a substitution.
-
class
lasio.reader.SectionParser(title, version=1.2)[source]¶ Parse lines from header sections.
Parameters: title (str) – title line of section. Used to understand different order formatting across the special sections ~C, ~P, ~W, and ~V, depending on version 1.2 or 2.0. Keyword Arguments: version (float) – version to parse according to. Default is 1.2.
-
lasio.reader.read_header_line(line, pattern=None, section_name=None)[source]¶ Read a line from a LAS header section.
The line is parsed with a regular expression – see LAS file specs for more details, but it should basically be in the format:
name.unit value : descr
Parameters: Returns: A dictionary with keys ‘name’, ‘unit’, ‘value’, and ‘descr’, each containing a string as value.
-
class
lasio.HeaderItem(mnemonic='', unit='', value='', descr='', data=None)[source]¶ Dictionary/namedtuple-style object for a LAS header line.
Parameters: These arguments are available for use as either items or attributes of the object.
-
HeaderItem.set_session_mnemonic_only(value)[source]¶ Set the mnemonic for session use.
See source comments for
lasio.HeaderItem.__init__for a more in-depth explanation.
Reading data¶
-
LASFile.__getitem__(key)[source]¶ Provide access to curve data.
Parameters: key (str, int) – either a curve mnemonic or the column index. Returns: 1D numpy.ndarray(the data for the curve)
-
LASFile.__setitem__(key, value)[source]¶ Append a curve.
Parameters: See
lasio.LASFile.append_curve_item()orlasio.LASFile.append_curve()for more details.
-
LASFile.get_curve(mnemonic)[source]¶ Return CurveItem object.
Parameters: mnemonic (str) – the name of the curve Returns: lasio.CurveItem(not just the data array)
-
LASFile.df()[source]¶ Return data as a
pandas.DataFramestructure.The first Curve of the LASFile object is used as the pandas DataFrame’s index.
-
LASFile.version¶ Header information from the Version (~V) section.
Returns: lasio.SectionItemsobject.
-
LASFile.well¶ Header information from the Well (~W) section.
Returns: lasio.SectionItemsobject.
-
LASFile.curves¶ Curve information and data from the Curves (~C) and data section..
Returns: lasio.SectionItemsobject.
-
LASFile.curvesdict¶ Curve information and data from the Curves (~C) and data section..
Returns: dict
-
LASFile.params¶ Header information from the Parameter (~P) section.
Returns: lasio.SectionItemsobject.
-
LASFile.other¶ Header information from the Other (~O) section.
Returns: str
-
LASFile.index¶ Return data from the first column of the LAS file data (depth/time).
-
LASFile.depth_m¶ Return the index as metres.
-
LASFile.depth_ft¶ Return the index as feet.
-
LASFile.data¶
Modifying data¶
-
LASFile.set_data(array_like, names=None, truncate=False)[source]¶ Set the data for the LAS; actually sets data on individual curves.
Parameters: array_like (array_like or
pandas.DataFrame) – 2-D data arrayKeyword Arguments: - names (list, optional) – used to replace the names of the existing
lasio.CurveItemobjects. - truncate (bool) – remove any columns which are not included in the Curves (~C) section.
Note: you can pass a
pandas.DataFrameto this method.- names (list, optional) – used to replace the names of the existing
-
LASFile.set_data_from_df(df, **kwargs)[source]¶ Set the LAS file data from a
pandas.DataFrame.Parameters: df (pandas.DataFrame) – curve mnemonics are the column names. The depth column for the curves must be the index of the DataFrame. Keyword arguments are passed to
lasio.LASFile.set_data().
-
LASFile.append_curve(mnemonic, data, unit='', descr='', value='')[source]¶ Add a curve.
Parameters: - mnemonic (str) – the curve mnemonic
- data (1D ndarray) – the curve data
Keyword Arguments:
-
LASFile.insert_curve(ix, mnemonic, data, unit='', descr='', value='')[source]¶ Insert a curve.
Parameters: Keyword Arguments:
-
LASFile.delete_curve(mnemonic=None, ix=None)[source]¶ Delete a curve.
Keyword Arguments: The index takes precedence over the mnemonic.
-
LASFile.append_curve_item(curve_item)[source]¶ Add a CurveItem.
Parameters: curve_item (lasio.CurveItem) –
-
LASFile.insert_curve_item(ix, curve_item)[source]¶ Insert a CurveItem.
Parameters: - ix (int) – position to insert CurveItem i.e. 0 for start
- curve_item (lasio.CurveItem) –
Writing data out¶
-
LASFile.write(file_ref, **kwargs)[source]¶ Write LAS file to disk.
Parameters: file_ref (open file-like object or str) – a file-like object opening for writing, or a filename. All
**kwargsare passed tolasio.writer.write()– please check the docstring of that function for more keyword arguments you can use here!Examples
>>> import lasio >>> las = lasio.read("tests/examples/sample.las") >>> with open('test_output.las', mode='w') as f: ... las.write(f, version=2.0) # <-- this method
-
lasio.writer.write(las, file_object, version=None, wrap=None, STRT=None, STOP=None, STEP=None, fmt='%.5f', column_fmt=None, len_numeric_field=None, data_width=79, header_width=60)[source]¶ Write a LAS files.
Parameters: - las (
lasio.LASFile) – - file_object (file-like object open for writing) – output
- version (float or None) – version of written file, either 1.2 or 2.
If this is None,
las.version.VERS.valuewill be used. - wrap (bool or None) – whether to wrap the output data section.
If this is None,
las.version.WRAP.valuewill be used. - STRT (float or None) – value to use as STRT (note the data will not be clipped). If this is None, the data value in the first column, first row will be used.
- STOP (float or None) – value to use as STOP (note the data will not be clipped). If this is None, the data value in the first column, last row will be used.
- STEP (float or None) – value to use as STEP (note the data will not be resampled and/or interpolated). If this is None, the STEP will be estimated from the first two rows of the first column.
- fmt (str) – Python string formatting operator for numeric data to be used.
- column_fmt (dict or None) – use this to set a different format string
for specific columns from the data ndarray. E.g. to use
'%.3f'for the depth column and'%.2f'for all the other columns, you would usefmt='%.2f', column_fmt={0: '%.3f'}. - len_numeric_field (int) – width of each numeric field column (must be greater than than all the formatted numeric values in the file).
- data_width (79) – width of data field in characters
Creating an output file is not the only side-effect of this function. It will also modify the STRT, STOP and STEP HeaderItems so that they correctly reflect the ~Data section’s units and the actual first, last, and interval values.
However, passing a version to this write() function only changes the version of the object written to. Example: las.write(myfile, version=2). Lasio’s internal-las-object version will remain separate and defined by las.version.VERS.value
You should avoid calling this function directly - instead use the
lasio.LASFile.write()method.- las (
-
lasio.writer.get_formatter_function(order, left_width=None, middle_width=None)[source]¶ Create function to format a LAS header item for output.
Parameters: order – format of item, either ‘descr:value’ or ‘value:descr’
Keyword Arguments: Returns: A function which takes a header item (e.g.
lasio.HeaderItem) as its single argument and which in turn returns a string which is the correctly formatted LAS header line.
-
lasio.writer.get_section_order_function(section, version, order_definitions={1.2: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['descr:value', ('value:descr', ['STRT', 'STOP', 'STEP', 'NULL', 'strt', 'stop', 'step', 'null'])]}, 2.0: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['value:descr']}, 3.0: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['value:descr']}})[source]¶ Get a function that returns the order per the mnemonic and section.
Parameters: Keyword Arguments: order_definitions (dict) – see source of defaults.py for more information
Returns: A function which takes a mnemonic (str) as its only argument, and in turn returns the order ‘value:descr’ or ‘descr:value’.
-
lasio.writer.get_section_widths(section_name, items, version, order_func)[source]¶ Find minimum section widths fitting the content in items.
Parameters: - section_name (str) – either ‘version’, ‘well’, ‘curves’, or ‘params’
- items (SectionItems) – section items
- version (float) – either 1.2 or 2.0
- order_func (func) – see
lasio.writer.get_section_order_function()
-
LASFile.to_csv(file_ref, mnemonics=True, units=True, units_loc='line', **kwargs)[source]¶ Export to a CSV file.
Parameters: file_ref (open file-like object or str) – a file-like object opening for writing, or a filename.
Keyword Arguments: - mnemonics (list, True, False) – write mnemonics as a header line at the start. If list, use the supplied items as mnemonics. If True, use the curve mnemonics.
- units (list, True, False) – as for mnemonics.
- units_loc (str or None) – either ‘line’, ‘[]’ or ‘()’. ‘line’ will put units on the line following the mnemonics (good for WellCAD). ‘[]’ and ‘()’ will put the units in either brackets or parentheses following the mnemonics, on the single header line (better for Excel)
- **kwargs – passed to
csv.writer. Note that iflineterminatoris not specified here, then it will be sent tocsv.writeraslineterminator='\n'.
-
LASFile.to_excel(filename)[source]¶ Export LAS file to a Microsoft Excel workbook.
This function will raise an
ImportErrorifopenpyxlis not installed.Parameters: filename (str) –
Custom exceptions¶
Test data¶
-
lasio.examples.open(filename, **kwargs)[source]¶ Open an example LAS file from lasio’s test suite.
Parameters: filename (str) – forward-slash separated filename of a LAS file from lasio’s test suite, starting from the “tests/examples” subfolder e.g. “1001178549.las” or “2.0/sample_2.0.las” Other keyword arguments are passed to lasio.LASFile. If lasio has been installed locally from source, then the local version of the example file will be opened. If lasio has not been installed from source then the LAS file will be downloaded from GitHub.
Returns: LASFile object
-
lasio.examples.open_github_example(filename, url_prefix='https://raw.githubusercontent.com/kinverarity1/lasio/master/tests/examples/', **kwargs)[source]¶ Open an example LAS file from lasio’s test suite on GitHub
Parameters: filename (str) – forward-slash separated filename of a LAS file from lasio’s test suite, starting from the “tests/examples” subfolder e.g. “1001178549.las” or “2.0/sample_2.0.las” Other keyword arguments are passed to lasio.LASFile.
Returns: LASFile object
-
lasio.examples.open_local_example(filename, **kwargs)[source]¶ Open an example LAS file from lasio’s test suite.
Parameters: filename (str) – forward-slash separated filename of a LAS file from lasio’s test suite, starting from the “tests/examples” subfolder e.g. “1001178549.las” or “2.0/sample_2.0.las” Other keyword arguments are passed to lasio.LASFile. If lasio has not been installed from source then an exception will be raised.
Returns: LASFile object
Contributing to lasio¶
lasio is an open source project released under the MIT License. It has grown over the years through the wonderful work of all these contributors:
- adamwulf
- ae3e
- ahjulstad
- eimerej
- dagrha
- dbhart
- dcslagel
- Fry484
- JustinGOSSES
- Jyhess
- kinverarity1
- kwinkunks
- MandarJKulkarni
- nasedil
- oliveirarodolfo
- roliveira
- trqmorgan
- VelizarVESSELINOV
Thank you also to everyone who has helped via email, in discussions on GitHub, and on software underground!
Your help is very welcome! No contribution is too small. You can help with the documentation, adding example notebooks, posting ideas or feature requests to GitHub, or by working on the code - or anything else!
Places you can help¶
- Please don’t hesitate to open a GitHub issue for any problems you are having with lasio, or any ideas for improvements. There are templates to guide you in how to file a bug report, or a request for a new feature or improvement. If you are not sure whether your issue fits under these categories, please go ahead and raise one anyway!
- Please feel free to contribute suggested changes. The easiest method is to fork lasio on GitHub and submit a pull request against the “master” branch. Don’t worry about getting all the details right, either way it’s still the most convenient way for me or other maintainers to see your changes in context.
- Example LAS files: if you have an interesting/difficult/silly/standards-challenged LAS file (any version) you would be willing to share with me, please do so! Email it to me at kinverarity@hotmail.com. The more examples we can incorporate into lasio’s regression testing, the better. If you have concerns about privacy, I’d suggest obfuscating with find-and-replace on various alpha (or numeric) characters before sending it on, and/or deleting any sensitive header lines.
How to make contributions¶
Contributions are always welcome to the code, documentation, or example notebooks. If you are making a contribution, please make sure you are working off the latest GitHub master. You will want to make your contributions in a branch taken from master, and then when you want to share your changes, you can publish them by “pushing” your branch to your GitHub fork of the lasio repository, and opening a PR (pull request) here.
First, create a fork of the lasio repository using the GitHub website. Then clone your fork locally to your computer:
$ git clone https://github.com/your-username/lasio
$ cd lasio
Your fork will be called the “origin” repository - you’ll need to know this for when you push/pull changes to and from your computer.
Adding kinverarity1/lasio as “upstream”¶
Now also add the kinverarity1/lasio repository as the “upstream” repository. This is so that when other people make changes to kinverarity1/lasio, you can “pull” those changes into your local copy:
$ git remote add upstream https://github.com/kinverarity1/lasio
To update the master branch of the local copy you have of your fork from the “upstream” repository:
$ git checkout master
$ git pull upstream master
And to update the GitHub fork from your local copy:
$ git checkout master
$ git push origin master
Making sure you have necessary development dependencies¶
There are some additional packages you needing for running unit/regression tests (pytest) and formatting Python code (black). You can install these easily by using:
$ pip install --editable .[test]
Making changes to the code¶
First, start by making sure your local copy is using the latest copy of code from “upstream” master (see above). Then create a branch - you can call it whatever is meaningful to you. We switch to master so that your changes are relative to the latest copy of the code in master:
$ git checkout master
$ git checkout -b your-branch-name
Switched to a new branch 'your-branch-name'
(your-branch-name) $
Then you can make your changes. To test them, make sure you have an “editable” installation of lasio in your Python environment. Shift to the root folder of the repository and run:
$ pip install -e .
Then to run all the tests:
$ pytest
Before publishing your changes please make the code is formatted using black:
$ black .
Then you can push your changes to your fork:
$ git push origin your-branch-name
And follow the instructions on your fork’s GitHub page to open a pull request (PR) for lasio!
Making changes to the documentation¶
Just as valuable as changes to the code, are changes or improvements to the Sphinx documentation! If you would like to help in this regard, you will need Sphinx and IPython installed:
$ pip install sphinx IPython sphinx_rtd_theme
Then create a new branch as above. The documentation is written in RestructuredText, and can be found in the docs/source subfolder of the lasio repository. If you have any changes, you can build a local copy of the HTML repository to test how it looks. First change into the docs folder:
$ cd docs
Then run this to generate a local copy of the HTML docs in the build/html folder:
$ make clean
$ make html
Once you are happy, please publish your branch and open a PR in the same way as above.
Email¶
Please feel free to email me at kinverarity@hotmail.com with any suggestions, criticisms, questions, example files.
Code of Conduct¶
Our Pledge¶
In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
Our Standards¶
Examples of behavior that contributes to creating a positive environment include:
- Using welcoming and inclusive language
- Being respectful of differing viewpoints and experiences
- Gracefully accepting constructive criticism
- Focusing on what is best for the community
- Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
- The use of sexualized language or imagery and unwelcome sexual attention or advances
- Trolling, insulting/derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others’ private information, such as a physical or electronic address, without explicit permission
- Other conduct which could reasonably be considered inappropriate in a professional setting
Our Responsibilities¶
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
Scope¶
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
Enforcement¶
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at kinverarity@hotmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obliged to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project’s leadership.
Attribution¶
This Code of Conduct is adapted from the Contributor Covenant version 1.4.
List of changes¶
Version 0.29 (14 April 2021)¶
- Fix #404 (lasio changes STEP with imprecise floating-point number behaviour; #432)
- Add option
len_numeric_field=-1,lhs_spacer=" ", andspacer=" "to writer.py:write (see #412; PR #418) - Fix #271 (Read quoted strings in data section properly; #423)
- Fix #427 (Change null_policy to handle small non-zero values; #429)
- Fix #417 (Fix parsing for empty ~Other section; #430)
- Fix #402 (fixes issue when unit starts with dot; #403)
- Fix #395 (Update doc examples to reflect new HeaderItem repr; #410)
- Fix #426 (Update urllib.request to be the preferred urllib; #428)
- Add check for pushed tag version to version tests (#396)
- Update GitHub Action Python CI testing (#399, #400)
- Improve
las_items.py:HeaderItem.__repr__truncation logic (#397) - Remove codecs import (unused) and fix typo (#406)
- Exclude LAS files from GitHubs Language Stats (#411)
- Re-add try-except check around call to reader.read_data_section_iterative() (#401)
- Remove reader.py:read_file_contents - unused code (see #401; #393)
- Add test for timestring with colon in ~Well section (see #419 - PR #420)
- Fix SyntaxWarning in writer.py (#425)
- Add bugfix and feature request issue templates to GitHub repository
- Apply
blackcode style to all Python files (#438, #398) - Update demo notebook for using logging levels with current behaviour
- Update contributing guide (#437, #441)
Version 0.28 (12 September 2020)¶
- Major re-write of reader code working towards LAS 3.0 support (#327; #347, #345, #353, #355, #358, #367, #368, #369)
- Fix #377 (writing “None” as the value instead of “”; #377)
- Fix #373 (enable GitHub Actions CI testing on MacOS, Windows, Ubuntu; #374, #387)
- Fix #363 (parse composite units such as “1000 lbf” correctly; #390)
- Fix #319 (allow skipping comment lines in data sections; #391)
- Avoid unnecessary exceptions on reading LAS 3.0 data sections (#385)
- Fix broken ReadTheDocs build
Version 0.26 (31 August 2020)¶
- This is the final version which works on Python 2.7 (#364)
- Fix #333 (header lines not parsed when colon is in description; #335)
- Fix #359 (sections not found when leading whitespace in line; #360, #361)
- Fix #350 (bug with NULL; #352)
- Fix #339 (0.1IN not recognised as index unit; #340, #349)
- Fix #31 (add command-line script to convert between LAS versions; #329)
- Fix #75 (add Cyrillic variant for metres; #330)
- Fix #326 (Support header-only LAS files–don’t lose the last header section before a missing ~A section)
- Improve documentation regarding deleting items and curves (#315, #325)
- Add deprecation markers (#331)
- Align json.dumps and LASFile.to_json() (#328)
- Fixes and updates to setup.py relating to the adoption of setuptools_scm (#312, #317, #318)
- Clean up and background changes related to future LAS 3.0 support: #334, #337, #338, #341, #342, #346, #348, #372
Version 0.25.1 (1 May 2020)¶
Version 0.25 (28 March 2020)¶
- Add stack_curves() method to allow joining a set of curves into a 2D array (issue #284, PR #293)
- Add lasio.examples module (#296)
- Fix #278 (leading zeroes were being stripped from API/UWI numbers)
- Fix #286 (error on trying to write a file with one row of data)
- Fix #258 (do not catch Ctrl+C when reading file)
- Fix #292 (improve error checking for when trying to write non-2D data)
- Fix #277 (allow pathlib objects to lasio.read)
- Fix #264 (allow periods in mnemonics to be retained in specific cases)
- Fix #201 (adjust descr parsing in ~P section to allow times in the descr, see PR #298)
- Fix #302 (change in str(datetime) handling)
- Fixes to JSON output (#300, #303)
- Fix #304 (add column_fmt argument to LASFile.write method)
Version 0.23¶
Version 0.22¶
Version 0.21¶
Version 0.20¶
- Fix #233 (pickling error lost Curve.data during multiprocessing)
- Fix #226 (do not issue warning on empty ~Parameter section)
- Revised default behaviour to using null_policy=’strict’ (ref. #227)
- Fix #221 (depths > 10000 were being rounded by default)
- Fix #225 (file handle leaked if exception during parsing)
Version 0.18¶
- Fix version numbering setup
- Fix #92 (can ignore blah blah lines in ~C section)
- Fix #209 (can now add curves with LASFile[‘mnemonic’] = [1, 2, 3])
- Fix #213 (LASFile.data is now a lazily generated property, with setter)
- Fix #218 (LASFile.append_curve was not adding data=[…] properly)
- Fix #216 (LASFile now raises KeyError for missing mnemonics)
- Fix #214 (first duplicate mnemonic when added was missing the :1)
Version 0.17¶
- Add Appveyor continuous integration testing
- Add example notebook for how to use python logging module
- Fix #160 (add methods to LASFile for inserting curves)
- Fix #155 (implement del keyword for header items)
- Fix #142 (implement slicing for SectionItems)
- Fix #135 (UWI numbers losing their leading zeros)
- Fix #153 (fix SectionItems pprint repr in Python 3)
- Fix #81 (accept header items with missing colon)
- Fix #71 (add Docker build for lasio to DockerHub)
- Fix #210 (allow upper/lowercase standardization of mnemonics on read)
- Document recent additions (nearly up to date) (in Sphinx docs)
Version 0.16¶
- Add read_policy and null_policy keywords - see documentation for details
- Fix bugs around files with missing ~V ~W ~P or ~C sections (#84 #85 #78)
- Fix #17 involving files with commas as a decimal mark
- Improve LASHeaderError traceback message
- Fix bug involving files with ~A but no data lines following
- Fix bug with blank line at start of file
- Fix bug involving missing or duplicate STRT, STOP and STEP mnemonics
Version 0.15.1¶
- Major performance improvements with both memory and speed
- Major improvement to read parser, now using iteration
- Add
LASFile.to_excel()andLASFile.to_csv()export methods - Improve
las2excelbulk.pyscript - Published new and updated Sphinx documentation
- Improved character encoding handling when
chardetnot installed autodetect_encoding=Trueby default- Allow reading of multiple non-standard header sections (#167, #168)
- Add flexibility in reading corrupted headers (
ignore_header_errors=True) - Add ability to avoid reading in data (
ignore_data=True) - Remove excessive debugging messages
- Fix bug #164 where
FEETwas not recognised asFT - Fix major globals() bug #141 affecting LASFile.add_curve
- Add command-line version script
$ lasioto show version number.
Version 0.14 and 0.15 skipped due to broken PyPI upload.
Version 0.13¶
- Other minor bug fixes inc inability to rename mnemonics in written LAS file.
Version 0.11.2¶
- Fix bug with not correctly figuring out units for LASFile.write()
- Add
LASFile.add_curve(CurveItem)method which automatically goes to the old method atLASFile.add_curve_raw(mnemonic=, data=, ...)if necessary, so it should be transparent to users
Version 0.11¶
- Reorganise code into modules
- various
Version 0.10¶
- Internal change to SectionItems for future LAS 3.0 support
- Added JSON encoder
- Added examples for using pandas DataFrame (.df attribute)
- LAS > Excel script refined (las2excel.py)
Version 0.9.1 (2015-11-11)¶
- pandas.DataFrame now as .df attribute, bugfix
Version 0.8 (2015-08-20)¶
- numerous bug fixes, API documentation added
Version 0.7 (2015-08-08)¶
- all tests passing on Python 2.6 through 3.4
Version 0.6 (2015-08-05)¶
- bugfixes and renamed from
las_readertolasio
Version 0.5 (2015-08-01)¶
- Improvements to writing LAS files
Version 0.4 (2015-07-26)¶
- Improved handling of character encodings, other internal improvements
Version 0.3 (2015-07-23)¶
- Added Python 3 support, now reads LAS 1.2 and 2.0