Metadata-Version: 2.1
Name: MLPet
Version: 0.0.1
Summary: Package to prepare well log data for ML projects.
Home-page: https://bitbucket.org/akerbp/petroml/
Author: Saghar Asadi
Author-email: saghar.asadi@akerbp.com
License: UNKNOWN
Description: # MLPet
        
        Preprocessing tools for Petrophysics ML projects at Eureka
        
        ## Quick start
        
        - Clone this repository
        
        - Install the package by running the following from the root directory (requires python 3.8 or later)
        
                python -m pip install --upgrade pip
                python setup.py install
        
        - Short example for pre-processing data prior to making a regression model:
        
                from mlpet.Datasets.shear import Sheardata
                # Instantiate an empty dataset object using the example settings and mappings provided
                ds = Sheardata(settings="support/settings_shear.yaml", mappings="support/mappings.yaml", folder_path="support/")
                # Populate the dataset with data from a file (support for multiple file formats and direct cdf data collection exists)
                ds.load_from_pickle("support/data/shear.pkl")
                # The original data will be kept in ds.df_original and will remain unchanged 
                print(ds.df_original.head())
                # Split the data into train-validation sets
                df_train_original, df_valid_original, valid_wells = ds.train_test_split(df=ds.df_original, test_size=0.3)
                # Preprocess the data for training
                df_train, train_key_wells, feats = ds.preprocess(df_train_original)
                # Preprocecss accepts some keyword arguments related to various steps (e.g. the key wells used for normalizing curves such as GR)
                df_valid, valid_key_wells, _ = ds.preprocess(df_valid_original, _normalize_curves={'key_wells':train_key_wells})
        
        
        - Short example for pre-processing data prior to making a classification model:
        
                from mlpet.Datasets.lithology import Lithologydata
                ds = Lithologydata(settings="support/settings_lithology.yaml", mappings="support/mappings.yaml", folder_path="support/")
                ds.load_from_pickle("support/data/lithology.pkl")
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
