Polire

pip install polire

The word "interpolation" has a 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

Please refer to the documentation to check out practical examples on real datasets.

Minimal example of interpolation

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

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 (temporarily unavailable)

from GPy.kern import Matern32 # or any other GPy kernel

# GP model
model = GP(Matern32(input_dim=2))

Regressors from sklearn

from sklearn.linear_model import LinearRegression # or any Scikit-learn regressor
from polire import GP, CustomInterpolator

# Sklearn model
model = CustomInterpolator(LinearRegression())

Extract spatial features from spatio-temporal dataset

# X and X_new are datasets as numpy arrays with first three dimensions as longitude, latitute and time.
# y is corresponding observations with X

from polire.preprocessing import SpatialFeatures
spatial = SpatialFeatures(n_closest=10)
Features = spatial.fit_transform(X, y)
Features_new = spatial.transform(X_new)

Citation

If you use this library, please cite the following paper:

@inproceedings{10.1145/3384419.3430407,
author = {Narayanan, S Deepak and Patel, Zeel B and Agnihotri, Apoorv and Batra, Nipun},
title = {A Toolkit for Spatial Interpolation and Sensor Placement},
year = {2020},
isbn = {9781450375900},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3384419.3430407},
doi = {10.1145/3384419.3430407},
booktitle = {Proceedings of the 18th Conference on Embedded Networked Sensor Systems},
pages = {653–654},
numpages = {2},
location = {Virtual Event, Japan},
series = {SenSys '20}
}