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When there are more than 90,000 coordinates (for example an area a bit larger than 300 pixels * 300 pixels), the variogram functions produces "std::bad_alloc" error. I would like to know if there is a…
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Dear authors,
I was looking for the python open source of Kriging to solve my works. Feel lucky to find this convenience and clean package. However, I found a small strange number (parameter) with …
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`gstat` looks promising
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Hi,
I've been encountering a problem I don't understand with the predict.gstat function.
I would like to use it to interpolate residuals using a cokriging model. Running the function with nsim=0…
lea-c updated
2 months ago
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More work could potentially be dedicated to improving the way that variograms are currently fit. There are many ways that the fitting can be done (what to minimise in the fit, for example, RMSE, MAE e…
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I ran into this problem while dealing with a large amount of data. I wonder whether replacing the `for` loop in `Variogram. _calc_groups()`. with `numpy.digitize()` might be an option. Not sure if the…
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The module `spatialstats.py` is starting to be a bit messy!
My thoughts right now:
- Move the spatial variogram `scikit-gstat` wrappers into a `stats/spatial.py` in GeoUtils,
- Move the N-D binni…
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Hello there !
I was wondering if it was possible to make this function faster : `skg.DirectionalVariogram(list(zip(data.x, data.y)), data.m, azimuth=90, maxlag=100, n_lags=5)`.
Knowing that length…
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Currently, the kriging matrix is build on the covariance function of the given model:
https://github.com/GeoStat-Framework/GSTools/blob/21c97fc7426864add2430addcaf465da8993d227/gstools/krige/base.py#…
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I am trying to produce kriging models from elevations points using the gstat package. I can fit models to the empirical variogram using exponential, spherical, and gaussian curves. However, despite th…