v1.3.1 'Pure Pink'

Release Notes

A bugfix release for GSTools v1.3.

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Enhancements

• Standalone use of Field class #166
• add social badges in README #169, #170

Bugfixes

• use oldest-supported-numpy to build cython extensions #165

v1.3.0 'Pure Pink'

Release Notes

A big step forward for GSTools. We now support geographical coordinates, directional variograms, auto-binning, arbitrary dimensions, normalizers and trends and much much more.

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Topics

Geographical Coordinates Support (#113)

• added boolean init parameter latlon to indicate a geographic model. When given, spatial dimension is fixed to dim=3, anis and angles will be ignored, since anisotropy is not well-defined on a sphere.
• add property field_dim to indicate the dimension of the resulting field. Will be 2 if latlon=True
• added yadrenko variogram, covariance and correlation method, since the geographic models are derived from standard models in 3D by plugging in the chordal distance of two points on a sphere derived from there great-circle distance zeta:
  • vario_yadrenko: given by variogram(2 * np.sin(zeta / 2))
  • cov_yadrenko: given by covariance(2 * np.sin(zeta / 2))
  • cor_yadrenko: given by correlation(2 * np.sin(zeta / 2))
• added plotting routines for yadrenko methods described above
• the isometrize and anisometrize methods will convert latlon tuples (given in degree) to points on the unit-sphere in 3D and vice versa
• representation of geographical models don't display the dim, anis and angles parameters, but latlon=True
• fit_variogram will expect an estimated variogram with great-circle distances given in radians
• Variogram estimation
  • latlon switch implemented in estimate_vario routine
  • will return a variogram estimated by the great-circle distance (haversine formula) given in radians
• Field
  • added plotting routines for latlon fields
  • no vector fields possible on latlon fields
  • corretly handle pos tuple for latlon fields

Krige Unification (#97)

• Swiss Army Knife for kriging: The Krige class now provides everything in one place
• "Kriging the mean" is now possible with the switch only_mean in the call routine
• Simple/Ordinary/Universal/ExtDrift/Detrended are only shortcuts to Krige with limited input parameter list
• We now use the covariance function to build up the kriging matrix (instead of variogram)
• An unbiased switch was added to enable simple kriging (where the unbiased condition is not given)
• An exact switch was added to allow smother results, if a nugget is present in the model
• An cond_err parameter was added, where measurement error variances can be given for each conditional point
• pseudo-inverse matrix is now used to solve the kriging system (can be disabled by the new switch pseudo_inv), this is equal to solving the system with least-squares and prevents numerical errors
• added options fit_normalizer and fit_variogram to automatically fit normalizer and variogram to given data

Directional Variograms and Auto-binning (#87, #106, #131)

• new routine name vario_estimate instead of vario_estimate_unstructured (old kept for legacy code) for simplicity
• new routine name vario_estimate_axis instead of vario_estimate_structured (old kept for legacy code) for simplicity
• vario_estimate
  • added simple automatic binning routine to determine bins from given data (one third of box diameter as max bin distance, sturges rule for number of bins)
  • allow to pass multiple fields for joint variogram estimation (e.g. for daily precipitation) on same mesh
  • no_data option added to allow missing values
  • masked fields
    • user can now pass a masked array (or a list of masked arrays) to deselect data points.
    • in addition, a mask keyword was added to provide an external mask
  • directional variograms
    • diretional variograms can now be estimated
    • either provide a list of direction vectors or angles for directions (spherical coordinates)
    • can be controlled by given angle tolerance and (optional) bandwidth
    • prepared for nD
  • structured fields (pos tuple describes axes) can now be passed to estimate an isotropic or directional variogram
  • distance calculation in cython routines in now independent of dimension
• vario_estimate_axis
  • estimation along array axis now possible in arbitrary dimensions
  • no_data option added to allow missing values (sovles #83)
  • axis can be given by name ("x", "y", "z") or axis number (0, 1, 2, 3, ...)

Better Variogram fitting (#78, #145)

• fixing sill possible now
• loss is now selectable for smoother handling of outliers
• r2 score can now be returned to get an impression of the goodness of fitting
• weights can be passed
• instead of deselecting parameters, one can also give fix values for each parameter
• default init guess for len_scale is now mean of given bin-centers
• default init guess for var and nugget is now mean of given variogram values

CovModel update (#109, #122, #157)

• add new rescale argument and attribute to the CovModel class to be able to rescale the len_scale (usefull for unit conversion or rescaling len_scale to coincide with the integral_scale like it's the case with the Gaussian model)
  See: #90, GeoStat-Framework/PyKrige#119
• added new len_rescaled attribute to the CovModel class, which is the rescaled len_scale: len_rescaled = len_scale / rescale
• new method default_rescale to provide default rescale factor (can be overridden)
• remove doctest calls
• docstring updates in CovModel and derived models
• updated all models to use the cor routine and make use of the rescale argument (See: #90)
• TPL models got a separate base class to not repeat code
• added new models (See: #88):
  • HyperSpherical: (Replaces the old Intersection model) Derived from the intersection of hyper-spheres in arbitrary dimensions. Coincides with the linear model in 1D, the circular model in 2D and the classical spherical model in 3D
  • SuperSpherical: like the HyperSpherical, but the shape parameter derived from dimension can be set by the user. Coincides with the HyperSpherical model by default
  • JBessel: a hole model valid in all dimensions. The shape parameter controls the dimension it was derived from. For nu=0.5 this model coincides with the well known wave hole model.
  • TPLSimple: a simple truncated power law controlled by a shape parameter nu. Coincides with the truncated linear model for nu=1
  • Cubic: to be compatible with scikit-gstat in the future
• all arguments are now stored as float internally (#157)
• string representation of the CovModel class is now using a float precision (CovModel._prec=3) to truncate longish output
• string representation of the CovModel class now only shows anis and angles if model is anisotropic resp. rotated
• dimension validity check: raise a warning, if given model is not valid in the desired dimension (See: #86)

Normalizer, Trend and Mean (#124)

• new normalize submodule containing power-transforms for data to gain normality
• Base-Class: Normalizer providing basic functionality including maximum likelihood fitting
• added: LogNormal, BoxCox, BoxCoxShift, YeoJohnson, Modulus and Manly
• normalizer, trend and mean can be passed to SRF, Krige and variogram estimation routines
  • A trend can be a callable function, that represents a trend in input data. For example a linear decrease of temperature with height.
  • The normalizer will be applied after the data was detrended, i.e. the trend was substracted from the data, in order to gain normality.
  • The mean is now interpreted as the mean of the normalized data. The user could also provide a callable mean, but it is mostly meant to be constant.

Arbitrary dimensions (#112)

• allow arbitrary dimensions in all routines (CovModel, Krige, SRF, variogram)
• anisotropy and rotation following a generalization of tait-bryan angles
• CovModel provides isometrize and anisometrize routines to convert points

New Class for Conditioned Random Fields (#130)

• THIS BREAKS BACKWARD COMPATIBILITY
• CondSRF replaces the conditioning feature of the SRF class, which was cumbersome and limited to Ordinary and Simple kriging
• CondSRF behaves similar to the SRF class, but instead of a covariance model, it takes a kriging class as input. With this kriging class, all conditioning related settings are defined.

Enhancements

• Python 3.9 Support #107
• add routines to format struct. ...

v1.3.0-rc2 'Pure Pink' 2. Release Candidate

Release Notes

A big step forward for GSTools. We now support geographical coordinates, directional variograms, auto-binning, arbitrary dimensions, normalizers and trends and much much more.

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Topics

Geographical Coordinates Support (#113)

• added boolean init parameter latlon to indicate a geographic model. When given, spatial dimension is fixed to dim=3, anis and angles will be ignored, since anisotropy is not well-defined on a sphere.
• add property field_dim to indicate the dimension of the resulting field. Will be 2 if latlon=True
• added yadrenko variogram, covariance and correlation method, since the geographic models are derived from standard models in 3D by plugging in the chordal distance of two points on a sphere derived from there great-circle distance zeta:
  • vario_yadrenko: given by variogram(2 * np.sin(zeta / 2))
  • cov_yadrenko: given by covariance(2 * np.sin(zeta / 2))
  • cor_yadrenko: given by correlation(2 * np.sin(zeta / 2))
• added plotting routines for yadrenko methods described above
• the isometrize and anisometrize methods will convert latlon tuples (given in degree) to points on the unit-sphere in 3D and vice versa
• representation of geographical models don't display the dim, anis and angles parameters, but latlon=True
• fit_variogram will expect an estimated variogram with great-circle distances given in radians
• Variogram estimation
  • latlon switch implemented in estimate_vario routine
  • will return a variogram estimated by the great-circle distance (haversine formula) given in radians
• Field
  • added plotting routines for latlon fields
  • no vector fields possible on latlon fields
  • corretly handle pos tuple for latlon fields

Krige Unification (#97)

• Swiss Army Knife for kriging: The Krige class now provides everything in one place
• "Kriging the mean" is now possible with the switch only_mean in the call routine
• Simple/Ordinary/Universal/ExtDrift/Detrended are only shortcuts to Krige with limited input parameter list
• We now use the covariance function to build up the kriging matrix (instead of variogram)
• An unbiased switch was added to enable simple kriging (where the unbiased condition is not given)
• An exact switch was added to allow smother results, if a nugget is present in the model
• An cond_err parameter was added, where measurement error variances can be given for each conditional point
• pseudo-inverse matrix is now used to solve the kriging system (can be disabled by the new switch pseudo_inv), this is equal to solving the system with least-squares and prevents numerical errors
• added options fit_normalizer and fit_variogram to automatically fit normalizer and variogram to given data

Directional Variograms and Auto-binning (#87, #106, #131)

• new routine name vario_estimate instead of vario_estimate_unstructured (old kept for legacy code) for simplicity
• new routine name vario_estimate_axis instead of vario_estimate_structured (old kept for legacy code) for simplicity
• vario_estimate
  • added simple automatic binning routine to determine bins from given data (one third of box diameter as max bin distance, sturges rule for number of bins)
  • allow to pass multiple fields for joint variogram estimation (e.g. for daily precipitation) on same mesh
  • no_data option added to allow missing values
  • masked fields
    • user can now pass a masked array (or a list of masked arrays) to deselect data points.
    • in addition, a mask keyword was added to provide an external mask
  • directional variograms
    • diretional variograms can now be estimated
    • either provide a list of direction vectors or angles for directions (spherical coordinates)
    • can be controlled by given angle tolerance and (optional) bandwidth
    • prepared for nD
  • structured fields (pos tuple describes axes) can now be passed to estimate an isotropic or directional variogram
  • distance calculation in cython routines in now independent of dimension
• vario_estimate_axis
  • estimation along array axis now possible in arbitrary dimensions
  • no_data option added to allow missing values (sovles #83)
  • axis can be given by name ("x", "y", "z") or axis number (0, 1, 2, 3, ...)

Better Variogram fitting (#78, #145)

• fixing sill possible now
• loss is now selectable for smoother handling of outliers
• r2 score can now be returned to get an impression of the goodness of fitting
• weights can be passed
• instead of deselecting parameters, one can also give fix values for each parameter
• default init guess for len_scale is now mean of given bin-centers
• default init guess for var and nugget is now mean of given variogram values

CovModel update (#109, #122)

• add new rescale argument and attribute to the CovModel class to be able to rescale the len_scale (usefull for unit conversion or rescaling len_scale to coincide with the integral_scale like it's the case with the Gaussian model)
  See: #90, GeoStat-Framework/PyKrige#119
• added new len_rescaled attribute to the CovModel class, which is the rescaled len_scale: len_rescaled = len_scale / rescale
• new method default_rescale to provide default rescale factor (can be overridden)
• remove doctest calls
• docstring updates in CovModel and derived models
• updated all models to use the cor routine and make use of the rescale argument (See: #90)
• TPL models got a separate base class to not repeat code
• added new models (See: #88):
  • HyperSpherical: (Replaces the old Intersection model) Derived from the intersection of hyper-spheres in arbitrary dimensions. Coincides with the linear model in 1D, the circular model in 2D and the classical spherical model in 3D
  • SuperSpherical: like the HyperSpherical, but the shape parameter derived from dimension can be set by the user. Coincides with the HyperSpherical model by default
  • JBessel: a hole model valid in all dimensions. The shape parameter controls the dimension it was derived from. For nu=0.5 this model coincides with the well known wave hole model.
  • TPLSimple: a simple truncated power law controlled by a shape parameter nu. Coincides with the truncated linear model for nu=1
  • Cubic: to be compatible with scikit-gstat in the future
• string representation of the CovModel class is now using a float precision (CovModel._prec=3) to truncate longish output
• dimension validity check: raise a warning, if given model is not valid in the desired dimension (See: #86)

Normalizer, Trend and Mean (#124)

• new normalize submodule containing power-transforms for data to gain normality
• Base-Class: Normalizer providing basic functionality including maximum likelihood fitting
• added: LogNormal, BoxCox, BoxCoxShift, YeoJohnson, Modulus and Manly
• normalizer, trend and mean can be passed to SRF, Krige and variogram estimation routines
  • A trend can be a callable function, that represents a trend in input data. For example a linear decrease of temperature with height.
  • The normalizer will be applied after the data was detrended, i.e. the trend was substracted from the data, in order to gain normality.
  • The mean is now interpreted as the mean of the normalized data. The user could also provide a callable mean, but it is mostly meant to be constant.

Arbitrary dimensions (#112)

• allow arbitrary dimensions in all routines (CovModel, Krige, SRF, variogram)
• anisotropy and rotation following a generalization of tait-bryan angles
• CovModel provides isometrize and anisometrize routines to convert points

New Class for Conditioned Random Fields (#130)

• THIS BREAKS BACKWARD COMPATIBILITY
• CondSRF replaces the conditioning feature of the SRF class, which was cumbersome and limited to Ordinary and Simple kriging
• CondSRF behaves similar to the SRF class, but instead of a covariance model, it takes a kriging class as input. With this kriging class, all conditioning related settings are defined.

Enhancements

• Python 3.9 Support #107
• add routines to format struct. pos tuple by given dim or shape
• add routine to format struct. pos tuple by given shape (variogram helper)
• remove field.tools subpackage
• support meshio>=4.0 and add as dependency
• PyVista mesh support #59
• added `EART...

v1.3.0-rc1 'Pure Pink' 1. Release Candidate

Release Notes

A big step forward for GSTools.

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Topics

Geographical Coordinates Support

• added boolean init parameter latlon to indicate a geographic model. When given, spatial dimension is fixed to dim=3, anis and angles will be ignored, since anisotropy is not well-defined on a sphere.
• add property field_dim to indicate the dimension of the resulting field. Will be 2 if latlon=True
• added yadrenko variogram, covariance and correlation method, since the geographic models are derived from standard models in 3D by plugging in the chordal distance of two points on a sphere derived from there great-circle distance zeta:
  • vario_yadrenko: given by variogram(2 * np.sin(zeta / 2))
  • cov_yadrenko: given by covariance(2 * np.sin(zeta / 2))
  • cor_yadrenko: given by correlation(2 * np.sin(zeta / 2))
• added plotting routines for yadrenko methods described above
• the isometrize and anisometrize methods will convert latlon tuples (given in degree) to points on the unit-sphere in 3D and vice versa
• representation of geographical models don't display the dim, anis and angles parameters, but latlon=True
• fit_variogram will expect an estimated variogram with great-circle distances given in radians
• Variogram estimation
  • latlon switch implemented in estimate_vario routine
  • will return a variogram estimated by the great-circle distance (haversine formula) given in radians
• Field
  • added plotting routines for latlon fields
  • no vector fields possible on latlon fields
  • corretly handle pos tuple for latlon fields

Krige Unification (#97)

• Swiss Army Knife for kriging: The Krige class now provides everything in one place
• "Kriging the mean" is now possible with the switch only_mean in the call routine
• Simple/Ordinary/Universal/ExtDrift/Detrended are only shortcuts to Krige with limited input parameter list
• We now use the covariance function to build up the kriging matrix (instead of variogram)
• An unbiased switch was added to enable simple kriging (where the unbiased condition is not given)
• An exact switch was added to allow smother results, if a nugget is present in the model
• An cond_err parameter was added, where measurement error variances can be given for each conditional point
• pseudo-inverse matrix is now used to solve the kriging system (can be disabled by the new switch pseudo_inv), this is equal to solving the system with least-squares and prevents numerical errors

Directional Variograms (#87, #106)

• new routine name vario_estimate instead of vario_estimate_unstructured (old kept for legacy code) for simplicity
• new routine name vario_estimate_axis instead of vario_estimate_structured (old kept for legacy code) for simplicity
• vario_estimate
  • allow to pass multiple fields for joint variogram estimation (e.g. for daily precipitation) on same mesh
  • no_data option added to allow missing values
  • masked fields
    • user can now pass a masked array (or a list of masked arrays) to deselect data points.
    • in addition, a mask keyword was added to provide an external mask
  • directional variograms
    • diretional variograms can now be estimated
    • either provide a list of direction vectors or angles for directions (spherical coordinates)
    • can be controlled by given angle tolerance and (optional) bandwidth
    • prepared for nD
  • structured fields (pos tuple describes axes) can now be passed to estimate an isotropic or directional variogram
  • distance calculation in cython routines in now independent of dimension
• vario_estimate_axis
  • estimation along array axis now possible in arbitrary dimensions
  • no_data option added to allow missing values (sovles #83)
  • axis can be given by name ("x", "y", "z") or axis number (0, 1, 2, 3, ...)

Better Variogram fitting (#78)

• fixing sill possible now
• loss is now selectable for smoother handling of outliers
• r2 score can now be returned to get an impression of the goodness of fitting
• weights can be passed
• instead of deselecting parameters, one can also give fix values for each parameter

CovModel update (#109, #122)

• add new rescale argument and attribute to the CovModel class to be able to rescale the len_scale (usefull for unit conversion or rescaling len_scale to coincide with the integral_scale like it's the case with the Gaussian model)
  See: #90, GeoStat-Framework/PyKrige#119
• added new len_rescaled attribute to the CovModel class, which is the rescaled len_scale: len_rescaled = len_scale / rescale
• new method default_rescale to provide default rescale factor (can be overridden)
• remove doctest calls
• docstring updates in CovModel and derived models
• updated all models to use the cor routine and make use of the rescale argument (See: #90)
• TPL models got a separate base class to not repeat code
• added new models (See: #88):
  • HyperSpherical: (Replaces the old Intersection model) Derived from the intersection of hyper-spheres in arbitrary dimensions. Coincides with the linear model in 1D, the circular model in 2D and the classical spherical model in 3D
  • SuperSpherical: like the HyperSpherical, but the shape parameter derived from dimension can be set by the user. Coincides with the HyperSpherical model by default
  • JBessel: a hole model valid in all dimensions. The shape parameter controls the dimension it was derived from. For nu=0.5 this model coincides with the well known wave hole model.
  • TPLSimple: a simple truncated power law controlled by a shape parameter nu. Coincides with the truncated linear model for nu=1
  • Cubic: to be compatible with scikit-gstat in the future
• string representation of the CovModel class is now using a float precision (CovModel._prec=3) to truncate longish output
• dimension validity check: raise a warning, if given model is not valid in the desired dimension (See: #86)

Arbitrary dimensions (#112)

• allow arbitrary dimensions in all routines (CovModel, Krige, SRF, variogram)
• anisotropy and rotation following a generalization of tait-bryan angles
• CovModel provides isometrize and anisometrize routines to convert points

Enhancements

• Python 3.9 Support #107
• add routines to format struct. pos tuple by given dim or shape
• add routine to format struct. pos tuple by given shape (variogram helper)
• remove field.tools subpackage
• support meshio>=4.0 and add as dependency
• PyVista mesh support #59
• added EARTH_RADIUS as constant providing earths radius in km (can be used to rescale models)
• add routines latlon2pos and pos2latlon to convert lat-lon coordinates to points on unit-sphere and vice versa
• a lot of new examples and tutorials

Changes

• drop usage of pos2xyz and xyz2pos
• remove structured option from generators (structured pos need to be converted first)
• explicitly assert dim=2,3 when generating vector fields
• simplify pre_pos routine to save pos tuple and reformat it an unstructured tuple
• simplify field shaping
• simplify plotting routines
• only the "unstructured" keyword is recognized everywhere, everything else is interpreted as "structured" (e.g. "rectilinear")
• use GitHub-Actions instead of TravisCI

Bugfixes

• typo in keyword argument for vario_estimate_structured #80
• isotropic rotation of SRF was not possible #100
• CovModel.opt_arg now sorted #103
• CovModel.fit: check if weights are given as a string (numpy comparison error) #111

v1.2.1 'Volatile Violet'

Release Notes

This is a bug-fix release.

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Bugfixes

• ModuleNotFoundError is not present in py35
• Fixing Cressie-Bug #76
• Adding analytical formula for integral scales of rational and stable model
• remove prange from IncomprRandMeth summators to prevent errors on Win and macOS

v1.2.0 'Volatile Violet'

Release Notes

This release comes with a totally reworked kriging sub-module, a new variogram estimator, Python3-only support and a set of minor bugfixes.

A shout out to @banesullivan for his work on the sphinx gallery and the pyvista interface!

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Enhancements

• different variogram estimator functions can now be used #51
• the TPLGaussian and TPLExponential now have analytical spectra #67
• added property is_isotropic to CovModel #67
• reworked the whole krige sub-module to provide multiple kriging methods #67
  • Simple
  • Ordinary
  • Universal
  • External Drift Kriging
  • Detrended Kriging
• a new transformation function for discrete fields has been added #70
• reworked tutorial section in the documentation #63
• pyvista interface #29

Changes

• Python versions 2.7 and 3.4 are no longer supported #40 #43
• CovModel: in 3D the input of anisotropy is now treated slightly different: #67
  • single given anisotropy value [e] is converted to [1, e] (it was [e, e] before)
  • two given length-scales [l_1, l_2] are converted to [l_1, l_2, l_2] (it was [l_1, l_2, l_1] before)

Bugfixes

• a race condition in the structured variogram estimation has been fixed #51
• multiple minor bugfixes

v1.2.0.rc2 'Volatile Violet' 2. Release Candidate

Release Notes

This release comes with a totally reworked kriging sub-module, a new variogram estimator, Python3-only support and a set of minor bugfixes.

A shout out to @banesullivan for his work on the sphinx gallery and the pivista interface!

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Enhancements

• different variogram estimator functions can now be used #51
• the TPLGaussian and TPLExponential now have analytical spectra #67
• added property is_isotropic to CovModel #67
• reworked the whole krige sub-module to provide multiple kriging methods #67
  • Simple
  • Ordinary
  • Universal
  • External Drift Kriging
  • Detrended Kriging
• a new transformation function for discrete fields has been added #70
• reworked tutorial section in the documentation #63
• pyvista interface #29

Changes

• Python versions 2.7 and 3.4 are no longer supported #40 #43
• CovModel: in 3D the input of anisotropy is now treated slightly different: #67
  • single given anisotropy value [e] is converted to [1, e] (it was [e, e] before)
  • two given length-scales [l_1, l_2] are converted to [l_1, l_2, l_2] (it was [l_1, l_2, l_1] before)

Bugfixes

• a race condition in the structured variogram estimation has been fixed #51
• multiple minor bugfixes

v1.2.0.rc1 'Volatile Violet' 1. Release Candidate

Release Notes

This release comes with a totally reworked kriging sub-module, a new variogram estimator, Python3-only support and a set of minor bugfixes.

A shout out to @banesullivan for his work on the sphinx gallery and the pivista interface!

Installation

You can install GSTools with conda:

conda install -c conda-forge gstools

or with pip:

pip install gstools

Documentation

The documentation can be found at: https://gstools.readthedocs.io/

What's new?

Enhancements

• different variogram estimator functions can now be used #51
• the TPLGaussian and TPLExponential now have analytical spectra #67
• added property is_isotropic to CovModel #67
• reworked the whole krige sub-module to provide multiple kriging methods #67
  • Simple
  • Ordinary
  • Universal
  • External Drift Kriging
  • Detrended Kriging
• a new transformation function for discrete fields has been added #70
• reworked tutorial section in the documentation #63
• pyvista interface #29

Changes

• Python versions 2.7 and 3.4 are no longer supported #40 #43
• CovModel: in 3D the input of anisotropy is now treated slightly different: #67
  • single given anisotropy value [e] is converted to [1, e] (it was [e, e] before)
  • two given length-scales [l_1, l_2] are converted to [l_1, l_2, l_2] (it was [l_1, l_2, l_1] before)

Bugfixes

• a race condition in the structured variogram estimation has been fixed #51
• multiple minor bugfixes

v1.1.1 'Reverberating Red'

Release Notes

Bugfix release

Installation

You can install GSTools with:

pip install -U gstools

For parallel compilation try (install the standard version of gstools first, to get all dependencies):

pip install --global-option="--openmp" -U gstools

What's new?

Enhancements

• added a changelog. See: fbea883

Changes

• deprecation warnings are now printed if Python versions 2.7 or 3.4 are used #40 #41

Bugfixes

• define spectral_density instead of spectrum in covariance models since Cov-base derives spectrum. See: 00f2747
• better boundaries for CovModel parameters. See: #37

v1.1.0 'Reverberating Red'

Release Notes

Woah! GSTools went parallel. And at the same time the humongous memory consumption of the field generation became very modest.

The second big news is that GSTools can now finally generate conditioned random fields and provides kriging.

Installation

You can install GSTools with:

pip install -U gstools

For parallel compilation try (maybe install the standard version of gstools first, to get all dependencies):

pip install --global-option="--openmp" -U gstools

Enhancements

• by using Cython for all the heavy computations, we could achieve quite some speed ups and reduce the memory consumption significantly #16
• parallel computation in Cython is now supported with the help of OpenMP and the performance increase is nearly linear with increasing cores #16
• new submodule krige providing simple (known mean) and ordinary (estimated mean) kriging working analogous to the srf class
• interface to pykrige to use the gstools CovModel with the pykrige routines (GeoStat-Framework/PyKrige#124)
• the srf class now provides a plot and a vtk_export routine
• incompressible flow fields can now be generated #14
• new submodule providing several field transformations like: Zinn&Harvey, log-normal, bimodal, ... #13
• Python 3.4 and 3.7 wheel support #19
• field can now be generated directly on meshes from meshio and ogs5py f4a3439
• the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible 29f7f1b
• tutorials on all important features of GSTools have been written for you guys #20
• a new interface to pyvista is provided to export fields to python vtk representation, which can be used for plotting, exploring and exporting fields #29

Changes

• the license was changed from GPL to LGPL in order to promote the use of this library #25
• the rotation angles are now interpreted in positive direction (counter clock wise)
• the force_moments keyword was removed from the SRF call method, it is now in provided as a field transformation #13
• drop support of python implementations of the variogram estimators #18
• the variogram_normed method was removed from the CovModel class due to redundance 25b1647
• the position vector of 1D fields does not have to be provided in a list-like object with length 1 a6f5be8

Bugfixes

• several minor bugfixes