Spatial interpolation Python module
Project description
PyInterpolate
PyInterpolate is designed as the Python library for geostatistics. It's role is to provide access to spatial statistics tools used in a wide range of studies.
Status
Beta version: package is tested and the main structure is preserved but future changes are very likely to occur.
Setup
Setup is described in the file SETUP.md: https://github.com/szymon-datalions/pyinterpolate/blob/master/SETUP.md
Commercial and scientific projects where library has been used
- Tick-Borne Disease Detector (Data Lions company) for the European Space Agency (2019-2020).
Community
Join our community in Discord: https://discord.gg/3EMuRkj
Bibliography
PyInterpolate was created thanks to many resources and all of them are pointed here:
- Armstrong M., Basic Linear Geostatistics, Springer 1998,
- GIS Algorithms by Ningchuan Xiao: https://uk.sagepub.com/en-gb/eur/gis-algorithms/book241284
- Pardo-Iguzquiza E., VARFIT: a fortran-77 program for fitting variogram models by weighted least squares, Computers & Geosciences 25, 251-261, 1999,
- Goovaerts P., Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units, Mathematical Geology 40(1), 101-128, 2008
- Deutsch C.V., Correcting for Negative Weights in Ordinary Kriging, Computers & Geosciences Vol.22, No.7, pp. 765-773, 1996
Requirements and dependencies
-
Python 3.7.6
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Numpy 1.18.3
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Scipy 1.4.1
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GeoPandas 0.7.0
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Fiona 1.18.13.post1 (Mac OS) / Fiona 1.8 (Linux)
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Rtree 0.9.4 (Mac OS), Rtree >= 0.8 & < 0.9 (Linux)
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Descartes 1.1.0
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Pyproj 2.6.0
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Shapely 1.7.0
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Matplotlib 3.2.1
Package structure
High level overview:
::
- pyinterpolate
- calculations - distance calculation
- data_processing - preparation of spatial data and data processing tasks,
- data visualization - interpolation of smooth surfaces as rasters,
- kriging - Ordinary Kriging, Simple Kriging, Poisson Kriging: centroid based, area-to-area, area-to-point,
- misc - compare different kriging techniques,
- semivariance - calculate semivariance, fit semivariograms and regularize semivariogram,
- tutorials - tutorials (Basic, Intermediate and Advanced)
Functions documentation
Pyinterpolate https://pyinterpolate.readthedocs.io/en/latest/
Development
- inverse distance weighting,
- point cloud variograms,
- semivariogram params management,
- semivariogram regularization with epidemiological data tutorial
Known Bugs
- (still) not detected!
Project details
Release history Release notifications | RSS feed
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