Metadata-Version: 2.1
Name: AlasKA
Version: 0.0.2
Summary: Automated well log mnemonics parser
Home-page: https://github.com/FRI-Energy-Analytics/AlasKA
Author: The AlasKA Developers
Author-email: destinydong@utexas.edu
Maintainer: Destiny Dong
Maintainer-email: destinydong@utexas.edu
License: MIT License
Keywords: geophysics,geology,reservoir engineering
Platform: Any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: lasio
Requires-Dist: pandas
Requires-Dist: torch
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: backports.functools-lru-cache

# AlasKA: The las file aliaser

[![DOI](https://zenodo.org/badge/288477124.svg)](https://zenodo.org/badge/latestdoi/288477124)

AlasKA is a Python package that reads mnemonics from LAS files and outputs an aliased dictionary of mnemonics and its aliases, as well as a list of mnemonics that cannot be found. It uses three different methods to find aliases to mnemonics: locates exact matches of a mnemonic in an alias dictionary, identifies keywords in mnemonics' description then returns alias from the keyword extractor, and predicts alias using all attributes of the curves.

## Sample Usage

```python
from alaska import Alias
from welly import Project
import lasio

path = "testcase.las"
a = Alias()
parsed, not_found = a.parse(path)
```

In this case, parsed is the aliased dictionary that contains mnemonics and its aliases, and not_found is the list of mnemonics that the aliaser did not find. Users can manually alias mnemonics in the not_found list and add them to the dictionary of aliased mnemonics

Parameters of the Alias class can be changed, and the defaults are the following

```python
a = Alias(dictionary=True, keyword_extractor=True, model=True, prob_cutoff=.5)
```

Users can choose which parser to use/not to use by setting the parsers to True/False. The prob_cutoff is the confidence the user wants the predictions made by model parser to have.

Then, the aliased mnemonics can be inputted into welly as demonstrated below.

```python
from welly import Project
p = Project.from_las(path)
data = p.df(keys=list(parsed.keys()), alias=parsed)
print(data)
```


