Metadata-Version: 2.1
Name: alaska
Version: 0.0.4
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
Description: # alaska: The las file aliaser
        
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4047049.svg)](https://doi.org/10.5281/zenodo.4047049)
        
        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)
        ```
        
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
