Metadata-Version: 2.1
Name: polire
Version: 0.1.3
Summary: A collection of interpolation methods.
Home-page: https://sustainability-lab.github.io/polire
Maintainer: Zeel B Patel, Apoorv Agnihotri, S Deepak Narayanan
Maintainer-email: patel_zeel@iitgn.ac.in, apoorv.agnihotri@iitgn.ac.in, deepak.narayanan@iitgn.ac.in
License: new BSD
Download-URL: https://sustainability-lab.github.io/polire
Description: ![example workflow](https://github.com/patel-zeel/polire/actions/workflows/python-package.yml/badge.svg)
        
        
        ## Polire
        
        The word "interpolation" has Latin origin and is composed of two words - Inter meaning between and Polire meaning to polish.
        
        This repository is a collection of several spatial interpolation algorithms. 
        
        ## Examples
        ### Minimal example of interpolation
        ```python
        import numpy as np
        from polire import Kriging
        
        # Data
        X = np.random.rand(10, 2) # Spatial 2D points
        y = np.random.rand(10) # Observations
        X_new = np.random.rand(100, 2) # New spatial points
        
        # Fit
        model = Kriging()
        model.fit(X, y)
        
        # Predict
        y_new = model.predict(X_new)
        ```
        
        ### Supported Interpolation Methods
        ```python
        from polire import (
            Kriging, # Best spatial unbiased predictor
            GP, # Gaussian process interpolator from GPy
            IDW, # Inverse distance weighting
            SpatialAverage,
            Spline,
            Trend,
            Random, # Predict uniformly within the observation range, a reasonable baseline
            NaturalNeighbor,
            CustomInterpolator # Supports any regressor from Scikit-learn
        )
        ```
        
        ### Use GP kernels from GPy and regressors from sklearn
        ```python
        from sklearn.linear_model import LinearRegression # or any Scikit-learn regressor
        from GPy.kern import Matern32 # or any other GPy kernel
        
        from polire import GP, CustomInterpolator
        
        # GP model
        model = GP(Matern32(input_dim=2))
        
        # Sklearn model
        model = CustomInterpolator(LinearRegression(normalize = True))
        ```
        
        ## More info
        
        Contributors:  [S Deepak Narayanan](https://github.com/sdeepaknarayanan), [Zeel B Patel](https://github.com/patel-zeel), [Apoorv Agnihotri](https://github.com/apoorvagnihotri), and [Nipun Batra](https://github.com/nipunbatra).
        
        This project is a part of Sustainability Lab at IIT Gandhinagar.
        
        Acknowledgement to sklearn template for helping to package into a PiPy package.
        
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
