%global _empty_manifest_terminate_build 0
Name:		python-lasio
Version:	0.30
Release:	1
Summary:	Read/write well data from Log ASCII Standard (LAS) files
License:	MIT
URL:		https://github.com/kinverarity1/lasio
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c6/16/59f214003f6dad6e83ce9c14175ece1be6983c3d21ffe35f1bd82520c7e5/lasio-0.30.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-pandas
Requires:	python3-cchardet
Requires:	python3-openpyxl
Requires:	python3-pandas
Requires:	python3-cchardet
Requires:	python3-openpyxl
Requires:	python3-pytest
Requires:	python3-pytest-cov
Requires:	python3-coverage
Requires:	python3-codecov
Requires:	python3-pytest-benchmark

%description
# lasio

[![License](http://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/kinverarity1/lasio/blob/master/LICENSE)

Read and write Log ASCII Standard files with Python.

This is a Python 3.3+ 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](https://www.cwls.org/products/#products-las). Support for LAS 3 is 
[being worked on](https://github.com/kinverarity1/lasio/issues/5). In
principle it is designed to read as many types of LAS files as possible,
including ones containing common errors or non-compliant formatting.

lasio is primarily for reading & writing data and metadata to and from LAS files. 
lasio does not mind whether LAS files meet the formal specification before reading
data from them; check out the project [lascheck](https://github.com/MandarJKulkarni/lascheck)
for doing that sort of thing. If you are working specifically with lithological or
stratigraphic data, you may find [striplog](https://github.com/agile-geoscience/striplog)
helpful, while if you are focused on working at the well level, please take a
look at [welly](https://github.com/agile-geoscience/welly), which provides
much more functionality in that area.

Note this is not a package for reading LiDAR data (also called "LAS files"); 
you may want to check out [laspy](https://github.com/laspy/laspy) for that.

lasio  [stopped](https://github.com/kinverarity1/lasio/issues/364) 
supporting Python 2.7 in August 2020. The final version of lasio with Python 2.7 support 
is version 0.26.

## Code of conduct

See our [code of conduct](https://lasio.readthedocs.io/en/latest/contributing.html#code-of-conduct).

## Documentation

See here for the [complete lasio package
documentation](https://lasio.readthedocs.io/en/latest/).

## Quick start

For the minimum working requirements, you'll need numpy installed. Install
lasio with:

```bash
$ pip install lasio
```

To make sure you have everything, use this to ensure pandas, cchardet, and
openpyxl are also installed:

```bash
$ pip install lasio[all]
```

Example session:

```python
>>> import lasio
```

You can read the file using a filename, file-like object, or URL:

```python
>>> las = lasio.read("sample_rev.las")
```

Data is accessible both directly as numpy arrays

```python
>>> las.keys()
['DEPT', 'DT', 'RHOB', 'NPHI', 'SFLU', 'SFLA', 'ILM', 'ILD']
>>> las['SFLU']
array([ 123.45,  123.45,  123.45, ...,  123.45,  123.45,  123.45])
>>> las['DEPT']
array([ 1670.   ,  1669.875,  1669.75 , ...,  1669.75 ,  1670.   ,
        1669.875])
```

and as ``CurveItem`` objects with associated metadata:

```python
>>> las.curves
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=1  DEPTH, original_mnemonic=DEPT, data.shape=(29897,)),
CurveItem(mnemonic=DT, unit=US/M, value=, descr=2  SONIC TRANSIT TIME, original_mnemonic=DT, data.shape=(29897,)),
CurveItem(mnemonic=RHOB, unit=K/M3, value=, descr=3  BULK DENSITY, original_mnemonic=RHOB, data.shape=(29897,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4   NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(29897,)),
CurveItem(mnemonic=SFLU, unit=OHMM, value=, descr=5  RXO RESISTIVITY, original_mnemonic=SFLU, data.shape=(29897,)),
CurveItem(mnemonic=SFLA, unit=OHMM, value=, descr=6  SHALLOW RESISTIVITY, original_mnemonic=SFLA, data.shape=(29897,)),
CurveItem(mnemonic=ILM, unit=OHMM, value=, descr=7  MEDIUM RESISTIVITY, original_mnemonic=ILM, data.shape=(29897,)),
CurveItem(mnemonic=ILD, unit=OHMM, value=, descr=8  DEEP RESISTIVITY, original_mnemonic=ILD, data.shape=(29897,))]
```

Header information is parsed into simple HeaderItem objects, and stored in a
dictionary for each section of the header:

```python
>>> las.version
[HeaderItem(mnemonic=VERS, unit=, value=1.2, descr=CWLS LOG ASCII STANDARD -VERSION 1.2, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=ONE LINE PER DEPTH STEP, original_mnemonic=WRAP)]
>>> las.well
[HeaderItem(mnemonic=STRT, unit=M, value=1670.0, descr=, original_mnemonic=STRT),
HeaderItem(mnemonic=STOP, unit=M, value=1660.0, descr=, original_mnemonic=STOP),
HeaderItem(mnemonic=STEP, unit=M, value=-0.125, descr=, original_mnemonic=STEP),
HeaderItem(mnemonic=NULL, unit=, value=-999.25, descr=, original_mnemonic=NULL),
HeaderItem(mnemonic=COMP, unit=, value=ANY OIL COMPANY LTD., descr=COMPANY, original_mnemonic=COMP),
HeaderItem(mnemonic=WELL, unit=, value=ANY ET AL OIL WELL #12, descr=WELL, original_mnemonic=WELL),
HeaderItem(mnemonic=FLD, unit=, value=EDAM, descr=FIELD, original_mnemonic=FLD),
HeaderItem(mnemonic=LOC, unit=, value=A9-16-49, descr=LOCATION, original_mnemonic=LOC),
HeaderItem(mnemonic=PROV, unit=, value=SASKATCHEWAN, descr=PROVINCE, original_mnemonic=PROV),
HeaderItem(mnemonic=SRVC, unit=, value=ANY LOGGING COMPANY LTD., descr=SERVICE COMPANY, original_mnemonic=SRVC),
HeaderItem(mnemonic=DATE, unit=, value=25-DEC-1988, descr=LOG DATE, original_mnemonic=DATE),
HeaderItem(mnemonic=UWI, unit=, value=100091604920, descr=UNIQUE WELL ID, original_mnemonic=UWI)]
>>> las.params
[HeaderItem(mnemonic=BHT, unit=DEGC, value=35.5, descr=BOTTOM HOLE TEMPERATURE, original_mnemonic=BHT),
HeaderItem(mnemonic=BS, unit=MM, value=200.0, descr=BIT SIZE, original_mnemonic=BS),
HeaderItem(mnemonic=FD, unit=K/M3, value=1000.0, descr=FLUID DENSITY, original_mnemonic=FD),
HeaderItem(mnemonic=MATR, unit=, value=0.0, descr=NEUTRON MATRIX(0=LIME,1=SAND,2=DOLO), original_mnemonic=MATR),
HeaderItem(mnemonic=MDEN, unit=, value=2710.0, descr=LOGGING MATRIX DENSITY, original_mnemonic=MDEN),
HeaderItem(mnemonic=RMF, unit=OHMM, value=0.216, descr=MUD FILTRATE RESISTIVITY, original_mnemonic=RMF),
HeaderItem(mnemonic=DFD, unit=K/M3, value=1525.0, descr=DRILL FLUID DENSITY, original_mnemonic=DFD)]
```

The data is stored as a 2D numpy array:

```python
>>> las.data
array([[ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       ...,
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ]])
```

You can also retrieve and load data as a ``pandas`` DataFrame, build LAS files
from scratch, write them back to disc, and export to Excel, amongst other
things.

See the [package documentation](https://lasio.readthedocs.io/en/latest/) for
more details.

## Contributing

Contributions are invited and welcome.

See [Contributing](https://lasio.readthedocs.io/en/latest/contributing.html) for how to get started.


## License

MIT




%package -n python3-lasio
Summary:	Read/write well data from Log ASCII Standard (LAS) files
Provides:	python-lasio
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-lasio
# lasio

[![License](http://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/kinverarity1/lasio/blob/master/LICENSE)

Read and write Log ASCII Standard files with Python.

This is a Python 3.3+ 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](https://www.cwls.org/products/#products-las). Support for LAS 3 is 
[being worked on](https://github.com/kinverarity1/lasio/issues/5). In
principle it is designed to read as many types of LAS files as possible,
including ones containing common errors or non-compliant formatting.

lasio is primarily for reading & writing data and metadata to and from LAS files. 
lasio does not mind whether LAS files meet the formal specification before reading
data from them; check out the project [lascheck](https://github.com/MandarJKulkarni/lascheck)
for doing that sort of thing. If you are working specifically with lithological or
stratigraphic data, you may find [striplog](https://github.com/agile-geoscience/striplog)
helpful, while if you are focused on working at the well level, please take a
look at [welly](https://github.com/agile-geoscience/welly), which provides
much more functionality in that area.

Note this is not a package for reading LiDAR data (also called "LAS files"); 
you may want to check out [laspy](https://github.com/laspy/laspy) for that.

lasio  [stopped](https://github.com/kinverarity1/lasio/issues/364) 
supporting Python 2.7 in August 2020. The final version of lasio with Python 2.7 support 
is version 0.26.

## Code of conduct

See our [code of conduct](https://lasio.readthedocs.io/en/latest/contributing.html#code-of-conduct).

## Documentation

See here for the [complete lasio package
documentation](https://lasio.readthedocs.io/en/latest/).

## Quick start

For the minimum working requirements, you'll need numpy installed. Install
lasio with:

```bash
$ pip install lasio
```

To make sure you have everything, use this to ensure pandas, cchardet, and
openpyxl are also installed:

```bash
$ pip install lasio[all]
```

Example session:

```python
>>> import lasio
```

You can read the file using a filename, file-like object, or URL:

```python
>>> las = lasio.read("sample_rev.las")
```

Data is accessible both directly as numpy arrays

```python
>>> las.keys()
['DEPT', 'DT', 'RHOB', 'NPHI', 'SFLU', 'SFLA', 'ILM', 'ILD']
>>> las['SFLU']
array([ 123.45,  123.45,  123.45, ...,  123.45,  123.45,  123.45])
>>> las['DEPT']
array([ 1670.   ,  1669.875,  1669.75 , ...,  1669.75 ,  1670.   ,
        1669.875])
```

and as ``CurveItem`` objects with associated metadata:

```python
>>> las.curves
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=1  DEPTH, original_mnemonic=DEPT, data.shape=(29897,)),
CurveItem(mnemonic=DT, unit=US/M, value=, descr=2  SONIC TRANSIT TIME, original_mnemonic=DT, data.shape=(29897,)),
CurveItem(mnemonic=RHOB, unit=K/M3, value=, descr=3  BULK DENSITY, original_mnemonic=RHOB, data.shape=(29897,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4   NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(29897,)),
CurveItem(mnemonic=SFLU, unit=OHMM, value=, descr=5  RXO RESISTIVITY, original_mnemonic=SFLU, data.shape=(29897,)),
CurveItem(mnemonic=SFLA, unit=OHMM, value=, descr=6  SHALLOW RESISTIVITY, original_mnemonic=SFLA, data.shape=(29897,)),
CurveItem(mnemonic=ILM, unit=OHMM, value=, descr=7  MEDIUM RESISTIVITY, original_mnemonic=ILM, data.shape=(29897,)),
CurveItem(mnemonic=ILD, unit=OHMM, value=, descr=8  DEEP RESISTIVITY, original_mnemonic=ILD, data.shape=(29897,))]
```

Header information is parsed into simple HeaderItem objects, and stored in a
dictionary for each section of the header:

```python
>>> las.version
[HeaderItem(mnemonic=VERS, unit=, value=1.2, descr=CWLS LOG ASCII STANDARD -VERSION 1.2, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=ONE LINE PER DEPTH STEP, original_mnemonic=WRAP)]
>>> las.well
[HeaderItem(mnemonic=STRT, unit=M, value=1670.0, descr=, original_mnemonic=STRT),
HeaderItem(mnemonic=STOP, unit=M, value=1660.0, descr=, original_mnemonic=STOP),
HeaderItem(mnemonic=STEP, unit=M, value=-0.125, descr=, original_mnemonic=STEP),
HeaderItem(mnemonic=NULL, unit=, value=-999.25, descr=, original_mnemonic=NULL),
HeaderItem(mnemonic=COMP, unit=, value=ANY OIL COMPANY LTD., descr=COMPANY, original_mnemonic=COMP),
HeaderItem(mnemonic=WELL, unit=, value=ANY ET AL OIL WELL #12, descr=WELL, original_mnemonic=WELL),
HeaderItem(mnemonic=FLD, unit=, value=EDAM, descr=FIELD, original_mnemonic=FLD),
HeaderItem(mnemonic=LOC, unit=, value=A9-16-49, descr=LOCATION, original_mnemonic=LOC),
HeaderItem(mnemonic=PROV, unit=, value=SASKATCHEWAN, descr=PROVINCE, original_mnemonic=PROV),
HeaderItem(mnemonic=SRVC, unit=, value=ANY LOGGING COMPANY LTD., descr=SERVICE COMPANY, original_mnemonic=SRVC),
HeaderItem(mnemonic=DATE, unit=, value=25-DEC-1988, descr=LOG DATE, original_mnemonic=DATE),
HeaderItem(mnemonic=UWI, unit=, value=100091604920, descr=UNIQUE WELL ID, original_mnemonic=UWI)]
>>> las.params
[HeaderItem(mnemonic=BHT, unit=DEGC, value=35.5, descr=BOTTOM HOLE TEMPERATURE, original_mnemonic=BHT),
HeaderItem(mnemonic=BS, unit=MM, value=200.0, descr=BIT SIZE, original_mnemonic=BS),
HeaderItem(mnemonic=FD, unit=K/M3, value=1000.0, descr=FLUID DENSITY, original_mnemonic=FD),
HeaderItem(mnemonic=MATR, unit=, value=0.0, descr=NEUTRON MATRIX(0=LIME,1=SAND,2=DOLO), original_mnemonic=MATR),
HeaderItem(mnemonic=MDEN, unit=, value=2710.0, descr=LOGGING MATRIX DENSITY, original_mnemonic=MDEN),
HeaderItem(mnemonic=RMF, unit=OHMM, value=0.216, descr=MUD FILTRATE RESISTIVITY, original_mnemonic=RMF),
HeaderItem(mnemonic=DFD, unit=K/M3, value=1525.0, descr=DRILL FLUID DENSITY, original_mnemonic=DFD)]
```

The data is stored as a 2D numpy array:

```python
>>> las.data
array([[ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       ...,
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ]])
```

You can also retrieve and load data as a ``pandas`` DataFrame, build LAS files
from scratch, write them back to disc, and export to Excel, amongst other
things.

See the [package documentation](https://lasio.readthedocs.io/en/latest/) for
more details.

## Contributing

Contributions are invited and welcome.

See [Contributing](https://lasio.readthedocs.io/en/latest/contributing.html) for how to get started.


## License

MIT




%package help
Summary:	Development documents and examples for lasio
Provides:	python3-lasio-doc
%description help
# lasio

[![License](http://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/kinverarity1/lasio/blob/master/LICENSE)

Read and write Log ASCII Standard files with Python.

This is a Python 3.3+ 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](https://www.cwls.org/products/#products-las). Support for LAS 3 is 
[being worked on](https://github.com/kinverarity1/lasio/issues/5). In
principle it is designed to read as many types of LAS files as possible,
including ones containing common errors or non-compliant formatting.

lasio is primarily for reading & writing data and metadata to and from LAS files. 
lasio does not mind whether LAS files meet the formal specification before reading
data from them; check out the project [lascheck](https://github.com/MandarJKulkarni/lascheck)
for doing that sort of thing. If you are working specifically with lithological or
stratigraphic data, you may find [striplog](https://github.com/agile-geoscience/striplog)
helpful, while if you are focused on working at the well level, please take a
look at [welly](https://github.com/agile-geoscience/welly), which provides
much more functionality in that area.

Note this is not a package for reading LiDAR data (also called "LAS files"); 
you may want to check out [laspy](https://github.com/laspy/laspy) for that.

lasio  [stopped](https://github.com/kinverarity1/lasio/issues/364) 
supporting Python 2.7 in August 2020. The final version of lasio with Python 2.7 support 
is version 0.26.

## Code of conduct

See our [code of conduct](https://lasio.readthedocs.io/en/latest/contributing.html#code-of-conduct).

## Documentation

See here for the [complete lasio package
documentation](https://lasio.readthedocs.io/en/latest/).

## Quick start

For the minimum working requirements, you'll need numpy installed. Install
lasio with:

```bash
$ pip install lasio
```

To make sure you have everything, use this to ensure pandas, cchardet, and
openpyxl are also installed:

```bash
$ pip install lasio[all]
```

Example session:

```python
>>> import lasio
```

You can read the file using a filename, file-like object, or URL:

```python
>>> las = lasio.read("sample_rev.las")
```

Data is accessible both directly as numpy arrays

```python
>>> las.keys()
['DEPT', 'DT', 'RHOB', 'NPHI', 'SFLU', 'SFLA', 'ILM', 'ILD']
>>> las['SFLU']
array([ 123.45,  123.45,  123.45, ...,  123.45,  123.45,  123.45])
>>> las['DEPT']
array([ 1670.   ,  1669.875,  1669.75 , ...,  1669.75 ,  1670.   ,
        1669.875])
```

and as ``CurveItem`` objects with associated metadata:

```python
>>> las.curves
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=1  DEPTH, original_mnemonic=DEPT, data.shape=(29897,)),
CurveItem(mnemonic=DT, unit=US/M, value=, descr=2  SONIC TRANSIT TIME, original_mnemonic=DT, data.shape=(29897,)),
CurveItem(mnemonic=RHOB, unit=K/M3, value=, descr=3  BULK DENSITY, original_mnemonic=RHOB, data.shape=(29897,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4   NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(29897,)),
CurveItem(mnemonic=SFLU, unit=OHMM, value=, descr=5  RXO RESISTIVITY, original_mnemonic=SFLU, data.shape=(29897,)),
CurveItem(mnemonic=SFLA, unit=OHMM, value=, descr=6  SHALLOW RESISTIVITY, original_mnemonic=SFLA, data.shape=(29897,)),
CurveItem(mnemonic=ILM, unit=OHMM, value=, descr=7  MEDIUM RESISTIVITY, original_mnemonic=ILM, data.shape=(29897,)),
CurveItem(mnemonic=ILD, unit=OHMM, value=, descr=8  DEEP RESISTIVITY, original_mnemonic=ILD, data.shape=(29897,))]
```

Header information is parsed into simple HeaderItem objects, and stored in a
dictionary for each section of the header:

```python
>>> las.version
[HeaderItem(mnemonic=VERS, unit=, value=1.2, descr=CWLS LOG ASCII STANDARD -VERSION 1.2, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=ONE LINE PER DEPTH STEP, original_mnemonic=WRAP)]
>>> las.well
[HeaderItem(mnemonic=STRT, unit=M, value=1670.0, descr=, original_mnemonic=STRT),
HeaderItem(mnemonic=STOP, unit=M, value=1660.0, descr=, original_mnemonic=STOP),
HeaderItem(mnemonic=STEP, unit=M, value=-0.125, descr=, original_mnemonic=STEP),
HeaderItem(mnemonic=NULL, unit=, value=-999.25, descr=, original_mnemonic=NULL),
HeaderItem(mnemonic=COMP, unit=, value=ANY OIL COMPANY LTD., descr=COMPANY, original_mnemonic=COMP),
HeaderItem(mnemonic=WELL, unit=, value=ANY ET AL OIL WELL #12, descr=WELL, original_mnemonic=WELL),
HeaderItem(mnemonic=FLD, unit=, value=EDAM, descr=FIELD, original_mnemonic=FLD),
HeaderItem(mnemonic=LOC, unit=, value=A9-16-49, descr=LOCATION, original_mnemonic=LOC),
HeaderItem(mnemonic=PROV, unit=, value=SASKATCHEWAN, descr=PROVINCE, original_mnemonic=PROV),
HeaderItem(mnemonic=SRVC, unit=, value=ANY LOGGING COMPANY LTD., descr=SERVICE COMPANY, original_mnemonic=SRVC),
HeaderItem(mnemonic=DATE, unit=, value=25-DEC-1988, descr=LOG DATE, original_mnemonic=DATE),
HeaderItem(mnemonic=UWI, unit=, value=100091604920, descr=UNIQUE WELL ID, original_mnemonic=UWI)]
>>> las.params
[HeaderItem(mnemonic=BHT, unit=DEGC, value=35.5, descr=BOTTOM HOLE TEMPERATURE, original_mnemonic=BHT),
HeaderItem(mnemonic=BS, unit=MM, value=200.0, descr=BIT SIZE, original_mnemonic=BS),
HeaderItem(mnemonic=FD, unit=K/M3, value=1000.0, descr=FLUID DENSITY, original_mnemonic=FD),
HeaderItem(mnemonic=MATR, unit=, value=0.0, descr=NEUTRON MATRIX(0=LIME,1=SAND,2=DOLO), original_mnemonic=MATR),
HeaderItem(mnemonic=MDEN, unit=, value=2710.0, descr=LOGGING MATRIX DENSITY, original_mnemonic=MDEN),
HeaderItem(mnemonic=RMF, unit=OHMM, value=0.216, descr=MUD FILTRATE RESISTIVITY, original_mnemonic=RMF),
HeaderItem(mnemonic=DFD, unit=K/M3, value=1525.0, descr=DRILL FLUID DENSITY, original_mnemonic=DFD)]
```

The data is stored as a 2D numpy array:

```python
>>> las.data
array([[ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       ...,
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ]])
```

You can also retrieve and load data as a ``pandas`` DataFrame, build LAS files
from scratch, write them back to disc, and export to Excel, amongst other
things.

See the [package documentation](https://lasio.readthedocs.io/en/latest/) for
more details.

## Contributing

Contributions are invited and welcome.

See [Contributing](https://lasio.readthedocs.io/en/latest/contributing.html) for how to get started.


## License

MIT




%prep
%autosetup -n lasio-0.30

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-lasio -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.30-1
- Package Spec generated