Rasters

This module provides useful methods for operating on 1D and 2D rasters such as making variogram or covariograms.

raster_to_struct_grid

wtools.geostats.raster.raster_to_struct_grid(datain, imeas='covar', rtol=1e-10)[source]

Create an auto-variogram or auto-covariance map from 1D or 2D rasters. This computes auto-variogram or auto-covariance maps from 1D or 2D rasters. This function computes variograms/covariances in the frequency domain via the Fast Fourier Transform (np.fftn).

Note

For viewing the results, please use the plot_struct_grid method from the plots module.

Note

Missing values, flagged as np.nan, are allowed.

Parameters:
  • datain (np.ndarray) – input arrray with raster in GeoEas format
  • imeas (str) – key indicating which structural measure to compute: 'var' for semi-variogram or 'covar' for covariogram.
  • gridspecs (list(GridSpec)) – array with grid specifications using GridSpec objects
  • rtol (float) – the tolerance. Default is 1e-10
Returns:

output array with variogram or covariogram map, depending on variogram choice, with size: in 1D: ( 2*nxOutHalf+1 ) or in 2D: ( 2*nxOutHalf+1 x 2*nxOutHalf+1 ).

output array with number of pairs available in each lag, of same size as outStruct
Return type:

tuple(np.ndarray, np.ndarray)

References

Originally implemented in MATLAB by:
Phaedon Kyriakidis, Department of Geography, University of California Santa Barbara, May 2005
Reimplemented into Python by:
Jonah Bartrand, Department of Geophysics, Colorado School of Mines, October 2018
Algorith based on:
Marcotte, D. (1996): Fast Variogram Computation with FFT, Computers & Geosciences, 22(10), 1175-1186.

suprts2modelcovFFT

wtools.geostats.raster.suprts2modelcovFFT(CovMapExtFFT, ind1Ext, sf1Ext, ind2Ext, sf2Ext)[source]

Integrated model covariances between 1 or 2 sets of arbitrary supports. Function to calculate array of TOTAL or AVERAGE model covariances between 1 or 2 sets of irregular supports, using convolution in the frequency domain (FFT-based). Integration or averaging is IMPLICIT in the pre-computed sampling functions (from discrsuprtsFFT).

Parameters:
  • CovMapExtFFT (np.ndarray) – Fourier transform of model covariance map evaluated at nodes of an extended MATLAB grid
  • ind1Ext – (nSup1 x 1) cell array with MATLAB indices of non-zero sampling function values for support set #1 in extended MATLAB grid
  • sf1Ext – (nSup1 x 1) cell array with sampling function values for support set #1
  • ind2Ext – Optional (nSup2 x 1) cell array with MATLAB indices of non-zero sampling function values for support set #2 in extended MATLAB grid
  • sf2Ext – Optional (nSup2 x 1) cell array with sampling function values for support set #2
Returns:

(nSup1 x nSup[1,2]) array with integrated covariances
Return type:

np.ndarray

References

Originally implemented in MATLAB by:
Phaedon Kyriakidis, Department of Geography, University of California Santa Barbara, May 2005
Reimplemented into Python by:
Bane Sullivan and Jonah Bartrand, Department of Geophysics, Colorado School of Mines, October 2018