CHANGELOG.md

Changelog

All notable changes to GSTools will be documented in this file.

1.5.2 - Nifty Neon - 2024-05

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)

1.5.1 - Nifty Neon - 2023-11

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

1.5.0 - Nifty Neon - 2023-06

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

1.4.1 - Sassy Sapphire - 2022-11

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

1.4.0 - Sassy Sapphire - 2022-08

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 point_data instead of point_arrays #237
• remove deprecated scipy (v1.9) method pinv2 #247
• change float comparison in tests #248
• Cython: solve -Wsometimes-uninitialized warning #255

1.3.5 - Pure Pink - 2022-01

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

1.3.4 - Pure Pink - 2021-11

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

1.3.3 - Pure Pink - 2021-08

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

1.3.2 - Pure Pink - 2021-07

Bugfixes

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

1.3.1 - Pure Pink - 2021-06

Enhancements

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

Bugfixes

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

1.3.0 - Pure Pink - 2021-04

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. 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 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
• several pylint fixes (#159)

1.2.1 - Volatile Violet - 2020-04-14

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

1.2.0 - Volatile Violet - 2020-03-20

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

1.1.1 - Reverberating Red - 2019-11-08

Enhancements

• added a changelog. See: commit 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: commit 00f2747
• better boundaries for CovModel parameters. See: #37

1.1.0 - Reverberating Red - 2019-10-01

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, see: commit f4a3439
• the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible, see: commit 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 commit 25b1647
• the position vector of 1D fields does not have to be provided in a list-like object with length 1 commit a6f5be8

Bugfixes

• several minor bugfixes

1.0.1 - Bouncy Blue - 2019-01-18

Bugfixes

• fixed Numpy and Cython version during build process

1.0.0 - Bouncy Blue - 2019-01-16

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

• 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

Bugfixes

• several minor bugfixes

0.4.0 - Glorious Green - 2018-07-17

Bugfixes

• import of cython functions put into a try-block

0.3.6 - Original Orange - 2018-07-17

First release of GSTools.