Kriging Toolkit for Python
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Updated
Sep 3, 2024 - Python
Kriging Toolkit for Python
GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more
Kriging | Poisson Kriging | Variogram Analysis
Geostatistics in Python
In this repository I publish the python code, that was part of my master thesis. The thesis can be found here, however its in German though, sry. :/
Implementation of image reparation and inpainting using Gaussian Conditional Simulation. Created as part of Unity Technologies research.
Spatial interpolation python package
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
"Space-Time Interpolation and Forecasting" - Predicting spatio-temporally distributed variables via space-time regression kriging using numpy and numba.
GeoKrige is a Python package designed for spatial interpolation using Kriging Methods. While primarily tailored for geospatial analysis, it is equally applicable to other spatial analysis tasks.
Laplacian-enhanced tensor learning for large-scale spatiotemporal traffic data kriging (estimation)
The repo is the official implementation for the paper: MoGERNN: An Inductive Traffic Predictor for Unobserved Locations.
An empirical study and comparison of Deterministic, Statistical, and ML Algorithms for the Spatial Modeling of significant wave height data from NOAA's National Data Buoy Center and other Environment-related datasets
POD+K in Real-Time Aeroelastic Pre-design Problem
Highly performant and scalable out-of-the-box gaussian process regression and Bernoulli classification. Built upon GPyTorch, with a familiar sklearn api.
Spatial interpolations using Kriging (Ordinary, Simple and External Drift) and IDW. Variogram fitting and clustering. Extraction of time series from raster data (GTiff and NC) to HDF5 or text. Take a look at the scripts in the "test" directory for how to use.
Space-Time Statistical Quality Control of Extreme Precipitation Observation
Iot sensor network to survey, analyze and send data on environmental variables (temperature, humidity, light, thermal sensation) to the Firebase cloud services platform (FireStore and Storage), through a Gateway (Raspberry Pi), communicating through NRF24L01 transceivers and RF24Network protocol communication.
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