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Recent Releases of gstools

gstools - v1.7.0 'Morphic Mint'

Release Notes

This great release brings not only one but two impressive new features to GSTools: Plurigaussian Fields and Sum-Models. Plurigaussian Fields provide a smart way to introduce structure to random fields and with Sum-Models you are finally able to add two or more covariance models to better capture spatial patterns. In addition, we outsourced the cython code of GSTools into a separate package GSTools-Cython, which makes GSTools itself a lightweight pure python package.

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

  • new feature: Plurigaussian simulations (PGS) (#370)
    • they simulate distributions of categorical data, e.g. lithofacies, hydrofacies, soil types, or cementitious materials
    • they naturally extend truncated Gaussian fields, which are already a part of GSTools through the field transformations
  • new feature: support for Sum-Models (#364)
    • added SumModel class
    • represents sum of covariance models
    • behaves just as a normal covariance model with kriging and field generation
    • covariance models can be added with overloaded + operator: model = m1 + m2
    • class is subscriptable to access sub-models by index: m1 = model[0]
    • included models will get a nugget of 0 and the nugget is stored separately in the sum-model
    • model variance is the sum of the sub-model variances
    • model length-scale is weighted sum of sub-model len-scales, where the weights are the ratios of the sub-models variance to the sum variance (motivated by the integral scale, which satisfies this relation)
    • anisotropy and rotation need to be the same for all included sub-models
    • parameters of the sub-models can be accessed by name with added index suffix: model[0].nu == model.nu_0
    • fitting: if len_scale is fixed, none of the len_scale_<i> can be fixed since len_scale is calculated from variance ratios
    • added Nugget class (empty SumModel)
    • allow len scale of 0 in CovModel to enable a pure nugget model
    • added zero_var and model attributes to Generator ABC to shortcut field generation for pure nugget models

Changes

  • outsourced cython code to a separate package GSTools-Cython (#376)
  • removed var_raw attribute from CovModel (was rarely used and only relevant for the truncated power law models)
    • BREAKING CHANGE (but not to many should be affected)
    • TPLCovModel now has a intensity attribute which calculates what var_raw was before
  • simplified variogram fitting (var_raw was a bad idea in the first place)
  • variogram plotting now handles a len-scale of 0 (to properly plot nugget models)
  • fitting: when sill is given and var and nugget are deselected from fitting, an error is raised if given var+nugget is not equal to sill (before, they were reset under the hood in a strange way)

Bugfixes

  • pnt_cnt was not recalculated in vario_estimate when a mask was applied, together with a given sample size this resulted in an IndexError most of the times (#378)

- Python
Published by MuellerSeb 10 months ago

gstools - v1.6.1 'Periodic Peach'

Release Notes

This release brings Python 3.13 support and provides some minor bugfixes.

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

  • added wheels for Python 3.13 #375

Bugfixes

  • fix readthedocs.com problems (https://github.com/GeoStat-Framework/GSTools/pull/375)
  • remove deprecated scipy dependency (https://github.com/GeoStat-Framework/GSTools/pull/375)

- Python
Published by LSchueler about 1 year ago

gstools - v1.6.0 'Periodic Peach'

Release Notes

This release includes a new spatial random field generator called "Fourier" which can generate periodic random fields.

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

  • add a new spatial random field generator called Fourier (#302)
    • create periodic spatial random fields with Fourier, by setting the period length with the keyword periodic
    • add examples to showcase how to use the new generator

Bugfixes

  • fix some Cython issues (#354)
    • update deprecated conditional compilation for OpenMP support
    • fix changing between Cython and Rust backends
    • improve documentation regarding parallelization
  • fix deprecated license identifier, now Zenodo releases work again (#356)

Changes

  • require gstools-core v1.0.0 at least (#361)

- Python
Published by LSchueler over 1 year ago

gstools - v1.5.2 'Nifty Neon'

Release Notes

This release brings better support for spatio-temporal models as well as some updates for models on geographic coordinates.

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

  • added global variable config.NUM_THREADS to select number of threads for parallel computation (#336)
  • speed up sampling with emcee by setting vectorize=True in EnsembleSampler (#346)
  • prepare numpy 2 support (#340)
    • at least numpy 2.0.0rc1 for building extensions (for Python 3.9 and above)
    • check multiple numpy and scipy versions in CI
    • fixed minimal versions for numpy
    • use np.asarray everywhere with np.atleast_(n)d
    • fix long/longlong integer issue in cython on windows by always using 64bit integers

Bugfixes

  • build docs with latest sphinx version (#340)
  • fixed zero division error in spectral density of Integral model (#347)
  • minor pylint fixes for used-before-assignment issues (#350)

Changes

  • require pyvista 0.40 at least (#340)
  • require matplotlib 3.7 at least (#350)
  • remove universal2 wheels for macos (we already provide separate intel and arm64 wheels) (#350)

- Python
Published by MuellerSeb almost 2 years ago

gstools - v1.5.1 'Nifty Neon'

Release Notes

This release brings better support for spatio-temporal models as well as some updates for models on geographic coordinates.

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

see #317

  • added wheels for Python 3.12
  • dropped support for Python 3.7 (EOL)
  • linted Cython files with cython-lint
  • use Cython 3 to build extensions

- Python
Published by MuellerSeb over 2 years ago

gstools - v1.5.0 'Nifty Neon'

Release Notes

This release brings better support for spatio-temporal models as well as some updates for models on geographic coordinates.

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

  • added temporal flag to CovModel to explicitly specify spatio-temporal models #308
    • rotation between spatial and temporal dimension will be ignored
    • added spatial_dim to CovModel to explicitly set spatial dimension for spatio-temporal models
    • if not using spatial_dim, the provided dim needs to include the possible temporal dimension
    • spatial_dim is always one less than field_dim for spatio-temporal models
    • also works with latlon=True to have a spatio-temporal model with geographic coordinates
    • all plotting routines respect this
    • the Field class now has a temporal attribute which forwards the model attribute
    • automatic variogram fitting in kriging classes for temporal=True and latlon=True will raise an error
  • added geo_scale to CovModel to have a more consistent way to set the units of the model length scale for geographic coordinates #308
    • no need to use rescale for this anymore (was rather a hack)
    • added gs.KM_SCALE which is the same as gs.EARTH_RADIUS for kilometer scaling
    • added gs.DEGREE_SCALE for great circle distance in degrees
    • added gs.RADIAN_SCALE for great circle distance in radians (default and previous behavior)
    • yadrenko variogram respects this and assumes the great circle distances is given in the respective unit
    • vario_estimate also has geo_scale now to control the units of the bins
  • vario_estimate now forwards additional kwargs to standard_bins (bin_no, max_dist) #308
  • added low and high arguments to uniform transformation #310

Changes

  • CovModels expect special arguments by keyword now #308
  • always use f-strings internally #283
  • removed verbose attribute from RandMeth classes #309
  • all arguments for RandMeth classes key-word-only now except model #309
  • rename "package" to "api" in doc structure #290

Bugfixes

  • latex equations were not rendered correctly in docs #290

- Python
Published by MuellerSeb over 2 years ago

gstools - v1.4.1 'Sassy Sapphire'

Release Notes

This release brings Python 3.11 support, a new covariance model and provides some minor bugfixes.

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

  • new (Exponential-) Integral model added #243
  • added wheels for Python 3.11 #272

Changes

  • API documentation is polished and fully auto-generated now #271

Bugfixes

  • fixed approximation of Matern.spectrum for big nu #243
  • GSTools had wrong version when installed from git archive #272
  • Field.plot: solve long-standing mpl slider bug #273

- Python
Published by MuellerSeb over 3 years ago

gstools - v1.4.0 'Sassy Sapphire'

Release Notes

This release drops Python 3.6 support, brings a new package structure, adds some usability improvements and provides some crucial bugfixes.

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

  • added Youtube tutorial to documentation #239
  • better support for custom generators #250 #259
  • add valid_value_types class variable to all field classes #250
  • PyKrige: fix passed variogram in case of latlon models #254
  • add bounds checks for optional arguments of CovModel when resetting by class attribute #255
  • minor coverage improvements #255
  • documentation: readability improvements #257

Changes

  • drop Python 3.6 support (setuptools>60 needs py>3.7) #241
  • move setup.cfg content to pyproject.toml (PEP 621) #241
  • move to src/ based package structure (better testing, building and structure) #241
  • use extension-helpers for openmp support in setup.py #241
  • increase minimal version of meshio to v5.1 #241

Bugfixes

  • Pyvista v0.32 deprecation warning: use pointdata instead of pointarrays #237
  • remove deprecated scipy (v1.9) method pinv2 #247
  • change float comparison in tests #248
  • Cython: solve -Wsometimes-uninitialized warning #255

- Python
Published by MuellerSeb over 3 years ago

gstools - v1.3.5 'Pure Pink'

Release Notes

Bugfix 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?

Changes

  • remove caps for dependencies #229
  • build linux wheels with manylinux2014 for all versions (CIBW v2.3.1) #227

Bugfixes

  • Field.mesh was not compatible with meshio v5.1+ #227

- Python
Published by MuellerSeb about 4 years ago

gstools - v1.3.4 'Pure Pink'

Release Notes

This release comes with wheels for Python 3.10 and a new optional package with re-implementations of the Cython routines in Rust called GSTools-Core.

You can install the rust package as an option with

pip install gstools[rust]

Or simply by

pip install gstools-core

If the package is present it will be used instead of the Cython routines.

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

  • add GSTools-Core as optional dependency #215
  • provide wheels for Python 3.10 #211
  • provide macOS wheels for Apple Silicon #211

Changes

  • remove unnecessary dim argument in Cython code #216

- Python
Published by MuellerSeb about 4 years ago

gstools - v1.3.3 'Pure Pink'

Release Notes

This release comes with a new storage framework for all Field classes and a better transform sub-module.

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

See: #197 - gstools.transform: - add keywords field, store, process and keep_mean to all transformations to control storage and respect normalizer - added apply_function transformation - added apply as wrapper for all transformations - added transform method to all Field (sub)classes as interface to transform.apply - added checks for normal fields to work smoothly with recently added normalizer submodule - Field: - allow naming fields when generating and control storage with store keyword - all subclasses now have the post_process keyword (apply mean, normalizer, trend) - added subscription to access fields by name (Field["field"]) - added set_pos method to set position tuple - allow reusing present pos tuple - added pos, mesh_type, field_names, field_shape, all_fields properties - CondSRF: - memory optimization by forwarding pos from underlying krige instance - only recalculate kriging field if pos tuple changed (optimized ensemble generation) - performance improvement by using np.asarray instead of np.array where possible - updated examples to use new features - added incomplete lower gamma function inc_gamma_low (for TPLGaussian spectral density) - filter nan values from cond_val array in all kriging routines #201

Bugfixes

  • inc_gamma was defined wrong for integer s < 0

- Python
Published by MuellerSeb over 4 years ago

gstools - v1.3.2 '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?

Bugfixes

  • vario_estimate was altering the input field unter certain circumstances #180
  • emcee v3.1 now requires nsteps in run_mcmc() to be integer (called in RNG.sample_ln_pdf) #184

- Python
Published by MuellerSeb over 4 years ago

gstools - 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

- Python
Published by MuellerSeb over 4 years ago

gstools - 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
  • varioestimateaxis
    • 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. 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
  • RandMeth class got a switch to select the sampling strategy
  • plotter for n-D fields added #141
  • antialias for contour plots of 2D fields #141
  • building from source is now configured with pyproject.toml to care about build dependencies, see #154

Changes

  • drop support for Python 3.5 #146
  • added a finit limit for shape-parameters in some CovModels #147
  • 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
  • parallel build now controlled by env-var GSTOOLS_BUILD_PARALLEL=1, see #154
  • install extra target for [dev] dropped, can be reproduced by pip install gstools[test, doc], see #154

Bugfixes

  • typo in keyword argument for varioestimatestructured #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
  • several pylint fixes (#159)

- Python
Published by MuellerSeb almost 5 years ago

gstools - 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 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
  • RandMeth class got a switch to select the sampling strategy
  • plotter for n-D fields added #141
  • antialias for contour plots of 2D fields #141
  • building from source is now configured with pyproject.toml to care about build dependencies, see #154

Changes

  • drop support for Python 3.5 #146
  • added a finit limit for shape-parameters in some CovModels #147
  • 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
  • parallel build now controlled by env-var GSTOOLS_BUILD_PARALLEL=1, see #154
  • install extra target for [dev] dropped, can be reproduced by pip install gstools[test, doc], see #154

Bugfixes

  • typo in keyword argument for varioestimatestructured #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

- Python
Published by MuellerSeb almost 5 years ago

gstools - 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, https://github.com/GeoStat-Framework/PyKrige/issues/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 varioestimatestructured #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

- Python
Published by MuellerSeb about 5 years ago

gstools - 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

- Python
Published by MuellerSeb almost 6 years ago

gstools - 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
    • two given length-scales [l1, l2] are converted to l1, l2, l_2

Bugfixes

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

- Python
Published by MuellerSeb almost 6 years ago

gstools - 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
    • two given length-scales [l1, l2] are converted to l1, l2, l_2

Bugfixes

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

- Python
Published by MuellerSeb almost 6 years ago

gstools - 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
    • two given length-scales [l1, l2] are converted to l1, l2, l_2

Bugfixes

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

- Python
Published by LSchueler almost 6 years ago

gstools - 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: https://github.com/GeoStat-Framework/GSTools/commit/fbea88300d0862393e52f4b7c3d2b15c2039498b

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: https://github.com/GeoStat-Framework/GSTools/commit/00f2747fd0503ff8806f2eebfba36acff813416b
  • better boundaries for CovModel parameters. See: https://github.com/GeoStat-Framework/GSTools/issues/37

- Python
Published by MuellerSeb over 6 years ago

gstools - 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 (https://github.com/bsmurphy/PyKrige/issues/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 f4a3439400b8
  • the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible 29f7f1b02
  • 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 25b164722ac6744ebc7e03f3c0bf1c30be1eba89
  • the position vector of 1D fields does not have to be provided in a list-like object with length 1 a6f5be8bf

Bugfixes

  • several minor bugfixes

- Python
Published by MuellerSeb over 6 years ago

gstools - v1.1.0.rc2 'Reverberating Red' 2. Release candidate

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

Since this is a pre-release you have to install it with:

pip install --pre -U gstools

For parallel compilation try:

pip install --pre --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 (https://github.com/bsmurphy/PyKrige/issues/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 f4a3439400b8
  • the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible 29f7f1b02
  • 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 25b164722ac6744ebc7e03f3c0bf1c30be1eba89
  • the position vector of 1D fields does not have to be provided in a list-like object with length 1 a6f5be8bf
  • we now require emcee version >= 3.0.0

Bugfixes

  • several minor bugfixes

- Python
Published by MuellerSeb over 6 years ago

gstools - v1.1.0.rc1 'Reverberating Red' 1. Release candidate

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 krging.

Installation

Since this is a pre-release you have to install it with:

pip install --pre -U gstools

For parallel compilation try:

pip install --pre --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 (https://github.com/GeoStat-Framework/GSTools/issues/16)
  • parallel computation in Cython is now supported with the help of OpenMP and the performance increase is nearly linear with increasing cores (https://github.com/GeoStat-Framework/GSTools/issues/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 (https://github.com/bsmurphy/PyKrige/issues/124)
  • the srf class now provides a plot and a vtk_export routine
  • in-compressible flow fields are now creatable (https://github.com/GeoStat-Framework/GSTools/issues/14)
  • new submodule providing several field transformations like: Zinn&Harvey, log-normal, bimodal, ... (https://github.com/GeoStat-Framework/GSTools/issues/13)
  • Python 3.4 and 3.7 wheel support (https://github.com/GeoStat-Framework/GSTools/issues/19)
  • field can now be generated directly on meshes from meshio and ogs5py (https://github.com/GeoStat-Framework/GSTools/commit/f4a3439400b81d8d9db81a5f7fbf6435f603cf05)
  • the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible (https://github.com/GeoStat-Framework/GSTools/commit/29f7f1b029866379ce881f44765f72534d757fae)

Changes

  • 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 (https://github.com/GeoStat-Framework/GSTools/issues/13)
  • drop support of python implementations of the variogram estimators (https://github.com/GeoStat-Framework/GSTools/issues/18)
  • the variogram_normed method was removed from the CovModel class due to redundance (https://github.com/GeoStat-Framework/GSTools/commit/25b164722ac6744ebc7e03f3c0bf1c30be1eba89)

Bugfixes

  • several minor bugfixes

- Python
Published by LSchueler over 6 years ago

gstools - v1.0.1 'Bouncy Blue'

Release Notes

Bugfix release. Fixed Numpy and Cython version during build process.

- Python
Published by MuellerSeb about 7 years ago

gstools - v1.0.0 'Bouncy Blue'

Release Notes

After a lot of hard work and some brilliant ideas, we are finally ready to release the first stable version of GSTools!

This release is mainly characterized by the CovModel, which lets you define arbitrary covariance models, including fractal power law models, simply by defining the variogram, or the correlation function. It's up to you.

But also the usability has become a major boost. And some workflows have become very intuitive, like estimating a variogram model and its parameters from data and creating new spatial random fields with these parameters and exporting them for different programs to use.

The tutorials will help new users get familiar with GSTools in no time.

For more details, see the following lists.

Enhancements

  • added a new covariance class, which allows the easy usage of arbitrary covariance models
  • added many predefined covariance models, including truncated power law models
  • added tutorials and examples, showing and explaining the main features of GSTools
  • variogram models can be fitted to data
  • prebuilt binaries for many Linux distributions, Mac OS and Windows, making the installation, especially of the Cython code, much easier
  • the generated fields can now easily be exported to vtk files
  • variance scaling is supported for coarser grids
  • added pure Python versions of the variogram estimators, in case somebody has problems compiling Cython code
  • the documentation is now a lot cleaner and easier to use
  • the code is a lot cleaner and more consistent now
  • unit tests are now automatically tested when new code is pushed
  • test coverage of code is shown
  • GeoStat Framework now has a website, visit us: https://geostat-framework.github.io/

Changes

One word of caution: This release is not downwards compatible with release v0.4.0.

  • SRF creation has been adapted for the CovModel
  • a tuple pos is now used instead of x, y, and z for the axes
  • renamed estimate_unstructured and estimate_structured to vario_estimate_unstructured and vario_estimate_structured for less ambiguity

- Python
Published by MuellerSeb about 7 years ago

gstools - 1.0rc12 'Bouncy Blue' 1. Release Candidate

Notes

First public release candidate (rc12) for version 1.0.

Enhancements

  • cleaner code
  • new CovModel to create covariance models
  • a lot of predefined covariance models (including truncated power law models)
  • interface change for SRF creation
  • use of pos instead of x, y and z for simplification
  • variogram fitting
  • vtk export
  • variance-scaling support
  • new documentation

- Python
Published by MuellerSeb about 7 years ago

gstools - 0.4.0 'Glorious Green'

- Python
Published by LSchueler over 7 years ago

gstools - 0.3.6 'Original Orange'

- Python
Published by LSchueler over 7 years ago